diff --git a/docs/Makefile b/docs/Makefile
index 13d195804cb..6645d85bdf2 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -86,10 +86,6 @@ vercel-build: install-vercel-deps build generate-references
 	mv langchain-api-docs-build/api_reference_build/html/* static/api_reference/
 	rm -rf langchain-api-docs-build
 	NODE_OPTIONS="--max-old-space-size=5000" yarn run docusaurus build
-	mv build v0.2
-	mkdir build
-	mv v0.2 build
-	mv build/v0.2/404.html build
 
 start:
 	cd $(OUTPUT_NEW_DIR) && yarn && yarn start --port=$(PORT)
diff --git a/docs/api_reference/guide_imports.json b/docs/api_reference/guide_imports.json
index 7c312af840f..e0fcde404bf 100644
--- a/docs/api_reference/guide_imports.json
+++ b/docs/api_reference/guide_imports.json
@@ -1 +1 @@
-{"ChatPromptTemplate": {"\ud83e\udd9c\ufe0f\ud83c\udfd3 LangServe": "https://python.langchain.com/v0.2/docs/langserve/", "Conceptual guide": "https://python.langchain.com/v0.2/docs/concepts/", "# Example": "https://python.langchain.com/v0.2/docs/versions/migrating_chains/map_rerank_docs_chain/", "# Legacy": "https://python.langchain.com/v0.2/docs/versions/migrating_chains/llm_router_chain/", "Load docs": "https://python.langchain.com/v0.2/docs/versions/migrating_chains/conversation_retrieval_chain/", "# Basic example (short documents)": "https://python.langchain.com/v0.2/docs/versions/migrating_chains/map_reduce_chain/", "How to add a semantic layer over graph database": "https://python.langchain.com/v0.2/docs/how_to/graph_semantic/", "How to handle long text when doing extraction": "https://python.langchain.com/v0.2/docs/how_to/extraction_long_text/", "How to add values to a chain's state": "https://python.langchain.com/v0.2/docs/how_to/assign/", "How to do per-user retrieval": "https://python.langchain.com/v0.2/docs/how_to/qa_per_user/", "How to track token usage in ChatModels": "https://python.langchain.com/v0.2/docs/how_to/chat_token_usage_tracking/", "How to create a custom LLM class": "https://python.langchain.com/v0.2/docs/how_to/custom_llm/", "How to inspect runnables": "https://python.langchain.com/v0.2/docs/how_to/inspect/", "How to handle cases where no queries are generated": "https://python.langchain.com/v0.2/docs/how_to/query_no_queries/", "How to use few shot examples in chat models": "https://python.langchain.com/v0.2/docs/how_to/few_shot_examples_chat/", "How to summarize text through iterative refinement": "https://python.langchain.com/v0.2/docs/how_to/summarize_refine/", "How to do tool/function calling": "https://python.langchain.com/v0.2/docs/how_to/function_calling/", "How to create tools": "https://python.langchain.com/v0.2/docs/how_to/custom_tools/", "How to use prompting alone (no tool calling) to do extraction": "https://python.langchain.com/v0.2/docs/how_to/extraction_parse/", "How to deal with large databases when doing SQL question-answering": "https://python.langchain.com/v0.2/docs/how_to/sql_large_db/", "How to use reference examples when doing extraction": "https://python.langchain.com/v0.2/docs/how_to/extraction_examples/", "How to handle multiple queries when doing query analysis": "https://python.langchain.com/v0.2/docs/how_to/query_multiple_queries/", "How to add fallbacks to a runnable": "https://python.langchain.com/v0.2/docs/how_to/fallbacks/", "How to propagate callbacks  constructor": "https://python.langchain.com/v0.2/docs/how_to/callbacks_constructor/", "How to map values to a graph database": "https://python.langchain.com/v0.2/docs/how_to/graph_mapping/", "How to save and load LangChain objects": "https://python.langchain.com/v0.2/docs/how_to/serialization/", "How to do question answering over CSVs": "https://python.langchain.com/v0.2/docs/how_to/sql_csv/", "How to stream results from your RAG application": "https://python.langchain.com/v0.2/docs/how_to/qa_streaming/", "How to get your RAG application to return sources": "https://python.langchain.com/v0.2/docs/how_to/qa_sources/", "How to summarize text through parallelization": "https://python.langchain.com/v0.2/docs/how_to/summarize_map_reduce/", "How to attach callbacks to a runnable": "https://python.langchain.com/v0.2/docs/how_to/callbacks_attach/", "How to handle tool errors": "https://python.langchain.com/v0.2/docs/how_to/tools_error/", "How to add tools to chatbots": "https://python.langchain.com/v0.2/docs/how_to/chatbots_tools/", "How to add default invocation args to a Runnable": "https://python.langchain.com/v0.2/docs/how_to/binding/", "How to convert Runnables as Tools": "https://python.langchain.com/v0.2/docs/how_to/convert_runnable_to_tool/", "How to stream events from a tool": "https://python.langchain.com/v0.2/docs/how_to/tool_stream_events/", "How to create a dynamic (self-constructing) chain": "https://python.langchain.com/v0.2/docs/how_to/dynamic_chain/", "How to create custom callback handlers": "https://python.langchain.com/v0.2/docs/how_to/custom_callbacks/", "How to stream runnables": "https://python.langchain.com/v0.2/docs/how_to/streaming/", "How to invoke runnables in parallel": "https://python.langchain.com/v0.2/docs/how_to/parallel/", "How to pass through arguments from one step to the next": "https://python.langchain.com/v0.2/docs/how_to/passthrough/", "How to retrieve using multiple vectors per document": "https://python.langchain.com/v0.2/docs/how_to/multi_vector/", "How to add chat history": "https://python.langchain.com/v0.2/docs/how_to/qa_chat_history_how_to/", "How to add message history": "https://python.langchain.com/v0.2/docs/how_to/message_history/", "How to add retrieval to chatbots": "https://python.langchain.com/v0.2/docs/how_to/chatbots_retrieval/", "How to handle multiple retrievers when doing query analysis": "https://python.langchain.com/v0.2/docs/how_to/query_multiple_retrievers/", "How to get a RAG application to add citations": "https://python.langchain.com/v0.2/docs/how_to/qa_citations/", "How to run custom functions": "https://python.langchain.com/v0.2/docs/how_to/functions/", "How to add memory to chatbots": "https://python.langchain.com/v0.2/docs/how_to/chatbots_memory/", "How deal with high cardinality categoricals when doing query analysis": "https://python.langchain.com/v0.2/docs/how_to/query_high_cardinality/", "How to return structured data from a model": "https://python.langchain.com/v0.2/docs/how_to/structured_output/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/v0.2/docs/how_to/tools_prompting/", "LangChain Expression Language Cheatsheet": "https://python.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/", "How to debug your LLM apps": "https://python.langchain.com/v0.2/docs/how_to/debugging/", "How to chain runnables": "https://python.langchain.com/v0.2/docs/how_to/sequence/", "Hybrid Search": "https://python.langchain.com/v0.2/docs/how_to/hybrid/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/v0.2/docs/how_to/migrate_agent/", "How to do query validation as part of SQL question-answering": "https://python.langchain.com/v0.2/docs/how_to/sql_query_checking/", "How to summarize text in a single LLM call": "https://python.langchain.com/v0.2/docs/how_to/summarize_stuff/", "How to use multimodal prompts": "https://python.langchain.com/v0.2/docs/how_to/multimodal_prompts/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/v0.2/docs/how_to/tools_few_shot/", "How to pass callbacks in at runtime": "https://python.langchain.com/v0.2/docs/how_to/callbacks_runtime/", "How to add examples to the prompt for query analysis": "https://python.langchain.com/v0.2/docs/how_to/query_few_shot/", "Facebook Messenger": "https://python.langchain.com/v0.2/docs/integrations/chat_loaders/facebook/", "LangSmith LLM Runs": "https://python.langchain.com/v0.2/docs/integrations/chat_loaders/langsmith_llm_runs/", "iMessage": "https://python.langchain.com/v0.2/docs/integrations/chat_loaders/imessage/", "NVIDIA NIMs ": "https://python.langchain.com/v0.2/docs/integrations/text_embedding/nvidia_ai_endpoints/", "AzureAISearchRetriever": "https://python.langchain.com/v0.2/docs/integrations/retrievers/azure_ai_search/", "You.com": "https://python.langchain.com/v0.2/docs/integrations/retrievers/you-retriever/", "Fleet AI Context": "https://python.langchain.com/v0.2/docs/integrations/retrievers/fleet_context/", "AskNews": "https://python.langchain.com/v0.2/docs/integrations/retrievers/asknews/", "WikipediaRetriever": "https://python.langchain.com/v0.2/docs/integrations/retrievers/wikipedia/", "TavilySearchAPIRetriever": "https://python.langchain.com/v0.2/docs/integrations/retrievers/tavily/", "Activeloop Deep Memory": "https://python.langchain.com/v0.2/docs/integrations/retrievers/activeloop/", "RAGatouille": "https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille/", "ArxivRetriever": "https://python.langchain.com/v0.2/docs/integrations/retrievers/arxiv/", "ElasticsearchRetriever": "https://python.langchain.com/v0.2/docs/integrations/retrievers/elasticsearch_retriever/", "Google Vertex AI Search": "https://python.langchain.com/v0.2/docs/integrations/retrievers/google_vertex_ai_search/", "Tavily Search": "https://python.langchain.com/v0.2/docs/integrations/tools/tavily_search/", "FinancialDatasets Toolkit": "https://python.langchain.com/v0.2/docs/integrations/tools/financial_datasets/", "Databricks Unity Catalog (UC)": "https://python.langchain.com/v0.2/docs/integrations/tools/databricks/", "Riza Code Interpreter": "https://python.langchain.com/v0.2/docs/integrations/tools/riza/", "Redis": "https://python.langchain.com/v0.2/docs/integrations/memory/redis_chat_message_history/", "Google SQL for MySQL": "https://python.langchain.com/v0.2/docs/integrations/memory/google_sql_mysql/", "Google AlloyDB for PostgreSQL": "https://python.langchain.com/v0.2/docs/integrations/memory/google_alloydb/", "ZepCloudChatMessageHistory": "https://python.langchain.com/v0.2/docs/integrations/memory/zep_cloud_chat_message_history/", "AWS DynamoDB": "https://python.langchain.com/v0.2/docs/integrations/memory/aws_dynamodb/", "Couchbase": "https://python.langchain.com/v0.2/docs/integrations/memory/couchbase_chat_message_history/", "MongoDB": "https://python.langchain.com/v0.2/docs/integrations/memory/mongodb_chat_message_history/", "SQL (SQLAlchemy)": "https://python.langchain.com/v0.2/docs/integrations/memory/sql_chat_message_history/", "Streamlit": "https://python.langchain.com/v0.2/docs/integrations/memory/streamlit_chat_message_history/", "Google El Carro Oracle": "https://python.langchain.com/v0.2/docs/integrations/memory/google_el_carro/", "SQLite": "https://python.langchain.com/v0.2/docs/integrations/memory/sqlite/", "Google SQL for PostgreSQL": "https://python.langchain.com/v0.2/docs/integrations/memory/google_sql_pg/", "Google SQL for SQL Server": "https://python.langchain.com/v0.2/docs/integrations/memory/google_sql_mssql/", "TiDB": "https://python.langchain.com/v0.2/docs/integrations/memory/tidb_chat_message_history/", "Kinetica Language To SQL Chat Model": "https://python.langchain.com/v0.2/docs/integrations/chat/kinetica/", "ChatFireworks": "https://python.langchain.com/v0.2/docs/integrations/chat/fireworks/", "ChatYI": "https://python.langchain.com/v0.2/docs/integrations/chat/yi/", "ChatAnthropic": "https://python.langchain.com/v0.2/docs/integrations/chat/anthropic/", "ChatGroq": "https://python.langchain.com/v0.2/docs/integrations/chat/groq/", "ChatGoogleGenerativeAI": "https://python.langchain.com/v0.2/docs/integrations/chat/google_generative_ai/", "OllamaFunctions": "https://python.langchain.com/v0.2/docs/integrations/chat/ollama_functions/", "ChatOpenAI": "https://python.langchain.com/v0.2/docs/integrations/chat/openai/", "ChatVertexAI": "https://python.langchain.com/v0.2/docs/integrations/chat/google_vertex_ai_palm/", "ChatBedrock": "https://python.langchain.com/v0.2/docs/integrations/chat/bedrock/", "JinaChat": "https://python.langchain.com/v0.2/docs/integrations/chat/jinachat/", "ChatOllama": "https://python.langchain.com/v0.2/docs/integrations/chat/ollama/", "ChatOCIGenAI": "https://python.langchain.com/v0.2/docs/integrations/chat/oci_generative_ai/", "AzureChatOpenAI": "https://python.langchain.com/v0.2/docs/integrations/chat/azure_chat_openai/", "Llama.cpp": "https://python.langchain.com/v0.2/docs/integrations/chat/llamacpp/", "ChatMistralAI": "https://python.langchain.com/v0.2/docs/integrations/chat/mistralai/", "ChatAI21": "https://python.langchain.com/v0.2/docs/integrations/chat/ai21/", "ChatDatabricks": "https://python.langchain.com/v0.2/docs/integrations/chat/databricks/", "ChatTogether": "https://python.langchain.com/v0.2/docs/integrations/chat/together/", "Llama2Chat": "https://python.langchain.com/v0.2/docs/integrations/chat/llama2_chat/", "Cohere": "https://python.langchain.com/v0.2/docs/integrations/providers/cohere/", "Eden AI": "https://python.langchain.com/v0.2/docs/integrations/chat/edenai/", "ChatWatsonx": "https://python.langchain.com/v0.2/docs/integrations/chat/ibm_watsonx/", "vLLM Chat": "https://python.langchain.com/v0.2/docs/integrations/chat/vllm/", "Yuan2.0": "https://python.langchain.com/v0.2/docs/integrations/chat/yuan2/", "Maritalk": "https://python.langchain.com/v0.2/docs/integrations/chat/maritalk/", "ChatPerplexity": "https://python.langchain.com/v0.2/docs/integrations/chat/perplexity/", "ChatUpstage": "https://python.langchain.com/v0.2/docs/integrations/chat/upstage/", "ChatNVIDIA": "https://python.langchain.com/v0.2/docs/integrations/chat/nvidia_ai_endpoints/", "Context": "https://python.langchain.com/v0.2/docs/integrations/callbacks/context/", "Fiddler": "https://python.langchain.com/v0.2/docs/integrations/callbacks/fiddler/", "UpTrain": "https://python.langchain.com/v0.2/docs/integrations/callbacks/uptrain/", "MLflow": "https://python.langchain.com/v0.2/docs/integrations/providers/mlflow_tracking/", "Weaviate": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/weaviate/", "Yellowbrick": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/yellowbrick/", "Jaguar Vector Database": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/jaguar/", "ApertureDB": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/aperturedb/", "Apache Cassandra": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/cassandra/", "OpenAI metadata tagger": "https://python.langchain.com/v0.2/docs/integrations/document_transformers/openai_metadata_tagger/", "Image captions": "https://python.langchain.com/v0.2/docs/integrations/document_loaders/image_captions/", "Figma": "https://python.langchain.com/v0.2/docs/integrations/document_loaders/figma/", "OllamaLLM": "https://python.langchain.com/v0.2/docs/integrations/llms/ollama/", "Build a Retrieval Augmented Generation (RAG) App": "https://python.langchain.com/v0.2/docs/tutorials/rag/", "Build a Local RAG Application": "https://python.langchain.com/v0.2/docs/tutorials/local_rag/", "Summarize Text": "https://python.langchain.com/v0.2/docs/tutorials/summarization/", "Build an Extraction Chain": "https://python.langchain.com/v0.2/docs/tutorials/extraction/", "Build a Chatbot": "https://python.langchain.com/v0.2/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/v0.2/docs/tutorials/qa_chat_history/", "Classify Text into Labels": "https://python.langchain.com/v0.2/docs/tutorials/classification/", "Build a Query Analysis System": "https://python.langchain.com/v0.2/docs/tutorials/query_analysis/", "Build a Simple LLM Application with LCEL": "https://python.langchain.com/v0.2/docs/tutorials/llm_chain/", "Build a PDF ingestion and Question/Answering system": "https://python.langchain.com/v0.2/docs/tutorials/pdf_qa/", "Vector stores and retrievers": "https://python.langchain.com/v0.2/docs/tutorials/retrievers/"}, "ChatAnthropic": {"\ud83e\udd9c\ufe0f\ud83c\udfd3 LangServe": "https://python.langchain.com/v0.2/docs/langserve/", "Conceptual guide": "https://python.langchain.com/v0.2/docs/concepts/", "How to use callbacks in async environments": "https://python.langchain.com/v0.2/docs/how_to/callbacks_async/", "How to route between sub-chains": "https://python.langchain.com/v0.2/docs/how_to/routing/", "How to track token usage in ChatModels": "https://python.langchain.com/v0.2/docs/how_to/chat_token_usage_tracking/", "How to merge consecutive messages of the same type": "https://python.langchain.com/v0.2/docs/how_to/merge_message_runs/", "How to parse XML output": "https://python.langchain.com/v0.2/docs/how_to/output_parser_xml/", "How to use prompting alone (no tool calling) to do extraction": "https://python.langchain.com/v0.2/docs/how_to/extraction_parse/", "How to handle rate limits": "https://python.langchain.com/v0.2/docs/how_to/chat_model_rate_limiting/", "How to add fallbacks to a runnable": "https://python.langchain.com/v0.2/docs/how_to/fallbacks/", "How to propagate callbacks  constructor": "https://python.langchain.com/v0.2/docs/how_to/callbacks_constructor/", "How to stream chat model responses": "https://python.langchain.com/v0.2/docs/how_to/chat_streaming/", "How to attach callbacks to a runnable": "https://python.langchain.com/v0.2/docs/how_to/callbacks_attach/", "How to filter messages": "https://python.langchain.com/v0.2/docs/how_to/filter_messages/", "How to create a dynamic (self-constructing) chain": "https://python.langchain.com/v0.2/docs/how_to/dynamic_chain/", "How to create custom callback handlers": "https://python.langchain.com/v0.2/docs/how_to/custom_callbacks/", "How to configure runtime chain internals": "https://python.langchain.com/v0.2/docs/how_to/configure/", "How to create a custom Output Parser": "https://python.langchain.com/v0.2/docs/how_to/output_parser_custom/", "Response metadata": "https://python.langchain.com/v0.2/docs/how_to/response_metadata/", "How to pass callbacks in at runtime": "https://python.langchain.com/v0.2/docs/how_to/callbacks_runtime/", "Anthropic": "https://python.langchain.com/v0.2/docs/integrations/platforms/anthropic/", "PlayWright Browser Toolkit": "https://python.langchain.com/v0.2/docs/integrations/tools/playwright/", "Riza Code Interpreter": "https://python.langchain.com/v0.2/docs/integrations/tools/riza/", "ChatAnthropic": "https://python.langchain.com/v0.2/docs/integrations/chat/anthropic/", "Log10": "https://python.langchain.com/v0.2/docs/integrations/providers/log10/", "Build an Agent": "https://python.langchain.com/v0.2/docs/tutorials/agents/"}, "ChatOpenAI": {"\ud83e\udd9c\ufe0f\ud83c\udfd3 LangServe": "https://python.langchain.com/v0.2/docs/langserve/", "Conceptual guide": "https://python.langchain.com/v0.2/docs/concepts/", "# Legacy": "https://python.langchain.com/v0.2/docs/versions/migrating_chains/llm_router_chain/", "Load docs": "https://python.langchain.com/v0.2/docs/versions/migrating_chains/retrieval_qa/", "# Example": "https://python.langchain.com/v0.2/docs/versions/migrating_chains/map_rerank_docs_chain/", "How to add a semantic layer over graph database": "https://python.langchain.com/v0.2/docs/how_to/graph_semantic/", "How to disable parallel tool calling": "https://python.langchain.com/v0.2/docs/how_to/tool_calling_parallel/", "How to add values to a chain's state": "https://python.langchain.com/v0.2/docs/how_to/assign/", "How to track token usage in ChatModels": "https://python.langchain.com/v0.2/docs/how_to/chat_token_usage_tracking/", "How to inspect runnables": "https://python.langchain.com/v0.2/docs/how_to/inspect/", "How to bind model-specific tools": "https://python.langchain.com/v0.2/docs/how_to/tools_model_specific/", "How to handle cases where no queries are generated": "https://python.langchain.com/v0.2/docs/how_to/query_no_queries/", "How to trim messages": "https://python.langchain.com/v0.2/docs/how_to/trim_messages/", "How to use few shot examples in chat models": "https://python.langchain.com/v0.2/docs/how_to/few_shot_examples_chat/", "How to use LangChain with different Pydantic versions": "https://python.langchain.com/v0.2/docs/how_to/pydantic_compatibility/", "How to use reference examples when doing extraction": "https://python.langchain.com/v0.2/docs/how_to/extraction_examples/", "How to handle multiple queries when doing query analysis": "https://python.langchain.com/v0.2/docs/how_to/query_multiple_queries/", "How to add 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"https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add retrieval to chatbots": "https://python.langchain.com/docs/how_to/chatbots_retrieval/", "How to summarize text in a single LLM call": "https://python.langchain.com/docs/how_to/summarize_stuff/", "RAGatouille": "https://python.langchain.com/docs/integrations/retrievers/ragatouille/", "ApertureDB": "https://python.langchain.com/docs/integrations/vectorstores/aperturedb/", "Jina Reranker": "https://python.langchain.com/docs/integrations/document_transformers/jina_rerank/", "Image captions": "https://python.langchain.com/docs/integrations/document_loaders/image_captions/", "Build a Retrieval Augmented Generation (RAG) App": "https://python.langchain.com/docs/tutorials/rag/", "Summarize Text": "https://python.langchain.com/docs/tutorials/summarization/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/", "Build a PDF ingestion and Question/Answering system": "https://python.langchain.com/docs/tutorials/pdf_qa/"}, "LLMMathChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/"}, "BaseMessage": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to use reference examples when doing extraction": "https://python.langchain.com/docs/how_to/extraction_examples/", "How to propagate callbacks  constructor": "https://python.langchain.com/docs/how_to/callbacks_constructor/", "How to attach callbacks to a runnable": "https://python.langchain.com/docs/how_to/callbacks_attach/", "How to create a custom chat model class": "https://python.langchain.com/docs/how_to/custom_chat_model/", "How to pass callbacks in at runtime": "https://python.langchain.com/docs/how_to/callbacks_runtime/", "How to add examples to the prompt for query analysis": "https://python.langchain.com/docs/how_to/query_few_shot/", "WeChat": "https://python.langchain.com/docs/integrations/chat_loaders/wechat/", "Discord": "https://python.langchain.com/docs/integrations/chat_loaders/discord/", "Chat Bot Feedback Template": "https://python.langchain.com/docs/templates/chat-bot-feedback/"}, "RunnableConfig": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/", "How to access the RunnableConfig from a tool": "https://python.langchain.com/docs/how_to/tool_configure/", "How to summarize text through iterative refinement": "https://python.langchain.com/docs/how_to/summarize_refine/", "How to handle tool errors": "https://python.langchain.com/docs/how_to/tools_error/", "How to stream events from a tool": "https://python.langchain.com/docs/how_to/tool_stream_events/", "How to run custom functions": "https://python.langchain.com/docs/how_to/functions/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "LangChain Expression Language Cheatsheet": "https://python.langchain.com/docs/how_to/lcel_cheatsheet/", "How to dispatch custom callback events": "https://python.langchain.com/docs/how_to/callbacks_custom_events/", "How to pass runtime secrets to runnables": "https://python.langchain.com/docs/how_to/runnable_runtime_secrets/", "Tavily Search": "https://python.langchain.com/docs/integrations/tools/tavily_search/"}, "tool": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/llm_math_chain/", "How to disable parallel tool calling": "https://python.langchain.com/docs/how_to/tool_calling_parallel/", "How to use tools in a chain": "https://python.langchain.com/docs/how_to/tools_chain/", "How to access the RunnableConfig from a tool": "https://python.langchain.com/docs/how_to/tool_configure/", "How to do tool/function calling": "https://python.langchain.com/docs/how_to/function_calling/", "How to pass run time values to tools": "https://python.langchain.com/docs/how_to/tool_runtime/", "How to add a human-in-the-loop for tools": "https://python.langchain.com/docs/how_to/tools_human/", "How to create tools": "https://python.langchain.com/docs/how_to/custom_tools/", "How to pass multimodal data directly to models": "https://python.langchain.com/docs/how_to/multimodal_inputs/", "How to force models to call a tool": "https://python.langchain.com/docs/how_to/tool_choice/", "How to handle tool errors": "https://python.langchain.com/docs/how_to/tools_error/", "How to stream events from a tool": "https://python.langchain.com/docs/how_to/tool_stream_events/", "How to stream runnables": "https://python.langchain.com/docs/how_to/streaming/", "How to pass tool outputs to chat models": "https://python.langchain.com/docs/how_to/tool_results_pass_to_model/", "How to add ad-hoc tool calling capability to LLMs and Chat Models": "https://python.langchain.com/docs/how_to/tools_prompting/", "How to return artifacts from a tool": "https://python.langchain.com/docs/how_to/tool_artifacts/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "How to stream tool calls": "https://python.langchain.com/docs/how_to/tool_streaming/", "How to pass runtime secrets to runnables": "https://python.langchain.com/docs/how_to/runnable_runtime_secrets/", "How to use few-shot prompting with tool calling": "https://python.langchain.com/docs/how_to/tools_few_shot/", "FinancialDatasets Toolkit": "https://python.langchain.com/docs/integrations/tools/financial_datasets/", "Exa Search": "https://python.langchain.com/docs/integrations/tools/exa_search/", "DeepInfra": "https://python.langchain.com/docs/integrations/chat/deepinfra/", "ChatOllama": "https://python.langchain.com/docs/integrations/chat/ollama/", "Llama.cpp": "https://python.langchain.com/docs/integrations/chat/llamacpp/", "Cohere": "https://python.langchain.com/docs/integrations/providers/cohere/", "Eden AI": "https://python.langchain.com/docs/integrations/chat/edenai/", "ChatTongyi": "https://python.langchain.com/docs/integrations/chat/tongyi/", "ChatPremAI": "https://python.langchain.com/docs/integrations/chat/premai/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "PremAI": "https://python.langchain.com/docs/integrations/providers/premai/", "Log, Trace, and Monitor": "https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey/", "Portkey": "https://python.langchain.com/docs/integrations/providers/portkey/index/", "JSONFormer": "https://python.langchain.com/docs/integrations/llms/jsonformer_experimental/"}, "MultiPromptChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/multi_prompt_chain/"}, "ConversationChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/"}, "ConversationBufferMemory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Memorize": "https://python.langchain.com/docs/integrations/tools/memorize/", "Gradio": "https://python.langchain.com/docs/integrations/tools/gradio_tools/", "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain/", "Reddit Search ": "https://python.langchain.com/docs/integrations/tools/reddit_search/", "Xata": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history/", "Llama2Chat": "https://python.langchain.com/docs/integrations/chat/llama2_chat/", "SAP HANA Cloud Vector Engine": "https://python.langchain.com/docs/integrations/vectorstores/sap_hanavector/", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor/"}, "InMemoryChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/"}, "RunnableWithMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to trim messages": "https://python.langchain.com/docs/how_to/trim_messages/", "How to add tools to chatbots": "https://python.langchain.com/docs/how_to/chatbots_tools/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "How to add message history": "https://python.langchain.com/docs/how_to/message_history/", "How to add memory to chatbots": "https://python.langchain.com/docs/how_to/chatbots_memory/", "How to migrate from legacy LangChain agents to LangGraph": "https://python.langchain.com/docs/how_to/migrate_agent/", "Redis": "https://python.langchain.com/docs/integrations/memory/redis_chat_message_history/", "Google SQL for MySQL": "https://python.langchain.com/docs/integrations/memory/google_sql_mysql/", "Google AlloyDB for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_alloydb/", "ZepCloudChatMessageHistory": "https://python.langchain.com/docs/integrations/memory/zep_cloud_chat_message_history/", "AWS DynamoDB": "https://python.langchain.com/docs/integrations/memory/aws_dynamodb/", "Couchbase": "https://python.langchain.com/docs/integrations/memory/couchbase_chat_message_history/", "MongoDB": "https://python.langchain.com/docs/integrations/memory/mongodb_chat_message_history/", "SQL (SQLAlchemy)": "https://python.langchain.com/docs/integrations/memory/sql_chat_message_history/", "Streamlit": "https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history/", "Google El Carro Oracle": "https://python.langchain.com/docs/integrations/memory/google_el_carro/", "SQLite": "https://python.langchain.com/docs/integrations/memory/sqlite/", "Google SQL for PostgreSQL": "https://python.langchain.com/docs/integrations/memory/google_sql_pg/", "Google SQL for SQL Server": "https://python.langchain.com/docs/integrations/memory/google_sql_mssql/", "TiDB": "https://python.langchain.com/docs/integrations/memory/tidb_chat_message_history/", "ChatNVIDIA": "https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "BaseChatMessageHistory": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/conversation_chain/", "Build an Agent with AgentExecutor (Legacy)": "https://python.langchain.com/docs/how_to/agent_executor/", "How to add chat history": "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/", "Build a Chatbot": "https://python.langchain.com/docs/tutorials/chatbot/", "Conversational RAG": "https://python.langchain.com/docs/tutorials/qa_chat_history/"}, "ConstitutionalChain": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "ConstitutionalPrinciple": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/"}, "OpenAI": {"# Legacy": "https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain/", "# Example": "https://python.langchain.com/docs/versions/migrating_chains/map_rerank_docs_chain/", "How to use output parsers to parse an LLM response into structured format": "https://python.langchain.com/docs/how_to/output_parser_structured/", "How to reorder retrieved results to mitigate the \"lost in the middle\" effect": "https://python.langchain.com/docs/how_to/long_context_reorder/", "How to add fallbacks to a runnable": "https://python.langchain.com/docs/how_to/fallbacks/", "How to do retrieval with contextual compression": "https://python.langchain.com/docs/how_to/contextual_compression/", "How to retry when a parsing error occurs": "https://python.langchain.com/docs/how_to/output_parser_retry/", "How to stream responses from an LLM": "https://python.langchain.com/docs/how_to/streaming_llm/", "How to cache LLM responses": "https://python.langchain.com/docs/how_to/llm_caching/", "How to track token usage for LLMs": "https://python.langchain.com/docs/how_to/llm_token_usage_tracking/", "Model caches": "https://python.langchain.com/docs/integrations/llm_caching/", "Milvus": "https://python.langchain.com/docs/integrations/retrievers/self_query/milvus_self_query/", "PGVector (Postgres)": "https://python.langchain.com/docs/integrations/retrievers/self_query/pgvector_self_query/", "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate/", "Databricks Vector Search": "https://python.langchain.com/docs/integrations/retrievers/self_query/databricks_vector_search/", "DingoDB": "https://python.langchain.com/docs/integrations/retrievers/self_query/dingo/", "OpenSearch": "https://python.langchain.com/docs/integrations/retrievers/self_query/opensearch_self_query/", "Elasticsearch": "https://python.langchain.com/docs/integrations/retrievers/self_query/elasticsearch_self_query/", "Chroma": "https://python.langchain.com/docs/integrations/retrievers/self_query/chroma_self_query/", "Timescale Vector (Postgres) ": "https://python.langchain.com/docs/integrations/retrievers/self_query/timescalevector_self_query/", "Astra DB (Cassandra)": "https://python.langchain.com/docs/integrations/retrievers/self_query/astradb/", "Pinecone": "https://python.langchain.com/docs/integrations/retrievers/self_query/pinecone/", "Supabase (Postgres)": "https://python.langchain.com/docs/integrations/retrievers/self_query/supabase_self_query/", "Redis": "https://python.langchain.com/docs/integrations/retrievers/self_query/redis_self_query/", "Deep Lake": "https://python.langchain.com/docs/integrations/retrievers/self_query/activeloop_deeplake_self_query/", "MongoDB Atlas": "https://python.langchain.com/docs/integrations/retrievers/self_query/mongodb_atlas/", "Qdrant": "https://python.langchain.com/docs/integrations/retrievers/self_query/qdrant_self_query/", "OpenAI": "https://python.langchain.com/docs/integrations/llms/openai/", "Jira Toolkit": "https://python.langchain.com/docs/integrations/tools/jira/", "Google Jobs": "https://python.langchain.com/docs/integrations/tools/google_jobs/", "Google Serper": "https://python.langchain.com/docs/integrations/tools/google_serper/", "Azure Cognitive Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_cognitive_services/", "Human as a tool": "https://python.langchain.com/docs/integrations/tools/human_tools/", "Natural Language API Toolkits": "https://python.langchain.com/docs/integrations/tools/openapi_nla/", "Steam Toolkit": "https://python.langchain.com/docs/integrations/tools/steam/", "JSON Toolkit": "https://python.langchain.com/docs/integrations/tools/json/", "Google Finance": "https://python.langchain.com/docs/integrations/tools/google_finance/", "ClickUp Toolkit": "https://python.langchain.com/docs/integrations/tools/clickup/", "AWS Lambda": "https://python.langchain.com/docs/integrations/tools/awslambda/", "Google Drive": "https://python.langchain.com/docs/integrations/tools/google_drive/", "OpenWeatherMap": "https://python.langchain.com/docs/integrations/tools/openweathermap/", "Eleven Labs Text2Speech": "https://python.langchain.com/docs/integrations/tools/eleven_labs_tts/", "Office365 Toolkit": "https://python.langchain.com/docs/integrations/tools/office365/", "Pandas Dataframe": "https://python.langchain.com/docs/integrations/tools/pandas/", "Zapier Natural Language Actions": "https://python.langchain.com/docs/integrations/tools/zapier/", "Lemon Agent": "https://python.langchain.com/docs/integrations/tools/lemonai/", "NASA Toolkit": "https://python.langchain.com/docs/integrations/tools/nasa/", "GraphQL": "https://python.langchain.com/docs/integrations/tools/graphql/", "SearchApi": "https://python.langchain.com/docs/integrations/providers/searchapi/", "Gradio": "https://python.langchain.com/docs/integrations/tools/gradio_tools/", "SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain/", "Azure AI Services Toolkit": "https://python.langchain.com/docs/integrations/tools/azure_ai_services/", "OpenAPI Toolkit": "https://python.langchain.com/docs/integrations/tools/openapi/", "Dall-E Image Generator": "https://python.langchain.com/docs/integrations/tools/dalle_image_generator/", "Gitlab Toolkit": "https://python.langchain.com/docs/integrations/tools/gitlab/", "Ionic Shopping Tool": "https://python.langchain.com/docs/integrations/tools/ionic_shopping/", "Zep Open Source Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory/", "Mot\u00f6rhead": "https://python.langchain.com/docs/integrations/memory/motorhead_memory/", "Zep Cloud Memory": "https://python.langchain.com/docs/integrations/memory/zep_memory_cloud/", "Confident": "https://python.langchain.com/docs/integrations/callbacks/confident/", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor/", "Fiddler": "https://python.langchain.com/docs/integrations/callbacks/fiddler/", "SageMaker Tracking": "https://python.langchain.com/docs/integrations/callbacks/sagemaker_tracking/", "Label Studio": "https://python.langchain.com/docs/integrations/callbacks/labelstudio/", "Comet Tracing": "https://python.langchain.com/docs/integrations/callbacks/comet_tracing/", "Argilla": "https://python.langchain.com/docs/integrations/callbacks/argilla/", "PromptLayer": "https://python.langchain.com/docs/integrations/callbacks/promptlayer/", "Streamlit": "https://python.langchain.com/docs/integrations/callbacks/streamlit/", "Trubrics": "https://python.langchain.com/docs/integrations/callbacks/trubrics/", "Infino": "https://python.langchain.com/docs/integrations/callbacks/infino/", "DSPy": "https://python.langchain.com/docs/integrations/providers/dspy/", "Comet": "https://python.langchain.com/docs/integrations/providers/comet_tracking/", "Aim": "https://python.langchain.com/docs/integrations/providers/aim_tracking/", "Weights & Biases": "https://python.langchain.com/docs/integrations/providers/wandb_tracking/", "Log10": "https://python.langchain.com/docs/integrations/providers/log10/", "LangChain Decorators \u2728": "https://python.langchain.com/docs/integrations/providers/langchain_decorators/", "Rebuff": "https://python.langchain.com/docs/integrations/providers/rebuff/", "Serper - Google Search API": "https://python.langchain.com/docs/integrations/providers/google_serper/", "Helicone": "https://python.langchain.com/docs/integrations/providers/helicone/", "Shale Protocol": "https://python.langchain.com/docs/integrations/providers/shaleprotocol/", "WhyLabs": "https://python.langchain.com/docs/integrations/providers/whylabs_profiling/", "WandB Tracing": "https://python.langchain.com/docs/integrations/providers/wandb_tracing/", "ClearML": "https://python.langchain.com/docs/integrations/providers/clearml_tracking/", "Ray Serve": "https://python.langchain.com/docs/integrations/providers/ray_serve/", "Identity-enabled RAG using PebbloRetrievalQA": "https://python.langchain.com/docs/integrations/providers/pebblo/pebblo_retrieval_qa/", "Apache Doris": "https://python.langchain.com/docs/integrations/vectorstores/apache_doris/", "Jaguar Vector Database": "https://python.langchain.com/docs/integrations/vectorstores/jaguar/", "StarRocks": "https://python.langchain.com/docs/integrations/vectorstores/starrocks/", "Marqo": "https://python.langchain.com/docs/integrations/vectorstores/marqo/", "Timescale Vector (Postgres)": "https://python.langchain.com/docs/integrations/vectorstores/timescalevector/", "Amazon Document DB": "https://python.langchain.com/docs/integrations/vectorstores/documentdb/", "VoyageAI Reranker": "https://python.langchain.com/docs/integrations/document_transformers/voyageai-reranker/", "Apify Dataset": "https://python.langchain.com/docs/integrations/document_loaders/apify_dataset/", "Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic/", "Docugami": 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"NotebookLoader": {"Jupyter Notebook": "https://python.langchain.com/docs/integrations/document_loaders/jupyter_notebook/"}, "OracleAutonomousDatabaseLoader": {"Oracle Autonomous Database": "https://python.langchain.com/docs/integrations/document_loaders/oracleadb_loader/"}, "LanguageParser": {"Source Code": "https://python.langchain.com/docs/integrations/document_loaders/source_code/"}, "SRTLoader": {"Subtitle": "https://python.langchain.com/docs/integrations/document_loaders/subtitle/"}, "MastodonTootsLoader": {"Mastodon": "https://python.langchain.com/docs/integrations/document_loaders/mastodon/"}, "AirbyteShopifyLoader": {"Airbyte Shopify (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_shopify/"}, "PyPDFDirectoryLoader": {"PyPDFDirectoryLoader": "https://python.langchain.com/docs/integrations/document_loaders/pypdfdirectory/"}, "PySparkDataFrameLoader": {"PySpark": "https://python.langchain.com/docs/integrations/document_loaders/pyspark_dataframe/"}, "AirbyteZendeskSupportLoader": {"Airbyte Zendesk Support (Deprecated)": "https://python.langchain.com/docs/integrations/document_loaders/airbyte_zendesk_support/"}, "CoNLLULoader": {"CoNLL-U": "https://python.langchain.com/docs/integrations/document_loaders/conll-u/"}, "MongodbLoader": {"MongoDB": "https://python.langchain.com/docs/integrations/document_loaders/mongodb/"}, "SitemapLoader": {"Sitemap": "https://python.langchain.com/docs/integrations/document_loaders/sitemap/"}, "YuqueLoader": {"Yuque": "https://python.langchain.com/docs/integrations/document_loaders/yuque/"}, "PDFMinerLoader": {"PDFMiner": "https://python.langchain.com/docs/integrations/document_loaders/pdfminer/"}, "PDFMinerPDFasHTMLLoader": {"PDFMiner": "https://python.langchain.com/docs/integrations/document_loaders/pdfminer/"}, "QuipLoader": {"Quip": "https://python.langchain.com/docs/integrations/document_loaders/quip/"}, "LangSmithLoader": {"LangSmithLoader": "https://python.langchain.com/docs/integrations/document_loaders/langsmith/"}, "MemgraphGraph": {"Memgraph": "https://python.langchain.com/docs/integrations/graphs/memgraph/"}, "GraphSparqlQAChain": {"RDFLib": "https://python.langchain.com/docs/integrations/graphs/rdflib_sparql/"}, "RdfGraph": {"RDFLib": "https://python.langchain.com/docs/integrations/graphs/rdflib_sparql/"}, "NebulaGraphQAChain": {"NebulaGraph": "https://python.langchain.com/docs/integrations/graphs/nebula_graph/"}, "NebulaGraph": {"NebulaGraph": "https://python.langchain.com/docs/integrations/graphs/nebula_graph/"}, "GremlinQAChain": {"Azure Cosmos DB for Apache Gremlin": "https://python.langchain.com/docs/integrations/graphs/azure_cosmosdb_gremlin/"}, "GraphIndexCreator": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "GraphQAChain": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "NetworkxEntityGraph": {"NetworkX": "https://python.langchain.com/docs/integrations/graphs/networkx/"}, "HugeGraphQAChain": {"HugeGraph": "https://python.langchain.com/docs/integrations/graphs/hugegraph/"}, "HugeGraph": {"HugeGraph": "https://python.langchain.com/docs/integrations/graphs/hugegraph/"}, "AGEGraph": {"Apache AGE": "https://python.langchain.com/docs/integrations/graphs/apache_age/"}, "KuzuQAChain": {"Kuzu": "https://python.langchain.com/docs/integrations/graphs/kuzu_db/"}, "KuzuGraph": {"Kuzu": "https://python.langchain.com/docs/integrations/graphs/kuzu_db/"}, "FalkorDBQAChain": {"FalkorDB": "https://python.langchain.com/docs/integrations/graphs/falkordb/"}, "FalkorDBGraph": {"FalkorDB": "https://python.langchain.com/docs/integrations/graphs/falkordb/"}, "ConversationBufferWindowMemory": {"Baseten": "https://python.langchain.com/docs/integrations/llms/baseten/", "OpaquePrompts": "https://python.langchain.com/docs/integrations/llms/opaqueprompts/"}, "Solar": {"Solar": 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"https://python.langchain.com/docs/integrations/llms/titan_takeoff/"}, "Friendli": {"Friendli": "https://python.langchain.com/docs/integrations/llms/friendli/"}, "Databricks": {"Databricks": "https://python.langchain.com/docs/integrations/llms/databricks/"}, "LMFormatEnforcer": {"LM Format Enforcer": "https://python.langchain.com/docs/integrations/llms/lmformatenforcer_experimental/"}, "VLLM": {"vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/"}, "VLLMOpenAI": {"vLLM": "https://python.langchain.com/docs/integrations/llms/vllm/"}, "CustomOpenAIContentFormatter": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "ContentFormatterBase": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "DollyContentFormatter": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "load_llm": {"Azure ML": "https://python.langchain.com/docs/integrations/llms/azure_ml/"}, "MapReduceChain": {"Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/"}, "ModelLaboratory": {"Manifest": "https://python.langchain.com/docs/integrations/llms/manifest/"}, "ExLlamaV2": {"ExLlamaV2": "https://python.langchain.com/docs/integrations/llms/exllamav2/"}, "RELLM": {"RELLM": "https://python.langchain.com/docs/integrations/llms/rellm_experimental/"}, "Moonshot": {"MoonshotChat": "https://python.langchain.com/docs/integrations/llms/moonshot/"}, "OpenLM": {"OpenLM": "https://python.langchain.com/docs/integrations/llms/openlm/"}, "CloudflareWorkersAI": {"Cloudflare Workers AI": "https://python.langchain.com/docs/integrations/llms/cloudflare_workersai/"}, "ChatGLM3": {"ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/"}, "ChatGLM": {"ChatGLM": "https://python.langchain.com/docs/integrations/llms/chatglm/"}, "Sambaverse": {"SambaNova": "https://python.langchain.com/docs/integrations/llms/sambanova/"}, "SambaStudio": {"SambaNova": 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"https://python.langchain.com/docs/tutorials/data_generation/"}, "DatasetGenerator": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_data_generation_chain": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}, "create_extraction_chain_pydantic": {"Generate Synthetic Data": "https://python.langchain.com/docs/tutorials/data_generation/"}}
\ No newline at end of file
diff --git a/docs/docs/additional_resources/arxiv_references.mdx b/docs/docs/additional_resources/arxiv_references.mdx
index afec022d344..bbd292c5ffa 100644
--- a/docs/docs/additional_resources/arxiv_references.mdx
+++ b/docs/docs/additional_resources/arxiv_references.mdx
@@ -13,45 +13,45 @@ From the opposite direction, scientists use `LangChain` in research and referenc
 
 | arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation|
 |------------------|---------|-------------------|------------------------|
-| `2403.14403v2` [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](http://arxiv.org/abs/2403.14403v2) | Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al. | 2024‑03‑21 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2403.14403v2` [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](http://arxiv.org/abs/2403.14403v2) | Soyeong Jeong, Jinheon Baek, Sukmin Cho,  et al. | 2024‑03‑21 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
 | `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren,  et al. | 2024‑02‑06 | `Cookbook:` [Self-Discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb)
-| `2402.03367v2` [RAG-Fusion: a New Take on Retrieval-Augmented Generation](http://arxiv.org/abs/2402.03367v2) | Zackary Rackauckas | 2024‑01‑31 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2402.03367v2` [RAG-Fusion: a New Take on Retrieval-Augmented Generation](http://arxiv.org/abs/2402.03367v2) | Zackary Rackauckas | 2024‑01‑31 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
 | `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli,  et al. | 2024‑01‑31 | `Cookbook:` [Raptor](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb)
-| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al. | 2024‑01‑29 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
-| `2401.08500v1` [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](http://arxiv.org/abs/2401.08500v1) | Tal Ridnik, Dedy Kredo, Itamar Friedman | 2024‑01‑16 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu,  et al. | 2024‑01‑29 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Cookbook:` [Langgraph Crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
+| `2401.08500v1` [Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering](http://arxiv.org/abs/2401.08500v1) | Tal Ridnik, Dedy Kredo, Itamar Friedman | 2024‑01‑16 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
 | `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux,  et al. | 2024‑01‑08 | `Cookbook:` [Together Ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
 | `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen,  et al. | 2023‑12‑11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
 | `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan,  et al. | 2023‑11‑15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
-| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang,  et al. | 2023‑10‑17 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Cookbook:` [Langgraph Self Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
-| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023‑10‑09 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [Stepback-Qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
+| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang,  et al. | 2023‑10‑17 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Cookbook:` [Langgraph Self Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
+| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen,  et al. | 2023‑10‑09 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [Stepback-Qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
 | `2307.15337v3` [Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation](http://arxiv.org/abs/2307.15337v3) | Xuefei Ning, Zinan Lin, Zixuan Zhou,  et al. | 2023‑07‑28 | `Template:` [skeleton-of-thought](https://python.langchain.com/docs/templates/skeleton-of-thought)
 | `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone,  et al. | 2023‑07‑18 | `Cookbook:` [Semi Structured Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
-| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt,  et al. | 2023‑07‑06 | `Docs:` [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
+| `2307.03172v3` [Lost in the Middle: How Language Models Use Long Contexts](http://arxiv.org/abs/2307.03172v3) | Nelson F. Liu, Kevin Lin, John Hewitt,  et al. | 2023‑07‑06 | `Docs:` [docs/how_to/long_context_reorder](https://python.langchain.com/docs/how_to/long_context_reorder)
 | `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He,  et al. | 2023‑05‑23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [Rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
 | `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023‑05‑15 | `API:` [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot), `Cookbook:` [Tree Of Thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
 | `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan,  et al. | 2023‑05‑06 | `Cookbook:` [Plan And Execute Agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
-| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023‑05‑03 | `Docs:` [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression), `API:` [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
+| `2305.02156v1` [Zero-Shot Listwise Document Reranking with a Large Language Model](http://arxiv.org/abs/2305.02156v1) | Xueguang Ma, Xinyu Zhang, Ronak Pradeep,  et al. | 2023‑05‑03 | `Docs:` [docs/how_to/contextual_compression](https://python.langchain.com/docs/how_to/contextual_compression), `API:` [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
 | `2304.08485v2` [Visual Instruction Tuning](http://arxiv.org/abs/2304.08485v2) | Haotian Liu, Chunyuan Li, Qingyang Wu,  et al. | 2023‑04‑17 | `Cookbook:` [Semi Structured Multi Modal Rag Llama2](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb), [Semi Structured And Multi Modal Rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb)
 | `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai,  et al. | 2023‑04‑07 | `Cookbook:` [Generative Agents Interactive Simulacra Of Human Behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb), [Multiagent Bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb)
 | `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani,  et al. | 2023‑03‑31 | `Cookbook:` [Camel Role Playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
 | `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan,  et al. | 2023‑03‑30 | `API:` [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [Hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
 | `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen,  et al. | 2023‑01‑24 | `API:` [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022‑12‑20 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [Hypothetical Document Embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
-| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al. | 2022‑12‑15 | `Docs:` [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
+| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin,  et al. | 2022‑12‑20 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts), `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [Hypothetical Document Embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
+| `2212.08073v1` [Constitutional AI: Harmlessness from AI Feedback](http://arxiv.org/abs/2212.08073v1) | Yuntao Bai, Saurav Kadavath, Sandipan Kundu,  et al. | 2022‑12‑15 | `Docs:` [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain)
 | `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande,  et al. | 2022‑12‑12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
 | `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz,  et al. | 2022‑11‑25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
 | `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou,  et al. | 2022‑11‑18 | `API:` [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), `Cookbook:` [Program Aided Language Model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
-| `2210.11934v2` [An Analysis of Fusion Functions for Hybrid Retrieval](http://arxiv.org/abs/2210.11934v2) | Sebastian Bruch, Siyu Gai, Amir Ingber | 2022‑10‑21 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
-| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022‑10‑06 | `Docs:` [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
-| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022‑09‑22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
-| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022‑05‑26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
+| `2210.11934v2` [An Analysis of Fusion Functions for Hybrid Retrieval](http://arxiv.org/abs/2210.11934v2) | Sebastian Bruch, Siyu Gai, Amir Ingber | 2022‑10‑21 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
+| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu,  et al. | 2022‑10‑06 | `Docs:` [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/docs/concepts), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
+| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan,  et al. | 2022‑09‑22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
+| `2205.13147v4` [Matryoshka Representation Learning](http://arxiv.org/abs/2205.13147v4) | Aditya Kusupati, Gantavya Bhatt, Aniket Rege,  et al. | 2022‑05‑26 | `Docs:` [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
 | `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022‑05‑25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
-| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
+| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022‑03‑15 | `Docs:` [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa), `API:` [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
 | `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher,  et al. | 2022‑02‑01 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
-| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
+| `2112.01488v3` [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](http://arxiv.org/abs/2112.01488v3) | Keshav Santhanam, Omar Khattab, Jon Saad-Falcon,  et al. | 2021‑12‑02 | `Docs:` [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
 | `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy,  et al. | 2021‑02‑26 | `API:` [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
-| `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder,  et al. | 2020‑05‑28 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
-| `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al. | 2020‑05‑22 | `Docs:` [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+| `2005.14165v4` [Language Models are Few-Shot Learners](http://arxiv.org/abs/2005.14165v4) | Tom B. Brown, Benjamin Mann, Nick Ryder,  et al. | 2020‑05‑28 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
+| `2005.11401v4` [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](http://arxiv.org/abs/2005.11401v4) | Patrick Lewis, Ethan Perez, Aleksandra Piktus,  et al. | 2020‑05‑22 | `Docs:` [docs/concepts](https://python.langchain.com/docs/concepts)
 | `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney,  et al. | 2019‑09‑11 | `API:` [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference), [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint)
 
 ## Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
@@ -60,7 +60,7 @@ From the opposite direction, scientists use `LangChain` in research and referenc
 - **arXiv id:** [2403.14403v2](http://arxiv.org/abs/2403.14403v2)  **Published Date:** 2024-03-21
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
 
 **Abstract:** Retrieval-Augmented Large Language Models (LLMs), which incorporate the
 non-parametric knowledge from external knowledge bases into LLMs, have emerged
@@ -113,7 +113,7 @@ commonalities with human reasoning patterns.
 - **arXiv id:** [2402.03367v2](http://arxiv.org/abs/2402.03367v2)  **Published Date:** 2024-01-31
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
 
 **Abstract:** Infineon has identified a need for engineers, account managers, and customers
 to rapidly obtain product information. This problem is traditionally addressed
@@ -159,7 +159,7 @@ benchmark by 20% in absolute accuracy.
 - **arXiv id:** [2401.15884v2](http://arxiv.org/abs/2401.15884v2)  **Published Date:** 2024-01-29
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
    - **Cookbook:** [langgraph_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb)
 
 **Abstract:** Large language models (LLMs) inevitably exhibit hallucinations since the
@@ -187,7 +187,7 @@ performance of RAG-based approaches.
 - **arXiv id:** [2401.08500v1](http://arxiv.org/abs/2401.08500v1)  **Published Date:** 2024-01-16
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
 
 **Abstract:** Code generation problems differ from common natural language problems - they
 require matching the exact syntax of the target language, identifying happy
@@ -293,7 +293,7 @@ outside the pre-training knowledge scope.
 - **arXiv id:** [2310.11511v1](http://arxiv.org/abs/2310.11511v1)  **Published Date:** 2023-10-17
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
    - **Cookbook:** [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
 
 **Abstract:** Despite their remarkable capabilities, large language models (LLMs) often
@@ -324,7 +324,7 @@ to these models.
 - **arXiv id:** [2310.06117v2](http://arxiv.org/abs/2310.06117v2)  **Published Date:** 2023-10-09
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
    - **Template:** [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting)
    - **Cookbook:** [stepback-qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
 
@@ -384,7 +384,7 @@ contribute to the responsible development of LLMs.
 - **arXiv id:** [2307.03172v3](http://arxiv.org/abs/2307.03172v3)  **Published Date:** 2023-07-06
 - **LangChain:**
 
-   - **Documentation:** [docs/how_to/long_context_reorder](https://python.langchain.com/v0.2/docs/how_to/long_context_reorder)
+   - **Documentation:** [docs/how_to/long_context_reorder](https://python.langchain.com/docs/how_to/long_context_reorder)
 
 **Abstract:** While recent language models have the ability to take long contexts as input,
 relatively little is known about how well they use longer context. We analyze
@@ -490,7 +490,7 @@ https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
 - **arXiv id:** [2305.02156v1](http://arxiv.org/abs/2305.02156v1)  **Published Date:** 2023-05-03
 - **LangChain:**
 
-   - **Documentation:** [docs/how_to/contextual_compression](https://python.langchain.com/v0.2/docs/how_to/contextual_compression)
+   - **Documentation:** [docs/how_to/contextual_compression](https://python.langchain.com/docs/how_to/contextual_compression)
    - **API Reference:** [langchain...LLMListwiseRerank](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html#langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank)
 
 **Abstract:** Supervised ranking methods based on bi-encoder or cross-encoder architectures
@@ -649,7 +649,7 @@ family, and discuss robustness and security.
 - **arXiv id:** [2212.10496v1](http://arxiv.org/abs/2212.10496v1)  **Published Date:** 2022-12-20
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
    - **API Reference:** [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder)
    - **Template:** [hyde](https://python.langchain.com/docs/templates/hyde)
    - **Cookbook:** [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
@@ -678,7 +678,7 @@ search, QA, fact verification) and languages~(e.g. sw, ko, ja).
 - **arXiv id:** [2212.08073v1](http://arxiv.org/abs/2212.08073v1)  **Published Date:** 2022-12-15
 - **LangChain:**
 
-   - **Documentation:** [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/constitutional_chain)
+   - **Documentation:** [docs/versions/migrating_chains/constitutional_chain](https://python.langchain.com/docs/versions/migrating_chains/constitutional_chain)
 
 **Abstract:** As AI systems become more capable, we would like to enlist their help to
 supervise other AIs. We experiment with methods for training a harmless AI
@@ -792,7 +792,7 @@ publicly available at http://reasonwithpal.com/ .
 - **arXiv id:** [2210.11934v2](http://arxiv.org/abs/2210.11934v2)  **Published Date:** 2022-10-21
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
 
 **Abstract:** We study hybrid search in text retrieval where lexical and semantic search
 are fused together with the intuition that the two are complementary in how
@@ -811,7 +811,7 @@ training examples to tune its only parameter to a target domain.
 - **arXiv id:** [2210.03629v3](http://arxiv.org/abs/2210.03629v3)  **Published Date:** 2022-10-06
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/tools/ionic_shopping](https://python.langchain.com/v0.2/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/v0.2/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/concepts](https://python.langchain.com/docs/concepts)
    - **API Reference:** [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
 
 **Abstract:** While large language models (LLMs) have demonstrated impressive capabilities
@@ -843,7 +843,7 @@ Project site with code: https://react-lm.github.io
 - **arXiv id:** [2209.10785v2](http://arxiv.org/abs/2209.10785v2)  **Published Date:** 2022-09-22
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/v0.2/docs/integrations/providers/activeloop_deeplake)
+   - **Documentation:** [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
 
 **Abstract:** Traditional data lakes provide critical data infrastructure for analytical
 workloads by enabling time travel, running SQL queries, ingesting data with
@@ -868,7 +868,7 @@ TensorFlow, JAX, and integrate with numerous MLOps tools.
 - **arXiv id:** [2205.13147v4](http://arxiv.org/abs/2205.13147v4)  **Published Date:** 2022-05-26
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/v0.2/docs/integrations/providers/snowflake)
+   - **Documentation:** [docs/integrations/providers/snowflake](https://python.langchain.com/docs/integrations/providers/snowflake)
 
 **Abstract:** Learned representations are a central component in modern ML systems, serving
 a multitude of downstream tasks. When training such representations, it is
@@ -925,7 +925,7 @@ encoders, mine bitexts, and validate the bitexts by training NMT systems.
 - **arXiv id:** [2204.00498v1](http://arxiv.org/abs/2204.00498v1)  **Published Date:** 2022-03-15
 - **LangChain:**
 
-   - **Documentation:** [docs/tutorials/sql_qa](https://python.langchain.com/v0.2/docs/tutorials/sql_qa)
+   - **Documentation:** [docs/tutorials/sql_qa](https://python.langchain.com/docs/tutorials/sql_qa)
    - **API Reference:** [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL)
 
 **Abstract:** We perform an empirical evaluation of Text-to-SQL capabilities of the Codex
@@ -971,7 +971,7 @@ reducing degenerate repetitions.
 - **arXiv id:** [2112.01488v3](http://arxiv.org/abs/2112.01488v3)  **Published Date:** 2021-12-02
 - **LangChain:**
 
-   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/v0.2/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/v0.2/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/v0.2/docs/integrations/providers/dspy)
+   - **Documentation:** [docs/integrations/retrievers/ragatouille](https://python.langchain.com/docs/integrations/retrievers/ragatouille), [docs/integrations/providers/ragatouille](https://python.langchain.com/docs/integrations/providers/ragatouille), [docs/concepts](https://python.langchain.com/docs/concepts), [docs/integrations/providers/dspy](https://python.langchain.com/docs/integrations/providers/dspy)
 
 **Abstract:** Neural information retrieval (IR) has greatly advanced search and other
 knowledge-intensive language tasks. While many neural IR methods encode queries
@@ -1022,7 +1022,7 @@ https://github.com/OpenAI/CLIP.
 - **arXiv id:** [2005.14165v4](http://arxiv.org/abs/2005.14165v4)  **Published Date:** 2020-05-28
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
 
 **Abstract:** Recent work has demonstrated substantial gains on many NLP tasks and
 benchmarks by pre-training on a large corpus of text followed by fine-tuning on
@@ -1055,7 +1055,7 @@ and of GPT-3 in general.
 - **arXiv id:** [2005.11401v4](http://arxiv.org/abs/2005.11401v4)  **Published Date:** 2020-05-22
 - **LangChain:**
 
-   - **Documentation:** [docs/concepts](https://python.langchain.com/v0.2/docs/concepts)
+   - **Documentation:** [docs/concepts](https://python.langchain.com/docs/concepts)
 
 **Abstract:** Large pre-trained language models have been shown to store factual knowledge
 in their parameters, and achieve state-of-the-art results when fine-tuned on
diff --git a/docs/docs/concepts.mdx b/docs/docs/concepts.mdx
index 0c5987229e2..e20b1794626 100644
--- a/docs/docs/concepts.mdx
+++ b/docs/docs/concepts.mdx
@@ -97,7 +97,7 @@ For guides on how to do specific tasks with LCEL, check out [the relevant how-to
 ### Runnable interface
 <span data-heading-keywords="invoke,runnable"></span>
 
-To make it as easy as possible to create custom chains, we've implemented a ["Runnable"](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. Many LangChain components implement the `Runnable` protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about below.
+To make it as easy as possible to create custom chains, we've implemented a ["Runnable"](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. Many LangChain components implement the `Runnable` protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about below.
 
 This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way.
 The standard interface includes:
@@ -380,17 +380,17 @@ LangChain has lots of different types of output parsers. This is a list of outpu
 
 | Name            | Supports Streaming | Has Format Instructions       | Calls LLM | Input Type                       | Output Type          | Description                                                                                                                                                                                                                                              |
 |-----------------|--------------------|-------------------------------|-----------|----------------------------------|----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [JSON](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html#langchain_core.output_parsers.json.JsonOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`               | JSON object          | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.                                    |
-| [XML](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`                 | `dict`               | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).                                                                                                                            |
-| [CSV](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser)           | ✅                  | ✅                             |           | `str` \| `Message`                 | `List[str]`          | Returns a list of comma separated values.                                                                                                                                                                                                                |
-| [OutputFixing](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)    |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.                                                                                              |
-| [RetryWithError](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html#langchain.output_parsers.retry.RetryWithErrorOutputParser)  |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions. |
-| [Pydantic](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html#langchain_core.output_parsers.pydantic.PydanticOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format.                                                                                                                                                                                     |
-| [YAML](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format. Uses YAML to encode it.                                                                                                                                                                                    |
-| [PandasDataFrame](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser.html#langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser) |                    | ✅                             |           | `str` \| `Message`                 | `dict`               | Useful for doing operations with pandas DataFrames.                                                                                                                                                                                                      |
-| [Enum](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html#langchain.output_parsers.enum.EnumOutputParser)            |                    | ✅                             |           | `str` \| `Message`                 | `Enum`               | Parses response into one of the provided enum values.                                                                                                                                                                                                    |
-| [Datetime](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html#langchain.output_parsers.datetime.DatetimeOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `datetime.datetime`  | Parses response into a datetime string.                                                                                                                                                                                                                  |
-| [Structured](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html#langchain.output_parsers.structured.StructuredOutputParser)      |                    | ✅                             |           | `str` \| `Message`                 | `Dict[str, str]`     | An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.                                            |
+| [JSON](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html#langchain_core.output_parsers.json.JsonOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`               | JSON object          | Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.                                    |
+| [XML](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html#langchain_core.output_parsers.xml.XMLOutputParser)            | ✅                  | ✅                             |           | `str` \| `Message`                 | `dict`               | Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).                                                                                                                            |
+| [CSV](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.list.CommaSeparatedListOutputParser.html#langchain_core.output_parsers.list.CommaSeparatedListOutputParser)           | ✅                  | ✅                             |           | `str` \| `Message`                 | `List[str]`          | Returns a list of comma separated values.                                                                                                                                                                                                                |
+| [OutputFixing](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)    |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.                                                                                              |
+| [RetryWithError](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryWithErrorOutputParser.html#langchain.output_parsers.retry.RetryWithErrorOutputParser)  |                    |                               | ✅         | `str` \| `Message`                 |                      | Wraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions. |
+| [Pydantic](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html#langchain_core.output_parsers.pydantic.PydanticOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format.                                                                                                                                                                                     |
+| [YAML](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `pydantic.BaseModel` | Takes a user defined Pydantic model and returns data in that format. Uses YAML to encode it.                                                                                                                                                                                    |
+| [PandasDataFrame](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser.html#langchain.output_parsers.pandas_dataframe.PandasDataFrameOutputParser) |                    | ✅                             |           | `str` \| `Message`                 | `dict`               | Useful for doing operations with pandas DataFrames.                                                                                                                                                                                                      |
+| [Enum](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.enum.EnumOutputParser.html#langchain.output_parsers.enum.EnumOutputParser)            |                    | ✅                             |           | `str` \| `Message`                 | `Enum`               | Parses response into one of the provided enum values.                                                                                                                                                                                                    |
+| [Datetime](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.datetime.DatetimeOutputParser.html#langchain.output_parsers.datetime.DatetimeOutputParser)        |                    | ✅                             |           | `str` \| `Message`                 | `datetime.datetime`  | Parses response into a datetime string.                                                                                                                                                                                                                  |
+| [Structured](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.structured.StructuredOutputParser.html#langchain.output_parsers.structured.StructuredOutputParser)      |                    | ✅                             |           | `str` \| `Message`                 | `Dict[str, str]`     | An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.                                            |
 
 For specifics on how to use output parsers, see the [relevant how-to guides here](/docs/how_to/#output-parsers).
 
@@ -501,7 +501,7 @@ For specifics on how to use retrievers, see the [relevant how-to guides here](/d
 For some techniques, such as [indexing and retrieval with multiple vectors per document](/docs/how_to/multi_vector/) or
 [caching embeddings](/docs/how_to/caching_embeddings/), having a form of key-value (KV) storage is helpful.
 
-LangChain includes a [`BaseStore`](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.BaseStore.html) interface,
+LangChain includes a [`BaseStore`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) interface,
 which allows for storage of arbitrary data. However, LangChain components that require KV-storage accept a
 more specific `BaseStore[str, bytes]` instance that stores binary data (referred to as a `ByteStore`), and internally take care of
 encoding and decoding data for their specific needs.
@@ -510,7 +510,7 @@ This means that as a user, you only need to think about one type of store rather
 
 #### Interface
 
-All [`BaseStores`](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows
+All [`BaseStores`](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.BaseStore.html) support the following interface. Note that the interface allows
 for modifying **multiple** key-value pairs at once:
 
 - `mget(key: Sequence[str]) -> List[Optional[bytes]]`: get the contents of multiple keys, returning `None` if the key does not exist
@@ -708,10 +708,10 @@ You can subscribe to these events by using the `callbacks` argument available th
 
 Callback handlers can either be `sync` or `async`:
 
-* Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
-* Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
+* Sync callback handlers implement the [BaseCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html) interface.
+* Async callback handlers implement the [AsyncCallbackHandler](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) interface.
 
-During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
+During run-time LangChain configures an appropriate callback manager (e.g., [CallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.CallbackManager.html) or [AsyncCallbackManager](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.AsyncCallbackManager.html) which will be responsible for calling the appropriate method on each "registered" callback handler when the event is triggered.
 
 #### Passing callbacks
 
@@ -779,7 +779,7 @@ For models (or other components) that don't support streaming natively, this ite
 you could still use the same general pattern when calling them. Using `.stream()` will also automatically call the model in streaming mode
 without the need to provide additional config.
 
-The type of each outputted chunk depends on the type of component - for example, chat models yield [`AIMessageChunks`](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html).
+The type of each outputted chunk depends on the type of component - for example, chat models yield [`AIMessageChunks`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html).
 Because this method is part of [LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel),
 you can handle formatting differences from different outputs using an [output parser](/docs/concepts/#output-parsers) to transform
 each yielded chunk.
@@ -827,10 +827,10 @@ including a table listing available events.
 #### Callbacks
 
 The lowest level way to stream outputs from LLMs in LangChain is via the [callbacks](/docs/concepts/#callbacks) system. You can pass a
-callback handler that handles the [`on_llm_new_token`](https://python.langchain.com/v0.2/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_new_token) event into LangChain components. When that component is invoked, any
+callback handler that handles the [`on_llm_new_token`](https://python.langchain.com/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_new_token) event into LangChain components. When that component is invoked, any
 [LLM](/docs/concepts/#llms) or [chat model](/docs/concepts/#chat-models) contained in the component calls
 the callback with the generated token. Within the callback, you could pipe the tokens into some other destination, e.g. a HTTP response.
-You can also handle the [`on_llm_end`](https://python.langchain.com/v0.2/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_end) event to perform any necessary cleanup.
+You can also handle the [`on_llm_end`](https://python.langchain.com/api_reference/langchain/callbacks/langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html#langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.on_llm_end) event to perform any necessary cleanup.
 
 You can see [this how-to section](/docs/how_to/#callbacks) for more specifics on using callbacks.
 
diff --git a/docs/docs/contributing/documentation/setup.mdx b/docs/docs/contributing/documentation/setup.mdx
index 2ac1cab7376..edbcb3ca1b0 100644
--- a/docs/docs/contributing/documentation/setup.mdx
+++ b/docs/docs/contributing/documentation/setup.mdx
@@ -12,7 +12,7 @@ It covers a wide array of topics, including tutorials, use cases, integrations,
 and more, offering extensive guidance on building with LangChain.
 The content for this documentation lives in the `/docs` directory of the monorepo.
 2. In-code Documentation: This is documentation of the codebase itself, which is also
-used to generate the externally facing [API Reference](https://python.langchain.com/v0.2/api_reference/langchain/index.html).
+used to generate the externally facing [API Reference](https://python.langchain.com/api_reference/langchain/index.html).
 The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that
 developers document their code well.
 
diff --git a/docs/docs/contributing/repo_structure.mdx b/docs/docs/contributing/repo_structure.mdx
index 63e180696a3..30459d55784 100644
--- a/docs/docs/contributing/repo_structure.mdx
+++ b/docs/docs/contributing/repo_structure.mdx
@@ -50,7 +50,7 @@ There are other files in the root directory level, but their presence should be
 ## Documentation
 
 The `/docs` directory contains the content for the documentation that is shown
-at https://python.langchain.com/ and the associated API Reference https://python.langchain.com/v0.2/api_reference/langchain/index.html.
+at https://python.langchain.com/ and the associated API Reference https://python.langchain.com/api_reference/langchain/index.html.
 
 See the [documentation](/docs/contributing/documentation/) guidelines to learn how to contribute to the documentation.
 
diff --git a/docs/docs/how_to/HTML_header_metadata_splitter.ipynb b/docs/docs/how_to/HTML_header_metadata_splitter.ipynb
index e7d6df55cc9..2b336ed8844 100644
--- a/docs/docs/how_to/HTML_header_metadata_splitter.ipynb
+++ b/docs/docs/how_to/HTML_header_metadata_splitter.ipynb
@@ -13,7 +13,7 @@
     "# How to split by HTML header \n",
     "## Description and motivation\n",
     "\n",
-    "[HTMLHeaderTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/html/langchain_text_splitters.html.HTMLHeaderTextSplitter.html) is a \"structure-aware\" chunker that splits text at the HTML element level and adds metadata for each header \"relevant\" to any given chunk. It can return chunks element by element or combine elements with the same metadata, with the objectives of (a) keeping related text grouped (more or less) semantically and (b) preserving context-rich information encoded in document structures. It can be used with other text splitters as part of a chunking pipeline.\n",
+    "[HTMLHeaderTextSplitter](https://python.langchain.com/api_reference/text_splitters/html/langchain_text_splitters.html.HTMLHeaderTextSplitter.html) is a \"structure-aware\" chunker that splits text at the HTML element level and adds metadata for each header \"relevant\" to any given chunk. It can return chunks element by element or combine elements with the same metadata, with the objectives of (a) keeping related text grouped (more or less) semantically and (b) preserving context-rich information encoded in document structures. It can be used with other text splitters as part of a chunking pipeline.\n",
     "\n",
     "It is analogous to the [MarkdownHeaderTextSplitter](/docs/how_to/markdown_header_metadata_splitter) for markdown files.\n",
     "\n",
diff --git a/docs/docs/how_to/MultiQueryRetriever.ipynb b/docs/docs/how_to/MultiQueryRetriever.ipynb
index cd57bb05ec8..4b093af87db 100644
--- a/docs/docs/how_to/MultiQueryRetriever.ipynb
+++ b/docs/docs/how_to/MultiQueryRetriever.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on a distance metric. But, retrieval may produce different results with subtle changes in query wording, or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious.\n",
     "\n",
-    "The [MultiQueryRetriever](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html) automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` can mitigate some of the limitations of the distance-based retrieval and get a richer set of results.\n",
+    "The [MultiQueryRetriever](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html) automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` can mitigate some of the limitations of the distance-based retrieval and get a richer set of results.\n",
     "\n",
     "Let's build a vectorstore using the [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post by Lilian Weng from the [RAG tutorial](/docs/tutorials/rag):"
    ]
@@ -161,9 +161,9 @@
    "source": [
     "#### Supplying your own prompt\n",
     "\n",
-    "Under the hood, `MultiQueryRetriever` generates queries using a specific [prompt](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html). To customize this prompt:\n",
+    "Under the hood, `MultiQueryRetriever` generates queries using a specific [prompt](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.multi_query.MultiQueryRetriever.html). To customize this prompt:\n",
     "\n",
-    "1. Make a [PromptTemplate](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.prompt.PromptTemplate.html) with an input variable for the question;\n",
+    "1. Make a [PromptTemplate](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.prompt.PromptTemplate.html) with an input variable for the question;\n",
     "2. Implement an [output parser](/docs/concepts#output-parsers) like the one below to split the result into a list of queries.\n",
     "\n",
     "The prompt and output parser together must support the generation of a list of queries."
diff --git a/docs/docs/how_to/add_scores_retriever.ipynb b/docs/docs/how_to/add_scores_retriever.ipynb
index d5270545266..80baff7d3eb 100644
--- a/docs/docs/how_to/add_scores_retriever.ipynb
+++ b/docs/docs/how_to/add_scores_retriever.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# How to add scores to retriever results\n",
     "\n",
-    "Retrievers will return sequences of [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) objects, which by default include no information about the process that retrieved them (e.g., a similarity score against a query). Here we demonstrate how to add retrieval scores to the `.metadata` of documents:\n",
+    "Retrievers will return sequences of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects, which by default include no information about the process that retrieved them (e.g., a similarity score against a query). Here we demonstrate how to add retrieval scores to the `.metadata` of documents:\n",
     "1. From [vectorstore retrievers](/docs/how_to/vectorstore_retriever);\n",
     "2. From higher-order LangChain retrievers, such as [SelfQueryRetriever](/docs/how_to/self_query) or [MultiVectorRetriever](/docs/how_to/multi_vector).\n",
     "\n",
@@ -15,7 +15,7 @@
     "\n",
     "## Create vector store\n",
     "\n",
-    "First we populate a vector store with some data. We will use a [PineconeVectorStore](https://python.langchain.com/v0.2/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html), but this guide is compatible with any LangChain vector store that implements a `.similarity_search_with_score` method."
+    "First we populate a vector store with some data. We will use a [PineconeVectorStore](https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html), but this guide is compatible with any LangChain vector store that implements a `.similarity_search_with_score` method."
    ]
   },
   {
@@ -263,7 +263,7 @@
     "\n",
     "To propagate similarity scores through this retriever, we can again subclass `MultiVectorRetriever` and override a method. This time we will override `_get_relevant_documents`.\n",
     "\n",
-    "First, we prepare some fake data. We generate fake \"whole documents\" and store them in a document store; here we will use a simple [InMemoryStore](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.InMemoryBaseStore.html)."
+    "First, we prepare some fake data. We generate fake \"whole documents\" and store them in a document store; here we will use a simple [InMemoryStore](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.InMemoryBaseStore.html)."
    ]
   },
   {
diff --git a/docs/docs/how_to/assign.ipynb b/docs/docs/how_to/assign.ipynb
index a48623ac2ce..59e4a0ed3ec 100644
--- a/docs/docs/how_to/assign.ipynb
+++ b/docs/docs/how_to/assign.ipynb
@@ -27,7 +27,7 @@
     "\n",
     ":::\n",
     "\n",
-    "An alternate way of [passing data through](/docs/how_to/passthrough) steps of a chain is to leave the current values of the chain state unchanged while assigning a new value under a given key. The [`RunnablePassthrough.assign()`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html#langchain_core.runnables.passthrough.RunnablePassthrough.assign) static method takes an input value and adds the extra arguments passed to the assign function.\n",
+    "An alternate way of [passing data through](/docs/how_to/passthrough) steps of a chain is to leave the current values of the chain state unchanged while assigning a new value under a given key. The [`RunnablePassthrough.assign()`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html#langchain_core.runnables.passthrough.RunnablePassthrough.assign) static method takes an input value and adds the extra arguments passed to the assign function.\n",
     "\n",
     "This is useful in the common [LangChain Expression Language](/docs/concepts/#langchain-expression-language) pattern of additively creating a dictionary to use as input to a later step.\n",
     "\n",
diff --git a/docs/docs/how_to/binding.ipynb b/docs/docs/how_to/binding.ipynb
index ce2b8a0a8dd..c25f038e2a3 100644
--- a/docs/docs/how_to/binding.ipynb
+++ b/docs/docs/how_to/binding.ipynb
@@ -27,7 +27,7 @@
     "\n",
     ":::\n",
     "\n",
-    "Sometimes we want to invoke a [`Runnable`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) within a [RunnableSequence](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html) with constant arguments that are not part of the output of the preceding Runnable in the sequence, and which are not part of the user input. We can use the [`Runnable.bind()`](https://python.langchain.com/v0.2/api_reference/langchain_core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.bind) method to set these arguments ahead of time.\n",
+    "Sometimes we want to invoke a [`Runnable`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) within a [RunnableSequence](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html) with constant arguments that are not part of the output of the preceding Runnable in the sequence, and which are not part of the user input. We can use the [`Runnable.bind()`](https://python.langchain.com/api_reference/langchain_core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.bind) method to set these arguments ahead of time.\n",
     "\n",
     "## Binding stop sequences\n",
     "\n",
diff --git a/docs/docs/how_to/callbacks_async.ipynb b/docs/docs/how_to/callbacks_async.ipynb
index d25dc0cd40c..1577cff54e8 100644
--- a/docs/docs/how_to/callbacks_async.ipynb
+++ b/docs/docs/how_to/callbacks_async.ipynb
@@ -14,7 +14,7 @@
     "- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
     ":::\n",
     "\n",
-    "If you are planning to use the async APIs, it is recommended to use and extend [`AsyncCallbackHandler`](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) to avoid blocking the event.\n",
+    "If you are planning to use the async APIs, it is recommended to use and extend [`AsyncCallbackHandler`](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html) to avoid blocking the event.\n",
     "\n",
     "\n",
     ":::{.callout-warning}\n",
diff --git a/docs/docs/how_to/callbacks_attach.ipynb b/docs/docs/how_to/callbacks_attach.ipynb
index d951247749a..79ca30cf2e3 100644
--- a/docs/docs/how_to/callbacks_attach.ipynb
+++ b/docs/docs/how_to/callbacks_attach.ipynb
@@ -17,7 +17,7 @@
     "\n",
     ":::\n",
     "\n",
-    "If you are composing a chain of runnables and want to reuse callbacks across multiple executions, you can attach callbacks with the [`.with_config()`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method. This saves you the need to pass callbacks in each time you invoke the chain.\n",
+    "If you are composing a chain of runnables and want to reuse callbacks across multiple executions, you can attach callbacks with the [`.with_config()`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method. This saves you the need to pass callbacks in each time you invoke the chain.\n",
     "\n",
     ":::{.callout-important}\n",
     "\n",
diff --git a/docs/docs/how_to/callbacks_runtime.ipynb b/docs/docs/how_to/callbacks_runtime.ipynb
index 895427195b2..a4b15416dfc 100644
--- a/docs/docs/how_to/callbacks_runtime.ipynb
+++ b/docs/docs/how_to/callbacks_runtime.ipynb
@@ -15,7 +15,7 @@
     "\n",
     ":::\n",
     "\n",
-    "In many cases, it is advantageous to pass in handlers instead when running the object. When we pass through [`CallbackHandlers`](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) using the `callbacks` keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an Agent, it will be used for all callbacks related to the agent and all the objects involved in the agent's execution, in this case, the Tools and LLM.\n",
+    "In many cases, it is advantageous to pass in handlers instead when running the object. When we pass through [`CallbackHandlers`](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) using the `callbacks` keyword arg when executing an run, those callbacks will be issued by all nested objects involved in the execution. For example, when a handler is passed through to an Agent, it will be used for all callbacks related to the agent and all the objects involved in the agent's execution, in this case, the Tools and LLM.\n",
     "\n",
     "This prevents us from having to manually attach the handlers to each individual nested object. Here's an example:"
    ]
diff --git a/docs/docs/how_to/character_text_splitter.ipynb b/docs/docs/how_to/character_text_splitter.ipynb
index 49c7e3dafd1..ab82464c48f 100644
--- a/docs/docs/how_to/character_text_splitter.ipynb
+++ b/docs/docs/how_to/character_text_splitter.ipynb
@@ -28,7 +28,7 @@
     "\n",
     "To obtain the string content directly, use `.split_text`.\n",
     "\n",
-    "To create LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) objects (e.g., for use in downstream tasks), use `.create_documents`."
+    "To create LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects (e.g., for use in downstream tasks), use `.create_documents`."
    ]
   },
   {
diff --git a/docs/docs/how_to/chat_models_universal_init.ipynb b/docs/docs/how_to/chat_models_universal_init.ipynb
index 1f728f59451..37f15212e3f 100644
--- a/docs/docs/how_to/chat_models_universal_init.ipynb
+++ b/docs/docs/how_to/chat_models_universal_init.ipynb
@@ -11,7 +11,7 @@
     "\n",
     ":::tip Supported models\n",
     "\n",
-    "See the [init_chat_model()](https://python.langchain.com/v0.2/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) API reference for a full list of supported integrations.\n",
+    "See the [init_chat_model()](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) API reference for a full list of supported integrations.\n",
     "\n",
     "Make sure you have the integration packages installed for any model providers you want to support. E.g. you should have `langchain-openai` installed to init an OpenAI model.\n",
     "\n",
@@ -140,7 +140,7 @@
    "source": [
     "## Inferring model provider\n",
     "\n",
-    "For common and distinct model names `init_chat_model()` will attempt to infer the model provider. See the [API reference](https://python.langchain.com/v0.2/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) for a full list of inference behavior. E.g. any model that starts with `gpt-3...` or `gpt-4...` will be inferred as using model provider `openai`."
+    "For common and distinct model names `init_chat_model()` will attempt to infer the model provider. See the [API reference](https://python.langchain.com/api_reference/langchain/chat_models/langchain.chat_models.base.init_chat_model.html) for a full list of inference behavior. E.g. any model that starts with `gpt-3...` or `gpt-4...` will be inferred as using model provider `openai`."
    ]
   },
   {
diff --git a/docs/docs/how_to/chat_streaming.ipynb b/docs/docs/how_to/chat_streaming.ipynb
index c1bd12be838..19f1cc86022 100644
--- a/docs/docs/how_to/chat_streaming.ipynb
+++ b/docs/docs/how_to/chat_streaming.ipynb
@@ -18,7 +18,7 @@
     "# How to stream chat model responses\n",
     "\n",
     "\n",
-    "All [chat models](https://python.langchain.com/v0.2/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) implement the [Runnable interface](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable), which comes with a **default** implementations of standard runnable methods (i.e. `ainvoke`, `batch`, `abatch`, `stream`, `astream`, `astream_events`).\n",
+    "All [chat models](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) implement the [Runnable interface](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable), which comes with a **default** implementations of standard runnable methods (i.e. `ainvoke`, `batch`, `abatch`, `stream`, `astream`, `astream_events`).\n",
     "\n",
     "The **default** streaming implementation provides an`Iterator` (or `AsyncIterator` for asynchronous streaming) that yields a single value: the final output from the underlying chat model provider.\n",
     "\n",
@@ -120,7 +120,7 @@
    "source": [
     "## Astream events\n",
     "\n",
-    "Chat models also support the standard [astream events](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) method.\n",
+    "Chat models also support the standard [astream events](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) method.\n",
     "\n",
     "This method is useful if you're streaming output from a larger LLM application that contains multiple steps (e.g., an LLM chain composed of a prompt, llm and parser)."
    ]
diff --git a/docs/docs/how_to/chat_token_usage_tracking.ipynb b/docs/docs/how_to/chat_token_usage_tracking.ipynb
index 24b77c38e16..f469b1bfb71 100644
--- a/docs/docs/how_to/chat_token_usage_tracking.ipynb
+++ b/docs/docs/how_to/chat_token_usage_tracking.ipynb
@@ -42,7 +42,7 @@
     "\n",
     "A number of model providers return token usage information as part of the chat generation response. When available, this information will be included on the `AIMessage` objects produced by the corresponding model.\n",
     "\n",
-    "LangChain `AIMessage` objects include a [usage_metadata](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.usage_metadata) attribute. When populated, this attribute will be a [UsageMetadata](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html) dictionary with standard keys (e.g., `\"input_tokens\"` and `\"output_tokens\"`).\n",
+    "LangChain `AIMessage` objects include a [usage_metadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.usage_metadata) attribute. When populated, this attribute will be a [UsageMetadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html) dictionary with standard keys (e.g., `\"input_tokens\"` and `\"output_tokens\"`).\n",
     "\n",
     "Examples:\n",
     "\n",
@@ -118,7 +118,7 @@
    "source": [
     "### Using AIMessage.response_metadata\n",
     "\n",
-    "Metadata from the model response is also included in the AIMessage [response_metadata](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.response_metadata) attribute. These data are typically not standardized. Note that different providers adopt different conventions for representing token counts:"
+    "Metadata from the model response is also included in the AIMessage [response_metadata](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.response_metadata) attribute. These data are typically not standardized. Note that different providers adopt different conventions for representing token counts:"
    ]
   },
   {
@@ -153,7 +153,7 @@
     "\n",
     "#### OpenAI\n",
     "\n",
-    "For example, OpenAI will return a message [chunk](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html) at the end of a stream with token usage information. This behavior is supported by `langchain-openai >= 0.1.9` and can be enabled by setting `stream_usage=True`. This attribute can also be set when `ChatOpenAI` is instantiated.\n",
+    "For example, OpenAI will return a message [chunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html) at the end of a stream with token usage information. This behavior is supported by `langchain-openai >= 0.1.9` and can be enabled by setting `stream_usage=True`. This attribute can also be set when `ChatOpenAI` is instantiated.\n",
     "\n",
     "```{=mdx}\n",
     ":::note\n",
diff --git a/docs/docs/how_to/chatbots_memory.ipynb b/docs/docs/how_to/chatbots_memory.ipynb
index a321aef825c..027594d71d2 100644
--- a/docs/docs/how_to/chatbots_memory.ipynb
+++ b/docs/docs/how_to/chatbots_memory.ipynb
@@ -140,7 +140,7 @@
     "\n",
     "## Chat history\n",
     "\n",
-    "It's perfectly fine to store and pass messages directly as an array, but we can use LangChain's built-in [message history class](https://python.langchain.com/v0.2/api_reference/langchain/index.html#module-langchain.memory) to store and load messages as well. Instances of this class are responsible for storing and loading chat messages from persistent storage. LangChain integrates with many providers - you can see a [list of integrations here](/docs/integrations/memory) - but for this demo we will use an ephemeral demo class.\n",
+    "It's perfectly fine to store and pass messages directly as an array, but we can use LangChain's built-in [message history class](https://python.langchain.com/api_reference/langchain/index.html#module-langchain.memory) to store and load messages as well. Instances of this class are responsible for storing and loading chat messages from persistent storage. LangChain integrates with many providers - you can see a [list of integrations here](/docs/integrations/memory) - but for this demo we will use an ephemeral demo class.\n",
     "\n",
     "Here's an example of the API:"
    ]
diff --git a/docs/docs/how_to/code_splitter.ipynb b/docs/docs/how_to/code_splitter.ipynb
index 5c83a51428d..7d65451595d 100644
--- a/docs/docs/how_to/code_splitter.ipynb
+++ b/docs/docs/how_to/code_splitter.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# How to split code\n",
     "\n",
-    "[RecursiveCharacterTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html) includes pre-built lists of separators that are useful for splitting text in a specific programming language.\n",
+    "[RecursiveCharacterTextSplitter](https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html) includes pre-built lists of separators that are useful for splitting text in a specific programming language.\n",
     "\n",
     "Supported languages are stored in the `langchain_text_splitters.Language` enum. They include:\n",
     "\n",
diff --git a/docs/docs/how_to/configure.ipynb b/docs/docs/how_to/configure.ipynb
index 80758c55727..3864a9ea80d 100644
--- a/docs/docs/how_to/configure.ipynb
+++ b/docs/docs/how_to/configure.ipynb
@@ -100,7 +100,7 @@
    "id": "b0f74589",
    "metadata": {},
    "source": [
-    "Above, we defined `temperature` as a [`ConfigurableField`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.utils.ConfigurableField.html#langchain_core.runnables.utils.ConfigurableField) that we can set at runtime. To do so, we use the [`with_config`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method like this:"
+    "Above, we defined `temperature` as a [`ConfigurableField`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.utils.ConfigurableField.html#langchain_core.runnables.utils.ConfigurableField) that we can set at runtime. To do so, we use the [`with_config`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config) method like this:"
    ]
   },
   {
diff --git a/docs/docs/how_to/contextual_compression.ipynb b/docs/docs/how_to/contextual_compression.ipynb
index 8d58db9cf15..5def4035eee 100644
--- a/docs/docs/how_to/contextual_compression.ipynb
+++ b/docs/docs/how_to/contextual_compression.ipynb
@@ -227,7 +227,7 @@
    "source": [
     "### `LLMListwiseRerank`\n",
     "\n",
-    "[LLMListwiseRerank](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html) uses [zero-shot listwise document reranking](https://arxiv.org/pdf/2305.02156) and functions similarly to `LLMChainFilter` as a robust but more expensive option. It is recommended to use a more powerful LLM.\n",
+    "[LLMListwiseRerank](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.listwise_rerank.LLMListwiseRerank.html) uses [zero-shot listwise document reranking](https://arxiv.org/pdf/2305.02156) and functions similarly to `LLMChainFilter` as a robust but more expensive option. It is recommended to use a more powerful LLM.\n",
     "\n",
     "Note that `LLMListwiseRerank` requires a model with the [with_structured_output](/docs/integrations/chat/) method implemented."
    ]
diff --git a/docs/docs/how_to/convert_runnable_to_tool.ipynb b/docs/docs/how_to/convert_runnable_to_tool.ipynb
index d8774194ad7..f2575b79011 100644
--- a/docs/docs/how_to/convert_runnable_to_tool.ipynb
+++ b/docs/docs/how_to/convert_runnable_to_tool.ipynb
@@ -42,13 +42,13 @@
    "source": [
     "LangChain [tools](/docs/concepts#tools) are interfaces that an agent, chain, or chat model can use to interact with the world. See [here](/docs/how_to/#tools) for how-to guides covering tool-calling, built-in tools, custom tools, and more information.\n",
     "\n",
-    "LangChain tools-- instances of [BaseTool](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.BaseTool.html)-- are [Runnables](/docs/concepts/#runnable-interface) with additional constraints that enable them to be invoked effectively by language models:\n",
+    "LangChain tools-- instances of [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html)-- are [Runnables](/docs/concepts/#runnable-interface) with additional constraints that enable them to be invoked effectively by language models:\n",
     "\n",
     "- Their inputs are constrained to be serializable, specifically strings and Python `dict` objects;\n",
     "- They contain names and descriptions indicating how and when they should be used;\n",
-    "- They may contain a detailed [args_schema](https://python.langchain.com/v0.2/docs/how_to/custom_tools/) for their arguments. That is, while a tool (as a `Runnable`) might accept a single `dict` input, the specific keys and type information needed to populate a dict should be specified in the `args_schema`.\n",
+    "- They may contain a detailed [args_schema](https://python.langchain.com/docs/how_to/custom_tools/) for their arguments. That is, while a tool (as a `Runnable`) might accept a single `dict` input, the specific keys and type information needed to populate a dict should be specified in the `args_schema`.\n",
     "\n",
-    "Runnables that accept string or `dict` input can be converted to tools using the [as_tool](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.as_tool) method, which allows for the specification of names, descriptions, and additional schema information for arguments."
+    "Runnables that accept string or `dict` input can be converted to tools using the [as_tool](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.as_tool) method, which allows for the specification of names, descriptions, and additional schema information for arguments."
    ]
   },
   {
@@ -180,7 +180,7 @@
    "id": "32b1a992-8997-4c98-8eb2-c9fe9431b799",
    "metadata": {},
    "source": [
-    "Alternatively, the schema can be fully specified by directly passing the desired [args_schema](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool.args_schema) for the tool:"
+    "Alternatively, the schema can be fully specified by directly passing the desired [args_schema](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool.args_schema) for the tool:"
    ]
   },
   {
@@ -331,7 +331,7 @@
    "id": "9ba737ac-43a2-4a6f-b855-5bd0305017f1",
    "metadata": {},
    "source": [
-    "We next create use a simple pre-built [LangGraph agent](https://python.langchain.com/v0.2/docs/tutorials/agents/) and provide it the tool:"
+    "We next create use a simple pre-built [LangGraph agent](https://python.langchain.com/docs/tutorials/agents/) and provide it the tool:"
    ]
   },
   {
diff --git a/docs/docs/how_to/custom_callbacks.ipynb b/docs/docs/how_to/custom_callbacks.ipynb
index d1564b40d1f..2579f8b8ae8 100644
--- a/docs/docs/how_to/custom_callbacks.ipynb
+++ b/docs/docs/how_to/custom_callbacks.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "LangChain has some built-in callback handlers, but you will often want to create your own handlers with custom logic.\n",
     "\n",
-    "To create a custom callback handler, we need to determine the [event(s)](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) we want our callback handler to handle as well as what we want our callback handler to do when the event is triggered. Then all we need to do is attach the callback handler to the object, for example via [the constructor](/docs/how_to/callbacks_constructor) or [at runtime](/docs/how_to/callbacks_runtime).\n",
+    "To create a custom callback handler, we need to determine the [event(s)](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) we want our callback handler to handle as well as what we want our callback handler to do when the event is triggered. Then all we need to do is attach the callback handler to the object, for example via [the constructor](/docs/how_to/callbacks_constructor) or [at runtime](/docs/how_to/callbacks_runtime).\n",
     "\n",
     "In the example below, we'll implement streaming with a custom handler.\n",
     "\n",
@@ -107,7 +107,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can see [this reference page](https://python.langchain.com/v0.2/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) for a list of events you can handle. Note that the `handle_chain_*` events run for most LCEL runnables.\n",
+    "You can see [this reference page](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html#langchain-core-callbacks-base-basecallbackhandler) for a list of events you can handle. Note that the `handle_chain_*` events run for most LCEL runnables.\n",
     "\n",
     "## Next steps\n",
     "\n",
diff --git a/docs/docs/how_to/custom_chat_model.ipynb b/docs/docs/how_to/custom_chat_model.ipynb
index 2a95b97d285..7aaa3c94c7c 100644
--- a/docs/docs/how_to/custom_chat_model.ipynb
+++ b/docs/docs/how_to/custom_chat_model.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "In this guide, we'll learn how to create a custom chat model using LangChain abstractions.\n",
     "\n",
-    "Wrapping your LLM with the standard [`BaseChatModel`](https://python.langchain.com/v0.2/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface allow you to use your LLM in existing LangChain programs with minimal code modifications!\n",
+    "Wrapping your LLM with the standard [`BaseChatModel`](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html) interface allow you to use your LLM in existing LangChain programs with minimal code modifications!\n",
     "\n",
     "As an bonus, your LLM will automatically become a LangChain `Runnable` and will benefit from some optimizations out of the box (e.g., batch via a threadpool), async support, the `astream_events` API, etc.\n",
     "\n",
@@ -503,7 +503,7 @@
     "\n",
     "Documentation:\n",
     "\n",
-    "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [APIReference](https://python.langchain.com/v0.2/api_reference/langchain/index.html).\n",
+    "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [APIReference](https://python.langchain.com/api_reference/langchain/index.html).\n",
     "* The class doc-string for the model contains a link to the model API if the model is powered by a service.\n",
     "\n",
     "Tests:\n",
diff --git a/docs/docs/how_to/custom_llm.ipynb b/docs/docs/how_to/custom_llm.ipynb
index 7681163983d..d56380332f4 100644
--- a/docs/docs/how_to/custom_llm.ipynb
+++ b/docs/docs/how_to/custom_llm.ipynb
@@ -402,7 +402,7 @@
     "\n",
     "Documentation:\n",
     "\n",
-    "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [APIReference](https://python.langchain.com/v0.2/api_reference/langchain/index.html).\n",
+    "* The model contains doc-strings for all initialization arguments, as these will be surfaced in the [APIReference](https://python.langchain.com/api_reference/langchain/index.html).\n",
     "* The class doc-string for the model contains a link to the model API if the model is powered by a service.\n",
     "\n",
     "Tests:\n",
diff --git a/docs/docs/how_to/custom_retriever.ipynb b/docs/docs/how_to/custom_retriever.ipynb
index 3bda108e2b1..c74b3546369 100644
--- a/docs/docs/how_to/custom_retriever.ipynb
+++ b/docs/docs/how_to/custom_retriever.ipynb
@@ -270,7 +270,7 @@
     "\n",
     "Documentation:\n",
     "\n",
-    "* The retriever contains doc-strings for all initialization arguments, as these will be surfaced in the [API Reference](https://python.langchain.com/v0.2/api_reference/langchain/index.html).\n",
+    "* The retriever contains doc-strings for all initialization arguments, as these will be surfaced in the [API Reference](https://python.langchain.com/api_reference/langchain/index.html).\n",
     "* The class doc-string for the model contains a link to any relevant APIs used for the retriever (e.g., if the retriever is retrieving from wikipedia, it'll be good to link to the wikipedia API!)\n",
     "\n",
     "Tests:\n",
diff --git a/docs/docs/how_to/custom_tools.ipynb b/docs/docs/how_to/custom_tools.ipynb
index cb5c9d604c2..73535e5c56d 100644
--- a/docs/docs/how_to/custom_tools.ipynb
+++ b/docs/docs/how_to/custom_tools.ipynb
@@ -20,9 +20,9 @@
     "\n",
     "1. Functions;\n",
     "2. LangChain [Runnables](/docs/concepts#runnable-interface);\n",
-    "3. By sub-classing from [BaseTool](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
+    "3. By sub-classing from [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html) -- This is the most flexible method, it provides the largest degree of control, at the expense of more effort and code.\n",
     "\n",
-    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method.\n",
+    "Creating tools from functions may be sufficient for most use cases, and can be done via a simple [@tool decorator](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html#langchain_core.tools.tool). If more configuration is needed-- e.g., specification of both sync and async implementations-- one can also use the [StructuredTool.from_function](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.StructuredTool.html#langchain_core.tools.StructuredTool.from_function) class method.\n",
     "\n",
     "In this guide we provide an overview of these methods.\n",
     "\n",
@@ -294,7 +294,7 @@
    "metadata": {},
    "source": [
     ":::{.callout-caution}\n",
-    "By default, `@tool(parse_docstring=True)` will raise `ValueError` if the docstring does not parse correctly. See [API Reference](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.tool.html) for detail and examples.\n",
+    "By default, `@tool(parse_docstring=True)` will raise `ValueError` if the docstring does not parse correctly. See [API Reference](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.tool.html) for detail and examples.\n",
     ":::"
    ]
   },
@@ -415,7 +415,7 @@
    "source": [
     "## Creating tools from Runnables\n",
     "\n",
-    "LangChain [Runnables](/docs/concepts#runnable-interface) that accept string or `dict` input can be converted to tools using the [as_tool](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.as_tool) method, which allows for the specification of names, descriptions, and additional schema information for arguments.\n",
+    "LangChain [Runnables](/docs/concepts#runnable-interface) that accept string or `dict` input can be converted to tools using the [as_tool](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.as_tool) method, which allows for the specification of names, descriptions, and additional schema information for arguments.\n",
     "\n",
     "Example usage:"
    ]
@@ -592,7 +592,7 @@
    "source": [
     "## How to create async tools\n",
     "\n",
-    "LangChain Tools implement the [Runnable interface 🏃](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html).\n",
+    "LangChain Tools implement the [Runnable interface 🏃](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html).\n",
     "\n",
     "All Runnables expose the `invoke` and `ainvoke` methods (as well as other methods like `batch`, `abatch`, `astream` etc).\n",
     "\n",
@@ -907,7 +907,7 @@
     "\n",
     "Sometimes there are artifacts of a tool's execution that we want to make accessible to downstream components in our chain or agent, but that we don't want to expose to the model itself. For example if a tool returns custom objects like Documents, we may want to pass some view or metadata about this output to the model without passing the raw output to the model. At the same time, we may want to be able to access this full output elsewhere, for example in downstream tools.\n",
     "\n",
-    "The Tool and [ToolMessage](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
+    "The Tool and [ToolMessage](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
     "\n",
     ":::info Requires ``langchain-core >= 0.2.19``\n",
     "\n",
diff --git a/docs/docs/how_to/document_loader_csv.ipynb b/docs/docs/how_to/document_loader_csv.ipynb
index 890006cfe4a..e8b89df72c5 100644
--- a/docs/docs/how_to/document_loader_csv.ipynb
+++ b/docs/docs/how_to/document_loader_csv.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "A [comma-separated values (CSV)](https://en.wikipedia.org/wiki/Comma-separated_values) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.\n",
     "\n",
-    "LangChain implements a [CSV Loader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.csv_loader.CSVLoader.html) that will load CSV files into a sequence of [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects. Each row of the CSV file is translated to one document."
+    "LangChain implements a [CSV Loader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.csv_loader.CSVLoader.html) that will load CSV files into a sequence of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects. Each row of the CSV file is translated to one document."
    ]
   },
   {
@@ -88,7 +88,7 @@
    "source": [
     "## Specify a column to identify the document source\n",
     "\n",
-    "The `\"source\"` key on [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) metadata can be set using a column of the CSV. Use the `source_column` argument to specify a source for the document created from each row. Otherwise `file_path` will be used as the source for all documents created from the CSV file.\n",
+    "The `\"source\"` key on [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) metadata can be set using a column of the CSV. Use the `source_column` argument to specify a source for the document created from each row. Otherwise `file_path` will be used as the source for all documents created from the CSV file.\n",
     "\n",
     "This is useful when using documents loaded from CSV files for chains that answer questions using sources."
    ]
diff --git a/docs/docs/how_to/document_loader_directory.ipynb b/docs/docs/how_to/document_loader_directory.ipynb
index 32b833ab14d..2f646e53682 100644
--- a/docs/docs/how_to/document_loader_directory.ipynb
+++ b/docs/docs/how_to/document_loader_directory.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# How to load documents from a directory\n",
     "\n",
-    "LangChain's [DirectoryLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.directory.DirectoryLoader.html) implements functionality for reading files from disk into LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects. Here we demonstrate:\n",
+    "LangChain's [DirectoryLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.directory.DirectoryLoader.html) implements functionality for reading files from disk into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects. Here we demonstrate:\n",
     "\n",
     "- How to load from a filesystem, including use of wildcard patterns;\n",
     "- How to use multithreading for file I/O;\n",
@@ -134,7 +134,7 @@
    "metadata": {},
    "source": [
     "## Change loader class\n",
-    "By default this uses the `UnstructuredLoader` class. To customize the loader, specify the loader class in the `loader_cls` kwarg. Below we show an example using [TextLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.text.TextLoader.html):"
+    "By default this uses the `UnstructuredLoader` class. To customize the loader, specify the loader class in the `loader_cls` kwarg. Below we show an example using [TextLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.text.TextLoader.html):"
    ]
   },
   {
diff --git a/docs/docs/how_to/document_loader_html.ipynb b/docs/docs/how_to/document_loader_html.ipynb
index db35d839a8c..b0f963ce2ff 100644
--- a/docs/docs/how_to/document_loader_html.ipynb
+++ b/docs/docs/how_to/document_loader_html.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "The HyperText Markup Language or [HTML](https://en.wikipedia.org/wiki/HTML) is the standard markup language for documents designed to be displayed in a web browser.\n",
     "\n",
-    "This covers how to load `HTML` documents into a LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects that we can use downstream.\n",
+    "This covers how to load `HTML` documents into a LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects that we can use downstream.\n",
     "\n",
     "Parsing HTML files often requires specialized tools. Here we demonstrate parsing via [Unstructured](https://unstructured-io.github.io/unstructured/) and [BeautifulSoup4](https://beautiful-soup-4.readthedocs.io/en/latest/), which can be installed via pip. Head over to the integrations page to find integrations with additional services, such as [Azure AI Document Intelligence](/docs/integrations/document_loaders/azure_document_intelligence) or [FireCrawl](/docs/integrations/document_loaders/firecrawl).\n",
     "\n",
diff --git a/docs/docs/how_to/document_loader_json.mdx b/docs/docs/how_to/document_loader_json.mdx
index b88e37aa801..e3f71683812 100644
--- a/docs/docs/how_to/document_loader_json.mdx
+++ b/docs/docs/how_to/document_loader_json.mdx
@@ -4,8 +4,8 @@
 
 [JSON Lines](https://jsonlines.org/) is a file format where each line is a valid JSON value.
 
-LangChain implements a [JSONLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) 
-to convert JSON and JSONL data into LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) 
+LangChain implements a [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) 
+to convert JSON and JSONL data into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) 
 objects. It uses a specified [jq schema](https://en.wikipedia.org/wiki/Jq_(programming_language)) to parse the JSON files, allowing for the extraction of specific fields into the content 
 and metadata of the LangChain Document.
 
diff --git a/docs/docs/how_to/document_loader_markdown.ipynb b/docs/docs/how_to/document_loader_markdown.ipynb
index 228703d5b14..385d3f687aa 100644
--- a/docs/docs/how_to/document_loader_markdown.ipynb
+++ b/docs/docs/how_to/document_loader_markdown.ipynb
@@ -9,14 +9,14 @@
     "\n",
     "[Markdown](https://en.wikipedia.org/wiki/Markdown) is a lightweight markup language for creating formatted text using a plain-text editor.\n",
     "\n",
-    "Here we cover how to load `Markdown` documents into LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects that we can use downstream.\n",
+    "Here we cover how to load `Markdown` documents into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects that we can use downstream.\n",
     "\n",
     "We will cover:\n",
     "\n",
     "- Basic usage;\n",
     "- Parsing of Markdown into elements such as titles, list items, and text.\n",
     "\n",
-    "LangChain implements an [UnstructuredMarkdownLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) object which requires the [Unstructured](https://unstructured-io.github.io/unstructured/) package. First we install it:"
+    "LangChain implements an [UnstructuredMarkdownLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) object which requires the [Unstructured](https://unstructured-io.github.io/unstructured/) package. First we install it:"
    ]
   },
   {
diff --git a/docs/docs/how_to/document_loader_office_file.mdx b/docs/docs/how_to/document_loader_office_file.mdx
index 30e6fa94d89..6d2ef5faad0 100644
--- a/docs/docs/how_to/document_loader_office_file.mdx
+++ b/docs/docs/how_to/document_loader_office_file.mdx
@@ -3,7 +3,7 @@
 The [Microsoft Office](https://www.office.com/) suite of productivity software includes Microsoft Word, Microsoft Excel, Microsoft PowerPoint, Microsoft Outlook, and Microsoft OneNote. It is available for Microsoft Windows and macOS operating systems. It is also available on Android and iOS.
 
 This covers how to load commonly used file formats including `DOCX`, `XLSX` and `PPTX` documents into a LangChain 
-[Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document)
+[Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document)
 object that we can use downstream.
 
 
diff --git a/docs/docs/how_to/document_loader_pdf.ipynb b/docs/docs/how_to/document_loader_pdf.ipynb
index 69b6462aad8..40218703e6d 100644
--- a/docs/docs/how_to/document_loader_pdf.ipynb
+++ b/docs/docs/how_to/document_loader_pdf.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "[Portable Document Format (PDF)](https://en.wikipedia.org/wiki/PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.\n",
     "\n",
-    "This guide covers how to load `PDF` documents into the LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) format that we use downstream.\n",
+    "This guide covers how to load `PDF` documents into the LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) format that we use downstream.\n",
     "\n",
     "LangChain integrates with a host of PDF parsers. Some are simple and relatively low-level; others will support OCR and image-processing, or perform advanced document layout analysis. The right choice will depend on your application. Below we enumerate the possibilities.\n",
     "\n",
@@ -183,7 +183,7 @@
    "source": [
     "## Using other PDF loaders\n",
     "\n",
-    "For a list of other PDF loaders to use, please see [this table](https://python.langchain.com/v0.2/docs/integrations/document_loaders/#pdfs)"
+    "For a list of other PDF loaders to use, please see [this table](https://python.langchain.com/docs/integrations/document_loaders/#pdfs)"
    ]
   }
  ],
diff --git a/docs/docs/how_to/ensemble_retriever.ipynb b/docs/docs/how_to/ensemble_retriever.ipynb
index 695e9493930..99098554f5e 100644
--- a/docs/docs/how_to/ensemble_retriever.ipynb
+++ b/docs/docs/how_to/ensemble_retriever.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# How to combine results from multiple retrievers\n",
     "\n",
-    "The [EnsembleRetriever](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html) supports ensembling of results from multiple retrievers. It is initialized with a list of [BaseRetriever](https://python.langchain.com/v0.2/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html) objects. EnsembleRetrievers rerank the results of the constituent retrievers based on the [Reciprocal Rank Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) algorithm.\n",
+    "The [EnsembleRetriever](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.ensemble.EnsembleRetriever.html) supports ensembling of results from multiple retrievers. It is initialized with a list of [BaseRetriever](https://python.langchain.com/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html) objects. EnsembleRetrievers rerank the results of the constituent retrievers based on the [Reciprocal Rank Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) algorithm.\n",
     "\n",
     "By leveraging the strengths of different algorithms, the `EnsembleRetriever` can achieve better performance than any single algorithm. \n",
     "\n",
@@ -14,7 +14,7 @@
     "\n",
     "## Basic usage\n",
     "\n",
-    "Below we demonstrate ensembling of a [BM25Retriever](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.bm25.BM25Retriever.html) with a retriever derived from the [FAISS vector store](https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html)."
+    "Below we demonstrate ensembling of a [BM25Retriever](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.bm25.BM25Retriever.html) with a retriever derived from the [FAISS vector store](https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html)."
    ]
   },
   {
diff --git a/docs/docs/how_to/extraction_examples.ipynb b/docs/docs/how_to/extraction_examples.ipynb
index d7e5741a387..5d18d4097c4 100644
--- a/docs/docs/how_to/extraction_examples.ipynb
+++ b/docs/docs/how_to/extraction_examples.ipynb
@@ -16,11 +16,11 @@
     "also with JSON more or prompt based techniques.\n",
     ":::\n",
     "\n",
-    "LangChain implements a [tool-call attribute](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls) on messages from LLMs that include tool calls. See our [how-to guide on tool calling](/docs/how_to/tool_calling) for more detail. To build reference examples for data extraction, we build a chat history containing a sequence of: \n",
+    "LangChain implements a [tool-call attribute](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage.tool_calls) on messages from LLMs that include tool calls. See our [how-to guide on tool calling](/docs/how_to/tool_calling) for more detail. To build reference examples for data extraction, we build a chat history containing a sequence of: \n",
     "\n",
-    "- [HumanMessage](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.human.HumanMessage.html) containing example inputs;\n",
-    "- [AIMessage](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html) containing example tool calls;\n",
-    "- [ToolMessage](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html) containing example tool outputs.\n",
+    "- [HumanMessage](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.human.HumanMessage.html) containing example inputs;\n",
+    "- [AIMessage](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html) containing example tool calls;\n",
+    "- [ToolMessage](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html) containing example tool outputs.\n",
     "\n",
     "LangChain adopts this convention for structuring tool calls into conversation across LLM model providers.\n",
     "\n",
diff --git a/docs/docs/how_to/extraction_long_text.ipynb b/docs/docs/how_to/extraction_long_text.ipynb
index 704f20da185..2e54de648d0 100644
--- a/docs/docs/how_to/extraction_long_text.ipynb
+++ b/docs/docs/how_to/extraction_long_text.ipynb
@@ -58,7 +58,7 @@
    "id": "ac000b03-33fc-414f-8f2c-3850df621a35",
    "metadata": {},
    "source": [
-    "Now we need some example data! Let's download an article about [cars from wikipedia](https://en.wikipedia.org/wiki/Car) and load it as a LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html)."
+    "Now we need some example data! Let's download an article about [cars from wikipedia](https://en.wikipedia.org/wiki/Car) and load it as a LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)."
    ]
   },
   {
@@ -289,7 +289,7 @@
    "id": "5b43d7e0-3c85-4d97-86c7-e8c984b60b0a",
    "metadata": {},
    "source": [
-    "Use [batch](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) functionality to run the extraction in **parallel** across each chunk! \n",
+    "Use [batch](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) functionality to run the extraction in **parallel** across each chunk! \n",
     "\n",
     ":::{.callout-tip}\n",
     "You can often use .batch() to parallelize the extractions! `.batch` uses a threadpool under the hood to help you parallelize workloads.\n",
diff --git a/docs/docs/how_to/extraction_parse.ipynb b/docs/docs/how_to/extraction_parse.ipynb
index 08b2de51605..5c115a509a4 100644
--- a/docs/docs/how_to/extraction_parse.ipynb
+++ b/docs/docs/how_to/extraction_parse.ipynb
@@ -237,7 +237,7 @@
     "\n",
     "If desired, it's easy to create a custom prompt and parser with `LangChain` and `LCEL`.\n",
     "\n",
-    "To create a custom parser, define a function to parse the output from the model (typically an [AIMessage](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html)) into an object of your choice.\n",
+    "To create a custom parser, define a function to parse the output from the model (typically an [AIMessage](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html)) into an object of your choice.\n",
     "\n",
     "See below for a simple implementation of a JSON parser."
    ]
diff --git a/docs/docs/how_to/few_shot_examples.ipynb b/docs/docs/how_to/few_shot_examples.ipynb
index 40fc6eea3f4..69ffd691bc0 100644
--- a/docs/docs/how_to/few_shot_examples.ipynb
+++ b/docs/docs/how_to/few_shot_examples.ipynb
@@ -29,7 +29,7 @@
     "\n",
     "In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance.\n",
     "\n",
-    "A few-shot prompt template can be constructed from either a set of examples, or from an [Example Selector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.base.BaseExampleSelector.html) class responsible for choosing a subset of examples from the defined set.\n",
+    "A few-shot prompt template can be constructed from either a set of examples, or from an [Example Selector](https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.base.BaseExampleSelector.html) class responsible for choosing a subset of examples from the defined set.\n",
     "\n",
     "This guide will cover few-shotting with string prompt templates. For a guide on few-shotting with chat messages for chat models, see [here](/docs/how_to/few_shot_examples_chat/).\n",
     "\n",
@@ -160,7 +160,7 @@
    "source": [
     "### Pass the examples and formatter to `FewShotPromptTemplate`\n",
     "\n",
-    "Finally, create a [`FewShotPromptTemplate`](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.few_shot.FewShotPromptTemplate.html) object. This object takes in the few-shot examples and the formatter for the few-shot examples. When this `FewShotPromptTemplate` is formatted, it formats the passed examples using the `example_prompt`, then and adds them to the final prompt before `suffix`:"
+    "Finally, create a [`FewShotPromptTemplate`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.few_shot.FewShotPromptTemplate.html) object. This object takes in the few-shot examples and the formatter for the few-shot examples. When this `FewShotPromptTemplate` is formatted, it formats the passed examples using the `example_prompt`, then and adds them to the final prompt before `suffix`:"
    ]
   },
   {
@@ -251,7 +251,7 @@
    "source": [
     "## Using an example selector\n",
     "\n",
-    "We will reuse the example set and the formatter from the previous section. However, instead of feeding the examples directly into the `FewShotPromptTemplate` object, we will feed them into an implementation of `ExampleSelector` called [`SemanticSimilarityExampleSelector`](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html) instance. This class selects few-shot examples from the initial set based on their similarity to the input. It uses an embedding model to compute the similarity between the input and the few-shot examples, as well as a vector store to perform the nearest neighbor search.\n",
+    "We will reuse the example set and the formatter from the previous section. However, instead of feeding the examples directly into the `FewShotPromptTemplate` object, we will feed them into an implementation of `ExampleSelector` called [`SemanticSimilarityExampleSelector`](https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html) instance. This class selects few-shot examples from the initial set based on their similarity to the input. It uses an embedding model to compute the similarity between the input and the few-shot examples, as well as a vector store to perform the nearest neighbor search.\n",
     "\n",
     "To show what it looks like, let's initialize an instance and call it in isolation:"
    ]
diff --git a/docs/docs/how_to/few_shot_examples_chat.ipynb b/docs/docs/how_to/few_shot_examples_chat.ipynb
index d01b24cd16f..5ccc06d9fcf 100644
--- a/docs/docs/how_to/few_shot_examples_chat.ipynb
+++ b/docs/docs/how_to/few_shot_examples_chat.ipynb
@@ -29,7 +29,7 @@
     "\n",
     "This guide covers how to prompt a chat model with example inputs and outputs. Providing the model with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance.\n",
     "\n",
-    "There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the [FewShotChatMessagePromptTemplate](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate.html?highlight=fewshot#langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate) as a flexible starting point, and you can modify or replace them as you see fit.\n",
+    "There does not appear to be solid consensus on how best to do few-shot prompting, and the optimal prompt compilation will likely vary by model. Because of this, we provide few-shot prompt templates like the [FewShotChatMessagePromptTemplate](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate.html?highlight=fewshot#langchain_core.prompts.few_shot.FewShotChatMessagePromptTemplate) as a flexible starting point, and you can modify or replace them as you see fit.\n",
     "\n",
     "The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.\n",
     "\n",
@@ -49,7 +49,7 @@
     "\n",
     "The basic components of the template are:\n",
     "- `examples`: A list of dictionary examples to include in the final prompt.\n",
-    "- `example_prompt`: converts each example into 1 or more messages through its [`format_messages`](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=format_messages#langchain_core.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n",
+    "- `example_prompt`: converts each example into 1 or more messages through its [`format_messages`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=format_messages#langchain_core.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n",
     "\n",
     "Below is a simple demonstration. First, define the examples you'd like to include. Let's give the LLM an unfamiliar mathematical operator, denoted by the \"🦜\" emoji:"
    ]
@@ -240,8 +240,8 @@
     "\n",
     "Sometimes you may want to select only a few examples from your overall set to show based on the input. For this, you can replace the `examples` passed into `FewShotChatMessagePromptTemplate` with an `example_selector`. The other components remain the same as above! Our dynamic few-shot prompt template would look like:\n",
     "\n",
-    "- `example_selector`: responsible for selecting few-shot examples (and the order in which they are returned) for a given input. These implement the [BaseExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.base.BaseExampleSelector.html?highlight=baseexampleselector#langchain_core.example_selectors.base.BaseExampleSelector) interface. A common example is the vectorstore-backed [SemanticSimilarityExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html?highlight=semanticsimilarityexampleselector#langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector)\n",
-    "- `example_prompt`: convert each example into 1 or more messages through its [`format_messages`](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=chatprompttemplate#langchain_core.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n",
+    "- `example_selector`: responsible for selecting few-shot examples (and the order in which they are returned) for a given input. These implement the [BaseExampleSelector](https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.base.BaseExampleSelector.html?highlight=baseexampleselector#langchain_core.example_selectors.base.BaseExampleSelector) interface. A common example is the vectorstore-backed [SemanticSimilarityExampleSelector](https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html?highlight=semanticsimilarityexampleselector#langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector)\n",
+    "- `example_prompt`: convert each example into 1 or more messages through its [`format_messages`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html?highlight=chatprompttemplate#langchain_core.prompts.chat.ChatPromptTemplate.format_messages) method. A common example would be to convert each example into one human message and one AI message response, or a human message followed by a function call message.\n",
     "\n",
     "These once again can be composed with other messages and chat templates to assemble your final prompt.\n",
     "\n",
diff --git a/docs/docs/how_to/filter_messages.ipynb b/docs/docs/how_to/filter_messages.ipynb
index 2c6d3076bcc..d5b94abad52 100644
--- a/docs/docs/how_to/filter_messages.ipynb
+++ b/docs/docs/how_to/filter_messages.ipynb
@@ -175,7 +175,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For a complete description of all arguments head to the API reference: https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.utils.filter_messages.html"
+    "For a complete description of all arguments head to the API reference: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.filter_messages.html"
    ]
   }
  ],
diff --git a/docs/docs/how_to/function_calling.ipynb b/docs/docs/how_to/function_calling.ipynb
index 541b26cc5cb..136b5c0d8fa 100644
--- a/docs/docs/how_to/function_calling.ipynb
+++ b/docs/docs/how_to/function_calling.ipynb
@@ -88,7 +88,7 @@
     "## Passing tools to LLMs\n",
     "\n",
     "Chat models supporting tool calling features implement a `.bind_tools` method, which \n",
-    "receives a list of LangChain [tool objects](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool) \n",
+    "receives a list of LangChain [tool objects](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool) \n",
     "and binds them to the chat model in its expected format. Subsequent invocations of the \n",
     "chat model will include tool schemas in its calls to the LLM.\n",
     "\n",
@@ -212,9 +212,9 @@
     "## Tool calls\n",
     "\n",
     "If tool calls are included in a LLM response, they are attached to the corresponding \n",
-    "[message](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage) \n",
-    "or [message chunk](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
-    "as a list of [tool call](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.ToolCall.html#langchain_core.messages.tool.ToolCall) \n",
+    "[message](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage) \n",
+    "or [message chunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "as a list of [tool call](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolCall.html#langchain_core.messages.tool.ToolCall) \n",
     "objects in the `.tool_calls` attribute. A `ToolCall` is a typed dict that includes a \n",
     "tool name, dict of argument values, and (optionally) an identifier. Messages with no \n",
     "tool calls default to an empty list for this attribute.\n",
@@ -258,7 +258,7 @@
     "The `.tool_calls` attribute should contain valid tool calls. Note that on occasion, \n",
     "model providers may output malformed tool calls (e.g., arguments that are not \n",
     "valid JSON). When parsing fails in these cases, instances \n",
-    "of [InvalidToolCall](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.InvalidToolCall.html#langchain_core.messages.tool.InvalidToolCall) \n",
+    "of [InvalidToolCall](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.InvalidToolCall.html#langchain_core.messages.tool.InvalidToolCall) \n",
     "are populated in the `.invalid_tool_calls` attribute. An `InvalidToolCall` can have \n",
     "a name, string arguments, identifier, and error message.\n",
     "\n",
@@ -298,8 +298,8 @@
     "### Streaming\n",
     "\n",
     "When tools are called in a streaming context, \n",
-    "[message chunks](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
-    "will be populated with [tool call chunk](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk) \n",
+    "[message chunks](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "will be populated with [tool call chunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk) \n",
     "objects in a list via the `.tool_call_chunks` attribute. A `ToolCallChunk` includes \n",
     "optional string fields for the tool `name`, `args`, and `id`, and includes an optional \n",
     "integer field `index` that can be used to join chunks together. Fields are optional \n",
@@ -307,7 +307,7 @@
     "that includes a substring of the arguments may have null values for the tool name and id).\n",
     "\n",
     "Because message chunks inherit from their parent message class, an \n",
-    "[AIMessageChunk](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "[AIMessageChunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
     "with tool call chunks will also include `.tool_calls` and `.invalid_tool_calls` fields. \n",
     "These fields are parsed best-effort from the message's tool call chunks.\n",
     "\n",
diff --git a/docs/docs/how_to/functions.ipynb b/docs/docs/how_to/functions.ipynb
index c2fb7905339..0d49277f513 100644
--- a/docs/docs/how_to/functions.ipynb
+++ b/docs/docs/how_to/functions.ipynb
@@ -26,7 +26,7 @@
     "\n",
     ":::\n",
     "\n",
-    "You can use arbitrary functions as [Runnables](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable). This is useful for formatting or when you need functionality not provided by other LangChain components, and custom functions used as Runnables are called [`RunnableLambdas`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html).\n",
+    "You can use arbitrary functions as [Runnables](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable). This is useful for formatting or when you need functionality not provided by other LangChain components, and custom functions used as Runnables are called [`RunnableLambdas`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html).\n",
     "\n",
     "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single dict input and unpacks it into multiple arguments.\n",
     "\n",
@@ -211,7 +211,7 @@
     "\n",
     "## Passing run metadata\n",
     "\n",
-    "Runnable lambdas can optionally accept a [RunnableConfig](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig) parameter, which they can use to pass callbacks, tags, and other configuration information to nested runs."
+    "Runnable lambdas can optionally accept a [RunnableConfig](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig) parameter, which they can use to pass callbacks, tags, and other configuration information to nested runs."
    ]
   },
   {
@@ -304,7 +304,7 @@
     "## Streaming\n",
     "\n",
     ":::{.callout-note}\n",
-    "[RunnableLambda](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html) is best suited for code that does not need to support streaming. If you need to support streaming (i.e., be able to operate on chunks of inputs and yield chunks of outputs), use [RunnableGenerator](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableGenerator.html) instead as in the example below.\n",
+    "[RunnableLambda](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html) is best suited for code that does not need to support streaming. If you need to support streaming (i.e., be able to operate on chunks of inputs and yield chunks of outputs), use [RunnableGenerator](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableGenerator.html) instead as in the example below.\n",
     ":::\n",
     "\n",
     "You can use generator functions (ie. functions that use the `yield` keyword, and behave like iterators) in a chain.\n",
diff --git a/docs/docs/how_to/graph_prompting.ipynb b/docs/docs/how_to/graph_prompting.ipynb
index f5a3ecda33c..0b83559e7e1 100644
--- a/docs/docs/how_to/graph_prompting.ipynb
+++ b/docs/docs/how_to/graph_prompting.ipynb
@@ -347,7 +347,7 @@
     "\n",
     "If we have enough examples, we may want to only include the most relevant ones in the prompt, either because they don't fit in the model's context window or because the long tail of examples distracts the model. And specifically, given any input we want to include the examples most relevant to that input.\n",
     "\n",
-    "We can do just this using an ExampleSelector. In this case we'll use a [SemanticSimilarityExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html), which will store the examples in the vector database of our choosing. At runtime it will perform a similarity search between the input and our examples, and return the most semantically similar ones: "
+    "We can do just this using an ExampleSelector. In this case we'll use a [SemanticSimilarityExampleSelector](https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html), which will store the examples in the vector database of our choosing. At runtime it will perform a similarity search between the input and our examples, and return the most semantically similar ones: "
    ]
   },
   {
diff --git a/docs/docs/how_to/index.mdx b/docs/docs/how_to/index.mdx
index f5500f9093c..90a6d77034d 100644
--- a/docs/docs/how_to/index.mdx
+++ b/docs/docs/how_to/index.mdx
@@ -9,7 +9,7 @@ Here you’ll find answers to “How do I….?” types of questions.
 These guides are *goal-oriented* and *concrete*; they're meant to help you complete a specific task.
 For conceptual explanations see the [Conceptual guide](/docs/concepts/).
 For end-to-end walkthroughs see [Tutorials](/docs/tutorials).
-For comprehensive descriptions of every class and function see the [API Reference](https://python.langchain.com/v0.2/api_reference/).
+For comprehensive descriptions of every class and function see the [API Reference](https://python.langchain.com/api_reference/).
 
 ## Installation
 
@@ -27,7 +27,7 @@ This highlights functionality that is core to using LangChain.
 
 ## LangChain Expression Language (LCEL)
 
-[LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol.
+[LangChain Expression Language](/docs/concepts/#langchain-expression-language-lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol.
 
 [**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
 
diff --git a/docs/docs/how_to/lcel_cheatsheet.ipynb b/docs/docs/how_to/lcel_cheatsheet.ipynb
index ac3e8d38b8f..fb67e0cd7cf 100644
--- a/docs/docs/how_to/lcel_cheatsheet.ipynb
+++ b/docs/docs/how_to/lcel_cheatsheet.ipynb
@@ -7,10 +7,10 @@
    "source": [
     "# LangChain Expression Language Cheatsheet\n",
     "\n",
-    "This is a quick reference for all the most important LCEL primitives. For more advanced usage see the [LCEL how-to guides](/docs/how_to/#langchain-expression-language-lcel) and the [full API reference](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html).\n",
+    "This is a quick reference for all the most important LCEL primitives. For more advanced usage see the [LCEL how-to guides](/docs/how_to/#langchain-expression-language-lcel) and the [full API reference](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html).\n",
     "\n",
     "### Invoke a runnable\n",
-    "#### [Runnable.invoke()](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.invoke) / [Runnable.ainvoke()](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.ainvoke)"
+    "#### [Runnable.invoke()](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.invoke) / [Runnable.ainvoke()](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.ainvoke)"
    ]
   },
   {
@@ -46,7 +46,7 @@
    "metadata": {},
    "source": [
     "### Batch a runnable\n",
-    "#### [Runnable.batch()](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.batch) / [Runnable.abatch()](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.abatch)"
+    "#### [Runnable.batch()](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.batch) / [Runnable.abatch()](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.abatch)"
    ]
   },
   {
@@ -82,7 +82,7 @@
    "metadata": {},
    "source": [
     "### Stream a runnable\n",
-    "#### [Runnable.stream()](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) / [Runnable.astream()](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream)"
+    "#### [Runnable.stream()](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) / [Runnable.astream()](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream)"
    ]
   },
   {
@@ -165,7 +165,7 @@
    "metadata": {},
    "source": [
     "### Invoke runnables in parallel\n",
-    "#### [RunnableParallel](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableParallel.html)"
+    "#### [RunnableParallel](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableParallel.html)"
    ]
   },
   {
@@ -202,7 +202,7 @@
    "metadata": {},
    "source": [
     "### Turn any function into a runnable\n",
-    "#### [RunnableLambda](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html)"
+    "#### [RunnableLambda](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html)"
    ]
   },
   {
@@ -240,7 +240,7 @@
    "metadata": {},
    "source": [
     "### Merge input and output dicts\n",
-    "#### [RunnablePassthrough.assign](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html)"
+    "#### [RunnablePassthrough.assign](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html)"
    ]
   },
   {
@@ -276,7 +276,7 @@
    "metadata": {},
    "source": [
     "### Include input dict in output dict\n",
-    "#### [RunnablePassthrough](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html)"
+    "#### [RunnablePassthrough](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html)"
    ]
   },
   {
@@ -316,7 +316,7 @@
    "metadata": {},
    "source": [
     "### Add default invocation args\n",
-    "#### [Runnable.bind](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.bind)"
+    "#### [Runnable.bind](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.bind)"
    ]
   },
   {
@@ -360,7 +360,7 @@
    "metadata": {},
    "source": [
     "### Add fallbacks\n",
-    "#### [Runnable.with_fallbacks](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_fallbacks)"
+    "#### [Runnable.with_fallbacks](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_fallbacks)"
    ]
   },
   {
@@ -397,7 +397,7 @@
    "metadata": {},
    "source": [
     "### Add retries\n",
-    "#### [Runnable.with_retry](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_retry)"
+    "#### [Runnable.with_retry](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_retry)"
    ]
   },
   {
@@ -449,7 +449,7 @@
    "metadata": {},
    "source": [
     "### Configure runnable execution\n",
-    "#### [RunnableConfig](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html)"
+    "#### [RunnableConfig](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html)"
    ]
   },
   {
@@ -487,7 +487,7 @@
    "metadata": {},
    "source": [
     "### Add default config to runnable\n",
-    "#### [Runnable.with_config](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config)"
+    "#### [Runnable.with_config](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_config)"
    ]
   },
   {
@@ -526,7 +526,7 @@
    "metadata": {},
    "source": [
     "### Make runnable attributes configurable\n",
-    "#### [Runnable.with_configurable_fields](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableSerializable.html#langchain_core.runnables.base.RunnableSerializable.configurable_fields)"
+    "#### [Runnable.with_configurable_fields](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSerializable.html#langchain_core.runnables.base.RunnableSerializable.configurable_fields)"
    ]
   },
   {
@@ -605,7 +605,7 @@
    "metadata": {},
    "source": [
     "### Make chain components configurable\n",
-    "#### [Runnable.with_configurable_alternatives](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableSerializable.html#langchain_core.runnables.base.RunnableSerializable.configurable_alternatives)"
+    "#### [Runnable.with_configurable_alternatives](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSerializable.html#langchain_core.runnables.base.RunnableSerializable.configurable_alternatives)"
    ]
   },
   {
@@ -745,7 +745,7 @@
    "metadata": {},
    "source": [
     "### Generate a stream of events\n",
-    "#### [Runnable.astream_events](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events)"
+    "#### [Runnable.astream_events](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events)"
    ]
   },
   {
@@ -817,7 +817,7 @@
    "metadata": {},
    "source": [
     "### Yield batched outputs as they complete\n",
-    "#### [Runnable.batch_as_completed](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.batch_as_completed) / [Runnable.abatch_as_completed](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.abatch_as_completed)"
+    "#### [Runnable.batch_as_completed](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.batch_as_completed) / [Runnable.abatch_as_completed](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.abatch_as_completed)"
    ]
   },
   {
@@ -858,7 +858,7 @@
    "metadata": {},
    "source": [
     "### Return subset of output dict\n",
-    "#### [Runnable.pick](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.pick)"
+    "#### [Runnable.pick](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.pick)"
    ]
   },
   {
@@ -893,7 +893,7 @@
    "metadata": {},
    "source": [
     "### Declaratively make a batched version of a runnable\n",
-    "#### [Runnable.map](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.map)"
+    "#### [Runnable.map](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.map)"
    ]
   },
   {
@@ -930,7 +930,7 @@
    "metadata": {},
    "source": [
     "### Get a graph representation of a runnable\n",
-    "#### [Runnable.get_graph](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.get_graph)"
+    "#### [Runnable.get_graph](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.get_graph)"
    ]
   },
   {
@@ -991,7 +991,7 @@
    "metadata": {},
    "source": [
     "### Get all prompts in a chain\n",
-    "#### [Runnable.get_prompts](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.get_prompts)"
+    "#### [Runnable.get_prompts](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.get_prompts)"
    ]
   },
   {
@@ -1071,7 +1071,7 @@
    "metadata": {},
    "source": [
     "### Add lifecycle listeners\n",
-    "#### [Runnable.with_listeners](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_listeners)"
+    "#### [Runnable.with_listeners](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.with_listeners)"
    ]
   },
   {
diff --git a/docs/docs/how_to/llm_token_usage_tracking.ipynb b/docs/docs/how_to/llm_token_usage_tracking.ipynb
index 4d17c92d5c3..8818ee3171d 100644
--- a/docs/docs/how_to/llm_token_usage_tracking.ipynb
+++ b/docs/docs/how_to/llm_token_usage_tracking.ipynb
@@ -24,7 +24,7 @@
     "\n",
     "There are some API-specific callback context managers that allow you to track token usage across multiple calls. You'll need to check whether such an integration is available for your particular model.\n",
     "\n",
-    "If such an integration is not available for your model, you can create a custom callback manager by adapting the implementation of the [OpenAI callback manager](https://python.langchain.com/v0.2/api_reference/community/callbacks/langchain_community.callbacks.openai_info.OpenAICallbackHandler.html).\n",
+    "If such an integration is not available for your model, you can create a custom callback manager by adapting the implementation of the [OpenAI callback manager](https://python.langchain.com/api_reference/community/callbacks/langchain_community.callbacks.openai_info.OpenAICallbackHandler.html).\n",
     "\n",
     "### OpenAI\n",
     "\n",
diff --git a/docs/docs/how_to/local_llms.ipynb b/docs/docs/how_to/local_llms.ipynb
index 08433bff349..86eedaa55aa 100644
--- a/docs/docs/how_to/local_llms.ipynb
+++ b/docs/docs/how_to/local_llms.ipynb
@@ -244,7 +244,7 @@
     "\n",
     "* E.g., for Llama 2 7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
     "* We can also specify a particular version from the [model list](https://github.com/jmorganca/ollama?tab=readme-ov-file#model-library), e.g., `ollama pull llama2:13b`\n",
-    "* See the full set of parameters on the [API reference page](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.ollama.Ollama.html)"
+    "* See the full set of parameters on the [API reference page](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.ollama.Ollama.html)"
    ]
   },
   {
@@ -280,9 +280,9 @@
     "\n",
     "For example, below we run inference on `llama2-13b` with 4 bit quantization downloaded from [HuggingFace](https://huggingface.co/TheBloke/Llama-2-13B-GGML/tree/main).\n",
     "\n",
-    "As noted above, see the [API reference](https://python.langchain.com/v0.2/api_reference/langchain/llms/langchain.llms.llamacpp.LlamaCpp.html?highlight=llamacpp#langchain.llms.llamacpp.LlamaCpp) for the full set of parameters. \n",
+    "As noted above, see the [API reference](https://python.langchain.com/api_reference/langchain/llms/langchain.llms.llamacpp.LlamaCpp.html?highlight=llamacpp#langchain.llms.llamacpp.LlamaCpp) for the full set of parameters. \n",
     "\n",
-    "From the [llama.cpp API reference docs](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.llamacpp.LlamaCpp.html), a few are worth commenting on:\n",
+    "From the [llama.cpp API reference docs](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.llamacpp.LlamaCpp.html), a few are worth commenting on:\n",
     "\n",
     "`n_gpu_layers`: number of layers to be loaded into GPU memory\n",
     "\n",
@@ -416,7 +416,7 @@
     "\n",
     "We can use model weights downloaded from [GPT4All](/docs/integrations/llms/gpt4all) model explorer.\n",
     "\n",
-    "Similar to what is shown above, we can run inference and use [the API reference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.gpt4all.GPT4All.html) to set parameters of interest."
+    "Similar to what is shown above, we can run inference and use [the API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.gpt4all.GPT4All.html) to set parameters of interest."
    ]
   },
   {
diff --git a/docs/docs/how_to/logprobs.ipynb b/docs/docs/how_to/logprobs.ipynb
index b216bb4a915..c15565e0b95 100644
--- a/docs/docs/how_to/logprobs.ipynb
+++ b/docs/docs/how_to/logprobs.ipynb
@@ -55,7 +55,7 @@
    "id": "f88ffa0d-f4a7-482c-88de-cbec501a79b1",
    "metadata": {},
    "source": [
-    "For the OpenAI API to return log probabilities we need to configure the `logprobs=True` param. Then, the logprobs are included on each output [`AIMessage`](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html) as part of the `response_metadata`:"
+    "For the OpenAI API to return log probabilities we need to configure the `logprobs=True` param. Then, the logprobs are included on each output [`AIMessage`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html) as part of the `response_metadata`:"
    ]
   },
   {
diff --git a/docs/docs/how_to/long_context_reorder.ipynb b/docs/docs/how_to/long_context_reorder.ipynb
index d1f805322f3..29bed05bbe0 100644
--- a/docs/docs/how_to/long_context_reorder.ipynb
+++ b/docs/docs/how_to/long_context_reorder.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "To mitigate the [\"lost in the middle\"](https://arxiv.org/abs/2307.03172) effect, you can re-order documents after retrieval such that the most relevant documents are positioned at extrema (e.g., the first and last pieces of context), and the least relevant documents are positioned in the middle. In some cases this can help surface the most relevant information to LLMs.\n",
     "\n",
-    "The [LongContextReorder](https://python.langchain.com/v0.2/api_reference/community/document_transformers/langchain_community.document_transformers.long_context_reorder.LongContextReorder.html) document transformer implements this re-ordering procedure. Below we demonstrate an example."
+    "The [LongContextReorder](https://python.langchain.com/api_reference/community/document_transformers/langchain_community.document_transformers.long_context_reorder.LongContextReorder.html) document transformer implements this re-ordering procedure. Below we demonstrate an example."
    ]
   },
   {
diff --git a/docs/docs/how_to/markdown_header_metadata_splitter.ipynb b/docs/docs/how_to/markdown_header_metadata_splitter.ipynb
index d480bd9c224..ad33be83991 100644
--- a/docs/docs/how_to/markdown_header_metadata_splitter.ipynb
+++ b/docs/docs/how_to/markdown_header_metadata_splitter.ipynb
@@ -17,7 +17,7 @@
     "When a full paragraph or document is embedded, the embedding process considers both the overall context and the relationships between the sentences and phrases within the text. This can result in a more comprehensive vector representation that captures the broader meaning and themes of the text.\n",
     "```\n",
     " \n",
-    "As mentioned, chunking often aims to keep text with common context together. With this in mind, we might want to specifically honor the structure of the document itself. For example, a markdown file is organized by headers. Creating chunks within specific header groups is an intuitive idea. To address this challenge, we can use [MarkdownHeaderTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/markdown/langchain_text_splitters.markdown.MarkdownHeaderTextSplitter.html). This will split a markdown file by a specified set of headers. \n",
+    "As mentioned, chunking often aims to keep text with common context together. With this in mind, we might want to specifically honor the structure of the document itself. For example, a markdown file is organized by headers. Creating chunks within specific header groups is an intuitive idea. To address this challenge, we can use [MarkdownHeaderTextSplitter](https://python.langchain.com/api_reference/text_splitters/markdown/langchain_text_splitters.markdown.MarkdownHeaderTextSplitter.html). This will split a markdown file by a specified set of headers. \n",
     "\n",
     "For example, if we want to split this markdown:\n",
     "```\n",
diff --git a/docs/docs/how_to/merge_message_runs.ipynb b/docs/docs/how_to/merge_message_runs.ipynb
index 074c79bfe1a..350eed6577f 100644
--- a/docs/docs/how_to/merge_message_runs.ipynb
+++ b/docs/docs/how_to/merge_message_runs.ipynb
@@ -214,7 +214,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For a complete description of all arguments head to the API reference: https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.utils.merge_message_runs.html"
+    "For a complete description of all arguments head to the API reference: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.merge_message_runs.html"
    ]
   }
  ],
diff --git a/docs/docs/how_to/message_history.ipynb b/docs/docs/how_to/message_history.ipynb
index 08cc51cb536..bb08967f62a 100644
--- a/docs/docs/how_to/message_history.ipynb
+++ b/docs/docs/how_to/message_history.ipynb
@@ -32,7 +32,7 @@
     "\n",
     ":::\n",
     "\n",
-    "Passing conversation state into and out a chain is vital when building a chatbot. The [`RunnableWithMessageHistory`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html#langchain_core.runnables.history.RunnableWithMessageHistory) class lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it. Specifically, it loads previous messages in the conversation BEFORE passing it to the Runnable, and it saves the generated response as a message AFTER calling the runnable. This class also enables multiple conversations by saving each conversation with a `session_id` - it then expects a `session_id` to be passed in the config when calling the runnable, and uses that to look up the relevant conversation history.\n",
+    "Passing conversation state into and out a chain is vital when building a chatbot. The [`RunnableWithMessageHistory`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html#langchain_core.runnables.history.RunnableWithMessageHistory) class lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it. Specifically, it loads previous messages in the conversation BEFORE passing it to the Runnable, and it saves the generated response as a message AFTER calling the runnable. This class also enables multiple conversations by saving each conversation with a `session_id` - it then expects a `session_id` to be passed in the config when calling the runnable, and uses that to look up the relevant conversation history.\n",
     "\n",
     "![index_diagram](../../static/img/message_history.png)\n",
     "\n",
diff --git a/docs/docs/how_to/migrate_agent.ipynb b/docs/docs/how_to/migrate_agent.ipynb
index b2a8ae70492..b08140ba179 100644
--- a/docs/docs/how_to/migrate_agent.ipynb
+++ b/docs/docs/how_to/migrate_agent.ipynb
@@ -31,7 +31,7 @@
     ":::\n",
     "\n",
     "Here we focus on how to move from legacy LangChain agents to more flexible [LangGraph](https://langchain-ai.github.io/langgraph/) agents.\n",
-    "LangChain agents (the [AgentExecutor](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
+    "LangChain agents (the [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor) in particular) have multiple configuration parameters.\n",
     "In this notebook we will show how those parameters map to the LangGraph react agent executor using the [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#create_react_agent) prebuilt helper method.\n",
     "\n",
     "#### Prerequisites\n",
@@ -112,7 +112,7 @@
    "id": "af002033-fe51-4d14-b47c-3e9b483c8395",
    "metadata": {},
    "source": [
-    "For the LangChain [AgentExecutor](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor), we define a prompt with a placeholder for the agent's scratchpad. The agent can be invoked as follows:"
+    "For the LangChain [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor), we define a prompt with a placeholder for the agent's scratchpad. The agent can be invoked as follows:"
    ]
   },
   {
@@ -383,7 +383,7 @@
    "source": [
     "### In LangChain\n",
     "\n",
-    "With LangChain's [AgentExecutor](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could add chat [Memory](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.memory) so it can engage in a multi-turn conversation."
+    "With LangChain's [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could add chat [Memory](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.memory) so it can engage in a multi-turn conversation."
    ]
   },
   {
@@ -541,7 +541,7 @@
     "\n",
     "### In LangChain\n",
     "\n",
-    "With LangChain's [AgentExecutor](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could iterate over the steps using the [stream](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) (or async `astream`) methods or the [iter](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter) method. LangGraph supports stepwise iteration using [stream](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) "
+    "With LangChain's [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could iterate over the steps using the [stream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) (or async `astream`) methods or the [iter](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter) method. LangGraph supports stepwise iteration using [stream](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.stream) "
    ]
   },
   {
@@ -1019,7 +1019,7 @@
     "\n",
     "### In LangChain\n",
     "\n",
-    "With LangChain's [AgentExecutor](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could configure an [early_stopping_method](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.early_stopping_method) to either return a string saying \"Agent stopped due to iteration limit or time limit.\" (`\"force\"`) or prompt the LLM a final time to respond (`\"generate\"`)."
+    "With LangChain's [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.iter), you could configure an [early_stopping_method](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.early_stopping_method) to either return a string saying \"Agent stopped due to iteration limit or time limit.\" (`\"force\"`) or prompt the LLM a final time to respond (`\"generate\"`)."
    ]
   },
   {
@@ -1130,7 +1130,7 @@
     "\n",
     "### In LangChain\n",
     "\n",
-    "With LangChain's [AgentExecutor](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor), you could trim the intermediate steps of long-running agents using [trim_intermediate_steps](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.trim_intermediate_steps), which is either an integer (indicating the agent should keep the last N steps) or a custom function.\n",
+    "With LangChain's [AgentExecutor](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor), you could trim the intermediate steps of long-running agents using [trim_intermediate_steps](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html#langchain.agents.agent.AgentExecutor.trim_intermediate_steps), which is either an integer (indicating the agent should keep the last N steps) or a custom function.\n",
     "\n",
     "For instance, we could trim the value so the agent only sees the most recent intermediate step."
    ]
diff --git a/docs/docs/how_to/multi_vector.ipynb b/docs/docs/how_to/multi_vector.ipynb
index 0ba3ec5c227..db01daee38a 100644
--- a/docs/docs/how_to/multi_vector.ipynb
+++ b/docs/docs/how_to/multi_vector.ipynb
@@ -9,17 +9,17 @@
     "\n",
     "It can often be useful to store multiple vectors per document. There are multiple use cases where this is beneficial. For example, we can embed multiple chunks of a document and associate those embeddings with the parent document, allowing retriever hits on the chunks to return the larger document.\n",
     "\n",
-    "LangChain implements a base [MultiVectorRetriever](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.multi_vector.MultiVectorRetriever.html), which simplifies this process. Much of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`.\n",
+    "LangChain implements a base [MultiVectorRetriever](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.multi_vector.MultiVectorRetriever.html), which simplifies this process. Much of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`.\n",
     "\n",
     "The methods to create multiple vectors per document include:\n",
     "\n",
-    "- Smaller chunks: split a document into smaller chunks, and embed those (this is [ParentDocumentRetriever](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html)).\n",
+    "- Smaller chunks: split a document into smaller chunks, and embed those (this is [ParentDocumentRetriever](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html)).\n",
     "- Summary: create a summary for each document, embed that along with (or instead of) the document.\n",
     "- Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document.\n",
     "\n",
     "Note that this also enables another method of adding embeddings - manually. This is useful because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control.\n",
     "\n",
-    "Below we walk through an example. First we instantiate some documents. We will index them in an (in-memory) [Chroma](/docs/integrations/providers/chroma/) vector store using [OpenAI](https://python.langchain.com/v0.2/docs/integrations/text_embedding/openai/) embeddings, but any LangChain vector store or embeddings model will suffice."
+    "Below we walk through an example. First we instantiate some documents. We will index them in an (in-memory) [Chroma](/docs/integrations/providers/chroma/) vector store using [OpenAI](https://python.langchain.com/docs/integrations/text_embedding/openai/) embeddings, but any LangChain vector store or embeddings model will suffice."
    ]
   },
   {
@@ -68,7 +68,7 @@
    "source": [
     "## Smaller chunks\n",
     "\n",
-    "Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks. This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. Note that this is what the [ParentDocumentRetriever](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html) does. Here we show what is going on under the hood.\n",
+    "Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks. This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. Note that this is what the [ParentDocumentRetriever](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.parent_document_retriever.ParentDocumentRetriever.html) does. Here we show what is going on under the hood.\n",
     "\n",
     "We will make a distinction between the vector store, which indexes embeddings of the (sub) documents, and the document store, which houses the \"parent\" documents and associates them with an identifier."
    ]
@@ -103,7 +103,7 @@
    "id": "d4feded4-856a-4282-91c3-53aabc62e6ff",
    "metadata": {},
    "source": [
-    "We next generate the \"sub\" documents by splitting the original documents. Note that we store the document identifier in the `metadata` of the corresponding [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) object."
+    "We next generate the \"sub\" documents by splitting the original documents. Note that we store the document identifier in the `metadata` of the corresponding [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) object."
    ]
   },
   {
@@ -207,7 +207,7 @@
    "id": "cdef8339-f9fa-4b3b-955f-ad9dbdf2734f",
    "metadata": {},
    "source": [
-    "The default search type the retriever performs on the vector database is a similarity search. LangChain vector stores also support searching via [Max Marginal Relevance](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search). This can be controlled via the `search_type` parameter of the retriever:"
+    "The default search type the retriever performs on the vector database is a similarity search. LangChain vector stores also support searching via [Max Marginal Relevance](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search). This can be controlled via the `search_type` parameter of the retriever:"
    ]
   },
   {
@@ -244,7 +244,7 @@
     "\n",
     "A summary may be able to distill more accurately what a chunk is about, leading to better retrieval. Here we show how to create summaries, and then embed those.\n",
     "\n",
-    "We construct a simple [chain](/docs/how_to/sequence) that will receive an input [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) object and generate a summary using a LLM.\n",
+    "We construct a simple [chain](/docs/how_to/sequence) that will receive an input [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) object and generate a summary using a LLM.\n",
     "\n",
     "```{=mdx}\n",
     "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
@@ -294,7 +294,7 @@
    "id": "3faa9fde-1b09-4849-a815-8b2e89c30a02",
    "metadata": {},
    "source": [
-    "Note that we can [batch](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) the chain accross documents:"
+    "Note that we can [batch](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) the chain accross documents:"
    ]
   },
   {
diff --git a/docs/docs/how_to/output_parser_fixing.ipynb b/docs/docs/how_to/output_parser_fixing.ipynb
index a227f6d0698..3ba61eaa4f6 100644
--- a/docs/docs/how_to/output_parser_fixing.ipynb
+++ b/docs/docs/how_to/output_parser_fixing.ipynb
@@ -131,7 +131,7 @@
    "id": "84498e02",
    "metadata": {},
    "source": [
-    "Find out api documentation for [OutputFixingParser](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)."
+    "Find out api documentation for [OutputFixingParser](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.fix.OutputFixingParser.html#langchain.output_parsers.fix.OutputFixingParser)."
    ]
   },
   {
diff --git a/docs/docs/how_to/output_parser_json.ipynb b/docs/docs/how_to/output_parser_json.ipynb
index 646b72514c3..4c4ac909085 100644
--- a/docs/docs/how_to/output_parser_json.ipynb
+++ b/docs/docs/how_to/output_parser_json.ipynb
@@ -30,7 +30,7 @@
    "id": "ae909b7a",
    "metadata": {},
    "source": [
-    "The [`JsonOutputParser`](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html) is one built-in option for prompting for and then parsing JSON output. While it is similar in functionality to the [`PydanticOutputParser`](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html), it also supports streaming back partial JSON objects.\n",
+    "The [`JsonOutputParser`](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html) is one built-in option for prompting for and then parsing JSON output. While it is similar in functionality to the [`PydanticOutputParser`](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html), it also supports streaming back partial JSON objects.\n",
     "\n",
     "Here's an example of how it can be used alongside [Pydantic](https://docs.pydantic.dev/) to conveniently declare the expected schema:"
    ]
diff --git a/docs/docs/how_to/output_parser_retry.ipynb b/docs/docs/how_to/output_parser_retry.ipynb
index 2ca4be59c78..9f772c7aa7d 100644
--- a/docs/docs/how_to/output_parser_retry.ipynb
+++ b/docs/docs/how_to/output_parser_retry.ipynb
@@ -244,7 +244,7 @@
    "id": "e3a2513a",
    "metadata": {},
    "source": [
-    "Find out api documentation for [RetryOutputParser](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html#langchain.output_parsers.retry.RetryOutputParser)."
+    "Find out api documentation for [RetryOutputParser](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.retry.RetryOutputParser.html#langchain.output_parsers.retry.RetryOutputParser)."
    ]
   },
   {
diff --git a/docs/docs/how_to/output_parser_xml.ipynb b/docs/docs/how_to/output_parser_xml.ipynb
index ff63ca53465..8b351e3269e 100644
--- a/docs/docs/how_to/output_parser_xml.ipynb
+++ b/docs/docs/how_to/output_parser_xml.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "LLMs from different providers often have different strengths depending on the specific data they are trianed on. This also means that some may be \"better\" and more reliable at generating output in formats other than JSON.\n",
     "\n",
-    "This guide shows you how to use the [`XMLOutputParser`](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html) to prompt models for XML output, then and parse that output into a usable format.\n",
+    "This guide shows you how to use the [`XMLOutputParser`](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html) to prompt models for XML output, then and parse that output into a usable format.\n",
     "\n",
     ":::{.callout-note}\n",
     "Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed XML.\n",
diff --git a/docs/docs/how_to/output_parser_yaml.ipynb b/docs/docs/how_to/output_parser_yaml.ipynb
index af11c084377..0a3a1715aed 100644
--- a/docs/docs/how_to/output_parser_yaml.ipynb
+++ b/docs/docs/how_to/output_parser_yaml.ipynb
@@ -48,7 +48,7 @@
    "id": "cc479f3a",
    "metadata": {},
    "source": [
-    "We use [Pydantic](https://docs.pydantic.dev) with the [`YamlOutputParser`](https://python.langchain.com/v0.2/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser) to declare our data model and give the model more context as to what type of YAML it should generate:"
+    "We use [Pydantic](https://docs.pydantic.dev) with the [`YamlOutputParser`](https://python.langchain.com/api_reference/langchain/output_parsers/langchain.output_parsers.yaml.YamlOutputParser.html#langchain.output_parsers.yaml.YamlOutputParser) to declare our data model and give the model more context as to what type of YAML it should generate:"
    ]
   },
   {
diff --git a/docs/docs/how_to/parallel.ipynb b/docs/docs/how_to/parallel.ipynb
index 5f1c23428b5..eedaea41731 100644
--- a/docs/docs/how_to/parallel.ipynb
+++ b/docs/docs/how_to/parallel.ipynb
@@ -26,7 +26,7 @@
     "\n",
     ":::\n",
     "\n",
-    "The [`RunnableParallel`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableParallel.html) primitive is essentially a dict whose values are runnables (or things that can be coerced to runnables, like functions). It runs all of its values in parallel, and each value is called with the overall input of the `RunnableParallel`. The final return value is a dict with the results of each value under its appropriate key.\n",
+    "The [`RunnableParallel`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableParallel.html) primitive is essentially a dict whose values are runnables (or things that can be coerced to runnables, like functions). It runs all of its values in parallel, and each value is called with the overall input of the `RunnableParallel`. The final return value is a dict with the results of each value under its appropriate key.\n",
     "\n",
     "## Formatting with `RunnableParallels`\n",
     "\n",
diff --git a/docs/docs/how_to/passthrough.ipynb b/docs/docs/how_to/passthrough.ipynb
index 8bd8e775ffc..efe185a7793 100644
--- a/docs/docs/how_to/passthrough.ipynb
+++ b/docs/docs/how_to/passthrough.ipynb
@@ -29,7 +29,7 @@
     ":::\n",
     "\n",
     "\n",
-    "When composing chains with several steps, sometimes you will want to pass data from previous steps unchanged for use as input to a later step. The [`RunnablePassthrough`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html) class allows you to do just this, and is typically is used in conjuction with a [RunnableParallel](/docs/how_to/parallel/) to pass data through to a later step in your constructed chains.\n",
+    "When composing chains with several steps, sometimes you will want to pass data from previous steps unchanged for use as input to a later step. The [`RunnablePassthrough`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html) class allows you to do just this, and is typically is used in conjuction with a [RunnableParallel](/docs/how_to/parallel/) to pass data through to a later step in your constructed chains.\n",
     "\n",
     "See the example below:"
    ]
diff --git a/docs/docs/how_to/prompts_composition.ipynb b/docs/docs/how_to/prompts_composition.ipynb
index ab648642042..899ea104524 100644
--- a/docs/docs/how_to/prompts_composition.ipynb
+++ b/docs/docs/how_to/prompts_composition.ipynb
@@ -102,7 +102,7 @@
    "source": [
     "A chat prompt is made up a of a list of messages. Similarly to the above example, we can concatenate chat prompt templates. Each new element is a new message in the final prompt.\n",
     "\n",
-    "First, let's initialize the a [`ChatPromptTemplate`](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html) with a [`SystemMessage`](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.system.SystemMessage.html)."
+    "First, let's initialize the a [`ChatPromptTemplate`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html) with a [`SystemMessage`](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.system.SystemMessage.html)."
    ]
   },
   {
@@ -123,7 +123,7 @@
    "metadata": {},
    "source": [
     "You can then easily create a pipeline combining it with other messages *or* message templates.\n",
-    "Use a `Message` when there is no variables to be formatted, use a `MessageTemplate` when there are variables to be formatted. You can also use just a string (note: this will automatically get inferred as a [`HumanMessagePromptTemplate`](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.chat.HumanMessagePromptTemplate.html).)"
+    "Use a `Message` when there is no variables to be formatted, use a `MessageTemplate` when there are variables to be formatted. You can also use just a string (note: this will automatically get inferred as a [`HumanMessagePromptTemplate`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.HumanMessagePromptTemplate.html).)"
    ]
   },
   {
@@ -183,7 +183,7 @@
    "id": "0a5892f9-e4d8-4b7c-b6a5-4651539b9734",
    "metadata": {},
    "source": [
-    "LangChain includes a class called [`PipelinePromptTemplate`](https://python.langchain.com/v0.2/api_reference/core/prompts/langchain_core.prompts.pipeline.PipelinePromptTemplate.html), which can be useful when you want to reuse parts of prompts. A PipelinePrompt consists of two main parts:\n",
+    "LangChain includes a class called [`PipelinePromptTemplate`](https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.pipeline.PipelinePromptTemplate.html), which can be useful when you want to reuse parts of prompts. A PipelinePrompt consists of two main parts:\n",
     "\n",
     "- Final prompt: The final prompt that is returned\n",
     "- Pipeline prompts: A list of tuples, consisting of a string name and a prompt template. Each prompt template will be formatted and then passed to future prompt templates as a variable with the same name."
diff --git a/docs/docs/how_to/pydantic_compatibility.md b/docs/docs/how_to/pydantic_compatibility.md
index 3e493951602..a0fd9f51373 100644
--- a/docs/docs/how_to/pydantic_compatibility.md
+++ b/docs/docs/how_to/pydantic_compatibility.md
@@ -175,4 +175,4 @@ If you need OpenAPI docs, your options are to either install Pydantic 1:
 or else to use the `APIHandler` object in LangChain to manually create the
 routes for your API.
 
-See: https://python.langchain.com/v0.2/docs/langserve/#pydantic
+See: https://python.langchain.com/docs/langserve/#pydantic
diff --git a/docs/docs/how_to/qa_chat_history_how_to.ipynb b/docs/docs/how_to/qa_chat_history_how_to.ipynb
index 3da169772d5..f65ac13cb38 100644
--- a/docs/docs/how_to/qa_chat_history_how_to.ipynb
+++ b/docs/docs/how_to/qa_chat_history_how_to.ipynb
@@ -102,7 +102,7 @@
    "source": [
     "## Chains {#chains}\n",
     "\n",
-    "In a conversational RAG application, queries issued to the retriever should be informed by the context of the conversation. LangChain provides a [create_history_aware_retriever](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) constructor to simplify this. It constructs a chain that accepts keys `input` and `chat_history` as input, and has the same output schema as a retriever. `create_history_aware_retriever` requires as inputs:  \n",
+    "In a conversational RAG application, queries issued to the retriever should be informed by the context of the conversation. LangChain provides a [create_history_aware_retriever](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) constructor to simplify this. It constructs a chain that accepts keys `input` and `chat_history` as input, and has the same output schema as a retriever. `create_history_aware_retriever` requires as inputs:  \n",
     "\n",
     "1. LLM;\n",
     "2. Retriever;\n",
@@ -155,7 +155,7 @@
    "id": "15f8ad59-19de-42e3-85a8-3ba95ee0bd43",
    "metadata": {},
    "source": [
-    "For the retriever, we will use [WebBaseLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `Chroma` vectorstore and then use its [.as_retriever](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/#langchain-expression-language) chains."
+    "For the retriever, we will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `Chroma` vectorstore and then use its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/#langchain-expression-language) chains."
    ]
   },
   {
@@ -260,9 +260,9 @@
     "\n",
     "Now we can build our full QA chain.\n",
     "\n",
-    "As in the [RAG tutorial](/docs/tutorials/rag), we will use [create_stuff_documents_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) to generate a `question_answer_chain`, with input keys `context`, `chat_history`, and `input`-- it accepts the retrieved context alongside the conversation history and query to generate an answer.\n",
+    "As in the [RAG tutorial](/docs/tutorials/rag), we will use [create_stuff_documents_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) to generate a `question_answer_chain`, with input keys `context`, `chat_history`, and `input`-- it accepts the retrieved context alongside the conversation history and query to generate an answer.\n",
     "\n",
-    "We build our final `rag_chain` with [create_retrieval_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html). This chain applies the `history_aware_retriever` and `question_answer_chain` in sequence, retaining intermediate outputs such as the retrieved context for convenience. It has input keys `input` and `chat_history`, and includes `input`, `chat_history`, `context`, and `answer` in its output."
+    "We build our final `rag_chain` with [create_retrieval_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html). This chain applies the `history_aware_retriever` and `question_answer_chain` in sequence, retaining intermediate outputs such as the retrieved context for convenience. It has input keys `input` and `chat_history`, and includes `input`, `chat_history`, `context`, and `answer` in its output."
    ]
   },
   {
@@ -305,7 +305,7 @@
     "1. An object for storing the chat history;\n",
     "2. An object that wraps our chain and manages updates to the chat history.\n",
     "\n",
-    "For these we will use [BaseChatMessageHistory](https://python.langchain.com/v0.2/api_reference/core/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html) and [RunnableWithMessageHistory](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html). The latter is a wrapper for an LCEL chain and a `BaseChatMessageHistory` that handles injecting chat history into inputs and updating it after each invocation.\n",
+    "For these we will use [BaseChatMessageHistory](https://python.langchain.com/api_reference/core/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html) and [RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html). The latter is a wrapper for an LCEL chain and a `BaseChatMessageHistory` that handles injecting chat history into inputs and updating it after each invocation.\n",
     "\n",
     "For a detailed walkthrough of how to use these classes together to create a stateful conversational chain, head to the [How to add message history (memory)](/docs/how_to/message_history/) LCEL how-to guide.\n",
     "\n",
diff --git a/docs/docs/how_to/qa_citations.ipynb b/docs/docs/how_to/qa_citations.ipynb
index 7dc28ac350e..1b27659a947 100644
--- a/docs/docs/how_to/qa_citations.ipynb
+++ b/docs/docs/how_to/qa_citations.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "We generally suggest using the first item of the list that works for your use-case. That is, if your model supports tool-calling, try methods 1 or 2; otherwise, or if those fail, advance down the list.\n",
     "\n",
-    "Let's first create a simple RAG chain. To start we'll just retrieve from Wikipedia using the [WikipediaRetriever](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html)."
+    "Let's first create a simple RAG chain. To start we'll just retrieve from Wikipedia using the [WikipediaRetriever](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html)."
    ]
   },
   {
@@ -253,7 +253,7 @@
    "source": [
     "## Function-calling\n",
     "\n",
-    "If your LLM of choice implements a [tool-calling](/docs/concepts#functiontool-calling) feature, you can use it to make the model specify which of the provided documents it's referencing when generating its answer. LangChain tool-calling models implement a `.with_structured_output` method which will force generation adhering to a desired schema (see for example [here](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.with_structured_output)).\n",
+    "If your LLM of choice implements a [tool-calling](/docs/concepts#functiontool-calling) feature, you can use it to make the model specify which of the provided documents it's referencing when generating its answer. LangChain tool-calling models implement a `.with_structured_output` method which will force generation adhering to a desired schema (see for example [here](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html#langchain_openai.chat_models.base.ChatOpenAI.with_structured_output)).\n",
     "\n",
     "### Cite documents\n",
     "\n",
@@ -616,7 +616,7 @@
     "\n",
     "1. We update the formatting function to wrap the retrieved context in XML tags;\n",
     "2. We do not use `.with_structured_output` (e.g., because it does not exist for a model);\n",
-    "3. We use [XMLOutputParser](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html) in place of `StrOutputParser` to parse the answer into a dict."
+    "3. We use [XMLOutputParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.xml.XMLOutputParser.html) in place of `StrOutputParser` to parse the answer into a dict."
    ]
   },
   {
@@ -711,7 +711,7 @@
    "source": [
     "## Retrieval post-processing\n",
     "\n",
-    "Another approach is to post-process our retrieved documents to compress the content, so that the source content is already minimal enough that we don't need the model to cite specific sources or spans. For example, we could break up each document into a sentence or two, embed those and keep only the most relevant ones. LangChain has some built-in components for this. Here we'll use a [RecursiveCharacterTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/text_splitter/langchain_text_splitters.RecursiveCharacterTextSplitter.html#langchain_text_splitters.RecursiveCharacterTextSplitter), which creates chunks of a sepacified size by splitting on separator substrings, and an [EmbeddingsFilter](https://python.langchain.com/v0.2/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html#langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter), which keeps only the texts with the most relevant embeddings.\n",
+    "Another approach is to post-process our retrieved documents to compress the content, so that the source content is already minimal enough that we don't need the model to cite specific sources or spans. For example, we could break up each document into a sentence or two, embed those and keep only the most relevant ones. LangChain has some built-in components for this. Here we'll use a [RecursiveCharacterTextSplitter](https://python.langchain.com/api_reference/text_splitters/text_splitter/langchain_text_splitters.RecursiveCharacterTextSplitter.html#langchain_text_splitters.RecursiveCharacterTextSplitter), which creates chunks of a sepacified size by splitting on separator substrings, and an [EmbeddingsFilter](https://python.langchain.com/api_reference/langchain/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html#langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter), which keeps only the texts with the most relevant embeddings.\n",
     "\n",
     "This approach effectively swaps our original retriever with an updated one that compresses the documents. To start, we build the retriever:"
    ]
diff --git a/docs/docs/how_to/qa_sources.ipynb b/docs/docs/how_to/qa_sources.ipynb
index 1ec1c854692..04da90e68c4 100644
--- a/docs/docs/how_to/qa_sources.ipynb
+++ b/docs/docs/how_to/qa_sources.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "We will cover two approaches:\n",
     "\n",
-    "1. Using the built-in [create_retrieval_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html), which returns sources by default;\n",
+    "1. Using the built-in [create_retrieval_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html), which returns sources by default;\n",
     "2. Using a simple [LCEL](/docs/concepts#langchain-expression-language-lcel) implementation, to show the operating principle.\n",
     "\n",
     "We will also show how to structure sources into the model response, such that a model can report what specific sources it used in generating its answer."
diff --git a/docs/docs/how_to/qa_streaming.ipynb b/docs/docs/how_to/qa_streaming.ipynb
index fecf20ca9ac..8eba1a8d9ce 100644
--- a/docs/docs/how_to/qa_streaming.ipynb
+++ b/docs/docs/how_to/qa_streaming.ipynb
@@ -328,7 +328,7 @@
    "id": "8b2d224d-2a82-418b-b562-01ea210b86ef",
    "metadata": {},
    "source": [
-    "More simply, we can use the [.pick](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.pick) method to select only the desired key:"
+    "More simply, we can use the [.pick](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.pick) method to select only the desired key:"
    ]
   },
   {
@@ -432,7 +432,7 @@
     "\n",
     "To stream intermediate output, we recommend use of the async `.astream_events` method. This method will stream output from all \"events\" in the chain, and can be quite verbose. We can filter using tags, event types, and other criteria, as we do here.\n",
     "\n",
-    "Below we show a typical `.astream_events` loop, where we pass in the chain input and emit desired results. See the [API reference](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) and [streaming guide](/docs/how_to/streaming) for more detail."
+    "Below we show a typical `.astream_events` loop, where we pass in the chain input and emit desired results. See the [API reference](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) and [streaming guide](/docs/how_to/streaming) for more detail."
    ]
   },
   {
diff --git a/docs/docs/how_to/recursive_json_splitter.ipynb b/docs/docs/how_to/recursive_json_splitter.ipynb
index 71d095a05fe..9936ed9ebcb 100644
--- a/docs/docs/how_to/recursive_json_splitter.ipynb
+++ b/docs/docs/how_to/recursive_json_splitter.ipynb
@@ -107,7 +107,7 @@
    "id": "3f05bc21-227e-4d2c-af51-16d69ad3cd7b",
    "metadata": {},
    "source": [
-    "To obtain LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) objects, use the `.create_documents` method:"
+    "To obtain LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects, use the `.create_documents` method:"
    ]
   },
   {
diff --git a/docs/docs/how_to/recursive_text_splitter.ipynb b/docs/docs/how_to/recursive_text_splitter.ipynb
index 623438f52ce..ce77b89f9d1 100644
--- a/docs/docs/how_to/recursive_text_splitter.ipynb
+++ b/docs/docs/how_to/recursive_text_splitter.ipynb
@@ -30,7 +30,7 @@
     "\n",
     "To obtain the string content directly, use `.split_text`.\n",
     "\n",
-    "To create LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) objects (e.g., for use in downstream tasks), use `.create_documents`."
+    "To create LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects (e.g., for use in downstream tasks), use `.create_documents`."
    ]
   },
   {
diff --git a/docs/docs/how_to/semantic-chunker.ipynb b/docs/docs/how_to/semantic-chunker.ipynb
index b59375c022f..35889ec53af 100644
--- a/docs/docs/how_to/semantic-chunker.ipynb
+++ b/docs/docs/how_to/semantic-chunker.ipynb
@@ -69,7 +69,7 @@
    "id": "774a5199-c2ff-43bc-bf07-87573e0b8db4",
    "metadata": {},
    "source": [
-    "To instantiate a [SemanticChunker](https://python.langchain.com/v0.2/api_reference/experimental/text_splitter/langchain_experimental.text_splitter.SemanticChunker.html), we must specify an embedding model. Below we will use [OpenAIEmbeddings](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.openai.OpenAIEmbeddings.html). "
+    "To instantiate a [SemanticChunker](https://python.langchain.com/api_reference/experimental/text_splitter/langchain_experimental.text_splitter.SemanticChunker.html), we must specify an embedding model. Below we will use [OpenAIEmbeddings](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.openai.OpenAIEmbeddings.html). "
    ]
   },
   {
@@ -92,7 +92,7 @@
    "source": [
     "## Split Text\n",
     "\n",
-    "We split text in the usual way, e.g., by invoking `.create_documents` to create LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) objects:"
+    "We split text in the usual way, e.g., by invoking `.create_documents` to create LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects:"
    ]
   },
   {
diff --git a/docs/docs/how_to/sequence.ipynb b/docs/docs/how_to/sequence.ipynb
index c4ae076baab..2420afb057b 100644
--- a/docs/docs/how_to/sequence.ipynb
+++ b/docs/docs/how_to/sequence.ipynb
@@ -31,7 +31,7 @@
     "\n",
     "One point about [LangChain Expression Language](/docs/concepts/#langchain-expression-language) is that any two runnables can be \"chained\" together into sequences. The output of the previous runnable's `.invoke()` call is passed as input to the next runnable. This can be done using the pipe operator (`|`), or the more explicit `.pipe()` method, which does the same thing.\n",
     "\n",
-    "The resulting [`RunnableSequence`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html) is itself a runnable, which means it can be invoked, streamed, or further chained just like any other runnable. Advantages of chaining runnables in this way are efficient streaming (the sequence will stream output as soon as it is available), and debugging and tracing with tools like [LangSmith](/docs/how_to/debugging).\n",
+    "The resulting [`RunnableSequence`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html) is itself a runnable, which means it can be invoked, streamed, or further chained just like any other runnable. Advantages of chaining runnables in this way are efficient streaming (the sequence will stream output as soon as it is available), and debugging and tracing with tools like [LangSmith](/docs/how_to/debugging).\n",
     "\n",
     "## The pipe operator: `|`\n",
     "\n",
diff --git a/docs/docs/how_to/serialization.ipynb b/docs/docs/how_to/serialization.ipynb
index 923835d5a40..bc89a76f0ab 100644
--- a/docs/docs/how_to/serialization.ipynb
+++ b/docs/docs/how_to/serialization.ipynb
@@ -12,9 +12,9 @@
     "- Secrets, such as API keys, are separated from other parameters and can be loaded back to the object on de-serialization;\n",
     "- De-serialization is kept compatible across package versions, so objects that were serialized with one version of LangChain can be properly de-serialized with another.\n",
     "\n",
-    "To save and load LangChain objects using this system, use the `dumpd`, `dumps`, `load`, and `loads` functions in the [load module](https://python.langchain.com/v0.2/api_reference/core/load.html) of `langchain-core`. These functions support JSON and JSON-serializable objects.\n",
+    "To save and load LangChain objects using this system, use the `dumpd`, `dumps`, `load`, and `loads` functions in the [load module](https://python.langchain.com/api_reference/core/load.html) of `langchain-core`. These functions support JSON and JSON-serializable objects.\n",
     "\n",
-    "All LangChain objects that inherit from [Serializable](https://python.langchain.com/v0.2/api_reference/core/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://python.langchain.com/v0.2/api_reference//python/core_api_reference.html#module-langchain_core.messages), [document objects](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/#retrievers)), and most [Runnables](/docs/concepts/#langchain-expression-language-lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
+    "All LangChain objects that inherit from [Serializable](https://python.langchain.com/api_reference/core/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://python.langchain.com/api_reference//python/core_api_reference.html#module-langchain_core.messages), [document objects](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/#retrievers)), and most [Runnables](/docs/concepts/#langchain-expression-language-lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
     "\n",
     "Below we walk through an example with a simple [LLM chain](/docs/tutorials/llm_chain).\n",
     "\n",
diff --git a/docs/docs/how_to/split_by_token.ipynb b/docs/docs/how_to/split_by_token.ipynb
index cf6c8814b4b..b816658c526 100644
--- a/docs/docs/how_to/split_by_token.ipynb
+++ b/docs/docs/how_to/split_by_token.ipynb
@@ -27,7 +27,7 @@
     "1. How the text is split: by character passed in.\n",
     "2. How the chunk size is measured: by `tiktoken` tokenizer.\n",
     "\n",
-    "[CharacterTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/character/langchain_text_splitters.character.CharacterTextSplitter.html), [RecursiveCharacterTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html), and [TokenTextSplitter](https://python.langchain.com/v0.2/api_reference/langchain_text_splitters/base/langchain_text_splitters.base.TokenTextSplitter.html) can be used with `tiktoken` directly."
+    "[CharacterTextSplitter](https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.CharacterTextSplitter.html), [RecursiveCharacterTextSplitter](https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html), and [TokenTextSplitter](https://python.langchain.com/api_reference/langchain_text_splitters/base/langchain_text_splitters.base.TokenTextSplitter.html) can be used with `tiktoken` directly."
    ]
   },
   {
@@ -59,7 +59,7 @@
    "id": "a3ba1d8a",
    "metadata": {},
    "source": [
-    "To split with a [CharacterTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/character/langchain_text_splitters.character.CharacterTextSplitter.html) and then merge chunks with `tiktoken`, use its `.from_tiktoken_encoder()` method. Note that splits from this method can be larger than the chunk size measured by the `tiktoken` tokenizer.\n",
+    "To split with a [CharacterTextSplitter](https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.CharacterTextSplitter.html) and then merge chunks with `tiktoken`, use its `.from_tiktoken_encoder()` method. Note that splits from this method can be larger than the chunk size measured by the `tiktoken` tokenizer.\n",
     "\n",
     "The `.from_tiktoken_encoder()` method takes either `encoding_name` as an argument (e.g. `cl100k_base`), or the `model_name` (e.g. `gpt-4`). All additional arguments like `chunk_size`, `chunk_overlap`, and `separators` are used to instantiate `CharacterTextSplitter`:"
    ]
@@ -279,7 +279,7 @@
    "source": [
     "## SentenceTransformers\n",
     "\n",
-    "The [SentenceTransformersTokenTextSplitter](https://python.langchain.com/v0.2/api_reference/text_splitters/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html) is a specialized text splitter for use with the sentence-transformer models. The default behaviour is to split the text into chunks that fit the token window of the sentence transformer model that you would like to use.\n",
+    "The [SentenceTransformersTokenTextSplitter](https://python.langchain.com/api_reference/text_splitters/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html) is a specialized text splitter for use with the sentence-transformer models. The default behaviour is to split the text into chunks that fit the token window of the sentence transformer model that you would like to use.\n",
     "\n",
     "To split text and constrain token counts according to the sentence-transformers tokenizer, instantiate a `SentenceTransformersTokenTextSplitter`. You can optionally specify:\n",
     "\n",
diff --git a/docs/docs/how_to/sql_csv.ipynb b/docs/docs/how_to/sql_csv.ipynb
index 88f39d6643d..d4a292b43af 100644
--- a/docs/docs/how_to/sql_csv.ipynb
+++ b/docs/docs/how_to/sql_csv.ipynb
@@ -609,7 +609,7 @@
    "source": [
     "### Agent\n",
     "\n",
-    "For complex questions it can be helpful for an LLM to be able to iteratively execute code while maintaining the inputs and outputs of its previous executions. This is where Agents come into play. They allow an LLM to decide how many times a tool needs to be invoked and keep track of the executions it's made so far. The [create_pandas_dataframe_agent](https://python.langchain.com/v0.2/api_reference/experimental/agents/langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent.html) is a built-in agent that makes it easy to work with dataframes:"
+    "For complex questions it can be helpful for an LLM to be able to iteratively execute code while maintaining the inputs and outputs of its previous executions. This is where Agents come into play. They allow an LLM to decide how many times a tool needs to be invoked and keep track of the executions it's made so far. The [create_pandas_dataframe_agent](https://python.langchain.com/api_reference/experimental/agents/langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent.html) is a built-in agent that makes it easy to work with dataframes:"
    ]
   },
   {
@@ -761,7 +761,7 @@
     "* [SQL tutorial](/docs/tutorials/sql_qa): Many of the challenges of working with SQL db's and CSV's are generic to any structured data type, so it's useful to read the SQL techniques even if you're using Pandas for CSV data analysis.\n",
     "* [Tool use](/docs/how_to/tool_calling): Guides on general best practices when working with chains and agents that invoke tools\n",
     "* [Agents](/docs/tutorials/agents): Understand the fundamentals of building LLM agents.\n",
-    "* Integrations: Sandboxed envs like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly), utilities like [SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), related agents like [Spark DataFrame agent](/docs/integrations/tools/spark_sql)."
+    "* Integrations: Sandboxed envs like [E2B](/docs/integrations/tools/e2b_data_analysis) and [Bearly](/docs/integrations/tools/bearly), utilities like [SQLDatabase](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase), related agents like [Spark DataFrame agent](/docs/integrations/tools/spark_sql)."
    ]
   }
  ],
diff --git a/docs/docs/how_to/sql_large_db.ipynb b/docs/docs/how_to/sql_large_db.ipynb
index 7fcc94cf2a5..5e27faa8f8e 100644
--- a/docs/docs/how_to/sql_large_db.ipynb
+++ b/docs/docs/how_to/sql_large_db.ipynb
@@ -55,7 +55,7 @@
     "* Run `.read Chinook_Sqlite.sql`\n",
     "* Test `SELECT * FROM Artist LIMIT 10;`\n",
     "\n",
-    "Now, `Chinhook.db` is in our directory and we can interface with it using the SQLAlchemy-driven [SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) class:"
+    "Now, `Chinhook.db` is in our directory and we can interface with it using the SQLAlchemy-driven [SQLDatabase](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) class:"
    ]
   },
   {
@@ -271,7 +271,7 @@
    "id": "04d52d01-1ccf-4753-b34a-0dcbc4921f78",
    "metadata": {},
    "source": [
-    "Now that we've got a chain that can output the relevant tables for any query we can combine this with our [create_sql_query_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html), which can accept a list of `table_names_to_use` to determine which table schemas are included in the prompt:"
+    "Now that we've got a chain that can output the relevant tables for any query we can combine this with our [create_sql_query_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html), which can accept a list of `table_names_to_use` to determine which table schemas are included in the prompt:"
    ]
   },
   {
diff --git a/docs/docs/how_to/sql_prompting.ipynb b/docs/docs/how_to/sql_prompting.ipynb
index 6dd22b081d3..990087edffe 100644
--- a/docs/docs/how_to/sql_prompting.ipynb
+++ b/docs/docs/how_to/sql_prompting.ipynb
@@ -6,11 +6,11 @@
    "source": [
     "# How to better prompt when doing SQL question-answering\n",
     "\n",
-    "In this guide we'll go over prompting strategies to improve SQL query generation using [create_sql_query_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html). We'll largely focus on methods for getting relevant database-specific information in your prompt.\n",
+    "In this guide we'll go over prompting strategies to improve SQL query generation using [create_sql_query_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html). We'll largely focus on methods for getting relevant database-specific information in your prompt.\n",
     "\n",
     "We will cover: \n",
     "\n",
-    "- How the dialect of the LangChain [SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) impacts the prompt of the chain;\n",
+    "- How the dialect of the LangChain [SQLDatabase](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) impacts the prompt of the chain;\n",
     "- How to format schema information into the prompt using `SQLDatabase.get_context`;\n",
     "- How to build and select few-shot examples to assist the model.\n",
     "\n",
@@ -84,7 +84,7 @@
    "source": [
     "## Dialect-specific prompting\n",
     "\n",
-    "One of the simplest things we can do is make our prompt specific to the SQL dialect we're using. When using the built-in [create_sql_query_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html) and [SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html), this is handled for you for any of the following dialects:"
+    "One of the simplest things we can do is make our prompt specific to the SQL dialect we're using. When using the built-in [create_sql_query_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html) and [SQLDatabase](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html), this is handled for you for any of the following dialects:"
    ]
   },
   {
@@ -629,7 +629,7 @@
     "\n",
     "If we have enough examples, we may want to only include the most relevant ones in the prompt, either because they don't fit in the model's context window or because the long tail of examples distracts the model. And specifically, given any input we want to include the examples most relevant to that input.\n",
     "\n",
-    "We can do just this using an ExampleSelector. In this case we'll use a [SemanticSimilarityExampleSelector](https://python.langchain.com/v0.2/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html), which will store the examples in the vector database of our choosing. At runtime it will perform a similarity search between the input and our examples, and return the most semantically similar ones.\n",
+    "We can do just this using an ExampleSelector. In this case we'll use a [SemanticSimilarityExampleSelector](https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html), which will store the examples in the vector database of our choosing. At runtime it will perform a similarity search between the input and our examples, and return the most semantically similar ones.\n",
     "\n",
     "We default to OpenAI embeddings here, but you can swap them out for the model provider of your choice."
    ]
diff --git a/docs/docs/how_to/streaming.ipynb b/docs/docs/how_to/streaming.ipynb
index 3615c790e49..85d1ba1d5e6 100644
--- a/docs/docs/how_to/streaming.ipynb
+++ b/docs/docs/how_to/streaming.ipynb
@@ -246,7 +246,7 @@
     "\n",
     "Let's build a simple chain using `LangChain Expression Language` (`LCEL`) that combines a prompt, model and a parser and verify that streaming works.\n",
     "\n",
-    "We will use [`StrOutputParser`](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) to parse the output from the model. This is a simple parser that extracts the `content` field from an `AIMessageChunk`, giving us the `token` returned by the model.\n",
+    "We will use [`StrOutputParser`](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html) to parse the output from the model. This is a simple parser that extracts the `content` field from an `AIMessageChunk`, giving us the `token` returned by the model.\n",
     "\n",
     ":::{.callout-tip}\n",
     "LCEL is a *declarative* way to specify a \"program\" by chainining together different LangChain primitives. Chains created using LCEL benefit from an automatic implementation of `stream` and `astream` allowing streaming of the final output. In fact, chains created with LCEL implement the entire standard Runnable interface.\n",
diff --git a/docs/docs/how_to/streaming_llm.ipynb b/docs/docs/how_to/streaming_llm.ipynb
index a6cf3766c1f..ea84079e723 100644
--- a/docs/docs/how_to/streaming_llm.ipynb
+++ b/docs/docs/how_to/streaming_llm.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# How to stream responses from an LLM\n",
     "\n",
-    "All `LLM`s implement the [Runnable interface](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable), which comes with **default** implementations of standard runnable methods (i.e. `ainvoke`, `batch`, `abatch`, `stream`, `astream`, `astream_events`).\n",
+    "All `LLM`s implement the [Runnable interface](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable), which comes with **default** implementations of standard runnable methods (i.e. `ainvoke`, `batch`, `abatch`, `stream`, `astream`, `astream_events`).\n",
     "\n",
     "The **default** streaming implementations provide an`Iterator` (or `AsyncIterator` for asynchronous streaming) that yields a single value: the final output from the underlying chat model provider.\n",
     "\n",
@@ -112,7 +112,7 @@
     "## Async event streaming\n",
     "\n",
     "\n",
-    "LLMs also support the standard [astream events](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) method.\n",
+    "LLMs also support the standard [astream events](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable.astream_events) method.\n",
     "\n",
     ":::{.callout-tip}\n",
     "\n",
diff --git a/docs/docs/how_to/structured_output.ipynb b/docs/docs/how_to/structured_output.ipynb
index 1c42e81ff50..a1d5c760bdc 100644
--- a/docs/docs/how_to/structured_output.ipynb
+++ b/docs/docs/how_to/structured_output.ipynb
@@ -574,7 +574,7 @@
     "\n",
     "If using JSON mode you'll have to still specify the desired schema in the model prompt. The schema you pass to `with_structured_output` will only be used for parsing the model outputs, it will not be passed to the model the way it is with tool calling.\n",
     "\n",
-    "To see if the model you're using supports JSON mode, check its entry in the [API reference](https://python.langchain.com/v0.2/api_reference/langchain/index.html).\n",
+    "To see if the model you're using supports JSON mode, check its entry in the [API reference](https://python.langchain.com/api_reference/langchain/index.html).\n",
     "\n",
     ":::"
    ]
@@ -653,7 +653,7 @@
     "\n",
     "### Using `PydanticOutputParser`\n",
     "\n",
-    "The following example uses the built-in [`PydanticOutputParser`](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html) to parse the output of a chat model prompted to match the given Pydantic schema. Note that we are adding `format_instructions` directly to the prompt from a method on the parser:"
+    "The following example uses the built-in [`PydanticOutputParser`](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.pydantic.PydanticOutputParser.html) to parse the output of a chat model prompted to match the given Pydantic schema. Note that we are adding `format_instructions` directly to the prompt from a method on the parser:"
    ]
   },
   {
diff --git a/docs/docs/how_to/summarize_map_reduce.ipynb b/docs/docs/how_to/summarize_map_reduce.ipynb
index 52b4cc6ad6c..72c50b81d0b 100644
--- a/docs/docs/how_to/summarize_map_reduce.ipynb
+++ b/docs/docs/how_to/summarize_map_reduce.ipynb
@@ -72,7 +72,7 @@
    "source": [
     "## Load documents\n",
     "\n",
-    "First we load in our documents. We will use [WebBaseLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load a blog post, and split the documents into smaller sub-documents."
+    "First we load in our documents. We will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load a blog post, and split the documents into smaller sub-documents."
    ]
   },
   {
diff --git a/docs/docs/how_to/summarize_stuff.ipynb b/docs/docs/how_to/summarize_stuff.ipynb
index 2aa67d8a829..415895a8c93 100644
--- a/docs/docs/how_to/summarize_stuff.ipynb
+++ b/docs/docs/how_to/summarize_stuff.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "LLMs can summarize and otherwise distill desired information from text, including large volumes of text. In many cases, especially for models with larger context windows, this can be adequately achieved via a single LLM call.\n",
     "\n",
-    "LangChain implements a simple [pre-built chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) that \"stuffs\" a prompt with the desired context for summarization and other purposes. In this guide we demonstrate how to use the chain."
+    "LangChain implements a simple [pre-built chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) that \"stuffs\" a prompt with the desired context for summarization and other purposes. In this guide we demonstrate how to use the chain."
    ]
   },
   {
diff --git a/docs/docs/how_to/tool_artifacts.ipynb b/docs/docs/how_to/tool_artifacts.ipynb
index bf630ced575..eebc3372cd5 100644
--- a/docs/docs/how_to/tool_artifacts.ipynb
+++ b/docs/docs/how_to/tool_artifacts.ipynb
@@ -18,7 +18,7 @@
     "\n",
     "Tools are utilities that can be called by a model, and whose outputs are designed to be fed back to a model. Sometimes, however, there are artifacts of a tool's execution that we want to make accessible to downstream components in our chain or agent, but that we don't want to expose to the model itself. For example if a tool returns a custom object, a dataframe or an image, we may want to pass some metadata about this output to the model without passing the actual output to the model. At the same time, we may want to be able to access this full output elsewhere, for example in downstream tools.\n",
     "\n",
-    "The Tool and [ToolMessage](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
+    "The Tool and [ToolMessage](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolMessage.html) interfaces make it possible to distinguish between the parts of the tool output meant for the model (this is the ToolMessage.content) and those parts which are meant for use outside the model (ToolMessage.artifact).\n",
     "\n",
     ":::info Requires ``langchain-core >= 0.2.19``\n",
     "\n",
diff --git a/docs/docs/how_to/tool_calling.ipynb b/docs/docs/how_to/tool_calling.ipynb
index 6558f4843b5..70faf4ce421 100644
--- a/docs/docs/how_to/tool_calling.ipynb
+++ b/docs/docs/how_to/tool_calling.ipynb
@@ -55,7 +55,7 @@
    "source": [
     "## Defining tool schemas\n",
     "\n",
-    "For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what it's arguments are. Chat models that support tool calling features implement a `.bind_tools()` method for passing tool schemas to the model. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain [Tool objects](https://python.langchain.com/v0.2/api_reference/core/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool). Subsequent invocations of the model will pass in these tool schemas along with the prompt.\n",
+    "For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what it's arguments are. Chat models that support tool calling features implement a `.bind_tools()` method for passing tool schemas to the model. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain [Tool objects](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html#langchain_core.tools.BaseTool). Subsequent invocations of the model will pass in these tool schemas along with the prompt.\n",
     "\n",
     "### Python functions\n",
     "Our tool schemas can be Python functions:"
@@ -263,7 +263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we can see our LLM generated arguments to a tool! You can look at the docs for [bind_tools()](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools) to learn about all the ways to customize how your LLM selects tools, as well as [this guide on how to force the LLM to call a tool](/docs/how_to/tool_choice/) rather than letting it decide."
+    "As we can see our LLM generated arguments to a tool! You can look at the docs for [bind_tools()](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.BaseChatOpenAI.html#langchain_openai.chat_models.base.BaseChatOpenAI.bind_tools) to learn about all the ways to customize how your LLM selects tools, as well as [this guide on how to force the LLM to call a tool](/docs/how_to/tool_choice/) rather than letting it decide."
    ]
   },
   {
@@ -273,9 +273,9 @@
     "## Tool calls\n",
     "\n",
     "If tool calls are included in a LLM response, they are attached to the corresponding \n",
-    "[message](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage) \n",
-    "or [message chunk](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
-    "as a list of [tool call](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.ToolCall.html#langchain_core.messages.tool.ToolCall) \n",
+    "[message](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessage.html#langchain_core.messages.ai.AIMessage) \n",
+    "or [message chunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "as a list of [tool call](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolCall.html#langchain_core.messages.tool.ToolCall) \n",
     "objects in the `.tool_calls` attribute.\n",
     "\n",
     "Note that chat models can call multiple tools at once.\n",
@@ -328,7 +328,7 @@
     "The `.tool_calls` attribute should contain valid tool calls. Note that on occasion, \n",
     "model providers may output malformed tool calls (e.g., arguments that are not \n",
     "valid JSON). When parsing fails in these cases, instances \n",
-    "of [InvalidToolCall](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.InvalidToolCall.html#langchain_core.messages.tool.InvalidToolCall) \n",
+    "of [InvalidToolCall](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.InvalidToolCall.html#langchain_core.messages.tool.InvalidToolCall) \n",
     "are populated in the `.invalid_tool_calls` attribute. An `InvalidToolCall` can have \n",
     "a name, string arguments, identifier, and error message.\n",
     "\n",
@@ -336,7 +336,7 @@
     "## Parsing\n",
     "\n",
     "If desired, [output parsers](/docs/how_to#output-parsers) can further process the output. For example, we can convert existing values populated on the `.tool_calls` to Pydantic objects using the\n",
-    "[PydanticToolsParser](https://python.langchain.com/v0.2/api_reference/core/output_parsers/langchain_core.output_parsers.openai_tools.PydanticToolsParser.html):"
+    "[PydanticToolsParser](https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.openai_tools.PydanticToolsParser.html):"
    ]
   },
   {
diff --git a/docs/docs/how_to/tool_configure.ipynb b/docs/docs/how_to/tool_configure.ipynb
index c67cc95399b..a97446e261a 100644
--- a/docs/docs/how_to/tool_configure.ipynb
+++ b/docs/docs/how_to/tool_configure.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "If you have a tool  that call chat models, retrievers, or other runnables, you may want to access internal events from those runnables or configure them with additional properties. This guide shows you how to manually pass parameters properly so that you can do this using the `astream_events()` method.\n",
     "\n",
-    "Tools are runnables, and you can treat them the same way as any other runnable at the interface level - you can call `invoke()`, `batch()`, and `stream()` on them as normal. However, when writing custom tools, you may want to invoke other runnables like chat models or retrievers. In order to properly trace and configure those sub-invocations, you'll need to manually access and pass in the tool's current [`RunnableConfig`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html) object. This guide show you some examples of how to do that.\n",
+    "Tools are runnables, and you can treat them the same way as any other runnable at the interface level - you can call `invoke()`, `batch()`, and `stream()` on them as normal. However, when writing custom tools, you may want to invoke other runnables like chat models or retrievers. In order to properly trace and configure those sub-invocations, you'll need to manually access and pass in the tool's current [`RunnableConfig`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.config.RunnableConfig.html) object. This guide show you some examples of how to do that.\n",
     "\n",
     ":::caution Compatibility\n",
     "\n",
diff --git a/docs/docs/how_to/tool_streaming.ipynb b/docs/docs/how_to/tool_streaming.ipynb
index 54a92e9bc62..b96868d80ff 100644
--- a/docs/docs/how_to/tool_streaming.ipynb
+++ b/docs/docs/how_to/tool_streaming.ipynb
@@ -7,8 +7,8 @@
     "# How to stream tool calls\n",
     "\n",
     "When tools are called in a streaming context, \n",
-    "[message chunks](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
-    "will be populated with [tool call chunk](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk) \n",
+    "[message chunks](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "will be populated with [tool call chunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.tool.ToolCallChunk.html#langchain_core.messages.tool.ToolCallChunk) \n",
     "objects in a list via the `.tool_call_chunks` attribute. A `ToolCallChunk` includes \n",
     "optional string fields for the tool `name`, `args`, and `id`, and includes an optional \n",
     "integer field `index` that can be used to join chunks together. Fields are optional \n",
@@ -16,7 +16,7 @@
     "that includes a substring of the arguments may have null values for the tool name and id).\n",
     "\n",
     "Because message chunks inherit from their parent message class, an \n",
-    "[AIMessageChunk](https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
+    "[AIMessageChunk](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.AIMessageChunk.html#langchain_core.messages.ai.AIMessageChunk) \n",
     "with tool call chunks will also include `.tool_calls` and `.invalid_tool_calls` fields. \n",
     "These fields are parsed best-effort from the message's tool call chunks.\n",
     "\n",
diff --git a/docs/docs/how_to/tools_builtin.ipynb b/docs/docs/how_to/tools_builtin.ipynb
index 47fd4055275..b2ebe356b5c 100644
--- a/docs/docs/how_to/tools_builtin.ipynb
+++ b/docs/docs/how_to/tools_builtin.ipynb
@@ -36,7 +36,7 @@
     "\n",
     "When using 3rd party tools, make sure that you understand how the tool works, what permissions\n",
     "it has. Read over its documentation and check if anything is required from you\n",
-    "from a security point of view. Please see our [security](https://python.langchain.com/v0.2/docs/security/) \n",
+    "from a security point of view. Please see our [security](https://python.langchain.com/docs/security/) \n",
     "guidelines for more information.\n",
     "\n",
     ":::\n",
diff --git a/docs/docs/how_to/tools_chain.ipynb b/docs/docs/how_to/tools_chain.ipynb
index c99044735bc..efa46fd7192 100644
--- a/docs/docs/how_to/tools_chain.ipynb
+++ b/docs/docs/how_to/tools_chain.ipynb
@@ -280,7 +280,7 @@
     "\n",
     "LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the [agent types here](/docs/concepts#agents).\n",
     "\n",
-    "We'll use the [tool calling agent](https://python.langchain.com/v0.2/api_reference/langchain/agents/langchain.agents.tool_calling_agent.base.create_tool_calling_agent.html), which is generally the most reliable kind and the recommended one for most use cases.\n",
+    "We'll use the [tool calling agent](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.tool_calling_agent.base.create_tool_calling_agent.html), which is generally the most reliable kind and the recommended one for most use cases.\n",
     "\n",
     "![agent](../../static/img/tool_agent.svg)"
    ]
diff --git a/docs/docs/how_to/tools_prompting.ipynb b/docs/docs/how_to/tools_prompting.ipynb
index d3f6bac4dc8..a42a048c26f 100644
--- a/docs/docs/how_to/tools_prompting.ipynb
+++ b/docs/docs/how_to/tools_prompting.ipynb
@@ -28,7 +28,7 @@
     "This guide assumes familiarity with the following concepts:\n",
     "\n",
     "- [LangChain Tools](/docs/concepts/#tools)\n",
-    "- [Function/tool calling](https://python.langchain.com/v0.2/docs/concepts/#functiontool-calling)\n",
+    "- [Function/tool calling](https://python.langchain.com/docs/concepts/#functiontool-calling)\n",
     "- [Chat models](/docs/concepts/#chat-models)\n",
     "- [LLMs](/docs/concepts/#llms)\n",
     "\n",
diff --git a/docs/docs/how_to/trim_messages.ipynb b/docs/docs/how_to/trim_messages.ipynb
index 0955ed2091f..eb8a44a7f4d 100644
--- a/docs/docs/how_to/trim_messages.ipynb
+++ b/docs/docs/how_to/trim_messages.ipynb
@@ -451,7 +451,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For a complete description of all arguments head to the API reference: https://python.langchain.com/v0.2/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html"
+    "For a complete description of all arguments head to the API reference: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html"
    ]
   }
  ],
diff --git a/docs/docs/how_to/vectorstore_retriever.ipynb b/docs/docs/how_to/vectorstore_retriever.ipynb
index f2655c2ca24..55408b91116 100644
--- a/docs/docs/how_to/vectorstore_retriever.ipynb
+++ b/docs/docs/how_to/vectorstore_retriever.ipynb
@@ -28,9 +28,9 @@
     "\n",
     "## Creating a retriever from a vectorstore\n",
     "\n",
-    "You can build a retriever from a vectorstore using its [.as_retriever](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method. Let's walk through an example.\n",
+    "You can build a retriever from a vectorstore using its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method. Let's walk through an example.\n",
     "\n",
-    "First we instantiate a vectorstore. We will use an in-memory [FAISS](https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html) vectorstore:"
+    "First we instantiate a vectorstore. We will use an in-memory [FAISS](https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html) vectorstore:"
    ]
   },
   {
@@ -77,7 +77,7 @@
    "id": "08f8b820-5912-49c1-9d76-40be0571dffb",
    "metadata": {},
    "source": [
-    "This creates a retriever (specifically a [VectorStoreRetriever](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html)), which we can use in the usual way:"
+    "This creates a retriever (specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html)), which we can use in the usual way:"
    ]
   },
   {
diff --git a/docs/docs/integrations/callbacks/streamlit.md b/docs/docs/integrations/callbacks/streamlit.md
index 90137951805..8cd81495bf5 100644
--- a/docs/docs/integrations/callbacks/streamlit.md
+++ b/docs/docs/integrations/callbacks/streamlit.md
@@ -37,7 +37,7 @@ st_callback = StreamlitCallbackHandler(st.container())
 ```
 
 Additional keyword arguments to customize the display behavior are described in the
-[API reference](https://python.langchain.com/v0.2/api_reference/langchain/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html).
+[API reference](https://python.langchain.com/api_reference/langchain/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html).
 
 ### Scenario 1: Using an Agent with Tools
 
diff --git a/docs/docs/integrations/chat/ai21.ipynb b/docs/docs/integrations/chat/ai21.ipynb
index bd1410d2d15..e16a62260cf 100644
--- a/docs/docs/integrations/chat/ai21.ipynb
+++ b/docs/docs/integrations/chat/ai21.ipynb
@@ -25,9 +25,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatAI21](https://python.langchain.com/v0.2/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21) | [langchain-ai21](https://python.langchain.com/v0.2/api_reference/ai21/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?style=flat-square&label=%20) |\n",
+    "| [ChatAI21](https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html#langchain_ai21.chat_models.ChatAI21) | [langchain-ai21](https://python.langchain.com/api_reference/ai21/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ai21?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ai21?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -290,7 +290,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatAI21 features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html"
+    "For detailed documentation of all ChatAI21 features and configurations head to the API reference: https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/anthropic.ipynb b/docs/docs/integrations/chat/anthropic.ipynb
index 30ce233d329..b813f6a9ee9 100644
--- a/docs/docs/integrations/chat/anthropic.ipynb
+++ b/docs/docs/integrations/chat/anthropic.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatAnthropic\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Anthropic [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatAnthropic features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html).\n",
+    "This notebook provides a quick overview for getting started with Anthropic [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatAnthropic features and configurations head to the [API reference](https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html).\n",
     "\n",
     "Anthropic has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Anthropic docs](https://docs.anthropic.com/en/docs/models-overview).\n",
     "\n",
@@ -31,9 +31,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/anthropic) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/anthropic) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatAnthropic](https://python.langchain.com/v0.2/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html) | [langchain-anthropic](https://python.langchain.com/v0.2/api_reference/anthropic/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-anthropic?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-anthropic?style=flat-square&label=%20) |\n",
+    "| [ChatAnthropic](https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html) | [langchain-anthropic](https://python.langchain.com/api_reference/anthropic/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-anthropic?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-anthropic?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -322,7 +322,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatAnthropic features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html"
+    "For detailed documentation of all ChatAnthropic features and configurations head to the API reference: https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/azure_chat_openai.ipynb b/docs/docs/integrations/chat/azure_chat_openai.ipynb
index 8b1454cae2f..a0883d34b71 100644
--- a/docs/docs/integrations/chat/azure_chat_openai.ipynb
+++ b/docs/docs/integrations/chat/azure_chat_openai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# AzureChatOpenAI\n",
     "\n",
-    "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
+    "This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
     "\n",
     "Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
     "\n",
@@ -30,9 +30,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/azure) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/azure) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [AzureChatOpenAI](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://python.langchain.com/v0.2/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
+    "| [AzureChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -321,7 +321,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html"
+    "For detailed documentation of all AzureChatOpenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/bedrock.ipynb b/docs/docs/integrations/chat/bedrock.ipynb
index 16b17743545..750f3918227 100644
--- a/docs/docs/integrations/chat/bedrock.ipynb
+++ b/docs/docs/integrations/chat/bedrock.ipynb
@@ -21,14 +21,14 @@
     "\n",
     "For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
     "\n",
-    "For detailed documentation of all ChatBedrock features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html).\n",
+    "For detailed documentation of all ChatBedrock features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/bedrock) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatBedrock](https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/v0.2/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
+    "| [ChatBedrock](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html) | [langchain-aws](https://python.langchain.com/api_reference/aws/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-aws?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-aws?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -225,7 +225,7 @@
    "source": [
     "## Bedrock Converse API\n",
     "\n",
-    "AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.\n",
+    "AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.\n",
     "\n",
     "We recommend using `ChatBedrockConverse` for users who do not need to use custom models.\n",
     "\n",
@@ -350,9 +350,9 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatBedrock features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
+    "For detailed documentation of all ChatBedrock features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html\n",
     "\n",
-    "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html"
+    "For detailed documentation of all ChatBedrockConverse features and configurations head to the API reference: https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/cerebras.ipynb b/docs/docs/integrations/chat/cerebras.ipynb
index ef3568d4d0d..41183d1ec33 100644
--- a/docs/docs/integrations/chat/cerebras.ipynb
+++ b/docs/docs/integrations/chat/cerebras.ipynb
@@ -35,7 +35,7 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/cerebras) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/cerebras) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
     "| [ChatCerebras](https://api.python.langchain.com/en/latest/chat_models/langchain_cerebras.chat_models.ChatCerebras.html) | [langchain-cerebras](https://api.python.langchain.com/en/latest/cerebras_api_reference.html) | ❌ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-cerebras?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-cerebras?style=flat-square&label=%20) |\n",
     "\n",
diff --git a/docs/docs/integrations/chat/cohere.ipynb b/docs/docs/integrations/chat/cohere.ipynb
index c63bb8e4a8c..7675306c265 100644
--- a/docs/docs/integrations/chat/cohere.ipynb
+++ b/docs/docs/integrations/chat/cohere.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "This notebook covers how to get started with [Cohere chat models](https://cohere.com/chat).\n",
     "\n",
-    "Head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
+    "Head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.cohere.ChatCohere.html) for detailed documentation of all attributes and methods."
    ]
   },
   {
diff --git a/docs/docs/integrations/chat/databricks.ipynb b/docs/docs/integrations/chat/databricks.ipynb
index 6da34df82b8..74c80b86cf5 100644
--- a/docs/docs/integrations/chat/databricks.ipynb
+++ b/docs/docs/integrations/chat/databricks.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "> [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform. \n",
     "\n",
-    "This notebook provides a quick overview for getting started with Databricks [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatDatabricks features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html).\n",
+    "This notebook provides a quick overview for getting started with Databricks [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatDatabricks features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -31,7 +31,7 @@
     "\n",
     "| Class | Package | Local | Serializable | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [ChatDatabricks](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html) | [langchain-databricks](https://python.langchain.com/v0.2/api_reference/databricks/index.html) | ❌ | beta | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-databricks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-databricks?style=flat-square&label=%20) |\n",
+    "| [ChatDatabricks](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html) | [langchain-databricks](https://python.langchain.com/api_reference/databricks/index.html) | ❌ | beta | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-databricks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-databricks?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -142,7 +142,7 @@
     "    endpoint=\"databricks-dbrx-instruct\",\n",
     "    temperature=0.1,\n",
     "    max_tokens=256,\n",
-    "    # See https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html for other supported parameters\n",
+    "    # See https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html for other supported parameters\n",
     ")"
    ]
   },
@@ -460,7 +460,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatDatabricks features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/databricks/chat_models/langchain_databricks.chat_models.ChatDatabricks.html"
+    "For detailed documentation of all ChatDatabricks features and configurations head to the API reference: https://python.langchain.com/api_reference/databricks/chat_models/langchain_databricks.chat_models.ChatDatabricks.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/fireworks.ipynb b/docs/docs/integrations/chat/fireworks.ipynb
index c1144ab5913..b43c5f56f8b 100644
--- a/docs/docs/integrations/chat/fireworks.ipynb
+++ b/docs/docs/integrations/chat/fireworks.ipynb
@@ -17,16 +17,16 @@
    "source": [
     "# ChatFireworks\n",
     "\n",
-    "This doc help you get started with Fireworks AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
+    "This doc help you get started with Fireworks AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
     "\n",
     "Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/fireworks) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/fireworks) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatFireworks](https://python.langchain.com/v0.2/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html) | [langchain-fireworks](https://python.langchain.com/v0.2/api_reference/fireworks/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-fireworks?style=flat-square&label=%20) |\n",
+    "| [ChatFireworks](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html) | [langchain-fireworks](https://python.langchain.com/api_reference/fireworks/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-fireworks?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -240,7 +240,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatFireworks features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
+    "For detailed documentation of all ChatFireworks features and configurations head to the API reference: https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/google_generative_ai.ipynb b/docs/docs/integrations/chat/google_generative_ai.ipynb
index 1eb87304c34..08aad6fefb4 100644
--- a/docs/docs/integrations/chat/google_generative_ai.ipynb
+++ b/docs/docs/integrations/chat/google_generative_ai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatGoogleGenerativeAI\n",
     "\n",
-    "This docs will help you get started with Google AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
+    "This docs will help you get started with Google AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
     "\n",
     "Google AI offers a number of different chat models. For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini).\n",
     "\n",
@@ -32,9 +32,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/google_generativeai) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_generativeai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGoogleGenerativeAI](https://python.langchain.com/v0.2/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) | [langchain-google-genai](https://python.langchain.com/v0.2/api_reference/google_genai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-genai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-genai?style=flat-square&label=%20) |\n",
+    "| [ChatGoogleGenerativeAI](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html) | [langchain-google-genai](https://python.langchain.com/api_reference/google_genai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-genai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-genai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -286,7 +286,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html"
+    "For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the API reference: https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
index 005a00a384e..7261adba160 100644
--- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatVertexAI\n",
     "\n",
-    "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
+    "This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
     "\n",
     "ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
     "\n",
@@ -30,9 +30,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/google_vertex_ai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatVertexAI](https://python.langchain.com/v0.2/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/v0.2/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square&label=%20) |\n",
+    "| [ChatVertexAI](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-google-vertexai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-google-vertexai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -241,7 +241,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/v0.2/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
+    "For detailed documentation of all ChatVertexAI features and configurations, like how to send multimodal inputs and configure safety settings, head to the API reference: https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/groq.ipynb b/docs/docs/integrations/chat/groq.ipynb
index bec7aff00f5..59898319b54 100644
--- a/docs/docs/integrations/chat/groq.ipynb
+++ b/docs/docs/integrations/chat/groq.ipynb
@@ -17,14 +17,14 @@
    "source": [
     "# ChatGroq\n",
     "\n",
-    "This will help you getting started with Groq [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models).\n",
+    "This will help you getting started with Groq [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatGroq features and configurations head to the [API reference](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html). For a list of all Groq models, visit this [link](https://console.groq.com/docs/models).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/groq) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/groq) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatGroq](https://python.langchain.com/v0.2/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html) | [langchain-groq](https://python.langchain.com/v0.2/api_reference/groq/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-groq?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-groq?style=flat-square&label=%20) |\n",
+    "| [ChatGroq](https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html) | [langchain-groq](https://python.langchain.com/api_reference/groq/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-groq?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-groq?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
@@ -249,7 +249,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
+    "For detailed documentation of all ChatGroq features and configurations head to the API reference: https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/huggingface.ipynb b/docs/docs/integrations/chat/huggingface.ipynb
index af03eb8d36b..b1ab4e16dfe 100644
--- a/docs/docs/integrations/chat/huggingface.ipynb
+++ b/docs/docs/integrations/chat/huggingface.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# ChatHuggingFace\n",
     "\n",
-    "This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
+    "This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -15,7 +15,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatHuggingFace](https://python.langchain.com/v0.2/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html) | [langchain-huggingface](https://python.langchain.com/v0.2/api_reference/huggingface/index.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_huggingface?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_huggingface?style=flat-square&label=%20) |\n",
+    "| [ChatHuggingFace](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html) | [langchain-huggingface](https://python.langchain.com/api_reference/huggingface/index.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_huggingface?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_huggingface?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -52,7 +52,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatHuggingFace](https://python.langchain.com/v0.2/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html) | [langchain_huggingface](https://python.langchain.com/v0.2/api_reference/huggingface/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_huggingface?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_huggingface?style=flat-square&label=%20) |\n",
+    "| [ChatHuggingFace](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html) | [langchain_huggingface](https://python.langchain.com/api_reference/huggingface/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_huggingface?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_huggingface?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -465,7 +465,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `ChatHuggingFace` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html"
+    "For detailed documentation of all `ChatHuggingFace` features and configurations head to the API reference: https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html"
    ]
   },
   {
@@ -474,7 +474,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatHuggingFace features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html"
+    "For detailed documentation of all ChatHuggingFace features and configurations head to the API reference: https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/ibm_watsonx.ipynb b/docs/docs/integrations/chat/ibm_watsonx.ipynb
index c0556454771..179790bbe1d 100644
--- a/docs/docs/integrations/chat/ibm_watsonx.ipynb
+++ b/docs/docs/integrations/chat/ibm_watsonx.ipynb
@@ -34,7 +34,7 @@
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
     "| ChatWatsonx | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |\n",
     "\n",
diff --git a/docs/docs/integrations/chat/llamacpp.ipynb b/docs/docs/integrations/chat/llamacpp.ipynb
index 97a12133046..dbd49b8d54d 100644
--- a/docs/docs/integrations/chat/llamacpp.ipynb
+++ b/docs/docs/integrations/chat/llamacpp.ipynb
@@ -32,7 +32,7 @@
     "### Integration details\n",
     "| Class | Package | Local | Serializable | JS support |\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [ChatLlamaCpp](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html) | [langchain-community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ |\n",
+    "| [ChatLlamaCpp](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -404,7 +404,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatLlamaCpp features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html"
+    "For detailed documentation of all ChatLlamaCpp features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/mistralai.ipynb b/docs/docs/integrations/chat/mistralai.ipynb
index 26b555738f5..94f4717108b 100644
--- a/docs/docs/integrations/chat/mistralai.ipynb
+++ b/docs/docs/integrations/chat/mistralai.ipynb
@@ -17,14 +17,14 @@
    "source": [
     "# ChatMistralAI\n",
     "\n",
-    "This will help you getting started with Mistral [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
+    "This will help you getting started with Mistral [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/mistral) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/mistral) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatMistralAI](https://python.langchain.com/v0.2/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) | [langchain_mistralai](https://python.langchain.com/v0.2/api_reference/mistralai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_mistralai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_mistralai?style=flat-square&label=%20) |\n",
+    "| [ChatMistralAI](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) | [langchain_mistralai](https://python.langchain.com/api_reference/mistralai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_mistralai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_mistralai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -234,7 +234,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "Head to the [API reference](https://python.langchain.com/v0.2/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
+    "Head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
index 0149c7c463a..58f5a01f912 100644
--- a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
+++ b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatNVIDIA\n",
     "\n",
-    "This will help you getting started with NVIDIA [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
+    "This will help you getting started with NVIDIA [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
     "\n",
     "## Overview\n",
     "The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",
@@ -41,7 +41,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatNVIDIA](https://python.langchain.com/v0.2/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html) | [langchain_nvidia_ai_endpoints](https://python.langchain.com/v0.2/api_reference/nvidia_ai_endpoints/index.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) |\n",
+    "| [ChatNVIDIA](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html) | [langchain_nvidia_ai_endpoints](https://python.langchain.com/api_reference/nvidia_ai_endpoints/index.html) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_nvidia_ai_endpoints?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -513,7 +513,7 @@
     "id": "79efa62d"
    },
    "source": [
-    "Like any other integration, ChatNVIDIA is fine to support chat utilities like RunnableWithMessageHistory which is analogous to using `ConversationChain`. Below, we show the [LangChain RunnableWithMessageHistory](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html) example applied to the `mistralai/mixtral-8x22b-instruct-v0.1` model."
+    "Like any other integration, ChatNVIDIA is fine to support chat utilities like RunnableWithMessageHistory which is analogous to using `ConversationChain`. Below, we show the [LangChain RunnableWithMessageHistory](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html) example applied to the `mistralai/mixtral-8x22b-instruct-v0.1` model."
    ]
   },
   {
@@ -617,7 +617,7 @@
    "source": [
     "## Tool calling\n",
     "\n",
-    "Starting in v0.2, `ChatNVIDIA` supports [bind_tools](https://python.langchain.com/v0.2/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.bind_tools).\n",
+    "Starting in v0.2, `ChatNVIDIA` supports [bind_tools](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html#langchain_core.language_models.chat_models.BaseChatModel.bind_tools).\n",
     "\n",
     "`ChatNVIDIA` provides integration with the variety of models on [build.nvidia.com](https://build.nvidia.com) as well as local NIMs. Not all these models are trained for tool calling. Be sure to select a model that does have tool calling for your experimention and applications."
    ]
@@ -680,7 +680,7 @@
    "id": "e08df68c",
    "metadata": {},
    "source": [
-    "See [How to use chat models to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling/) for additional examples."
+    "See [How to use chat models to call tools](https://python.langchain.com/docs/how_to/tool_calling/) for additional examples."
    ]
   },
   {
@@ -729,7 +729,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `ChatNVIDIA` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html"
+    "For detailed documentation of all `ChatNVIDIA` features and configurations head to the API reference: https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/oci_generative_ai.ipynb b/docs/docs/integrations/chat/oci_generative_ai.ipynb
index 9c32baee5f8..563a9dc0c6f 100644
--- a/docs/docs/integrations/chat/oci_generative_ai.ipynb
+++ b/docs/docs/integrations/chat/oci_generative_ai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatOCIGenAI\n",
     "\n",
-    "This notebook provides a quick overview for getting started with OCIGenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIGenAI features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html).\n",
+    "This notebook provides a quick overview for getting started with OCIGenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIGenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html).\n",
     "\n",
     "Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API.\n",
     "Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. Detailed documentation of the service and API is available __[here](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)__ and __[here](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai/20231130/)__.\n",
@@ -26,9 +26,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/oci_generative_ai) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/oci_generative_ai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOCIGenAI](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html) | [langchain-community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-oci-generative-ai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-oci-generative-ai?style=flat-square&label=%20) |\n",
+    "| [ChatOCIGenAI](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-oci-generative-ai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-oci-generative-ai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -162,7 +162,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatOCIGenAI features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html"
+    "For detailed documentation of all ChatOCIGenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/ollama.ipynb b/docs/docs/integrations/chat/ollama.ipynb
index 091a5562753..1d9b5de0403 100644
--- a/docs/docs/integrations/chat/ollama.ipynb
+++ b/docs/docs/integrations/chat/ollama.ipynb
@@ -435,7 +435,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatOllama features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html"
+    "For detailed documentation of all ChatOllama features and configurations head to the API reference: https://python.langchain.com/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/openai.ipynb b/docs/docs/integrations/chat/openai.ipynb
index a0f6ca6a203..1aa43bcd08f 100644
--- a/docs/docs/integrations/chat/openai.ipynb
+++ b/docs/docs/integrations/chat/openai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# ChatOpenAI\n",
     "\n",
-    "This notebook provides a quick overview for getting started with OpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+    "This notebook provides a quick overview for getting started with OpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
     "\n",
     "OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [OpenAI docs](https://platform.openai.com/docs/models).\n",
     "\n",
@@ -36,9 +36,9 @@
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOpenAI](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/v0.2/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
+    "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -441,7 +441,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatOpenAI features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html"
+    "For detailed documentation of all ChatOpenAI features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/symblai_nebula.ipynb b/docs/docs/integrations/chat/symblai_nebula.ipynb
index beb16374cda..a2a6c2d7444 100644
--- a/docs/docs/integrations/chat/symblai_nebula.ipynb
+++ b/docs/docs/integrations/chat/symblai_nebula.ipynb
@@ -269,7 +269,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "Check out the [API reference](https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.symblai_nebula.ChatNebula.html) for more detail."
+    "Check out the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.symblai_nebula.ChatNebula.html) for more detail."
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/together.ipynb b/docs/docs/integrations/chat/together.ipynb
index 4a3088b82a6..9cbdbfe47be 100644
--- a/docs/docs/integrations/chat/together.ipynb
+++ b/docs/docs/integrations/chat/together.ipynb
@@ -18,16 +18,16 @@
     "# ChatTogether\n",
     "\n",
     "\n",
-    "This page will help you get started with Together AI [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
+    "This page will help you get started with Together AI [chat models](../../concepts.mdx#chat-models). For detailed documentation of all ChatTogether features and configurations head to the [API reference](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html).\n",
     "\n",
     "[Together AI](https://www.together.ai/) offers an API to query [50+ leading open-source models](https://docs.together.ai/docs/chat-models)\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/togetherai) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/togetherai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatTogether](https://python.langchain.com/v0.2/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html) | [langchain-together](https://python.langchain.com/v0.2/api_reference/together/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-together?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-together?style=flat-square&label=%20) |\n",
+    "| [ChatTogether](https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html) | [langchain-together](https://python.langchain.com/api_reference/together/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-together?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-together?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](../../how_to/tool_calling.ipynb) | [Structured output](../../how_to/structured_output.ipynb) | JSON mode | [Image input](../../how_to/multimodal_inputs.ipynb) | Audio input | Video input | [Token-level streaming](../../how_to/chat_streaming.ipynb) | Native async | [Token usage](../../how_to/chat_token_usage_tracking.ipynb) | [Logprobs](../../how_to/logprobs.ipynb) |\n",
@@ -239,7 +239,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatTogether features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
+    "For detailed documentation of all ChatTogether features and configurations head to the API reference: https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/chat/vllm.ipynb b/docs/docs/integrations/chat/vllm.ipynb
index 121ee281805..3fa481fe4d4 100644
--- a/docs/docs/integrations/chat/vllm.ipynb
+++ b/docs/docs/integrations/chat/vllm.ipynb
@@ -20,13 +20,13 @@
     "vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
     "\n",
     "## Overview\n",
-    "This will help you getting started with vLLM [chat models](/docs/concepts/#chat-models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
+    "This will help you getting started with vLLM [chat models](/docs/concepts/#chat-models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
     "\n",
     "### Integration details\n",
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOpenAI](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain_openai](https://python.langchain.com/v0.2/api_reference/openai/) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_openai?style=flat-square&label=%20) |\n",
+    "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain_openai](https://python.langchain.com/api_reference/openai/) | ✅ | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_openai?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "Specific model features-- such as tool calling, support for multi-modal inputs, support for token-level streaming, etc.-- will depend on the hosted model.\n",
@@ -214,7 +214,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all features and configurations exposed via `langchain-openai`, head to the API reference: https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html\n",
+    "For detailed documentation of all features and configurations exposed via `langchain-openai`, head to the API reference: https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html\n",
     "\n",
     "Refer to the vLLM [documentation](https://docs.vllm.ai/en/latest/) as well."
    ]
diff --git a/docs/docs/integrations/chat/yi.ipynb b/docs/docs/integrations/chat/yi.ipynb
index 3349bcb2a9d..27f1915e574 100644
--- a/docs/docs/integrations/chat/yi.ipynb
+++ b/docs/docs/integrations/chat/yi.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# ChatYI\n",
     "\n",
-    "This will help you getting started with Yi [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
+    "This will help you getting started with Yi [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
     "\n",
     "[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
     "\n",
@@ -16,7 +16,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatYi](https://python.langchain.com/v0.2/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "| [ChatYi](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
     "\n",
     "### Model features\n",
     "| [Tool calling](/docs/how_to/tool_calling) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
@@ -199,7 +199,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ChatYi features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
+    "For detailed documentation of all ChatYi features and configurations head to the API reference: https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.yi.ChatYi.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/arxiv.ipynb b/docs/docs/integrations/document_loaders/arxiv.ipynb
index 6c86b534bd2..b88d11365ec 100644
--- a/docs/docs/integrations/document_loaders/arxiv.ipynb
+++ b/docs/docs/integrations/document_loaders/arxiv.ipynb
@@ -188,7 +188,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all ArxivLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.arxiv.ArxivLoader.html#langchain_community.document_loaders.arxiv.ArxivLoader"
+    "For detailed documentation of all ArxivLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.arxiv.ArxivLoader.html#langchain_community.document_loaders.arxiv.ArxivLoader"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/box.ipynb b/docs/docs/integrations/document_loaders/box.ipynb
index d7aa96b388b..5f65ab5a074 100644
--- a/docs/docs/integrations/document_loaders/box.ipynb
+++ b/docs/docs/integrations/document_loaders/box.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# BoxLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Box [document loader](/docs/integrations/document_loaders/). For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html).\n",
+    "This notebook provides a quick overview for getting started with Box [document loader](/docs/integrations/document_loaders/). For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html).\n",
     "\n",
     "\n",
     "## Overview\n",
@@ -34,7 +34,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [BoxLoader](https://python.langchain.com/v0.2/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) | [langchain_box](https://python.langchain.com/v0.2/api_reference/box/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [BoxLoader](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html) | [langchain_box](https://python.langchain.com/api_reference/box/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -244,7 +244,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html)\n",
+    "For detailed documentation of all BoxLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/document_loaders/langchain_box.document_loaders.box.BoxLoader.html)\n",
     "\n",
     "\n",
     "## Help\n",
diff --git a/docs/docs/integrations/document_loaders/bshtml.ipynb b/docs/docs/integrations/document_loaders/bshtml.ipynb
index 5c4b8450c01..b23ba8dca4b 100644
--- a/docs/docs/integrations/document_loaders/bshtml.ipynb
+++ b/docs/docs/integrations/document_loaders/bshtml.ipynb
@@ -7,7 +7,7 @@
     "# BSHTMLLoader\n",
     "\n",
     "\n",
-    "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
+    "This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
     "\n",
     "\n",
     "## Overview\n",
@@ -16,7 +16,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [BSHTMLLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [BSHTMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -215,7 +215,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all BSHTMLLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
+    "For detailed documentation of all BSHTMLLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/dedoc.ipynb b/docs/docs/integrations/document_loaders/dedoc.ipynb
index 130977d5a51..1406243192d 100644
--- a/docs/docs/integrations/document_loaders/dedoc.ipynb
+++ b/docs/docs/integrations/document_loaders/dedoc.ipynb
@@ -23,9 +23,9 @@
     "\n",
     "| Class                                                                                                                                                | Package                                                                                        | Local | Serializable | JS support |\n",
     "|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----:|:------------:|:----------:|\n",
-    "| [DedocFileLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocFileLoader.html)       | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) |   ❌   |     beta     |     ❌      |\n",
-    "| [DedocPDFLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.DedocPDFLoader.html)           | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) |   ❌   |     beta     |     ❌      | \n",
-    "| [DedocAPIFileLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocAPIFileLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) |   ❌   |     beta     |     ❌      | \n",
+    "| [DedocFileLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocFileLoader.html)       | [langchain_community](https://python.langchain.com/api_reference/community/index.html) |   ❌   |     beta     |     ❌      |\n",
+    "| [DedocPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.DedocPDFLoader.html)           | [langchain_community](https://python.langchain.com/api_reference/community/index.html) |   ❌   |     beta     |     ❌      | \n",
+    "| [DedocAPIFileLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocAPIFileLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) |   ❌   |     beta     |     ❌      | \n",
     "\n",
     "\n",
     "### Loader features\n",
@@ -161,9 +161,9 @@
     "\n",
     "For detailed information on configuring and calling `Dedoc` loaders, please see the API references: \n",
     "\n",
-    "* https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocFileLoader.html\n",
-    "* https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.DedocPDFLoader.html\n",
-    "* https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocAPIFileLoader.html"
+    "* https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocFileLoader.html\n",
+    "* https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.DedocPDFLoader.html\n",
+    "* https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.dedoc.DedocAPIFileLoader.html"
    ]
   },
   {
diff --git a/docs/docs/integrations/document_loaders/firecrawl.ipynb b/docs/docs/integrations/document_loaders/firecrawl.ipynb
index 025bb08c2a1..b26bb12054c 100644
--- a/docs/docs/integrations/document_loaders/firecrawl.ipynb
+++ b/docs/docs/integrations/document_loaders/firecrawl.ipynb
@@ -13,9 +13,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/web_loaders/firecrawl/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/firecrawl/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [FireCrawlLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.firecrawl.FireCrawlLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [FireCrawlLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.firecrawl.FireCrawlLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -102,7 +102,7 @@
     {
      "data": {
       "text/plain": [
-       "Document(metadata={'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}, page_content='Introducing [Smart Crawl!](https://www.firecrawl.dev/smart-crawl)\\n Join the waitlist to turn any website into an API with AI\\n\\n\\n\\n[🔥 Firecrawl](/)\\n\\n*   [Playground](/playground)\\n    \\n*   [Docs](https://docs.firecrawl.dev)\\n    \\n*   [Pricing](/pricing)\\n    \\n*   [Blog](/blog)\\n    \\n*   Beta Features\\n\\n[Log In](/signin)\\n[Log In](/signin)\\n[Sign Up](/signin/signup)\\n 8.9k\\n\\n[💥 Get 2 months free with yearly plan](/pricing)\\n\\nTurn websites into  \\n_LLM-ready_ data\\n=====================================\\n\\nPower your AI apps with clean data crawled from any website. It\\'s also open-source.\\n\\nStart for free (500 credits)Start for free[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nA product by\\n\\n[![Mendable Logo](https://www.firecrawl.dev/images/mendable_logo_transparent.png)Mendable](https://mendable.ai)\\n\\n![Example Webpage](https://www.firecrawl.dev/multiple-websites.png)\\n\\nCrawl, Scrape, Clean\\n--------------------\\n\\nWe crawl all accessible subpages and give you clean markdown for each. No sitemap required.\\n\\n    \\n      [\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/\",\\\\\\n          \"markdown\": \"## Welcome to Firecrawl\\\\\\n            Firecrawl is a web scraper that allows you to extract the content of a webpage.\"\\\\\\n        },\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/features\",\\\\\\n          \"markdown\": \"## Features\\\\\\n            Discover how Firecrawl\\'s cutting-edge features can \\\\\\n            transform your data operations.\"\\\\\\n        },\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/pricing\",\\\\\\n          \"markdown\": \"## Pricing Plans\\\\\\n            Choose the perfect plan that fits your needs.\"\\\\\\n        },\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/about\",\\\\\\n          \"markdown\": \"## About Us\\\\\\n            Learn more about Firecrawl\\'s mission and the \\\\\\n            team behind our innovative platform.\"\\\\\\n        }\\\\\\n      ]\\n      \\n\\nNote: The markdown has been edited for display purposes.\\n\\nTrusted by Top Companies\\n------------------------\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/zapier.png)](https://www.zapier.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/gamma.svg)](https://gamma.app)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nvidia-com.png)](https://www.nvidia.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/teller-io.svg)](https://www.teller.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/stackai.svg)](https://www.stack-ai.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/palladiumdigital-co-uk.svg)](https://www.palladiumdigital.co.uk)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/worldwide-casting-com.svg)](https://www.worldwide-casting.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/open-gov-sg.png)](https://www.open.gov.sg)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/bain-com.svg)](https://www.bain.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/demand-io.svg)](https://www.demand.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nocodegarden-io.png)](https://www.nocodegarden.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/cyberagent-co-jp.svg)](https://www.cyberagent.co.jp)\\n\\nIntegrate today\\n---------------\\n\\nEnhance your applications with top-tier web scraping and crawling capabilities.\\n\\n#### Scrape\\n\\nExtract markdown or structured data from websites quickly and efficiently.\\n\\n#### Crawling\\n\\nNavigate and retrieve data from all accessible subpages, even without a sitemap.\\n\\nNode.js\\n\\nPython\\n\\ncURL\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\n9\\n\\n10\\n\\n// npm install @mendable/firecrawl-js  \\n  \\nimport FirecrawlApp from \\'@mendable/firecrawl-js\\';  \\n  \\nconst app \\\\= new FirecrawlApp({ apiKey: \"fc-YOUR\\\\_API\\\\_KEY\" });  \\n  \\n// Scrape a website:  \\nconst scrapeResult \\\\= await app.scrapeUrl(\\'firecrawl.dev\\');  \\n  \\nconsole.log(scrapeResult.data.markdown)\\n\\n#### Use well-known tools\\n\\nAlready fully integrated with the greatest existing tools and workflows.\\n\\n[![LlamaIndex](https://www.firecrawl.dev/logos/llamaindex.svg)](https://docs.llamaindex.ai/en/stable/examples/data_connectors/WebPageDemo/#using-firecrawl-reader/)\\n[![Langchain](https://www.firecrawl.dev/integrations/langchain.png)](https://python.langchain.com/v0.2/docs/integrations/document_loaders/firecrawl/)\\n[![Dify](https://www.firecrawl.dev/logos/dify.png)](https://dify.ai/blog/dify-ai-blog-integrated-with-firecrawl/)\\n[![Dify](https://www.firecrawl.dev/integrations/langflow_2.png)](https://www.langflow.org/)\\n[![Flowise](https://www.firecrawl.dev/integrations/flowise.png)](https://flowiseai.com/)\\n[![CrewAI](https://www.firecrawl.dev/integrations/crewai.png)](https://crewai.com/)\\n\\n#### Start for free, scale easily\\n\\nKick off your journey for free and scale seamlessly as your project expands.\\n\\n[Try it out](/signin/signup)\\n\\n#### Open-source\\n\\nDeveloped transparently and collaboratively. Join our community of contributors.\\n\\n[Check out our repo](https://github.com/mendableai/firecrawl)\\n\\nWe handle the hard stuff\\n------------------------\\n\\nRotating proxies, caching, rate limits, js-blocked content and more\\n\\n#### Crawling\\n\\nFirecrawl crawls all accessible subpages, even without a sitemap.\\n\\n#### Dynamic content\\n\\nFirecrawl gathers data even if a website uses javascript to render content.\\n\\n#### To Markdown\\n\\nFirecrawl returns clean, well formatted markdown - ready for use in LLM applications\\n\\n#### Crawling Orchestration\\n\\nFirecrawl orchestrates the crawling process in parallel for the fastest results.\\n\\n#### Caching\\n\\nFirecrawl caches content, so you don\\'t have to wait for a full scrape unless new content exists.\\n\\n#### Built for AI\\n\\nBuilt by LLM engineers, for LLM engineers. Giving you clean data the way you want it.\\n\\nOur wall of love\\n\\nDon\\'t take our word for it\\n--------------------------\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\nFlexible Pricing\\n----------------\\n\\nStart for free, then scale as you grow\\n\\nYearly (17% off)Yearly (2 months free)Monthly\\n\\nFree Plan\\n---------\\n\\n500 credits\\n\\n$0/month\\n\\n*   Scrape 500 pages\\n*   5 /scrape per min\\n*   1 /crawl per min\\n\\nGet Started\\n\\nHobby\\n-----\\n\\n3,000 credits\\n\\n$16/month\\n\\n*   Scrape 3,000 pages\\n*   10 /scrape per min\\n*   3 /crawl per min\\n\\nSubscribe\\n\\nStandardMost Popular\\n--------------------\\n\\n100,000 credits\\n\\n$83/month\\n\\n*   Scrape 100,000 pages\\n*   50 /scrape per min\\n*   10 /crawl per min\\n\\nSubscribe\\n\\nGrowth\\n------\\n\\n500,000 credits\\n\\n$333/month\\n\\n*   Scrape 500,000 pages\\n*   500 /scrape per min\\n*   50 /crawl per min\\n*   Priority Support\\n\\nSubscribe\\n\\nEnterprise Plan\\n---------------\\n\\nUnlimited credits. Custom RPMs.\\n\\nTalk to us\\n\\n*   Top priority support\\n*   Feature Acceleration\\n*   SLAs\\n*   Account Manager\\n*   Custom rate limits volume\\n*   Custom concurrency limits\\n*   Beta features access\\n*   CEO\\'s number\\n\\n\\\\* a /scrape refers to the [scrape](https://docs.firecrawl.dev/api-reference/endpoint/scrape)\\n API endpoint.\\n\\n\\\\* a /crawl refers to the [crawl](https://docs.firecrawl.dev/api-reference/endpoint/crawl)\\n API endpoint.\\n\\nScrape Credits\\n--------------\\n\\nScrape credits are consumed for each API request, varying by endpoint and feature.\\n\\n| Features | Credits per page |\\n| --- | --- |\\n| Scrape(/scrape) | 1   |\\n| Crawl(/crawl) | 1   |\\n| Search(/search) | 1   |\\n| Scrape + LLM extraction (/scrape) | 50  |\\n\\n[🔥](/)\\n\\nReady to _Build?_\\n-----------------\\n\\nStart scraping web data for your AI apps today.  \\nNo credit card needed.\\n\\n[Get Started](/signin)\\n\\n[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nFAQ\\n---\\n\\nFrequently asked questions about Firecrawl\\n\\n#### General\\n\\nWhat is Firecrawl?\\n\\nFirecrawl turns entire websites into clean, LLM-ready markdown or structured data. Scrape, crawl and extract the web with a single API. Ideal for AI companies looking to empower their LLM applications with web data.\\n\\nWhat sites work?\\n\\nFirecrawl is best suited for business websites, docs and help centers. We currently don\\'t support social media platforms.\\n\\nWho can benefit from using Firecrawl?\\n\\nFirecrawl is tailored for LLM engineers, data scientists, AI researchers, and developers looking to harness web data for training machine learning models, market research, content aggregation, and more. It simplifies the data preparation process, allowing professionals to focus on insights and model development.\\n\\nIs Firecrawl open-source?\\n\\nYes, it is. You can check out the repository on GitHub. Keep in mind that this repository is currently in its early stages of development. We are in the process of merging custom modules into this mono repository.\\n\\n#### Scraping & Crawling\\n\\nHow does Firecrawl handle dynamic content on websites?\\n\\nUnlike traditional web scrapers, Firecrawl is equipped to handle dynamic content rendered with JavaScript. It ensures comprehensive data collection from all accessible subpages, making it a reliable tool for scraping websites that rely heavily on JS for content delivery.\\n\\nWhy is it not crawling all the pages?\\n\\nThere are a few reasons why Firecrawl may not be able to crawl all the pages of a website. Some common reasons include rate limiting, and anti-scraping mechanisms, disallowing the crawler from accessing certain pages. If you\\'re experiencing issues with the crawler, please reach out to our support team at hello@firecrawl.com.\\n\\nCan Firecrawl crawl websites without a sitemap?\\n\\nYes, Firecrawl can access and crawl all accessible subpages of a website, even in the absence of a sitemap. This feature enables users to gather data from a wide array of web sources with minimal setup.\\n\\nWhat formats can Firecrawl convert web data into?\\n\\nFirecrawl specializes in converting web data into clean, well-formatted markdown. This format is particularly suited for LLM applications, offering a structured yet flexible way to represent web content.\\n\\nHow does Firecrawl ensure the cleanliness of the data?\\n\\nFirecrawl employs advanced algorithms to clean and structure the scraped data, removing unnecessary elements and formatting the content into readable markdown. This process ensures that the data is ready for use in LLM applications without further preprocessing.\\n\\nIs Firecrawl suitable for large-scale data scraping projects?\\n\\nAbsolutely. Firecrawl offers various pricing plans, including a Scale plan that supports scraping of millions of pages. With features like caching and scheduled syncs, it\\'s designed to efficiently handle large-scale data scraping and continuous updates, making it ideal for enterprises and large projects.\\n\\nDoes it respect robots.txt?\\n\\nYes, Firecrawl crawler respects the rules set in a website\\'s robots.txt file. If you notice any issues with the way Firecrawl interacts with your website, you can adjust the robots.txt file to control the crawler\\'s behavior. Firecrawl user agent name is \\'FirecrawlAgent\\'. If you notice any behavior that is not expected, please let us know at hello@firecrawl.com.\\n\\nWhat measures does Firecrawl take to handle web scraping challenges like rate limits and caching?\\n\\nFirecrawl is built to navigate common web scraping challenges, including reverse proxies, rate limits, and caching. It smartly manages requests and employs caching techniques to minimize bandwidth usage and avoid triggering anti-scraping mechanisms, ensuring reliable data collection.\\n\\nDoes Firecrawl handle captcha or authentication?\\n\\nFirecrawl avoids captcha by using stealth proxyies. When it encounters captcha, it attempts to solve it automatically, but this is not always possible. We are working to add support for more captcha solving methods. Firecrawl can handle authentication by providing auth headers to the API.\\n\\n#### API Related\\n\\nWhere can I find my API key?\\n\\nClick on the dashboard button on the top navigation menu when logged in and you will find your API key in the main screen and under API Keys.\\n\\n#### Billing\\n\\nIs Firecrawl free?\\n\\nFirecrawl is free for the first 500 scraped pages (500 free credits). After that, you can upgrade to our Standard or Scale plans for more credits.\\n\\nIs there a pay per use plan instead of monthly?\\n\\nNo we do not currently offer a pay per use plan, instead you can upgrade to our Standard or Growth plans for more credits and higher rate limits.\\n\\nHow many credit does scraping, crawling, and extraction cost?\\n\\nScraping costs 1 credit per page. Crawling costs 1 credit per page.\\n\\nDo you charge for failed requests (scrape, crawl, extract)?\\n\\nWe do not charge for any failed requests (scrape, crawl, extract). Please contact support at help@firecrawl.dev if you have any questions.\\n\\nWhat payment methods do you accept?\\n\\nWe accept payments through Stripe which accepts most major credit cards, debit cards, and PayPal.\\n\\n[🔥](/)\\n\\n© A product by Mendable.ai - All rights reserved.\\n\\n[StatusStatus](https://firecrawl.betteruptime.com)\\n[Terms of ServiceTerms of Service](/terms-of-service)\\n[Privacy PolicyPrivacy Policy](/privacy-policy)\\n\\n[Twitter](https://twitter.com/mendableai)\\n[GitHub](https://github.com/mendableai)\\n[Discord](https://discord.gg/gSmWdAkdwd)\\n\\n###### Helpful Links\\n\\n*   [Status](https://firecrawl.betteruptime.com/)\\n    \\n*   [Pricing](/pricing)\\n    \\n*   [Blog](https://www.firecrawl.dev/blog)\\n    \\n*   [Docs](https://docs.firecrawl.dev)\\n    \\n\\nBacked by![Y Combinator Logo](https://www.firecrawl.dev/images/yc.svg)\\n\\n![SOC 2 Type II](https://www.firecrawl.dev/soc2type2badge.png)\\n\\n###### Resources\\n\\n*   [Community](#0)\\n    \\n*   [Terms of service](#0)\\n    \\n*   [Collaboration features](#0)\\n    \\n\\n###### Legals\\n\\n*   [Refund policy](#0)\\n    \\n*   [Terms & Conditions](#0)\\n    \\n*   [Privacy policy](#0)\\n    \\n*   [Brand Kit](#0)')"
+       "Document(metadata={'ogUrl': 'https://www.firecrawl.dev/', 'title': 'Home - Firecrawl', 'robots': 'follow, index', 'ogImage': 'https://www.firecrawl.dev/og.png?123', 'ogTitle': 'Firecrawl', 'sitemap': {'lastmod': '2024-08-12T00:28:16.681Z', 'changefreq': 'weekly'}, 'keywords': 'Firecrawl,Markdown,Data,Mendable,Langchain', 'sourceURL': 'https://www.firecrawl.dev/', 'ogSiteName': 'Firecrawl', 'description': 'Firecrawl crawls and converts any website into clean markdown.', 'ogDescription': 'Turn any website into LLM-ready data.', 'pageStatusCode': 200, 'ogLocaleAlternate': []}, page_content='Introducing [Smart Crawl!](https://www.firecrawl.dev/smart-crawl)\\n Join the waitlist to turn any website into an API with AI\\n\\n\\n\\n[🔥 Firecrawl](/)\\n\\n*   [Playground](/playground)\\n    \\n*   [Docs](https://docs.firecrawl.dev)\\n    \\n*   [Pricing](/pricing)\\n    \\n*   [Blog](/blog)\\n    \\n*   Beta Features\\n\\n[Log In](/signin)\\n[Log In](/signin)\\n[Sign Up](/signin/signup)\\n 8.9k\\n\\n[💥 Get 2 months free with yearly plan](/pricing)\\n\\nTurn websites into  \\n_LLM-ready_ data\\n=====================================\\n\\nPower your AI apps with clean data crawled from any website. It\\'s also open-source.\\n\\nStart for free (500 credits)Start for free[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nA product by\\n\\n[![Mendable Logo](https://www.firecrawl.dev/images/mendable_logo_transparent.png)Mendable](https://mendable.ai)\\n\\n![Example Webpage](https://www.firecrawl.dev/multiple-websites.png)\\n\\nCrawl, Scrape, Clean\\n--------------------\\n\\nWe crawl all accessible subpages and give you clean markdown for each. No sitemap required.\\n\\n    \\n      [\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/\",\\\\\\n          \"markdown\": \"## Welcome to Firecrawl\\\\\\n            Firecrawl is a web scraper that allows you to extract the content of a webpage.\"\\\\\\n        },\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/features\",\\\\\\n          \"markdown\": \"## Features\\\\\\n            Discover how Firecrawl\\'s cutting-edge features can \\\\\\n            transform your data operations.\"\\\\\\n        },\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/pricing\",\\\\\\n          \"markdown\": \"## Pricing Plans\\\\\\n            Choose the perfect plan that fits your needs.\"\\\\\\n        },\\\\\\n        {\\\\\\n          \"url\": \"https://www.firecrawl.dev/about\",\\\\\\n          \"markdown\": \"## About Us\\\\\\n            Learn more about Firecrawl\\'s mission and the \\\\\\n            team behind our innovative platform.\"\\\\\\n        }\\\\\\n      ]\\n      \\n\\nNote: The markdown has been edited for display purposes.\\n\\nTrusted by Top Companies\\n------------------------\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/zapier.png)](https://www.zapier.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/gamma.svg)](https://gamma.app)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nvidia-com.png)](https://www.nvidia.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/teller-io.svg)](https://www.teller.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/stackai.svg)](https://www.stack-ai.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/palladiumdigital-co-uk.svg)](https://www.palladiumdigital.co.uk)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/worldwide-casting-com.svg)](https://www.worldwide-casting.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/open-gov-sg.png)](https://www.open.gov.sg)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/bain-com.svg)](https://www.bain.com)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/demand-io.svg)](https://www.demand.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/nocodegarden-io.png)](https://www.nocodegarden.io)\\n\\n[![Customer Logo](https://www.firecrawl.dev/logos/cyberagent-co-jp.svg)](https://www.cyberagent.co.jp)\\n\\nIntegrate today\\n---------------\\n\\nEnhance your applications with top-tier web scraping and crawling capabilities.\\n\\n#### Scrape\\n\\nExtract markdown or structured data from websites quickly and efficiently.\\n\\n#### Crawling\\n\\nNavigate and retrieve data from all accessible subpages, even without a sitemap.\\n\\nNode.js\\n\\nPython\\n\\ncURL\\n\\n1\\n\\n2\\n\\n3\\n\\n4\\n\\n5\\n\\n6\\n\\n7\\n\\n8\\n\\n9\\n\\n10\\n\\n// npm install @mendable/firecrawl-js  \\n  \\nimport FirecrawlApp from \\'@mendable/firecrawl-js\\';  \\n  \\nconst app \\\\= new FirecrawlApp({ apiKey: \"fc-YOUR\\\\_API\\\\_KEY\" });  \\n  \\n// Scrape a website:  \\nconst scrapeResult \\\\= await app.scrapeUrl(\\'firecrawl.dev\\');  \\n  \\nconsole.log(scrapeResult.data.markdown)\\n\\n#### Use well-known tools\\n\\nAlready fully integrated with the greatest existing tools and workflows.\\n\\n[![LlamaIndex](https://www.firecrawl.dev/logos/llamaindex.svg)](https://docs.llamaindex.ai/en/stable/examples/data_connectors/WebPageDemo/#using-firecrawl-reader/)\\n[![Langchain](https://www.firecrawl.dev/integrations/langchain.png)](https://python.langchain.com/docs/integrations/document_loaders/firecrawl/)\\n[![Dify](https://www.firecrawl.dev/logos/dify.png)](https://dify.ai/blog/dify-ai-blog-integrated-with-firecrawl/)\\n[![Dify](https://www.firecrawl.dev/integrations/langflow_2.png)](https://www.langflow.org/)\\n[![Flowise](https://www.firecrawl.dev/integrations/flowise.png)](https://flowiseai.com/)\\n[![CrewAI](https://www.firecrawl.dev/integrations/crewai.png)](https://crewai.com/)\\n\\n#### Start for free, scale easily\\n\\nKick off your journey for free and scale seamlessly as your project expands.\\n\\n[Try it out](/signin/signup)\\n\\n#### Open-source\\n\\nDeveloped transparently and collaboratively. Join our community of contributors.\\n\\n[Check out our repo](https://github.com/mendableai/firecrawl)\\n\\nWe handle the hard stuff\\n------------------------\\n\\nRotating proxies, caching, rate limits, js-blocked content and more\\n\\n#### Crawling\\n\\nFirecrawl crawls all accessible subpages, even without a sitemap.\\n\\n#### Dynamic content\\n\\nFirecrawl gathers data even if a website uses javascript to render content.\\n\\n#### To Markdown\\n\\nFirecrawl returns clean, well formatted markdown - ready for use in LLM applications\\n\\n#### Crawling Orchestration\\n\\nFirecrawl orchestrates the crawling process in parallel for the fastest results.\\n\\n#### Caching\\n\\nFirecrawl caches content, so you don\\'t have to wait for a full scrape unless new content exists.\\n\\n#### Built for AI\\n\\nBuilt by LLM engineers, for LLM engineers. Giving you clean data the way you want it.\\n\\nOur wall of love\\n\\nDon\\'t take our word for it\\n--------------------------\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Greg Kamradt](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-02.0afeb750.jpg&w=96&q=75)\\n\\nGreg Kamradt\\n\\n[@GregKamradt](https://twitter.com/GregKamradt/status/1780300642197840307)\\n\\nLLM structured data via API, handling requests, cleaning, and crawling. Enjoyed the early preview.\\n\\n![Amit Naik](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-03.ff5dbe11.jpg&w=96&q=75)\\n\\nAmit Naik\\n\\n[@suprgeek](https://twitter.com/suprgeek/status/1780338213351035254)\\n\\n#llm success with RAG relies on Retrieval. Firecrawl by @mendableai structures web content for processing. 👏\\n\\n![Jerry Liu](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-04.76bef0df.jpg&w=96&q=75)\\n\\nJerry Liu\\n\\n[@jerryjliu0](https://twitter.com/jerryjliu0/status/1781122933349572772)\\n\\nFirecrawl is awesome 🔥 Turns web pages into structured markdown for LLM apps, thanks to @mendableai.\\n\\n![Bardia Pourvakil](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-01.025350bc.jpeg&w=96&q=75)\\n\\nBardia Pourvakil\\n\\n[@thepericulum](https://twitter.com/thepericulum/status/1781397799487078874)\\n\\nThese guys ship. I wanted types for their node SDK, and less than an hour later, I got them. Can\\'t recommend them enough.\\n\\n![latentsauce 🧘🏽](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-07.c2285d35.jpeg&w=96&q=75)\\n\\nlatentsauce 🧘🏽\\n\\n[@latentsauce](https://twitter.com/latentsauce/status/1781738253927735331)\\n\\nFirecrawl simplifies data preparation significantly, exactly what I was hoping for. Thank you for creating Firecrawl ❤️❤️❤️\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\n![Michael Ning](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-05.76d7cd3e.png&w=96&q=75)\\n\\nMichael Ning\\n\\n[](#)\\n\\nFirecrawl is impressive, saving us 2/3 the tokens and allowing gpt3.5turbo use over gpt4. Major savings in time and money.\\n\\n![Alex Reibman 🖇️](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-06.4ee7cf5a.jpeg&w=96&q=75)\\n\\nAlex Reibman 🖇️\\n\\n[@AlexReibman](https://twitter.com/AlexReibman/status/1780299595484131836)\\n\\nMoved our internal agent\\'s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps.\\n\\n![Michael](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-08.0bed40be.jpeg&w=96&q=75)\\n\\nMichael\\n\\n[@michael\\\\_chomsky](#)\\n\\nI really like some of the design decisions Firecrawl made, so I really want to share with others.\\n\\n![Paul Scott](https://www.firecrawl.dev/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ftestimonial-09.d303b5b4.png&w=96&q=75)\\n\\nPaul Scott\\n\\n[@palebluepaul](https://twitter.com/palebluepaul)\\n\\nAppreciating your lean approach, Firecrawl ticks off everything on our list without the cost prohibitive overkill.\\n\\nFlexible Pricing\\n----------------\\n\\nStart for free, then scale as you grow\\n\\nYearly (17% off)Yearly (2 months free)Monthly\\n\\nFree Plan\\n---------\\n\\n500 credits\\n\\n$0/month\\n\\n*   Scrape 500 pages\\n*   5 /scrape per min\\n*   1 /crawl per min\\n\\nGet Started\\n\\nHobby\\n-----\\n\\n3,000 credits\\n\\n$16/month\\n\\n*   Scrape 3,000 pages\\n*   10 /scrape per min\\n*   3 /crawl per min\\n\\nSubscribe\\n\\nStandardMost Popular\\n--------------------\\n\\n100,000 credits\\n\\n$83/month\\n\\n*   Scrape 100,000 pages\\n*   50 /scrape per min\\n*   10 /crawl per min\\n\\nSubscribe\\n\\nGrowth\\n------\\n\\n500,000 credits\\n\\n$333/month\\n\\n*   Scrape 500,000 pages\\n*   500 /scrape per min\\n*   50 /crawl per min\\n*   Priority Support\\n\\nSubscribe\\n\\nEnterprise Plan\\n---------------\\n\\nUnlimited credits. Custom RPMs.\\n\\nTalk to us\\n\\n*   Top priority support\\n*   Feature Acceleration\\n*   SLAs\\n*   Account Manager\\n*   Custom rate limits volume\\n*   Custom concurrency limits\\n*   Beta features access\\n*   CEO\\'s number\\n\\n\\\\* a /scrape refers to the [scrape](https://docs.firecrawl.dev/api-reference/endpoint/scrape)\\n API endpoint.\\n\\n\\\\* a /crawl refers to the [crawl](https://docs.firecrawl.dev/api-reference/endpoint/crawl)\\n API endpoint.\\n\\nScrape Credits\\n--------------\\n\\nScrape credits are consumed for each API request, varying by endpoint and feature.\\n\\n| Features | Credits per page |\\n| --- | --- |\\n| Scrape(/scrape) | 1   |\\n| Crawl(/crawl) | 1   |\\n| Search(/search) | 1   |\\n| Scrape + LLM extraction (/scrape) | 50  |\\n\\n[🔥](/)\\n\\nReady to _Build?_\\n-----------------\\n\\nStart scraping web data for your AI apps today.  \\nNo credit card needed.\\n\\n[Get Started](/signin)\\n\\n[Talk to us](https://calendly.com/d/cj83-ngq-knk/meet-firecrawl)\\n\\nFAQ\\n---\\n\\nFrequently asked questions about Firecrawl\\n\\n#### General\\n\\nWhat is Firecrawl?\\n\\nFirecrawl turns entire websites into clean, LLM-ready markdown or structured data. Scrape, crawl and extract the web with a single API. Ideal for AI companies looking to empower their LLM applications with web data.\\n\\nWhat sites work?\\n\\nFirecrawl is best suited for business websites, docs and help centers. We currently don\\'t support social media platforms.\\n\\nWho can benefit from using Firecrawl?\\n\\nFirecrawl is tailored for LLM engineers, data scientists, AI researchers, and developers looking to harness web data for training machine learning models, market research, content aggregation, and more. It simplifies the data preparation process, allowing professionals to focus on insights and model development.\\n\\nIs Firecrawl open-source?\\n\\nYes, it is. You can check out the repository on GitHub. Keep in mind that this repository is currently in its early stages of development. We are in the process of merging custom modules into this mono repository.\\n\\n#### Scraping & Crawling\\n\\nHow does Firecrawl handle dynamic content on websites?\\n\\nUnlike traditional web scrapers, Firecrawl is equipped to handle dynamic content rendered with JavaScript. It ensures comprehensive data collection from all accessible subpages, making it a reliable tool for scraping websites that rely heavily on JS for content delivery.\\n\\nWhy is it not crawling all the pages?\\n\\nThere are a few reasons why Firecrawl may not be able to crawl all the pages of a website. Some common reasons include rate limiting, and anti-scraping mechanisms, disallowing the crawler from accessing certain pages. If you\\'re experiencing issues with the crawler, please reach out to our support team at hello@firecrawl.com.\\n\\nCan Firecrawl crawl websites without a sitemap?\\n\\nYes, Firecrawl can access and crawl all accessible subpages of a website, even in the absence of a sitemap. This feature enables users to gather data from a wide array of web sources with minimal setup.\\n\\nWhat formats can Firecrawl convert web data into?\\n\\nFirecrawl specializes in converting web data into clean, well-formatted markdown. This format is particularly suited for LLM applications, offering a structured yet flexible way to represent web content.\\n\\nHow does Firecrawl ensure the cleanliness of the data?\\n\\nFirecrawl employs advanced algorithms to clean and structure the scraped data, removing unnecessary elements and formatting the content into readable markdown. This process ensures that the data is ready for use in LLM applications without further preprocessing.\\n\\nIs Firecrawl suitable for large-scale data scraping projects?\\n\\nAbsolutely. Firecrawl offers various pricing plans, including a Scale plan that supports scraping of millions of pages. With features like caching and scheduled syncs, it\\'s designed to efficiently handle large-scale data scraping and continuous updates, making it ideal for enterprises and large projects.\\n\\nDoes it respect robots.txt?\\n\\nYes, Firecrawl crawler respects the rules set in a website\\'s robots.txt file. If you notice any issues with the way Firecrawl interacts with your website, you can adjust the robots.txt file to control the crawler\\'s behavior. Firecrawl user agent name is \\'FirecrawlAgent\\'. If you notice any behavior that is not expected, please let us know at hello@firecrawl.com.\\n\\nWhat measures does Firecrawl take to handle web scraping challenges like rate limits and caching?\\n\\nFirecrawl is built to navigate common web scraping challenges, including reverse proxies, rate limits, and caching. It smartly manages requests and employs caching techniques to minimize bandwidth usage and avoid triggering anti-scraping mechanisms, ensuring reliable data collection.\\n\\nDoes Firecrawl handle captcha or authentication?\\n\\nFirecrawl avoids captcha by using stealth proxyies. When it encounters captcha, it attempts to solve it automatically, but this is not always possible. We are working to add support for more captcha solving methods. Firecrawl can handle authentication by providing auth headers to the API.\\n\\n#### API Related\\n\\nWhere can I find my API key?\\n\\nClick on the dashboard button on the top navigation menu when logged in and you will find your API key in the main screen and under API Keys.\\n\\n#### Billing\\n\\nIs Firecrawl free?\\n\\nFirecrawl is free for the first 500 scraped pages (500 free credits). After that, you can upgrade to our Standard or Scale plans for more credits.\\n\\nIs there a pay per use plan instead of monthly?\\n\\nNo we do not currently offer a pay per use plan, instead you can upgrade to our Standard or Growth plans for more credits and higher rate limits.\\n\\nHow many credit does scraping, crawling, and extraction cost?\\n\\nScraping costs 1 credit per page. Crawling costs 1 credit per page.\\n\\nDo you charge for failed requests (scrape, crawl, extract)?\\n\\nWe do not charge for any failed requests (scrape, crawl, extract). Please contact support at help@firecrawl.dev if you have any questions.\\n\\nWhat payment methods do you accept?\\n\\nWe accept payments through Stripe which accepts most major credit cards, debit cards, and PayPal.\\n\\n[🔥](/)\\n\\n© A product by Mendable.ai - All rights reserved.\\n\\n[StatusStatus](https://firecrawl.betteruptime.com)\\n[Terms of ServiceTerms of Service](/terms-of-service)\\n[Privacy PolicyPrivacy Policy](/privacy-policy)\\n\\n[Twitter](https://twitter.com/mendableai)\\n[GitHub](https://github.com/mendableai)\\n[Discord](https://discord.gg/gSmWdAkdwd)\\n\\n###### Helpful Links\\n\\n*   [Status](https://firecrawl.betteruptime.com/)\\n    \\n*   [Pricing](/pricing)\\n    \\n*   [Blog](https://www.firecrawl.dev/blog)\\n    \\n*   [Docs](https://docs.firecrawl.dev)\\n    \\n\\nBacked by![Y Combinator Logo](https://www.firecrawl.dev/images/yc.svg)\\n\\n![SOC 2 Type II](https://www.firecrawl.dev/soc2type2badge.png)\\n\\n###### Resources\\n\\n*   [Community](#0)\\n    \\n*   [Terms of service](#0)\\n    \\n*   [Collaboration features](#0)\\n    \\n\\n###### Legals\\n\\n*   [Refund policy](#0)\\n    \\n*   [Terms & Conditions](#0)\\n    \\n*   [Privacy policy](#0)\\n    \\n*   [Brand Kit](#0)')"
       ]
      },
      "execution_count": 4,
@@ -214,7 +214,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `FireCrawlLoader` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.firecrawl.FireCrawlLoader.html"
+    "For detailed documentation of all `FireCrawlLoader` features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.firecrawl.FireCrawlLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/json.ipynb b/docs/docs/integrations/document_loaders/json.ipynb
index 02716f79d9b..299bc1aa75f 100644
--- a/docs/docs/integrations/document_loaders/json.ipynb
+++ b/docs/docs/integrations/document_loaders/json.ipynb
@@ -6,16 +6,16 @@
    "source": [
     "# JSONLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
+    "This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
     "\n",
     "- TODO: Add any other relevant links, like information about underlying API, etc.\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/json/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/json/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [JSONLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [JSONLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -320,7 +320,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all JSONLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
+    "For detailed documentation of all JSONLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/langsmith.ipynb b/docs/docs/integrations/document_loaders/langsmith.ipynb
index 5921bd1c035..9a8dc2aee3d 100644
--- a/docs/docs/integrations/document_loaders/langsmith.ipynb
+++ b/docs/docs/integrations/document_loaders/langsmith.ipynb
@@ -15,14 +15,14 @@
    "source": [
     "# LangSmithLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with the LangSmith [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all LangSmithLoader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html).\n",
+    "This notebook provides a quick overview for getting started with the LangSmith [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all LangSmithLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [LangSmithLoader](https://python.langchain.com/v0.2/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html) | [langchain-core](https://python.langchain.com/v0.2/api_reference/core/index.html) | ❌ | ❌ | ❌ | \n",
+    "| [LangSmithLoader](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html) | [langchain-core](https://python.langchain.com/api_reference/core/index.html) | ❌ | ❌ | ❌ | \n",
     "\n",
     "### Loader features\n",
     "| Source | Lazy loading | Native async\n",
@@ -266,7 +266,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all LangSmithLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html"
+    "For detailed documentation of all LangSmithLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/mathpix.ipynb b/docs/docs/integrations/document_loaders/mathpix.ipynb
index 03fcab6f6f1..bdbcbb96adc 100644
--- a/docs/docs/integrations/document_loaders/mathpix.ipynb
+++ b/docs/docs/integrations/document_loaders/mathpix.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [MathPixPDFLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [MathPixPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -150,7 +150,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all MathpixPDFLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
+    "For detailed documentation of all MathpixPDFLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/pdfminer.ipynb b/docs/docs/integrations/document_loaders/pdfminer.ipynb
index 1cf468e2698..ccb5fff8a23 100644
--- a/docs/docs/integrations/document_loaders/pdfminer.ipynb
+++ b/docs/docs/integrations/document_loaders/pdfminer.ipynb
@@ -12,7 +12,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PDFMinerLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFMinerLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [PDFMinerLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFMinerLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -289,7 +289,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all PDFMinerLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFMinerLoader.html"
+    "For detailed documentation of all PDFMinerLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFMinerLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/pdfplumber.ipynb b/docs/docs/integrations/document_loaders/pdfplumber.ipynb
index 6fe5a130792..cfa43817f10 100644
--- a/docs/docs/integrations/document_loaders/pdfplumber.ipynb
+++ b/docs/docs/integrations/document_loaders/pdfplumber.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PDFPlumberLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [PDFPlumberLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -155,7 +155,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all PDFPlumberLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
+    "For detailed documentation of all PDFPlumberLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/pymupdf.ipynb b/docs/docs/integrations/document_loaders/pymupdf.ipynb
index 191b0902d4c..65c92cb6ef0 100644
--- a/docs/docs/integrations/document_loaders/pymupdf.ipynb
+++ b/docs/docs/integrations/document_loaders/pymupdf.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PyMuPDFLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [PyMuPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -157,7 +157,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all PyMuPDFLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html"
+    "For detailed documentation of all PyMuPDFLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/pypdfdirectory.ipynb b/docs/docs/integrations/document_loaders/pypdfdirectory.ipynb
index 08726cf7a1b..69c49aca36d 100644
--- a/docs/docs/integrations/document_loaders/pypdfdirectory.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfdirectory.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PyPDFDirectoryLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [PyPDFDirectoryLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -159,7 +159,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
+    "For detailed documentation of all PyPDFDirectoryLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/pypdfium2.ipynb b/docs/docs/integrations/document_loaders/pypdfium2.ipynb
index 93cfa9106ad..48ca5ec0232 100644
--- a/docs/docs/integrations/document_loaders/pypdfium2.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfium2.ipynb
@@ -7,14 +7,14 @@
     "# PyPDFium2Loader\n",
     "\n",
     "\n",
-    "This notebook provides a quick overview for getting started with PyPDFium2 [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html).\n",
+    "This notebook provides a quick overview for getting started with PyPDFium2 [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PyPDFium2Loader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [PyPDFium2Loader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -160,7 +160,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all PyPDFium2Loader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html"
+    "For detailed documentation of all PyPDFium2Loader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/pypdfloader.ipynb b/docs/docs/integrations/document_loaders/pypdfloader.ipynb
index 6b72fecca4e..5085aa1c6c5 100644
--- a/docs/docs/integrations/document_loaders/pypdfloader.ipynb
+++ b/docs/docs/integrations/document_loaders/pypdfloader.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# PyPDFLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all DocumentLoader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
+    "This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all DocumentLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
     "\n",
     "\n",
     "## Overview\n",
@@ -15,7 +15,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [PyPDFLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [PyPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -174,7 +174,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `PyPDFLoader` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html"
+    "For detailed documentation of all `PyPDFLoader` features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/recursive_url.ipynb b/docs/docs/integrations/document_loaders/recursive_url.ipynb
index 5c62fafd745..98c87977435 100644
--- a/docs/docs/integrations/document_loaders/recursive_url.ipynb
+++ b/docs/docs/integrations/document_loaders/recursive_url.ipynb
@@ -12,9 +12,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/web_loaders/recursive_url_loader/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/recursive_url_loader/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [RecursiveUrlLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [RecursiveUrlLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -314,7 +314,7 @@
    "source": [
     "This looks much nicer!\n",
     "\n",
-    "You can similarly pass in a `metadata_extractor` to customize how Document metadata is extracted from the HTTP response. See the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html) for more on this."
+    "You can similarly pass in a `metadata_extractor` to customize how Document metadata is extracted from the HTTP response. See the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html) for more on this."
    ]
   },
   {
@@ -326,7 +326,7 @@
     "\n",
     "These examples show just a few of the ways in which you can modify the default `RecursiveUrlLoader`, but there are many more modifications that can be made to best fit your use case. Using the parameters `link_regex` and `exclude_dirs` can help you filter out unwanted URLs, `aload()` and `alazy_load()` can be used for aynchronous loading, and more.\n",
     "\n",
-    "For detailed information on configuring and calling the ``RecursiveUrlLoader``, please see the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html."
+    "For detailed information on configuring and calling the ``RecursiveUrlLoader``, please see the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html."
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/scrapingant.ipynb b/docs/docs/integrations/document_loaders/scrapingant.ipynb
index 5cc86dbbe01..42e58ade57c 100644
--- a/docs/docs/integrations/document_loaders/scrapingant.ipynb
+++ b/docs/docs/integrations/document_loaders/scrapingant.ipynb
@@ -18,7 +18,7 @@
     "\n",
     "| Class                                                                                                                                                    | Package                                                                                        | Local | Serializable | JS support |\n",
     "|:---------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----:|:------------:|:----------:|\n",
-    "| [ScrapingAntLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.scrapingant.ScrapingAntLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) |   ❌   |      ❌       |     ❌      | \n",
+    "| [ScrapingAntLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.scrapingant.ScrapingAntLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) |   ❌   |      ❌       |     ❌      | \n",
     "\n",
     "### Loader features\n",
     "|      Source       | Document Lazy Loading | Async Support |\n",
diff --git a/docs/docs/integrations/document_loaders/sitemap.ipynb b/docs/docs/integrations/document_loaders/sitemap.ipynb
index 699519d28a8..e930b12de31 100644
--- a/docs/docs/integrations/document_loaders/sitemap.ipynb
+++ b/docs/docs/integrations/document_loaders/sitemap.ipynb
@@ -13,9 +13,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/web_loaders/sitemap/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/sitemap/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [SiteMapLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [SiteMapLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -352,7 +352,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all SiteMapLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader"
+    "For detailed documentation of all SiteMapLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html#langchain_community.document_loaders.sitemap.SitemapLoader"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/unstructured_file.ipynb b/docs/docs/integrations/document_loaders/unstructured_file.ipynb
index f9a118698b6..b376721eb81 100644
--- a/docs/docs/integrations/document_loaders/unstructured_file.ipynb
+++ b/docs/docs/integrations/document_loaders/unstructured_file.ipynb
@@ -7,16 +7,16 @@
    "source": [
     "# Unstructured\n",
     "\n",
-    "This notebook covers how to use `Unstructured` [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders) to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
+    "This notebook covers how to use `Unstructured` [document loader](https://python.langchain.com/docs/concepts/#document-loaders) to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
     "\n",
     "Please see [this guide](../../integrations/providers/unstructured.mdx) for more instructions on setting up Unstructured locally, including setting up required system dependencies.\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredLoader](https://python.langchain.com/v0.2/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/unstructured/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [UnstructuredLoader](https://python.langchain.com/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html) | [langchain_community](https://python.langchain.com/api_reference/unstructured/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -526,7 +526,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `UnstructuredLoader` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html"
+    "For detailed documentation of all `UnstructuredLoader` features and configurations head to the API reference: https://python.langchain.com/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb b/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
index 9854268ec70..0645950990d 100644
--- a/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
+++ b/docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
@@ -6,15 +6,15 @@
    "source": [
     "# UnstructuredMarkdownLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
+    "This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredMarkdownLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ❌ | ❌ | ✅ | \n",
+    "| [UnstructuredMarkdownLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -241,7 +241,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
+    "For detailed documentation of all UnstructuredMarkdownLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb b/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb
index 25b162873b9..546e09674cf 100644
--- a/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb
+++ b/docs/docs/integrations/document_loaders/unstructured_pdfloader.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "## Overview\n",
     "\n",
-    "[Unstructured](https://unstructured-io.github.io/unstructured/) supports a common interface for working with unstructured or semi-structured file formats, such as Markdown or PDF. LangChain's [UnstructuredPDFLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html) integrates with Unstructured to parse PDF documents into LangChain [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) objects.\n",
+    "[Unstructured](https://unstructured-io.github.io/unstructured/) supports a common interface for working with unstructured or semi-structured file formats, such as Markdown or PDF. LangChain's [UnstructuredPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html) integrates with Unstructured to parse PDF documents into LangChain [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects.\n",
     "\n",
     "Please see [this page](/docs/integrations/providers/unstructured/) for more information on installing system requirements.\n",
     "\n",
@@ -16,9 +16,9 @@
     "### Integration details\n",
     "\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredPDFLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [UnstructuredPDFLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -256,7 +256,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all UnstructuredPDFLoader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html"
+    "For detailed documentation of all UnstructuredPDFLoader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/web_base.ipynb b/docs/docs/integrations/document_loaders/web_base.ipynb
index 4f0f21709ee..6eceb401ee5 100644
--- a/docs/docs/integrations/document_loaders/web_base.ipynb
+++ b/docs/docs/integrations/document_loaders/web_base.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "| Class | Package | Local | Serializable | JS support|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [WebBaseLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
+    "| [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ❌ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -370,7 +370,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `WebBaseLoader` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html"
+    "For detailed documentation of all `WebBaseLoader` features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_loaders/xml.ipynb b/docs/docs/integrations/document_loaders/xml.ipynb
index 19677816e79..0e936b07bae 100644
--- a/docs/docs/integrations/document_loaders/xml.ipynb
+++ b/docs/docs/integrations/document_loaders/xml.ipynb
@@ -7,16 +7,16 @@
    "source": [
     "# UnstructuredXMLLoader\n",
     "\n",
-    "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/v0.2/docs/concepts/#document-loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
+    "This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/#document-loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
     "\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/document_loaders/file_loaders/unstructured/)|\n",
     "| :--- | :--- | :---: | :---: |  :---: |\n",
-    "| [UnstructuredXMLLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
+    "| [UnstructuredXMLLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ❌ | ✅ | \n",
     "### Loader features\n",
     "| Source | Document Lazy Loading | Native Async Support\n",
     "| :---: | :---: | :---: | \n",
@@ -174,7 +174,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
+    "For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/document_transformers/google_cloud_vertexai_rerank.ipynb b/docs/docs/integrations/document_transformers/google_cloud_vertexai_rerank.ipynb
index 65b8f540e63..12ce774a1a8 100644
--- a/docs/docs/integrations/document_transformers/google_cloud_vertexai_rerank.ipynb
+++ b/docs/docs/integrations/document_transformers/google_cloud_vertexai_rerank.ipynb
@@ -546,7 +546,7 @@
     "id": "ud_cnGszb1i9"
    },
    "source": [
-    "Let's inspect a couple of reranked documents. We observe that the retriever still returns the relevant Langchain type [documents](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) but as part of the metadata field, we also recieve the `relevance_score` from the Ranking API."
+    "Let's inspect a couple of reranked documents. We observe that the retriever still returns the relevant Langchain type [documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) but as part of the metadata field, we also recieve the `relevance_score` from the Ranking API."
    ]
   },
   {
diff --git a/docs/docs/integrations/llm_caching.ipynb b/docs/docs/integrations/llm_caching.ipynb
index f0f4a7ce585..b64a10ca127 100644
--- a/docs/docs/integrations/llm_caching.ipynb
+++ b/docs/docs/integrations/llm_caching.ipynb
@@ -2754,35 +2754,35 @@
     }
    },
    "source": [
-    "**Cache** classes are implemented by inheriting the [BaseCache](https://python.langchain.com/v0.2/api_reference/core/caches/langchain_core.caches.BaseCache.html) class.\n",
+    "**Cache** classes are implemented by inheriting the [BaseCache](https://python.langchain.com/api_reference/core/caches/langchain_core.caches.BaseCache.html) class.\n",
     "\n",
     "This table lists all 21 derived classes with links to the API Reference.\n",
     "\n",
     "\n",
     "| Namespace 🔻 | Class |\n",
     "|------------|---------|\n",
-    "| langchain_astradb.cache | [AstraDBCache](https://python.langchain.com/v0.2/api_reference/astradb/cache/langchain_astradb.cache.AstraDBCache.html) |\n",
-    "| langchain_astradb.cache | [AstraDBSemanticCache](https://python.langchain.com/v0.2/api_reference/astradb/cache/langchain_astradb.cache.AstraDBSemanticCache.html) |\n",
-    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) |\n",
-    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) |\n",
-    "| langchain_community.cache | [AzureCosmosDBSemanticCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.AzureCosmosDBSemanticCache.html) |\n",
-    "| langchain_community.cache | [CassandraCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.CassandraCache.html) |\n",
-    "| langchain_community.cache | [CassandraSemanticCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.CassandraSemanticCache.html) |\n",
-    "| langchain_community.cache | [GPTCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.GPTCache.html) |\n",
-    "| langchain_community.cache | [InMemoryCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.InMemoryCache.html) |\n",
-    "| langchain_community.cache | [MomentoCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.MomentoCache.html) |\n",
-    "| langchain_community.cache | [OpenSearchSemanticCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.OpenSearchSemanticCache.html) |\n",
-    "| langchain_community.cache | [RedisSemanticCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.RedisSemanticCache.html) |\n",
-    "| langchain_community.cache | [SingleStoreDBSemanticCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.SingleStoreDBSemanticCache.html) |\n",
-    "| langchain_community.cache | [SQLAlchemyCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.SQLAlchemyCache.html) |\n",
-    "| langchain_community.cache | [SQLAlchemyMd5Cache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.SQLAlchemyMd5Cache.html) |\n",
-    "| langchain_community.cache | [UpstashRedisCache](https://python.langchain.com/v0.2/api_reference/community/cache/langchain_community.cache.UpstashRedisCache.html) |\n",
-    "| langchain_core.caches | [InMemoryCache](https://python.langchain.com/v0.2/api_reference/core/caches/langchain_core.caches.InMemoryCache.html) |\n",
-    "| langchain_elasticsearch.cache | [ElasticsearchCache](https://python.langchain.com/v0.2/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchCache.html) |\n",
-    "| langchain_mongodb.cache | [MongoDBAtlasSemanticCache](https://python.langchain.com/v0.2/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBAtlasSemanticCache.html) |\n",
-    "| langchain_mongodb.cache | [MongoDBCache](https://python.langchain.com/v0.2/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBCache.html) |\n",
-    "| langchain_couchbase.cache | [CouchbaseCache](https://python.langchain.com/v0.2/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseCache.html) |\n",
-    "| langchain_couchbase.cache | [CouchbaseSemanticCache](https://python.langchain.com/v0.2/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseSemanticCache.html) |\n"
+    "| langchain_astradb.cache | [AstraDBCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBCache.html) |\n",
+    "| langchain_astradb.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/astradb/cache/langchain_astradb.cache.AstraDBSemanticCache.html) |\n",
+    "| langchain_community.cache | [AstraDBCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBCache.html) |\n",
+    "| langchain_community.cache | [AstraDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AstraDBSemanticCache.html) |\n",
+    "| langchain_community.cache | [AzureCosmosDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.AzureCosmosDBSemanticCache.html) |\n",
+    "| langchain_community.cache | [CassandraCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraCache.html) |\n",
+    "| langchain_community.cache | [CassandraSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.CassandraSemanticCache.html) |\n",
+    "| langchain_community.cache | [GPTCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.GPTCache.html) |\n",
+    "| langchain_community.cache | [InMemoryCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.InMemoryCache.html) |\n",
+    "| langchain_community.cache | [MomentoCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.MomentoCache.html) |\n",
+    "| langchain_community.cache | [OpenSearchSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.OpenSearchSemanticCache.html) |\n",
+    "| langchain_community.cache | [RedisSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.RedisSemanticCache.html) |\n",
+    "| langchain_community.cache | [SingleStoreDBSemanticCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.SingleStoreDBSemanticCache.html) |\n",
+    "| langchain_community.cache | [SQLAlchemyCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.SQLAlchemyCache.html) |\n",
+    "| langchain_community.cache | [SQLAlchemyMd5Cache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.SQLAlchemyMd5Cache.html) |\n",
+    "| langchain_community.cache | [UpstashRedisCache](https://python.langchain.com/api_reference/community/cache/langchain_community.cache.UpstashRedisCache.html) |\n",
+    "| langchain_core.caches | [InMemoryCache](https://python.langchain.com/api_reference/core/caches/langchain_core.caches.InMemoryCache.html) |\n",
+    "| langchain_elasticsearch.cache | [ElasticsearchCache](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchCache.html) |\n",
+    "| langchain_mongodb.cache | [MongoDBAtlasSemanticCache](https://python.langchain.com/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBAtlasSemanticCache.html) |\n",
+    "| langchain_mongodb.cache | [MongoDBCache](https://python.langchain.com/api_reference/mongodb/cache/langchain_mongodb.cache.MongoDBCache.html) |\n",
+    "| langchain_couchbase.cache | [CouchbaseCache](https://python.langchain.com/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseCache.html) |\n",
+    "| langchain_couchbase.cache | [CouchbaseSemanticCache](https://python.langchain.com/api_reference/couchbase/cache/langchain_couchbase.cache.CouchbaseSemanticCache.html) |\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/llms/ai21.ipynb b/docs/docs/integrations/llms/ai21.ipynb
index f48e29c9c66..cf83e033e0d 100644
--- a/docs/docs/integrations/llms/ai21.ipynb
+++ b/docs/docs/integrations/llms/ai21.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# AI21LLM\n",
     "\n",
-    "This example goes over how to use LangChain to interact with `AI21` Jurassic models. To use the Jamba model, use the [ChatAI21 object](https://python.langchain.com/v0.2/docs/integrations/chat/ai21/) instead.\n",
+    "This example goes over how to use LangChain to interact with `AI21` Jurassic models. To use the Jamba model, use the [ChatAI21 object](https://python.langchain.com/docs/integrations/chat/ai21/) instead.\n",
     "\n",
     "[See a full list of AI21 models and tools on LangChain.](https://pypi.org/project/langchain-ai21/)\n",
     "\n",
diff --git a/docs/docs/integrations/llms/cohere.ipynb b/docs/docs/integrations/llms/cohere.ipynb
index 003e035e349..566667cca46 100644
--- a/docs/docs/integrations/llms/cohere.ipynb
+++ b/docs/docs/integrations/llms/cohere.ipynb
@@ -15,14 +15,14 @@
     "\n",
     ">[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.\n",
     "\n",
-    "Head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html) for detailed documentation of all attributes and methods.\n",
+    "Head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html) for detailed documentation of all attributes and methods.\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/llms/cohere/) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/llms/cohere/) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [Cohere](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n"
+    "| [Cohere](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n"
    ]
   },
   {
@@ -261,7 +261,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `Cohere` llm features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html"
+    "For detailed documentation of all `Cohere` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/community/llms/langchain_community.llms.cohere.Cohere.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/llms/databricks.ipynb b/docs/docs/integrations/llms/databricks.ipynb
index 9f13defa2a7..a08ae1516f0 100644
--- a/docs/docs/integrations/llms/databricks.ipynb
+++ b/docs/docs/integrations/llms/databricks.ipynb
@@ -10,7 +10,7 @@
     "> [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform.\n",
     "\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Databricks [LLM models](https://python.langchain.com/v0.2/docs/concepts/#llms). For detailed documentation of all features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/llms/langchain_community.llms.databricks.Databricks.html).\n",
+    "This notebook provides a quick overview for getting started with Databricks [LLM models](https://python.langchain.com/docs/concepts/#llms). For detailed documentation of all features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.databricks.Databricks.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -33,7 +33,7 @@
     "* Only supports synchronous invocation. Streaming or async APIs are not supported.\n",
     "* `batch` API is not supported.\n",
     "\n",
-    "To use those features, please use the new [ChatDatabricks](https://python.langchain.com/v0.2/docs/integrations/chat/databricks) class instead. `ChatDatabricks` supports all APIs of `ChatModel` including streaming, async, batch, etc.\n"
+    "To use those features, please use the new [ChatDatabricks](https://python.langchain.com/docs/integrations/chat/databricks) class instead. `ChatDatabricks` supports all APIs of `ChatModel` including streaming, async, batch, etc.\n"
    ]
   },
   {
diff --git a/docs/docs/integrations/llms/fireworks.ipynb b/docs/docs/integrations/llms/fireworks.ipynb
index cdc8308e74d..16e4ca4b37b 100644
--- a/docs/docs/integrations/llms/fireworks.ipynb
+++ b/docs/docs/integrations/llms/fireworks.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.1/docs/integrations/llms/fireworks/) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [Fireworks](https://python.langchain.com/v0.2/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks) | [langchain_fireworks](https://python.langchain.com/v0.2/api_reference/fireworks/index.html) | ❌ | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_fireworks?style=flat-square&label=%20) |"
+    "| [Fireworks](https://python.langchain.com/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks) | [langchain_fireworks](https://python.langchain.com/api_reference/fireworks/index.html) | ❌ | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_fireworks?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_fireworks?style=flat-square&label=%20) |"
    ]
   },
   {
@@ -282,7 +282,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `Fireworks` LLM features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks"
+    "For detailed documentation of all `Fireworks` LLM features and configurations head to the API reference: https://python.langchain.com/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html#langchain_fireworks.llms.Fireworks"
    ]
   }
  ],
diff --git a/docs/docs/integrations/llms/gpt4all.ipynb b/docs/docs/integrations/llms/gpt4all.ipynb
index 6c09903f291..d612f5b8252 100644
--- a/docs/docs/integrations/llms/gpt4all.ipynb
+++ b/docs/docs/integrations/llms/gpt4all.ipynb
@@ -84,7 +84,7 @@
     "\n",
     "---\n",
     "\n",
-    "This integration does not yet support streaming in chunks via the [`.stream()`](https://python.langchain.com/v0.2/docs/how_to/streaming/) method. The below example uses a callback handler with `streaming=True`:"
+    "This integration does not yet support streaming in chunks via the [`.stream()`](https://python.langchain.com/docs/how_to/streaming/) method. The below example uses a callback handler with `streaming=True`:"
    ]
   },
   {
diff --git a/docs/docs/integrations/llms/openai.ipynb b/docs/docs/integrations/llms/openai.ipynb
index 2bd4f6a67d6..58de2dec493 100644
--- a/docs/docs/integrations/llms/openai.ipynb
+++ b/docs/docs/integrations/llms/openai.ipynb
@@ -26,9 +26,9 @@
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [ChatOpenAI](https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/v0.2/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
+    "| [ChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html) | [langchain-openai](https://python.langchain.com/api_reference/openai/index.html) | ❌ | beta | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-openai?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-openai?style=flat-square&label=%20) |\n",
     "\n",
     "\n",
     "## Setup\n",
@@ -230,7 +230,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `OpenAI` llm features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
+    "For detailed documentation of all `OpenAI` llm features and configurations head to the API reference: https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/memory/couchbase_chat_message_history.ipynb b/docs/docs/integrations/memory/couchbase_chat_message_history.ipynb
index 4f615e827b1..1ef4fc59523 100644
--- a/docs/docs/integrations/memory/couchbase_chat_message_history.ipynb
+++ b/docs/docs/integrations/memory/couchbase_chat_message_history.ipynb
@@ -187,7 +187,7 @@
    "metadata": {},
    "source": [
     "## Chaining\n",
-    "The chat message history class can be used with [LCEL Runnables](https://python.langchain.com/v0.2/docs/how_to/message_history/)"
+    "The chat message history class can be used with [LCEL Runnables](https://python.langchain.com/docs/how_to/message_history/)"
    ]
   },
   {
diff --git a/docs/docs/integrations/platforms/aws.mdx b/docs/docs/integrations/platforms/aws.mdx
index c13c6e9aac8..5b46b2e6b58 100755
--- a/docs/docs/integrations/platforms/aws.mdx
+++ b/docs/docs/integrations/platforms/aws.mdx
@@ -35,9 +35,9 @@ from langchain_aws import ChatBedrock
 ```
 
 ### Bedrock Converse
-AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.
+AWS has recently released the Bedrock Converse API which provides a unified conversational interface for Bedrock models. This API does not yet support custom models. You can see a list of all [models that are supported here](https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html). To improve reliability the ChatBedrock integration will switch to using the Bedrock Converse API as soon as it has feature parity with the existing Bedrock API. Until then a separate [ChatBedrockConverse](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) integration has been released.
 
-We recommend using `ChatBedrockConverse` for users who do not need to use custom models. See the [docs](/docs/integrations/chat/bedrock/#bedrock-converse-api) and [API reference](https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) for more detail.
+We recommend using `ChatBedrockConverse` for users who do not need to use custom models. See the [docs](/docs/integrations/chat/bedrock/#bedrock-converse-api) and [API reference](https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock_converse.ChatBedrockConverse.html) for more detail.
 
 ```python
 from langchain_aws import ChatBedrockConverse
diff --git a/docs/docs/integrations/platforms/microsoft.mdx b/docs/docs/integrations/platforms/microsoft.mdx
index f6adefb1e49..e54c8cb24c7 100644
--- a/docs/docs/integrations/platforms/microsoft.mdx
+++ b/docs/docs/integrations/platforms/microsoft.mdx
@@ -440,11 +440,11 @@ from langchain_community.agent_toolkits import azure_ai_services
 
 The `azure_ai_services` toolkit includes the following tools:
 
-- Image Analysis: [AzureAiServicesImageAnalysisTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.image_analysis.AzureAiServicesImageAnalysisTool.html)
-- Document Intelligence: [AzureAiServicesDocumentIntelligenceTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.document_intelligence.AzureAiServicesDocumentIntelligenceTool.html)
-- Speech to Text: [AzureAiServicesSpeechToTextTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.speech_to_text.AzureAiServicesSpeechToTextTool.html)
-- Text to Speech: [AzureAiServicesTextToSpeechTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.text_to_speech.AzureAiServicesTextToSpeechTool.html)
-- Text Analytics for Health: [AzureAiServicesTextAnalyticsForHealthTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.azure_ai_services.text_analytics_for_health.AzureAiServicesTextAnalyticsForHealthTool.html)
+- Image Analysis: [AzureAiServicesImageAnalysisTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.azure_ai_services.image_analysis.AzureAiServicesImageAnalysisTool.html)
+- Document Intelligence: [AzureAiServicesDocumentIntelligenceTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.azure_ai_services.document_intelligence.AzureAiServicesDocumentIntelligenceTool.html)
+- Speech to Text: [AzureAiServicesSpeechToTextTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.azure_ai_services.speech_to_text.AzureAiServicesSpeechToTextTool.html)
+- Text to Speech: [AzureAiServicesTextToSpeechTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.azure_ai_services.text_to_speech.AzureAiServicesTextToSpeechTool.html)
+- Text Analytics for Health: [AzureAiServicesTextAnalyticsForHealthTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.azure_ai_services.text_analytics_for_health.AzureAiServicesTextAnalyticsForHealthTool.html)
 
 
 ### Microsoft Office 365 email and calendar
diff --git a/docs/docs/integrations/providers/apify.mdx b/docs/docs/integrations/providers/apify.mdx
index f3ee4fd6230..172d0f12c31 100644
--- a/docs/docs/integrations/providers/apify.mdx
+++ b/docs/docs/integrations/providers/apify.mdx
@@ -27,7 +27,7 @@ You can use the `ApifyWrapper` to run Actors on the Apify platform.
 from langchain_community.utilities import ApifyWrapper
 ```
 
-For more information on this wrapper, see [the API reference](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.apify.ApifyWrapper.html).
+For more information on this wrapper, see [the API reference](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.apify.ApifyWrapper.html).
 
 
 ## Document loader
diff --git a/docs/docs/integrations/providers/mlflow_tracking.ipynb b/docs/docs/integrations/providers/mlflow_tracking.ipynb
index aadc3f9611c..b30f2850649 100644
--- a/docs/docs/integrations/providers/mlflow_tracking.ipynb
+++ b/docs/docs/integrations/providers/mlflow_tracking.ipynb
@@ -523,7 +523,7 @@
    "metadata": {},
    "source": [
     "#### Where to Pass the Callback\n",
-    " LangChain supports two ways of passing callback instances: (1) Request time callbacks - pass them to the `invoke` method or bind with `with_config()` (2) Constructor callbacks - set them in the chain constructor. When using the `MlflowLangchainTracer` as a callback, you **must use request time callbacks**. Setting it in the constructor instead will only apply the callback to the top-level object, preventing it from being propagated to child components, resulting in incomplete traces. For more information on this behavior, please refer to [Callbacks Documentation](https://python.langchain.com/v0.2/docs/concepts/#callbacks) for more details.\n",
+    " LangChain supports two ways of passing callback instances: (1) Request time callbacks - pass them to the `invoke` method or bind with `with_config()` (2) Constructor callbacks - set them in the chain constructor. When using the `MlflowLangchainTracer` as a callback, you **must use request time callbacks**. Setting it in the constructor instead will only apply the callback to the top-level object, preventing it from being propagated to child components, resulting in incomplete traces. For more information on this behavior, please refer to [Callbacks Documentation](https://python.langchain.com/docs/concepts/#callbacks) for more details.\n",
     "\n",
     "```python\n",
     "# OK\n",
diff --git a/docs/docs/integrations/providers/redis.mdx b/docs/docs/integrations/providers/redis.mdx
index a6682ef25c9..1850ca312e8 100644
--- a/docs/docs/integrations/providers/redis.mdx
+++ b/docs/docs/integrations/providers/redis.mdx
@@ -132,7 +132,7 @@ Redis can be used to persist LLM conversations.
 
 ### Vector Store Retriever Memory
 
-For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](https://python.langchain.com/v0.2/api_reference/langchain/memory/langchain.memory.vectorstore.VectorStoreRetrieverMemory.html).
+For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](https://python.langchain.com/api_reference/langchain/memory/langchain.memory.vectorstore.VectorStoreRetrieverMemory.html).
 
 ### Chat Message History Memory
 For a detailed example of Redis to cache conversation message history, see [this notebook](/docs/integrations/memory/redis_chat_message_history).
diff --git a/docs/docs/integrations/retrievers/arxiv.ipynb b/docs/docs/integrations/retrievers/arxiv.ipynb
index d8458f4bd61..a334a2bc1eb 100644
--- a/docs/docs/integrations/retrievers/arxiv.ipynb
+++ b/docs/docs/integrations/retrievers/arxiv.ipynb
@@ -19,9 +19,9 @@
     "\n",
     ">[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.\n",
     "\n",
-    "This notebook shows how to retrieve scientific articles from Arxiv.org into the [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
+    "This notebook shows how to retrieve scientific articles from Arxiv.org into the [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
     "\n",
-    "For detailed documentation of all `ArxivRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html).\n",
+    "For detailed documentation of all `ArxivRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html).\n",
     "\n",
     "### Integration details\n",
     "\n",
@@ -79,7 +79,7 @@
     "- optional `load_all_available_meta`: default=False. By default only the most important fields downloaded: `Published` (date when document was published/last updated), `Title`, `Authors`, `Summary`. If True, other fields also downloaded.\n",
     "- `get_full_documents`: boolean, default False. Determines whether to fetch full text of documents.\n",
     "\n",
-    "See [API reference](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html) for more detail."
+    "See [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html) for more detail."
    ]
   },
   {
@@ -299,7 +299,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `ArxivRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html)."
+    "For detailed documentation of all `ArxivRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/retrievers/azure_ai_search.ipynb b/docs/docs/integrations/retrievers/azure_ai_search.ipynb
index c308d46319d..6b5ce1f3600 100644
--- a/docs/docs/integrations/retrievers/azure_ai_search.ipynb
+++ b/docs/docs/integrations/retrievers/azure_ai_search.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "`AzureAISearchRetriever` is an integration module that returns documents from an unstructured query. It's based on the BaseRetriever class and it targets the 2023-11-01 stable REST API version of Azure AI Search, which means it supports vector indexing and queries.\n",
     "\n",
-    "This guide will help you getting started with the Azure AI Search [retriever](/docs/concepts/#retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
+    "This guide will help you getting started with the Azure AI Search [retriever](/docs/concepts/#retrievers). For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html).\n",
     "\n",
     "`AzureAISearchRetriever` replaces `AzureCognitiveSearchRetriever`, which will soon be deprecated. We recommend switching to the newer version that's based on the most recent stable version of the search APIs.\n",
     "\n",
@@ -336,7 +336,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html)."
+    "For detailed documentation of all `AzureAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/retrievers/bedrock.ipynb b/docs/docs/integrations/retrievers/bedrock.ipynb
index c5dbf925db7..b674b1175b7 100644
--- a/docs/docs/integrations/retrievers/bedrock.ipynb
+++ b/docs/docs/integrations/retrievers/bedrock.ipynb
@@ -163,7 +163,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `AmazonKnowledgeBasesRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/aws/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html)."
+    "For detailed documentation of all `AmazonKnowledgeBasesRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/aws/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/retrievers/box.ipynb b/docs/docs/integrations/retrievers/box.ipynb
index 8860c479e75..311e3235d1a 100644
--- a/docs/docs/integrations/retrievers/box.ipynb
+++ b/docs/docs/integrations/retrievers/box.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# BoxRetriever\n",
     "\n",
-    "This will help you getting started with the Box [retriever](/docs/concepts/#retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
+    "This will help you getting started with the Box [retriever](/docs/concepts/#retrievers). For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
     "\n",
     "# Overview\n",
     "\n",
@@ -35,7 +35,7 @@
     "\n",
     "| Retriever | Self-host | Cloud offering | Package |\n",
     "| :--- | :--- | :---: | :---: |\n",
-    "[BoxRetriever](https://python.langchain.com/v0.2/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html) | ❌ | ✅ | langchain-box |\n",
+    "[BoxRetriever](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html) | ❌ | ✅ | langchain-box |\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -290,7 +290,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
+    "For detailed documentation of all BoxRetriever features and configurations head to the [API reference](https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html).\n",
     "\n",
     "\n",
     "## Help\n",
diff --git a/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb b/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb
index 774c20165bd..07181b82227 100644
--- a/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb
+++ b/docs/docs/integrations/retrievers/elasticsearch_retriever.ipynb
@@ -21,7 +21,7 @@
     "\n",
     "The `ElasticsearchRetriever` is a generic wrapper to enable flexible access to all `Elasticsearch` features through the [Query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html).  For most use cases the other classes (`ElasticsearchStore`, `ElasticsearchEmbeddings`, etc.) should suffice, but if they don't you can use `ElasticsearchRetriever`.\n",
     "\n",
-    "This guide will help you getting started with the Elasticsearch [retriever](/docs/concepts/#retrievers). For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html).\n",
+    "This guide will help you getting started with the Elasticsearch [retriever](/docs/concepts/#retrievers). For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html).\n",
     "\n",
     "### Integration details\n",
     "\n",
@@ -677,7 +677,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html)."
+    "For detailed documentation of all `ElasticsearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb b/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb
index 6310e34e1c3..60352ca64d8 100644
--- a/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb
+++ b/docs/docs/integrations/retrievers/google_vertex_ai_search.ipynb
@@ -23,7 +23,7 @@
     "\n",
     "This notebook demonstrates how to configure `Vertex AI Search` and use the Vertex AI Search [retriever](/docs/concepts/#retrievers). The Vertex AI Search retriever encapsulates the [Python client library](https://cloud.google.com/generative-ai-app-builder/docs/libraries#client-libraries-install-python) and uses it to access the [Search Service API](https://cloud.google.com/python/docs/reference/discoveryengine/latest/google.cloud.discoveryengine_v1beta.services.search_service).\n",
     "\n",
-    "For detailed documentation of all `VertexAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/google_community/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html).\n",
+    "For detailed documentation of all `VertexAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html).\n",
     "\n",
     "### Integration details\n",
     "\n",
@@ -444,7 +444,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `VertexAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/google_community/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html)."
+    "For detailed documentation of all `VertexAISearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/retrievers/index.mdx b/docs/docs/integrations/retrievers/index.mdx
index 451a2c5c646..6d749441e6f 100644
--- a/docs/docs/integrations/retrievers/index.mdx
+++ b/docs/docs/integrations/retrievers/index.mdx
@@ -12,7 +12,7 @@ It is more general than a vector store.
 A retriever does not need to be able to store documents, only to return (or retrieve) them.
 Retrievers can be created from vector stores, but are also broad enough to include [Wikipedia search](/docs/integrations/retrievers/wikipedia/) and [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/).
 
-Retrievers accept a string query as input and return a list of [Documents](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) as output.
+Retrievers accept a string query as input and return a list of [Documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) as output.
 
 For specifics on how to use retrievers, see the [relevant how-to guides here](/docs/how_to/#retrievers).
 
diff --git a/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb b/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb
index f77f589271c..fa498e2abe0 100644
--- a/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb
+++ b/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "> [Milvus](https://milvus.io/docs) is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.\n",
     "\n",
-    "This will help you getting started with the Milvus Hybrid Search [retriever](/docs/concepts/#retrievers), which combines the strengths of both dense and sparse vector search. For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html).\n",
+    "This will help you getting started with the Milvus Hybrid Search [retriever](/docs/concepts/#retrievers), which combines the strengths of both dense and sparse vector search. For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html).\n",
     "\n",
     "See also the Milvus Multi-Vector Search [docs](https://milvus.io/docs/multi-vector-search.md).\n",
     "\n",
@@ -611,7 +611,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html)."
+    "For detailed documentation of all `MilvusCollectionHybridSearchRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/retrievers/tavily.ipynb b/docs/docs/integrations/retrievers/tavily.ipynb
index 739eb1d274f..53467d4e863 100644
--- a/docs/docs/integrations/retrievers/tavily.ipynb
+++ b/docs/docs/integrations/retrievers/tavily.ipynb
@@ -202,7 +202,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `TavilySearchAPIRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html)."
+    "For detailed documentation of all `TavilySearchAPIRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/retrievers/wikipedia.ipynb b/docs/docs/integrations/retrievers/wikipedia.ipynb
index 97c3b808830..239bf0d67a9 100644
--- a/docs/docs/integrations/retrievers/wikipedia.ipynb
+++ b/docs/docs/integrations/retrievers/wikipedia.ipynb
@@ -20,7 +20,7 @@
     "## Overview\n",
     ">[Wikipedia](https://wikipedia.org/) is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. `Wikipedia` is the largest and most-read reference work in history.\n",
     "\n",
-    "This notebook shows how to retrieve wiki pages from `wikipedia.org` into the [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
+    "This notebook shows how to retrieve wiki pages from `wikipedia.org` into the [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) format that is used downstream.\n",
     "\n",
     "### Integration details\n",
     "\n",
@@ -236,7 +236,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `WikipediaRetriever` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html#langchain-community-retrievers-wikipedia-wikipediaretriever)."
+    "For detailed documentation of all `WikipediaRetriever` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html#langchain-community-retrievers-wikipedia-wikipediaretriever)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/stores/astradb.ipynb b/docs/docs/integrations/stores/astradb.ipynb
index 1d16d83477b..c38cf5b7a0d 100644
--- a/docs/docs/integrations/stores/astradb.ipynb
+++ b/docs/docs/integrations/stores/astradb.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# AstraDBByteStore\n",
     "\n",
-    "This will help you get started with Astra DB [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `AstraDBByteStore` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html).\n",
+    "This will help you get started with Astra DB [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `AstraDBByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -29,7 +29,7 @@
     "\n",
     "| Class | Package | Local | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [AstraDBByteStore](https://python.langchain.com/v0.2/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html) | [langchain_astradb](https://python.langchain.com/v0.2/api_reference/astradb/index.html) | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_astradb?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_astradb?style=flat-square&label=%20) |\n",
+    "| [AstraDBByteStore](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html) | [langchain_astradb](https://python.langchain.com/api_reference/astradb/index.html) | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_astradb?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_astradb?style=flat-square&label=%20) |\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -187,7 +187,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `AstraDBByteStore` features and configurations, head to the API reference: https://python.langchain.com/v0.2/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html"
+    "For detailed documentation of all `AstraDBByteStore` features and configurations, head to the API reference: https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/stores/cassandra.ipynb b/docs/docs/integrations/stores/cassandra.ipynb
index 20e77cb8d5b..9c31d12864f 100644
--- a/docs/docs/integrations/stores/cassandra.ipynb
+++ b/docs/docs/integrations/stores/cassandra.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# CassandraByteStore\n",
     "\n",
-    "This will help you get started with Cassandra [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `CassandraByteStore` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html).\n",
+    "This will help you get started with Cassandra [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `CassandraByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -27,9 +27,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/v0.2/docs/integrations/stores/cassandra_storage) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/cassandra_storage) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [CassandraByteStore](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "| [CassandraByteStore](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -199,7 +199,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `CassandraByteStore` features and configurations, head to the API reference: https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html"
+    "For detailed documentation of all `CassandraByteStore` features and configurations, head to the API reference: https://python.langchain.com/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/stores/elasticsearch.ipynb b/docs/docs/integrations/stores/elasticsearch.ipynb
index 99ffa7510ba..370886867ed 100644
--- a/docs/docs/integrations/stores/elasticsearch.ipynb
+++ b/docs/docs/integrations/stores/elasticsearch.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# ElasticsearchEmbeddingsCache\n",
     "\n",
-    "This will help you get started with Elasticsearch [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `ElasticsearchEmbeddingsCache` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html).\n",
+    "This will help you get started with Elasticsearch [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `ElasticsearchEmbeddingsCache` features and configurations head to the [API reference](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -29,7 +29,7 @@
     "\n",
     "| Class | Package | Local | JS support | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [ElasticsearchEmbeddingsCache](https://python.langchain.com/v0.2/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html) | [langchain_elasticsearch](https://python.langchain.com/v0.2/api_reference/elasticsearch/index.html) | ✅ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_elasticsearch?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_elasticsearch?style=flat-square&label=%20) |\n",
+    "| [ElasticsearchEmbeddingsCache](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html) | [langchain_elasticsearch](https://python.langchain.com/api_reference/elasticsearch/index.html) | ✅ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_elasticsearch?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_elasticsearch?style=flat-square&label=%20) |\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -218,7 +218,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `ElasticsearchEmbeddingsCache` features and configurations, head to the API reference: https://python.langchain.com/v0.2/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html"
+    "For detailed documentation of all `ElasticsearchEmbeddingsCache` features and configurations, head to the API reference: https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/stores/file_system.ipynb b/docs/docs/integrations/stores/file_system.ipynb
index b6af812ae97..1a0baa82dd4 100644
--- a/docs/docs/integrations/stores/file_system.ipynb
+++ b/docs/docs/integrations/stores/file_system.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# LocalFileStore\n",
     "\n",
-    "This will help you get started with local filesystem [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all LocalFileStore features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html).\n",
+    "This will help you get started with local filesystem [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all LocalFileStore features and configurations head to the [API reference](https://python.langchain.com/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -27,9 +27,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/v0.2/docs/integrations/stores/file_system) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/file_system) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [LocalFileStore](https://python.langchain.com/v0.2/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html) | [langchain](https://python.langchain.com/v0.2/api_reference/langchain/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain?style=flat-square&label=%20) |"
+    "| [LocalFileStore](https://python.langchain.com/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html) | [langchain](https://python.langchain.com/api_reference/langchain/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain?style=flat-square&label=%20) |"
    ]
   },
   {
@@ -184,7 +184,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `LocalFileStore` features and configurations, head to the API reference: https://python.langchain.com/v0.2/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html"
+    "For detailed documentation of all `LocalFileStore` features and configurations, head to the API reference: https://python.langchain.com/api_reference/langchain/storage/langchain.storage.file_system.LocalFileStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/stores/in_memory.ipynb b/docs/docs/integrations/stores/in_memory.ipynb
index 1b5927a6a94..7f81222dab6 100644
--- a/docs/docs/integrations/stores/in_memory.ipynb
+++ b/docs/docs/integrations/stores/in_memory.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# InMemoryByteStore\n",
     "\n",
-    "This guide will help you get started with in-memory [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `InMemoryByteStore` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html).\n",
+    "This guide will help you get started with in-memory [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `InMemoryByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -27,9 +27,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/v0.2/docs/integrations/stores/in_memory/) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/in_memory/) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [InMemoryByteStore](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html) | [langchain_core](https://python.langchain.com/v0.2/api_reference/core/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_core?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_core?style=flat-square&label=%20) |"
+    "| [InMemoryByteStore](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html) | [langchain_core](https://python.langchain.com/api_reference/core/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_core?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_core?style=flat-square&label=%20) |"
    ]
   },
   {
@@ -156,7 +156,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `InMemoryByteStore` features and configurations, head to the API reference: https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html"
+    "For detailed documentation of all `InMemoryByteStore` features and configurations, head to the API reference: https://python.langchain.com/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/stores/redis.ipynb b/docs/docs/integrations/stores/redis.ipynb
index 7e59142b00a..1a90576acf9 100644
--- a/docs/docs/integrations/stores/redis.ipynb
+++ b/docs/docs/integrations/stores/redis.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# RedisStore\n",
     "\n",
-    "This will help you get started with Redis [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `RedisStore` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html).\n",
+    "This will help you get started with Redis [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `RedisStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -27,9 +27,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/v0.2/docs/integrations/stores/ioredis_storage) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/ioredis_storage) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [RedisStore](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "| [RedisStore](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ✅ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -160,7 +160,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `RedisStore` features and configurations, head to the API reference: https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html"
+    "For detailed documentation of all `RedisStore` features and configurations, head to the API reference: https://python.langchain.com/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/stores/upstash_redis.ipynb b/docs/docs/integrations/stores/upstash_redis.ipynb
index 2811536f8f4..c512e9b376c 100644
--- a/docs/docs/integrations/stores/upstash_redis.ipynb
+++ b/docs/docs/integrations/stores/upstash_redis.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# UpstashRedisByteStore\n",
     "\n",
-    "This will help you get started with Upstash redis [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `UpstashRedisByteStore` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html).\n",
+    "This will help you get started with Upstash redis [key-value stores](/docs/concepts/#key-value-stores). For detailed documentation of all `UpstashRedisByteStore` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -29,9 +29,9 @@
     "\n",
     "### Integration details\n",
     "\n",
-    "| Class | Package | Local | [JS support](https://js.langchain.com/v0.2/docs/integrations/stores/upstash_redis_storage) | Package downloads | Package latest |\n",
+    "| Class | Package | Local | [JS support](https://js.langchain.com/docs/integrations/stores/upstash_redis_storage) | Package downloads | Package latest |\n",
     "| :--- | :--- | :---: | :---: |  :---: | :---: |\n",
-    "| [UpstashRedisByteStore](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html) | [langchain_community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
+    "| [UpstashRedisByteStore](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html) | [langchain_community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ✅ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_community?style=flat-square&label=%20) |\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -180,7 +180,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `UpstashRedisByteStore` features and configurations, head to the API reference: https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html"
+    "For detailed documentation of all `UpstashRedisByteStore` features and configurations, head to the API reference: https://python.langchain.com/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/ai21.ipynb b/docs/docs/integrations/text_embedding/ai21.ipynb
index d773d289d0e..c48d3e24a62 100644
--- a/docs/docs/integrations/text_embedding/ai21.ipynb
+++ b/docs/docs/integrations/text_embedding/ai21.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# AI21Embeddings\n",
     "\n",
-    "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n",
+    "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -242,7 +242,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n"
+    "For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/azureopenai.ipynb b/docs/docs/integrations/text_embedding/azureopenai.ipynb
index d12013b272f..7f5281e21e9 100644
--- a/docs/docs/integrations/text_embedding/azureopenai.ipynb
+++ b/docs/docs/integrations/text_embedding/azureopenai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# AzureOpenAIEmbeddings\n",
     "\n",
-    "This will help you get started with AzureOpenAI embedding models using LangChain. For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n",
+    "This will help you get started with AzureOpenAI embedding models using LangChain. For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -250,7 +250,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n"
+    "For detailed documentation on `AzureOpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/cohere.ipynb b/docs/docs/integrations/text_embedding/cohere.ipynb
index f0114e81166..073aa6183b0 100644
--- a/docs/docs/integrations/text_embedding/cohere.ipynb
+++ b/docs/docs/integrations/text_embedding/cohere.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# CohereEmbeddings\n",
     "\n",
-    "This will help you get started with Cohere embedding models using LangChain. For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n",
+    "This will help you get started with Cohere embedding models using LangChain. For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -239,7 +239,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n"
+    "For detailed documentation on `CohereEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/databricks.ipynb b/docs/docs/integrations/text_embedding/databricks.ipynb
index 68c39f75a5a..ae6b6ab8c03 100644
--- a/docs/docs/integrations/text_embedding/databricks.ipynb
+++ b/docs/docs/integrations/text_embedding/databricks.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "> [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform.\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Databricks [embedding models](/docs/concepts/#embedding-models). For detailed documentation of all `DatabricksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html).\n",
+    "This notebook provides a quick overview for getting started with Databricks [embedding models](/docs/concepts/#embedding-models). For detailed documentation of all `DatabricksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html).\n",
     "\n",
     "\n",
     "\n",
@@ -244,7 +244,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `DatabricksEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html).\n"
+    "For detailed documentation on `DatabricksEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.databricks.DatabricksEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/fireworks.ipynb b/docs/docs/integrations/text_embedding/fireworks.ipynb
index 1370d1e13da..32fe0134b88 100644
--- a/docs/docs/integrations/text_embedding/fireworks.ipynb
+++ b/docs/docs/integrations/text_embedding/fireworks.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# FireworksEmbeddings\n",
     "\n",
-    "This will help you get started with Fireworks embedding models using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html).\n",
+    "This will help you get started with Fireworks embedding models using LangChain. For detailed documentation on `FireworksEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -239,7 +239,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation of all `FireworksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html)."
+    "For detailed documentation of all `FireworksEmbeddings` features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb b/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
index 42a40cf6fe9..4e08f468ecb 100644
--- a/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
+++ b/docs/docs/integrations/text_embedding/google_vertex_ai_palm.ipynb
@@ -18,14 +18,14 @@
    "source": [
     "# Google Vertex AI Embeddings \n",
     "\n",
-    "This will help you get started with Google Vertex AI Embeddings models using LangChain. For detailed documentation on `Google Vertex AI Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n",
+    "This will help you get started with Google Vertex AI Embeddings models using LangChain. For detailed documentation on `Google Vertex AI Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
     "\n",
     "| Provider | Package |\n",
     "|:--------:|:-------:|\n",
-    "| [Google](https://python.langchain.com/v0.2/docs/integrations/platforms/google/) | [langchain-google-vertexai](https://python.langchain.com/v0.2/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
+    "| [Google](https://python.langchain.com/docs/integrations/platforms/google/) | [langchain-google-vertexai](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -289,7 +289,7 @@
     "## API Reference\n",
     "\n",
     "For detailed documentation on `Google Vertex AI Embeddings\n",
-    "` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n"
+    "` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/ipex_llm.ipynb b/docs/docs/integrations/text_embedding/ipex_llm.ipynb
index 2a44ff40448..e8d7c2dd0e8 100644
--- a/docs/docs/integrations/text_embedding/ipex_llm.ipynb
+++ b/docs/docs/integrations/text_embedding/ipex_llm.ipynb
@@ -70,7 +70,7 @@
    "metadata": {},
    "source": [
     "API Reference\n",
-    "- [IpexLLMBgeEmbeddings](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html)"
+    "- [IpexLLMBgeEmbeddings](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html)"
    ]
   },
   {
diff --git a/docs/docs/integrations/text_embedding/ipex_llm_gpu.ipynb b/docs/docs/integrations/text_embedding/ipex_llm_gpu.ipynb
index cca30c6745e..ca3ad124e6f 100644
--- a/docs/docs/integrations/text_embedding/ipex_llm_gpu.ipynb
+++ b/docs/docs/integrations/text_embedding/ipex_llm_gpu.ipynb
@@ -133,7 +133,7 @@
    "metadata": {},
    "source": [
     "API Reference\n",
-    "- [IpexLLMBgeEmbeddings](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html)"
+    "- [IpexLLMBgeEmbeddings](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings.html)"
    ]
   },
   {
diff --git a/docs/docs/integrations/text_embedding/mistralai.ipynb b/docs/docs/integrations/text_embedding/mistralai.ipynb
index 8efe1e0f2bb..70ce6f10496 100644
--- a/docs/docs/integrations/text_embedding/mistralai.ipynb
+++ b/docs/docs/integrations/text_embedding/mistralai.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# MistralAIEmbeddings\n",
     "\n",
-    "This will help you get started with MistralAI embedding models using LangChain. For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n",
+    "This will help you get started with MistralAI embedding models using LangChain. For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -238,7 +238,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n"
+    "For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/nomic.ipynb b/docs/docs/integrations/text_embedding/nomic.ipynb
index 52dbfca37e1..51c8773b2db 100644
--- a/docs/docs/integrations/text_embedding/nomic.ipynb
+++ b/docs/docs/integrations/text_embedding/nomic.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# NomicEmbeddings\n",
     "\n",
-    "This will help you get started with Nomic embedding models using LangChain. For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n",
+    "This will help you get started with Nomic embedding models using LangChain. For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -259,7 +259,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n"
+    "For detailed documentation on `NomicEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/ollama.ipynb b/docs/docs/integrations/text_embedding/ollama.ipynb
index 8464b9ea5a0..0cc76641485 100644
--- a/docs/docs/integrations/text_embedding/ollama.ipynb
+++ b/docs/docs/integrations/text_embedding/ollama.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# OllamaEmbeddings\n",
     "\n",
-    "This will help you get started with Ollama embedding models using LangChain. For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n",
+    "This will help you get started with Ollama embedding models using LangChain. For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -248,7 +248,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n"
+    "For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/openai.ipynb b/docs/docs/integrations/text_embedding/openai.ipynb
index 0696a27f268..cea8618fea3 100644
--- a/docs/docs/integrations/text_embedding/openai.ipynb
+++ b/docs/docs/integrations/text_embedding/openai.ipynb
@@ -18,7 +18,7 @@
    "source": [
     "# OpenAIEmbeddings\n",
     "\n",
-    "This will help you get started with OpenAI embedding models using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n",
+    "This will help you get started with OpenAI embedding models using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n",
     "\n",
     "\n",
     "## Overview\n",
@@ -244,7 +244,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n"
+    "For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/together.ipynb b/docs/docs/integrations/text_embedding/together.ipynb
index dcaeaaf4e44..b64159879f9 100644
--- a/docs/docs/integrations/text_embedding/together.ipynb
+++ b/docs/docs/integrations/text_embedding/together.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# TogetherEmbeddings\n",
     "\n",
-    "This will help you get started with Together embedding models using LangChain. For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n",
+    "This will help you get started with Together embedding models using LangChain. For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n",
     "\n",
     "## Overview\n",
     "### Integration details\n",
@@ -249,7 +249,7 @@
    "source": [
     "## API Reference\n",
     "\n",
-    "For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n"
+    "For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n"
    ]
   }
  ],
diff --git a/docs/docs/integrations/text_embedding/zhipuai.ipynb b/docs/docs/integrations/text_embedding/zhipuai.ipynb
index 78d531b35bd..de33980c564 100644
--- a/docs/docs/integrations/text_embedding/zhipuai.ipynb
+++ b/docs/docs/integrations/text_embedding/zhipuai.ipynb
@@ -25,7 +25,7 @@
     "\n",
     "| Provider | Package |\n",
     "|:--------:|:-------:|\n",
-    "| [ZhipuAI](/docs/integrations/providers/zhipuai/) | [langchain-community](https://python.langchain.com/v0.2/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html) |\n",
+    "| [ZhipuAI](/docs/integrations/providers/zhipuai/) | [langchain-community](https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.zhipuai.ZhipuAIEmbeddings.html) |\n",
     "\n",
     "## Setup\n",
     "\n",
diff --git a/docs/docs/integrations/tools/connery.ipynb b/docs/docs/integrations/tools/connery.ipynb
index 0162f4592ad..5070996bba9 100644
--- a/docs/docs/integrations/tools/connery.ipynb
+++ b/docs/docs/integrations/tools/connery.ipynb
@@ -257,8 +257,8 @@
     "\n",
     "For detailed documentation of all Connery features and configurations head to the API reference:\n",
     "\n",
-    "- Toolkit: https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.connery.toolkit.ConneryToolkit.html\n",
-    "- Tool: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.connery.service.ConneryService.html"
+    "- Toolkit: https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.connery.toolkit.ConneryToolkit.html\n",
+    "- Tool: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.connery.service.ConneryService.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/ddg.ipynb b/docs/docs/integrations/tools/ddg.ipynb
index 8e650c640be..d436b349cd6 100644
--- a/docs/docs/integrations/tools/ddg.ipynb
+++ b/docs/docs/integrations/tools/ddg.ipynb
@@ -153,7 +153,7 @@
    "source": [
     "## Related\n",
     "\n",
-    "- [How to use a chat model to call tools](https://python.langchain.com/v0.2/docs/how_to/tool_calling/)"
+    "- [How to use a chat model to call tools](https://python.langchain.com/docs/how_to/tool_calling/)"
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/financial_datasets.ipynb b/docs/docs/integrations/tools/financial_datasets.ipynb
index 6239379ad62..2b6a91ca685 100644
--- a/docs/docs/integrations/tools/financial_datasets.ipynb
+++ b/docs/docs/integrations/tools/financial_datasets.ipynb
@@ -281,7 +281,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `FinancialDatasetsToolkit` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.financial_datasets.toolkit.FinancialDatasetsToolkit.html)."
+    "For detailed documentation of all `FinancialDatasetsToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.financial_datasets.toolkit.FinancialDatasetsToolkit.html)."
    ]
   },
   {
diff --git a/docs/docs/integrations/tools/github.ipynb b/docs/docs/integrations/tools/github.ipynb
index d6348a2f31e..d9cfd289ecf 100644
--- a/docs/docs/integrations/tools/github.ipynb
+++ b/docs/docs/integrations/tools/github.ipynb
@@ -9,7 +9,7 @@
     "The `Github` toolkit contains tools that enable an LLM agent to interact with a github repository. \n",
     "The tool is a wrapper for the [PyGitHub](https://github.com/PyGithub/PyGithub) library. \n",
     "\n",
-    "For detailed documentation of all GithubToolkit features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html).\n",
+    "For detailed documentation of all GithubToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html).\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -279,7 +279,7 @@
       "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
       "Name: get_issue\n",
       "\n",
-      "{\"number\": 24888, \"title\": \"Standardize KV-Store Docs\", \"body\": \"To make our KV-store integrations as easy to use as possible we need to make sure the docs for them are thorough and standardized. There are two parts to this: updating the KV-store docstrings and updating the actual integration docs.\\r\\n\\r\\nThis needs to be done for each KV-store integration, ideally with one PR per KV-store.\\r\\n\\r\\nRelated to broader issues #21983 and #22005.\\r\\n\\r\\n## Docstrings\\r\\nEach KV-store class docstring should have the sections shown in the [Appendix](#appendix) below. The sections should have input and output code blocks when relevant.\\r\\n\\r\\nTo build a preview of the API docs for the package you're working on run (from root of repo):\\r\\n\\r\\n```shell\\r\\nmake api_docs_clean; make api_docs_quick_preview API_PKG=openai\\r\\n```\\r\\n\\r\\nwhere `API_PKG=` should be the parent directory that houses the edited package (e.g. community, openai, anthropic, huggingface, together, mistralai, groq, fireworks, etc.). This should be quite fast for all the partner packages.\\r\\n\\r\\n## Doc pages\\r\\nEach KV-store [docs page](https://python.langchain.com/v0.2/docs/integrations/stores/) should follow [this template](https://github.com/langchain-ai/langchain/blob/master/libs/cli/langchain_cli/integration_template/docs/kv_store.ipynb).\\r\\n\\r\\nHere is an example: https://python.langchain.com/v0.2/docs/integrations/stores/in_memory/\\r\\n\\r\\nYou can use the `langchain-cli` to quickly get started with a new chat model integration docs page (run from root of repo):\\r\\n\\r\\n```shell\\r\\npoetry run pip install -e libs/cli\\r\\npoetry run langchain-cli integration create-doc --name \\\"foo-bar\\\" --name-class FooBar --component-type kv_store --destination-dir ./docs/docs/integrations/stores/\\r\\n```\\r\\n\\r\\nwhere `--name` is the integration package name without the \\\"langchain-\\\" prefix and `--name-class` is the class name without the \\\"ByteStore\\\" suffix. This will create a template doc with some autopopulated fields at docs/docs/integrations/stores/foo_bar.ipynb.\\r\\n\\r\\nTo build a preview of the docs you can run (from root):\\r\\n\\r\\n```shell\\r\\nmake docs_clean\\r\\nmake docs_build\\r\\ncd docs/build/output-new\\r\\nyarn\\r\\nyarn start\\r\\n```\\r\\n\\r\\n## Appendix\\r\\nExpected sections for the KV-store class docstring.\\r\\n\\r\\n```python\\r\\n    \\\"\\\"\\\"__ModuleName__ completion KV-store integration.\\r\\n\\r\\n    # TODO: Replace with relevant packages, env vars.\\r\\n    Setup:\\r\\n        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.\\r\\n\\r\\n        .. code-block:: bash\\r\\n\\r\\n            pip install -U __package_name__\\r\\n            export __MODULE_NAME___API_KEY=\\\"your-api-key\\\"\\r\\n\\r\\n    # TODO: Populate with relevant params.\\r\\n    Key init args \\u2014 client params:\\r\\n        api_key: Optional[str]\\r\\n            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.\\r\\n\\r\\n    See full list of supported init args and their descriptions in the params section.\\r\\n\\r\\n    # TODO: Replace with relevant init params.\\r\\n    Instantiate:\\r\\n        .. code-block:: python\\r\\n\\r\\n            from __module_name__ import __ModuleName__ByteStore\\r\\n\\r\\n            kv_store = __ModuleName__ByteStore(\\r\\n                # api_key=\\\"...\\\",\\r\\n                # other params...\\r\\n            )\\r\\n\\r\\n    Set keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_pairs = [\\r\\n                [\\\"key1\\\", \\\"value1\\\"],\\r\\n                [\\\"key2\\\", \\\"value2\\\"],\\r\\n            ]\\r\\n\\r\\n            kv_store.mset(kv_pairs)\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n    Get keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_store.mget([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n            # TODO: Example output.\\r\\n\\r\\n    Delete keys:\\r\\n        ..code-block:: python\\r\\n\\r\\n            kv_store.mdelete([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        ..code-block:: python\\r\\n    \\\"\\\"\\\"  # noqa: E501\\r\\n```\", \"comments\": \"[]\", \"opened_by\": \"jacoblee93\"}\n",
+      "{\"number\": 24888, \"title\": \"Standardize KV-Store Docs\", \"body\": \"To make our KV-store integrations as easy to use as possible we need to make sure the docs for them are thorough and standardized. There are two parts to this: updating the KV-store docstrings and updating the actual integration docs.\\r\\n\\r\\nThis needs to be done for each KV-store integration, ideally with one PR per KV-store.\\r\\n\\r\\nRelated to broader issues #21983 and #22005.\\r\\n\\r\\n## Docstrings\\r\\nEach KV-store class docstring should have the sections shown in the [Appendix](#appendix) below. The sections should have input and output code blocks when relevant.\\r\\n\\r\\nTo build a preview of the API docs for the package you're working on run (from root of repo):\\r\\n\\r\\n```shell\\r\\nmake api_docs_clean; make api_docs_quick_preview API_PKG=openai\\r\\n```\\r\\n\\r\\nwhere `API_PKG=` should be the parent directory that houses the edited package (e.g. community, openai, anthropic, huggingface, together, mistralai, groq, fireworks, etc.). This should be quite fast for all the partner packages.\\r\\n\\r\\n## Doc pages\\r\\nEach KV-store [docs page](https://python.langchain.com/docs/integrations/stores/) should follow [this template](https://github.com/langchain-ai/langchain/blob/master/libs/cli/langchain_cli/integration_template/docs/kv_store.ipynb).\\r\\n\\r\\nHere is an example: https://python.langchain.com/docs/integrations/stores/in_memory/\\r\\n\\r\\nYou can use the `langchain-cli` to quickly get started with a new chat model integration docs page (run from root of repo):\\r\\n\\r\\n```shell\\r\\npoetry run pip install -e libs/cli\\r\\npoetry run langchain-cli integration create-doc --name \\\"foo-bar\\\" --name-class FooBar --component-type kv_store --destination-dir ./docs/docs/integrations/stores/\\r\\n```\\r\\n\\r\\nwhere `--name` is the integration package name without the \\\"langchain-\\\" prefix and `--name-class` is the class name without the \\\"ByteStore\\\" suffix. This will create a template doc with some autopopulated fields at docs/docs/integrations/stores/foo_bar.ipynb.\\r\\n\\r\\nTo build a preview of the docs you can run (from root):\\r\\n\\r\\n```shell\\r\\nmake docs_clean\\r\\nmake docs_build\\r\\ncd docs/build/output-new\\r\\nyarn\\r\\nyarn start\\r\\n```\\r\\n\\r\\n## Appendix\\r\\nExpected sections for the KV-store class docstring.\\r\\n\\r\\n```python\\r\\n    \\\"\\\"\\\"__ModuleName__ completion KV-store integration.\\r\\n\\r\\n    # TODO: Replace with relevant packages, env vars.\\r\\n    Setup:\\r\\n        Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.\\r\\n\\r\\n        .. code-block:: bash\\r\\n\\r\\n            pip install -U __package_name__\\r\\n            export __MODULE_NAME___API_KEY=\\\"your-api-key\\\"\\r\\n\\r\\n    # TODO: Populate with relevant params.\\r\\n    Key init args \\u2014 client params:\\r\\n        api_key: Optional[str]\\r\\n            __ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.\\r\\n\\r\\n    See full list of supported init args and their descriptions in the params section.\\r\\n\\r\\n    # TODO: Replace with relevant init params.\\r\\n    Instantiate:\\r\\n        .. code-block:: python\\r\\n\\r\\n            from __module_name__ import __ModuleName__ByteStore\\r\\n\\r\\n            kv_store = __ModuleName__ByteStore(\\r\\n                # api_key=\\\"...\\\",\\r\\n                # other params...\\r\\n            )\\r\\n\\r\\n    Set keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_pairs = [\\r\\n                [\\\"key1\\\", \\\"value1\\\"],\\r\\n                [\\\"key2\\\", \\\"value2\\\"],\\r\\n            ]\\r\\n\\r\\n            kv_store.mset(kv_pairs)\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n    Get keys:\\r\\n        .. code-block:: python\\r\\n\\r\\n            kv_store.mget([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        .. code-block:: python\\r\\n\\r\\n            # TODO: Example output.\\r\\n\\r\\n    Delete keys:\\r\\n        ..code-block:: python\\r\\n\\r\\n            kv_store.mdelete([\\\"key1\\\", \\\"key2\\\"])\\r\\n\\r\\n        ..code-block:: python\\r\\n    \\\"\\\"\\\"  # noqa: E501\\r\\n```\", \"comments\": \"[]\", \"opened_by\": \"jacoblee93\"}\n",
       "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
       "\n",
       "The title of issue 24888 is \"Standardize KV-Store Docs\".\n"
@@ -303,7 +303,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `GithubToolkit` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html)."
+    "For detailed documentation of all `GithubToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.github.toolkit.GitHubToolkit.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/gmail.ipynb b/docs/docs/integrations/tools/gmail.ipynb
index e3502fc6b1e..5ab7825cc21 100644
--- a/docs/docs/integrations/tools/gmail.ipynb
+++ b/docs/docs/integrations/tools/gmail.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Gmail Toolkit\n",
     "\n",
-    "This will help you getting started with the GMail [toolkit](/docs/concepts/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
+    "This will help you getting started with the GMail [toolkit](/docs/concepts/#toolkits). This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.toolkit.GmailToolkit.html).\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -142,11 +142,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- [GmailCreateDraft](https://python.langchain.com/v0.2/api_reference/google_community/gmail/langchain_google_community.gmail.create_draft.GmailCreateDraft.html)\n",
-    "- [GmailSendMessage](https://python.langchain.com/v0.2/api_reference/google_community/gmail/langchain_google_community.gmail.send_message.GmailSendMessage.html)\n",
-    "- [GmailSearch](https://python.langchain.com/v0.2/api_reference/google_community/gmail/langchain_google_community.gmail.search.GmailSearch.html)\n",
-    "- [GmailGetMessage](https://python.langchain.com/v0.2/api_reference/google_community/gmail/langchain_google_community.gmail.get_message.GmailGetMessage.html)\n",
-    "- [GmailGetThread](https://python.langchain.com/v0.2/api_reference/google_community/gmail/langchain_google_community.gmail.get_thread.GmailGetThread.html)"
+    "- [GmailCreateDraft](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.create_draft.GmailCreateDraft.html)\n",
+    "- [GmailSendMessage](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.send_message.GmailSendMessage.html)\n",
+    "- [GmailSearch](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.search.GmailSearch.html)\n",
+    "- [GmailGetMessage](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.get_message.GmailGetMessage.html)\n",
+    "- [GmailGetThread](https://python.langchain.com/api_reference/google_community/gmail/langchain_google_community.gmail.get_thread.GmailGetThread.html)"
    ]
   },
   {
@@ -243,7 +243,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `GmailToolkit` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.gmail.toolkit.GmailToolkit.html)."
+    "For detailed documentation of all `GmailToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.gmail.toolkit.GmailToolkit.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/jina_search.ipynb b/docs/docs/integrations/tools/jina_search.ipynb
index 07aa274e2ff..511085a579a 100644
--- a/docs/docs/integrations/tools/jina_search.ipynb
+++ b/docs/docs/integrations/tools/jina_search.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# Jina Search\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Jina [tool](/docs/integrations/tools/). For detailed documentation of all Jina features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html).\n",
+    "This notebook provides a quick overview for getting started with Jina [tool](/docs/integrations/tools/). For detailed documentation of all Jina features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html).\n",
     "\n",
     "## Overview\n",
     "\n",
@@ -25,7 +25,7 @@
     "\n",
     "| Class | Package | Serializable | JS support |  Package latest |\n",
     "| :--- | :--- | :---: | :---: | :---: |\n",
-    "| [JinaSearch](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html) | [langchain-community](https://python.langchain.com/v0.2/api_reference/community/) | ❌ | ❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
+    "| [JinaSearch](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html) | [langchain-community](https://python.langchain.com/api_reference/community/) | ❌ | ❌ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
     "\n",
     "### Tool features\n",
     "| [Returns artifact](/docs/how_to/tool_artifacts/) | Native async | Return data | Pricing |\n",
@@ -256,7 +256,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all Jina features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html"
+    "For detailed documentation of all Jina features and configurations head to the API reference: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.jina_search.tool.JinaSearch.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/nvidia_riva.ipynb b/docs/docs/integrations/tools/nvidia_riva.ipynb
index 1cc5b9c964b..f09badc0a77 100644
--- a/docs/docs/integrations/tools/nvidia_riva.ipynb
+++ b/docs/docs/integrations/tools/nvidia_riva.ipynb
@@ -527,7 +527,7 @@
     "## 6. Create Additional Chain Components\n",
     "As usual, declare the other parts of the chain. In this case, it's just a prompt template and an LLM.\n",
     "\n",
-    "You can use any [LangChain compatible LLM](https://python.langchain.com/v0.1/docs/integrations/llms/) in the chain. In this example, we use a [Mixtral8x7b NIM from NVIDIA](https://python.langchain.com/v0.2/docs/integrations/chat/nvidia_ai_endpoints/). NVIDIA NIMs are supported in LangChain via the `langchain-nvidia-ai-endpoints` package, so you can easily build applications with best in class throughput and latency. \n",
+    "You can use any [LangChain compatible LLM](https://python.langchain.com/v0.1/docs/integrations/llms/) in the chain. In this example, we use a [Mixtral8x7b NIM from NVIDIA](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/). NVIDIA NIMs are supported in LangChain via the `langchain-nvidia-ai-endpoints` package, so you can easily build applications with best in class throughput and latency. \n",
     "\n",
     "LangChain compatible NVIDIA LLMs from [NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) can also be used by following these [instructions](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints). "
    ]
@@ -547,7 +547,7 @@
    "id": "1744eec9",
    "metadata": {},
    "source": [
-    "Follow the [instructions for LangChain](https://python.langchain.com/v0.2/docs/integrations/chat/nvidia_ai_endpoints/) to use NVIDIA NIM in your speech-enabled LangChain application. \n",
+    "Follow the [instructions for LangChain](https://python.langchain.com/docs/integrations/chat/nvidia_ai_endpoints/) to use NVIDIA NIM in your speech-enabled LangChain application. \n",
     "\n",
     "Set your key for NVIDIA API catalog, where NIMs are hosted for you to try."
    ]
diff --git a/docs/docs/integrations/tools/polygon.ipynb b/docs/docs/integrations/tools/polygon.ipynb
index 058aa509f17..cac533d91ee 100644
--- a/docs/docs/integrations/tools/polygon.ipynb
+++ b/docs/docs/integrations/tools/polygon.ipynb
@@ -436,7 +436,7 @@
    "source": [
     "### API reference\n",
     "\n",
-    "For detailed documentation of all the Polygon IO toolkit features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.polygon.toolkit.PolygonToolkit.html"
+    "For detailed documentation of all the Polygon IO toolkit features and configurations head to the API reference: https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.polygon.toolkit.PolygonToolkit.html"
    ]
   },
   {
@@ -654,10 +654,10 @@
     "\n",
     "For detailed documentation of all Polygon IO tools head to the API reference for each:\n",
     "\n",
-    "- Aggregate: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.polygon.aggregates.PolygonAggregates.html\n",
-    "- Financials: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.polygon.financials.PolygonFinancials.html\n",
-    "- Last Quote: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.polygon.last_quote.PolygonLastQuote.html\n",
-    "- Ticker News: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.polygon.ticker_news.PolygonTickerNews.html"
+    "- Aggregate: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.polygon.aggregates.PolygonAggregates.html\n",
+    "- Financials: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.polygon.financials.PolygonFinancials.html\n",
+    "- Last Quote: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.polygon.last_quote.PolygonLastQuote.html\n",
+    "- Ticker News: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.polygon.ticker_news.PolygonTickerNews.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/python.ipynb b/docs/docs/integrations/tools/python.ipynb
index 75b51550a92..d2fffb0a31d 100644
--- a/docs/docs/integrations/tools/python.ipynb
+++ b/docs/docs/integrations/tools/python.ipynb
@@ -15,7 +15,7 @@
     ":::{.callout-caution}\n",
     "Python REPL can execute arbitrary code on the host machine (e.g., delete files, make network requests). Use with caution.\n",
     "\n",
-    "For more information general security guidelines, please see https://python.langchain.com/v0.2/docs/security/.\n",
+    "For more information general security guidelines, please see https://python.langchain.com/docs/security/.\n",
     ":::"
    ]
   },
diff --git a/docs/docs/integrations/tools/requests.ipynb b/docs/docs/integrations/tools/requests.ipynb
index 5fbcc1b3a7f..0db60b0aa98 100644
--- a/docs/docs/integrations/tools/requests.ipynb
+++ b/docs/docs/integrations/tools/requests.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "We can use the Requests [toolkit](/docs/concepts/#toolkits) to construct agents that generate HTTP requests.\n",
     "\n",
-    "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html).\n",
+    "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html).\n",
     "\n",
     "## ⚠️ Security note ⚠️\n",
     "There are inherent risks in giving models discretion to execute real-world actions. Take precautions to mitigate these risks:\n",
@@ -225,11 +225,11 @@
    "id": "a21a6ca4-d650-4b7d-a944-1a8771b5293a",
    "metadata": {},
    "source": [
-    "- [RequestsGetTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsGetTool.html)\n",
-    "- [RequestsPostTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPostTool.html)\n",
-    "- [RequestsPatchTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPatchTool.html)\n",
-    "- [RequestsPutTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPutTool.html)\n",
-    "- [RequestsDeleteTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsDeleteTool.html)"
+    "- [RequestsGetTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsGetTool.html)\n",
+    "- [RequestsPostTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPostTool.html)\n",
+    "- [RequestsPatchTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPatchTool.html)\n",
+    "- [RequestsPutTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsPutTool.html)\n",
+    "- [RequestsDeleteTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.requests.tool.RequestsDeleteTool.html)"
    ]
   },
   {
@@ -323,7 +323,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html)."
+    "For detailed documentation of all API toolkit features and configurations head to the API reference for [RequestsToolkit](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.RequestsToolkit.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/slack.ipynb b/docs/docs/integrations/tools/slack.ipynb
index cbc119b3881..3a516eeb0d7 100644
--- a/docs/docs/integrations/tools/slack.ipynb
+++ b/docs/docs/integrations/tools/slack.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Slack Toolkit\n",
     "\n",
-    "This will help you getting started with the Slack [toolkit](/docs/concepts/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
+    "This will help you getting started with the Slack [toolkit](/docs/concepts/#toolkits). For detailed documentation of all SlackToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html).\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -137,10 +137,10 @@
    "source": [
     "This toolkit loads:\n",
     "\n",
-    "- [SlackGetChannel](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.slack.get_channel.SlackGetChannel.html)\n",
-    "- [SlackGetMessage](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.slack.get_message.SlackGetMessage.html)\n",
-    "- [SlackScheduleMessage](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.slack.schedule_message.SlackScheduleMessage.html)\n",
-    "- [SlackSendMessage](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.slack.send_message.SlackSendMessage.html)"
+    "- [SlackGetChannel](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.get_channel.SlackGetChannel.html)\n",
+    "- [SlackGetMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.get_message.SlackGetMessage.html)\n",
+    "- [SlackScheduleMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.schedule_message.SlackScheduleMessage.html)\n",
+    "- [SlackSendMessage](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.slack.send_message.SlackSendMessage.html)"
    ]
   },
   {
@@ -246,7 +246,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `SlackToolkit` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
+    "For detailed documentation of all `SlackToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.slack.toolkit.SlackToolkit.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/sql_database.ipynb b/docs/docs/integrations/tools/sql_database.ipynb
index 714a65a3241..251409ddf9f 100644
--- a/docs/docs/integrations/tools/sql_database.ipynb
+++ b/docs/docs/integrations/tools/sql_database.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# SQLDatabase Toolkit\n",
     "\n",
-    "This will help you getting started with the SQL Database [toolkit](/docs/concepts/#toolkits). For detailed documentation of all `SQLDatabaseToolkit` features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html).\n",
+    "This will help you getting started with the SQL Database [toolkit](/docs/concepts/#toolkits). For detailed documentation of all `SQLDatabaseToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html).\n",
     "\n",
     "Tools within the `SQLDatabaseToolkit` are designed to interact with a `SQL` database. \n",
     "\n",
@@ -80,8 +80,8 @@
     "\n",
     "The `SQLDatabaseToolkit` toolkit requires:\n",
     "\n",
-    "- a [SQLDatabase](https://python.langchain.com/v0.2/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) object;\n",
-    "- a LLM or chat model (for instantiating the [QuerySQLCheckerTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLCheckerTool.html) tool).\n",
+    "- a [SQLDatabase](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.sql_database.SQLDatabase.html) object;\n",
+    "- a LLM or chat model (for instantiating the [QuerySQLCheckerTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLCheckerTool.html) tool).\n",
     "\n",
     "Below, we instantiate the toolkit with these objects. Let's first create a database object.\n",
     "\n",
@@ -216,10 +216,10 @@
    "source": [
     "API references:\n",
     "\n",
-    "- [QuerySQLDataBaseTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLDataBaseTool.html)\n",
-    "- [InfoSQLDatabaseTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.sql_database.tool.InfoSQLDatabaseTool.html)\n",
-    "- [ListSQLDatabaseTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.sql_database.tool.ListSQLDatabaseTool.html)\n",
-    "- [QuerySQLCheckerTool](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLCheckerTool.html)"
+    "- [QuerySQLDataBaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLDataBaseTool.html)\n",
+    "- [InfoSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.InfoSQLDatabaseTool.html)\n",
+    "- [ListSQLDatabaseTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.ListSQLDatabaseTool.html)\n",
+    "- [QuerySQLCheckerTool](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.sql_database.tool.QuerySQLCheckerTool.html)"
    ]
   },
   {
@@ -561,7 +561,7 @@
    "source": [
     "## Specific functionality\n",
     "\n",
-    "`SQLDatabaseToolkit` implements a [.get_context](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html#langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.get_context) method as a convenience for use in prompts or other contexts.\n",
+    "`SQLDatabaseToolkit` implements a [.get_context](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html#langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.get_context) method as a convenience for use in prompts or other contexts.\n",
     "\n",
     "**⚠️ Disclaimer ⚠️** : The agent may generate insert/update/delete queries. When this is not expected, use a custom prompt or create a SQL users without write permissions.\n",
     "\n",
@@ -586,7 +586,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all SQLDatabaseToolkit features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html)."
+    "For detailed documentation of all SQLDatabaseToolkit features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit.html)."
    ]
   }
  ],
diff --git a/docs/docs/integrations/tools/tavily_search.ipynb b/docs/docs/integrations/tools/tavily_search.ipynb
index 1c2099ec288..a8c6de9e1cd 100644
--- a/docs/docs/integrations/tools/tavily_search.ipynb
+++ b/docs/docs/integrations/tools/tavily_search.ipynb
@@ -18,9 +18,9 @@
     "## Overview\n",
     "\n",
     "### Integration details\n",
-    "| Class | Package | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/tools/tavily_search) |  Package latest |\n",
+    "| Class | Package | Serializable | [JS support](https://js.langchain.com/docs/integrations/tools/tavily_search) |  Package latest |\n",
     "| :--- | :--- | :---: | :---: | :---: |\n",
-    "| [TavilySearchResults](https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.tavily_search.tool.TavilySearchResults.html) | [langchain-community](https://python.langchain.com/v0.2/api_reference/community/index.html) | ❌ | ✅ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
+    "| [TavilySearchResults](https://python.langchain.com/api_reference/community/tools/langchain_community.tools.tavily_search.tool.TavilySearchResults.html) | [langchain-community](https://python.langchain.com/api_reference/community/index.html) | ❌ | ✅ |  ![PyPI - Version](https://img.shields.io/pypi/v/langchain-community?style=flat-square&label=%20) |\n",
     "\n",
     "### Tool features\n",
     "| [Returns artifact](/docs/how_to/tool_artifacts/) | Native async | Return data | Pricing |\n",
@@ -350,7 +350,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all TavilySearchResults features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/tools/langchain_community.tools.tavily_search.tool.TavilySearchResults.html"
+    "For detailed documentation of all TavilySearchResults features and configurations head to the API reference: https://python.langchain.com/api_reference/community/tools/langchain_community.tools.tavily_search.tool.TavilySearchResults.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/astradb.ipynb b/docs/docs/integrations/vectorstores/astradb.ipynb
index 9ef52d0cb37..17969ef5ee7 100644
--- a/docs/docs/integrations/vectorstores/astradb.ipynb
+++ b/docs/docs/integrations/vectorstores/astradb.ipynb
@@ -410,7 +410,7 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/v0.2/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
+    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
    ]
   },
   {
@@ -459,9 +459,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -507,7 +507,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `AstraDBVectorStore` features and configurations head to the API reference:https://python.langchain.com/v0.2/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html"
+    "For detailed documentation of all `AstraDBVectorStore` features and configurations head to the API reference:https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/chroma.ipynb b/docs/docs/integrations/vectorstores/chroma.ipynb
index c7049904d37..57968060e59 100644
--- a/docs/docs/integrations/vectorstores/chroma.ipynb
+++ b/docs/docs/integrations/vectorstores/chroma.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "This notebook covers how to get started with the `Chroma` vector store.\n",
     "\n",
-    ">[Chroma](https://docs.trychroma.com/getting-started) is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0. View the full docs of `Chroma` at [this page](https://docs.trychroma.com/reference/py-collection), and find the API reference for the LangChain integration at [this page](https://python.langchain.com/v0.2/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html).\n",
+    ">[Chroma](https://docs.trychroma.com/getting-started) is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0. View the full docs of `Chroma` at [this page](https://docs.trychroma.com/reference/py-collection), and find the API reference for the LangChain integration at [this page](https://python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html).\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -423,11 +423,11 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/v0.2/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html).\n",
+    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html).\n",
     "\n",
     "### Query by turning into retriever\n",
     "\n",
-    "You can also transform the vector store into a retriever for easier usage in your chains. For more information on the different search types and kwargs you can pass, please visit the API reference [here](https://python.langchain.com/v0.2/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html#langchain_chroma.vectorstores.Chroma.as_retriever)."
+    "You can also transform the vector store into a retriever for easier usage in your chains. For more information on the different search types and kwargs you can pass, please visit the API reference [here](https://python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html#langchain_chroma.vectorstores.Chroma.as_retriever)."
    ]
   },
   {
@@ -463,9 +463,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -475,7 +475,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `Chroma` vector store features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html"
+    "For detailed documentation of all `Chroma` vector store features and configurations head to the API reference: https://python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/clickhouse.ipynb b/docs/docs/integrations/vectorstores/clickhouse.ipynb
index 035be6fed01..64fb5d43695 100644
--- a/docs/docs/integrations/vectorstores/clickhouse.ipynb
+++ b/docs/docs/integrations/vectorstores/clickhouse.ipynb
@@ -322,7 +322,7 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `Clickhouse` vector store check out the [API reference](https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html)."
+    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `Clickhouse` vector store check out the [API reference](https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html)."
    ]
   },
   {
@@ -360,9 +360,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -380,7 +380,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `AstraDBVectorStore` features and configurations head to the API reference:https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html"
+    "For detailed documentation of all `AstraDBVectorStore` features and configurations head to the API reference:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/couchbase.ipynb b/docs/docs/integrations/vectorstores/couchbase.ipynb
index d3b00f80c16..2e28d6d78a6 100644
--- a/docs/docs/integrations/vectorstores/couchbase.ipynb
+++ b/docs/docs/integrations/vectorstores/couchbase.ipynb
@@ -678,9 +678,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -742,7 +742,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `CouchbaseVectorStore` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/couchbase/vectorstores/langchain_couchbase.vectorstores.CouchbaseVectorStore.html"
+    "For detailed documentation of all `CouchbaseVectorStore` features and configurations head to the API reference: https://python.langchain.com/api_reference/couchbase/vectorstores/langchain_couchbase.vectorstores.CouchbaseVectorStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb
index 72f0ab50970..74d7390e3d0 100644
--- a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb
+++ b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb
@@ -496,9 +496,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
diff --git a/docs/docs/integrations/vectorstores/elasticsearch.ipynb b/docs/docs/integrations/vectorstores/elasticsearch.ipynb
index 3eaf6bc04bd..b13f40a36d7 100644
--- a/docs/docs/integrations/vectorstores/elasticsearch.ipynb
+++ b/docs/docs/integrations/vectorstores/elasticsearch.ipynb
@@ -473,9 +473,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -601,7 +601,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `ElasticSearchStore` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/elasticsearch/vectorstores/langchain_elasticsearch.vectorstores.ElasticsearchStore.html"
+    "For detailed documentation of all `ElasticSearchStore` features and configurations head to the API reference: https://python.langchain.com/api_reference/elasticsearch/vectorstores/langchain_elasticsearch.vectorstores.ElasticsearchStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/faiss.ipynb b/docs/docs/integrations/vectorstores/faiss.ipynb
index 60106946982..09a1a759b59 100644
--- a/docs/docs/integrations/vectorstores/faiss.ipynb
+++ b/docs/docs/integrations/vectorstores/faiss.ipynb
@@ -328,7 +328,7 @@
     "#### Other search methods\n",
     "\n",
     "\n",
-    "There are a variety of other ways to search a FAISS vector store. For a complete list of those methods, please refer to the [API Reference](https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html)\n",
+    "There are a variety of other ways to search a FAISS vector store. For a complete list of those methods, please refer to the [API Reference](https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html)\n",
     "\n",
     "### Query by turning into retriever\n",
     "\n",
@@ -366,9 +366,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -510,7 +510,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `FAISS` vector store features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html"
+    "For detailed documentation of all `FAISS` vector store features and configurations head to the API reference: https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/milvus.ipynb b/docs/docs/integrations/vectorstores/milvus.ipynb
index 6bf4bf793b1..984a3fa6c6d 100644
--- a/docs/docs/integrations/vectorstores/milvus.ipynb
+++ b/docs/docs/integrations/vectorstores/milvus.ipynb
@@ -359,7 +359,7 @@
    "id": "14db337f",
    "metadata": {},
    "source": [
-    "For a full list of all the search options available when using the `Milvus` vector store, you can visit the [API reference](https://python.langchain.com/v0.2/api_reference/milvus/vectorstores/langchain_milvus.vectorstores.milvus.Milvus.html).\n",
+    "For a full list of all the search options available when using the `Milvus` vector store, you can visit the [API reference](https://python.langchain.com/api_reference/milvus/vectorstores/langchain_milvus.vectorstores.milvus.Milvus.html).\n",
     "\n",
     "### Query by turning into retriever\n",
     "\n",
@@ -397,9 +397,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -528,7 +528,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/milvus/vectorstores/langchain_milvus.vectorstores.milvus.Milvus.html"
+    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/milvus/vectorstores/langchain_milvus.vectorstores.milvus.Milvus.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
index be2ea109084..140dfe179ed 100644
--- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
+++ b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
@@ -412,7 +412,7 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/v0.2/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
+    "There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for `AstraDBVectorStore` check out the [API reference](https://python.langchain.com/api_reference/astradb/vectorstores/langchain_astradb.vectorstores.AstraDBVectorStore.html)."
    ]
   },
   {
@@ -461,9 +461,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -485,7 +485,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `MongoDBAtlasVectorSearch` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/mongodb/index.html"
+    "For detailed documentation of all `MongoDBAtlasVectorSearch` features and configurations head to the API reference: https://python.langchain.com/api_reference/mongodb/index.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/pgvector.ipynb b/docs/docs/integrations/vectorstores/pgvector.ipynb
index 855d655f43f..ebfa261edc5 100644
--- a/docs/docs/integrations/vectorstores/pgvector.ipynb
+++ b/docs/docs/integrations/vectorstores/pgvector.ipynb
@@ -400,7 +400,7 @@
    "id": "8d40db8c",
    "metadata": {},
    "source": [
-    "For a full list of the different searches you can execute on a `PGVector` vector store, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/postgres/vectorstores/langchain_postgres.vectorstores.PGVector.html).\n",
+    "For a full list of the different searches you can execute on a `PGVector` vector store, please refer to the [API reference](https://python.langchain.com/api_reference/postgres/vectorstores/langchain_postgres.vectorstores.PGVector.html).\n",
     "\n",
     "### Query by turning into retriever\n",
     "\n",
@@ -438,9 +438,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -450,7 +450,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/postgres/vectorstores/langchain_postgres.vectorstores.PGVector.html"
+    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/postgres/vectorstores/langchain_postgres.vectorstores.PGVector.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/docs/integrations/vectorstores/pinecone.ipynb
index bea9748f185..eb30997458e 100644
--- a/docs/docs/integrations/vectorstores/pinecone.ipynb
+++ b/docs/docs/integrations/vectorstores/pinecone.ipynb
@@ -371,7 +371,7 @@
    "source": [
     "#### Other search methods\n",
     "\n",
-    "There are more search methods (such as MMR) not listed in this notebook, to find all of them be sure to read the [API reference](https://python.langchain.com/v0.2/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html).\n",
+    "There are more search methods (such as MMR) not listed in this notebook, to find all of them be sure to read the [API reference](https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html).\n",
     "\n",
     "### Query by turning into retriever\n",
     "\n",
@@ -412,9 +412,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -424,7 +424,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html"
+    "For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/qdrant.ipynb b/docs/docs/integrations/vectorstores/qdrant.ipynb
index 4e6331e60a3..6762389edb6 100644
--- a/docs/docs/integrations/vectorstores/qdrant.ipynb
+++ b/docs/docs/integrations/vectorstores/qdrant.ipynb
@@ -472,7 +472,7 @@
     "To search with only dense vectors,\n",
     "\n",
     "- The `retrieval_mode` parameter should be set to `RetrievalMode.DENSE`(default).\n",
-    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided to the `embedding` parameter."
+    "- A [dense embeddings](https://python.langchain.com/docs/integrations/text_embedding/) value should be provided to the `embedding` parameter."
    ]
   },
   {
@@ -556,7 +556,7 @@
     "To perform a hybrid search using dense and sparse vectors with score fusion,\n",
     "\n",
     "- The `retrieval_mode` parameter should be set to `RetrievalMode.HYBRID`.\n",
-    "- A [dense embeddings](https://python.langchain.com/v0.2/docs/integrations/text_embedding/) value should be provided to the `embedding` parameter.\n",
+    "- A [dense embeddings](https://python.langchain.com/docs/integrations/text_embedding/) value should be provided to the `embedding` parameter.\n",
     "- An implementation of the [`SparseEmbeddings`](https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py) interface using any sparse embeddings provider has to be provided as value to the `sparse_embedding` parameter.\n",
     "\n",
     "Note that if you've added documents with the `HYBRID` mode, you can switch to any retrieval mode when searching. Since both the dense and sparse vectors are available in the collection."
@@ -628,7 +628,7 @@
    "id": "525e3582",
    "metadata": {},
    "source": [
-    "For a full list of all the search functions available for a `QdrantVectorStore`, read the [API reference](https://python.langchain.com/v0.2/api_reference/qdrant/qdrant/langchain_qdrant.qdrant.QdrantVectorStore.html)\n",
+    "For a full list of all the search functions available for a `QdrantVectorStore`, read the [API reference](https://python.langchain.com/api_reference/qdrant/qdrant/langchain_qdrant.qdrant.QdrantVectorStore.html)\n",
     "\n",
     "### Metadata filtering\n",
     "\n",
@@ -717,9 +717,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -814,7 +814,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all `QdrantVectorStore` features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/qdrant/qdrant/langchain_qdrant.qdrant.QdrantVectorStore.html"
+    "For detailed documentation of all `QdrantVectorStore` features and configurations head to the API reference: https://python.langchain.com/api_reference/qdrant/qdrant/langchain_qdrant.qdrant.QdrantVectorStore.html"
    ]
   }
  ],
diff --git a/docs/docs/integrations/vectorstores/redis.ipynb b/docs/docs/integrations/vectorstores/redis.ipynb
index 56af92a0632..c8e1de0a113 100644
--- a/docs/docs/integrations/vectorstores/redis.ipynb
+++ b/docs/docs/integrations/vectorstores/redis.ipynb
@@ -764,9 +764,9 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/v0.2/docs/tutorials/#working-with-external-knowledge)\n",
-    "- [How-to: Question and answer with RAG](https://python.langchain.com/v0.2/docs/how_to/#qa-with-rag)\n",
-    "- [Retrieval conceptual docs](https://python.langchain.com/v0.2/docs/concepts/#retrieval)"
+    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
+    "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]
   },
   {
@@ -1110,7 +1110,7 @@
    "source": [
     "## API reference\n",
     "\n",
-    "For detailed documentation of all RedisVectorStore features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/redis/vectorstores/langchain_redis.vectorstores.RedisVectorStore.html"
+    "For detailed documentation of all RedisVectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/redis/vectorstores/langchain_redis.vectorstores.RedisVectorStore.html"
    ]
   }
  ],
diff --git a/docs/docs/introduction.mdx b/docs/docs/introduction.mdx
index 436e2255100..00cb9b09dc2 100644
--- a/docs/docs/introduction.mdx
+++ b/docs/docs/introduction.mdx
@@ -61,7 +61,7 @@ Explore the full list of LangChain tutorials [here](/docs/tutorials), and check
 ## [How-to guides](/docs/how_to)
 
 [Here](/docs/how_to) you’ll find short answers to “How do I….?” types of questions.
-These how-to guides don’t cover topics in depth – you’ll find that material in the [Tutorials](/docs/tutorials) and the [API Reference](https://python.langchain.com/v0.2/api_reference/).
+These how-to guides don’t cover topics in depth – you’ll find that material in the [Tutorials](/docs/tutorials) and the [API Reference](https://python.langchain.com/api_reference/).
 However, these guides will help you quickly accomplish common tasks.
 
 Check out [LangGraph-specific how-tos here](https://langchain-ai.github.io/langgraph/how-tos/).
@@ -72,7 +72,7 @@ Introductions to all the key parts of LangChain you’ll need to know! [Here](/d
 
 For a deeper dive into LangGraph concepts, check out [this page](https://langchain-ai.github.io/langgraph/concepts/).
 
-## [API reference](https://python.langchain.com/v0.2/api_reference/)
+## [API reference](https://python.langchain.com/api_reference/)
 Head to the reference section for full documentation of all classes and methods in the LangChain Python packages.
 
 ## Ecosystem
diff --git a/docs/docs/tutorials/qa_chat_history.ipynb b/docs/docs/tutorials/qa_chat_history.ipynb
index bfdfee6b51f..594438c6d38 100644
--- a/docs/docs/tutorials/qa_chat_history.ipynb
+++ b/docs/docs/tutorials/qa_chat_history.ipynb
@@ -278,7 +278,7 @@
     "\n",
     "We'll use a prompt that includes a `MessagesPlaceholder` variable under the name \"chat_history\". This allows us to pass in a list of Messages to the prompt using the \"chat_history\" input key, and these messages will be inserted after the system message and before the human message containing the latest question.\n",
     "\n",
-    "Note that we leverage a helper function [create_history_aware_retriever](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) for this step, which manages the case where `chat_history` is empty, and otherwise applies `prompt | llm | StrOutputParser() | retriever` in sequence.\n",
+    "Note that we leverage a helper function [create_history_aware_retriever](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) for this step, which manages the case where `chat_history` is empty, and otherwise applies `prompt | llm | StrOutputParser() | retriever` in sequence.\n",
     "\n",
     "`create_history_aware_retriever` constructs a chain that accepts keys `input` and `chat_history` as input, and has the same output schema as a retriever."
    ]
@@ -322,9 +322,9 @@
     "\n",
     "Now we can build our full QA chain. This is as simple as updating the retriever to be our new `history_aware_retriever`.\n",
     "\n",
-    "Again, we will use [create_stuff_documents_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) to generate a `question_answer_chain`, with input keys `context`, `chat_history`, and `input`-- it accepts the retrieved context alongside the conversation history and query to generate an answer. A more detailed explaination is over [here](/docs/tutorials/rag/#built-in-chains)\n",
+    "Again, we will use [create_stuff_documents_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) to generate a `question_answer_chain`, with input keys `context`, `chat_history`, and `input`-- it accepts the retrieved context alongside the conversation history and query to generate an answer. A more detailed explaination is over [here](/docs/tutorials/rag/#built-in-chains)\n",
     "\n",
-    "We build our final `rag_chain` with [create_retrieval_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html). This chain applies the `history_aware_retriever` and `question_answer_chain` in sequence, retaining intermediate outputs such as the retrieved context for convenience. It has input keys `input` and `chat_history`, and includes `input`, `chat_history`, `context`, and `answer` in its output."
+    "We build our final `rag_chain` with [create_retrieval_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html). This chain applies the `history_aware_retriever` and `question_answer_chain` in sequence, retaining intermediate outputs such as the retrieved context for convenience. It has input keys `input` and `chat_history`, and includes `input`, `chat_history`, `context`, and `answer` in its output."
    ]
   },
   {
@@ -416,7 +416,7 @@
     "\n",
     "For this we can use:\n",
     "\n",
-    "- [BaseChatMessageHistory](https://python.langchain.com/v0.2/api_reference/langchain/index.html#module-langchain.memory): Store chat history.\n",
+    "- [BaseChatMessageHistory](https://python.langchain.com/api_reference/langchain/index.html#module-langchain.memory): Store chat history.\n",
     "- [RunnableWithMessageHistory](/docs/how_to/message_history): Wrapper for an LCEL chain and a `BaseChatMessageHistory` that handles injecting chat history into inputs and updating it after each invocation.\n",
     "\n",
     "For a detailed walkthrough of how to use these classes together to create a stateful conversational chain, head to the [How to add message history (memory)](/docs/how_to/message_history) LCEL page.\n",
diff --git a/docs/docs/tutorials/rag.ipynb b/docs/docs/tutorials/rag.ipynb
index f7b93fef437..6c2aa9bc546 100644
--- a/docs/docs/tutorials/rag.ipynb
+++ b/docs/docs/tutorials/rag.ipynb
@@ -228,7 +228,7 @@
     "[DocumentLoaders](/docs/concepts#document-loaders)\n",
     "for this, which are objects that load in data from a source and return a\n",
     "list of\n",
-    "[Documents](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html).\n",
+    "[Documents](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html).\n",
     "A `Document` is an object with some `page_content` (str) and `metadata`\n",
     "(dict).\n",
     "\n",
@@ -316,7 +316,7 @@
     "  Detailed documentation on how to use `DocumentLoaders`.\n",
     "- [Integrations](/docs/integrations/document_loaders/): 160+\n",
     "  integrations to choose from.\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/core/document_loaders/langchain_core.document_loaders.base.BaseLoader.html):\n",
+    "- [Interface](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.base.BaseLoader.html):\n",
     "  API reference  for the base interface.\n",
     "\n",
     "\n",
@@ -430,14 +430,14 @@
     "- Learn more about splitting text using different methods by reading the [how-to docs](/docs/how_to#text-splitters)\n",
     "- [Code (py or js)](/docs/integrations/document_loaders/source_code)\n",
     "- [Scientific papers](/docs/integrations/document_loaders/grobid)\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/text_splitters/base/langchain_text_splitters.base.TextSplitter.html): API reference for the base interface.\n",
+    "- [Interface](https://python.langchain.com/api_reference/text_splitters/base/langchain_text_splitters.base.TextSplitter.html): API reference for the base interface.\n",
     "\n",
     "`DocumentTransformer`: Object that performs a transformation on a list\n",
     "of `Document` objects.\n",
     "\n",
     "- [Docs](/docs/how_to#text-splitters): Detailed documentation on how to use `DocumentTransformers`\n",
     "- [Integrations](/docs/integrations/document_transformers/)\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.transformers.BaseDocumentTransformer.html): API reference for the base interface.\n",
+    "- [Interface](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.transformers.BaseDocumentTransformer.html): API reference for the base interface.\n",
     "\n",
     "## 3. Indexing: Store {#indexing-store}\n",
     "\n",
@@ -483,14 +483,14 @@
     "\n",
     "- [Docs](/docs/how_to/embed_text): Detailed documentation on how to use embeddings.\n",
     "- [Integrations](/docs/integrations/text_embedding/): 30+ integrations to choose from.\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/core/embeddings/langchain_core.embeddings.Embeddings.html): API reference for the base interface.\n",
+    "- [Interface](https://python.langchain.com/api_reference/core/embeddings/langchain_core.embeddings.Embeddings.html): API reference for the base interface.\n",
     "\n",
     "`VectorStore`: Wrapper around a vector database, used for storing and\n",
     "querying embeddings.\n",
     "\n",
     "- [Docs](/docs/how_to/vectorstores): Detailed documentation on how to use vector stores.\n",
     "- [Integrations](/docs/integrations/vectorstores/): 40+ integrations to choose from.\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html): API reference for the base interface.\n",
+    "- [Interface](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html): API reference for the base interface.\n",
     "\n",
     "This completes the **Indexing** portion of the pipeline. At this point\n",
     "we have a query-able vector store containing the chunked contents of our\n",
@@ -592,7 +592,7 @@
     "    Retriever](/docs/how_to/self_query).\n",
     "- [Integrations](/docs/integrations/retrievers/): Integrations\n",
     "  with retrieval services.\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html):\n",
+    "- [Interface](https://python.langchain.com/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html):\n",
     "  API reference for the base interface.\n",
     "\n",
     "## 5. Retrieval and Generation: Generate {#retrieval-and-generation-generate}\n",
@@ -721,14 +721,14 @@
    "source": [
     "Let's dissect the LCEL to understand what's going on.\n",
     "\n",
-    "First: each of these components (`retriever`, `prompt`, `llm`, etc.) are instances of [Runnable](/docs/concepts#langchain-expression-language-lcel). This means that they implement the same methods-- such as sync and async `.invoke`, `.stream`, or `.batch`-- which makes them easier to connect together. They can be connected into a [RunnableSequence](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html)-- another Runnable-- via the `|` operator.\n",
+    "First: each of these components (`retriever`, `prompt`, `llm`, etc.) are instances of [Runnable](/docs/concepts#langchain-expression-language-lcel). This means that they implement the same methods-- such as sync and async `.invoke`, `.stream`, or `.batch`-- which makes them easier to connect together. They can be connected into a [RunnableSequence](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html)-- another Runnable-- via the `|` operator.\n",
     "\n",
-    "LangChain will automatically cast certain objects to runnables when met with the `|` operator. Here, `format_docs` is cast to a [RunnableLambda](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html), and the dict with `\"context\"` and `\"question\"` is cast to a [RunnableParallel](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.RunnableParallel.html). The details are less important than the bigger point, which is that each object is a Runnable.\n",
+    "LangChain will automatically cast certain objects to runnables when met with the `|` operator. Here, `format_docs` is cast to a [RunnableLambda](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html), and the dict with `\"context\"` and `\"question\"` is cast to a [RunnableParallel](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableParallel.html). The details are less important than the bigger point, which is that each object is a Runnable.\n",
     "\n",
     "Let's trace how the input question flows through the above runnables.\n",
     "\n",
     "As we've seen above, the input to `prompt` is expected to be a dict with keys `\"context\"` and `\"question\"`. So the first element of this chain builds runnables that will calculate both of these from the input question:\n",
-    "- `retriever | format_docs` passes the question through the retriever, generating [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) objects, and then to `format_docs` to generate strings;\n",
+    "- `retriever | format_docs` passes the question through the retriever, generating [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects, and then to `format_docs` to generate strings;\n",
     "- `RunnablePassthrough()` passes through the input question unchanged.\n",
     "\n",
     "That is, if you constructed\n",
@@ -749,8 +749,8 @@
     "\n",
     "If preferred, LangChain includes convenience functions that implement the above LCEL. We compose two functions:\n",
     "\n",
-    "- [create_stuff_documents_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) specifies how retrieved context is fed into a prompt and LLM. In this case, we will \"stuff\" the contents into the prompt -- i.e., we will include all retrieved context without any summarization or other processing. It largely implements our above `rag_chain`, with input keys `context` and `input`-- it generates an answer using retrieved context and query.\n",
-    "- [create_retrieval_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html) adds the retrieval step and propagates the retrieved context through the chain, providing it alongside the final answer. It has input key `input`, and includes `input`, `context`, and `answer` in its output."
+    "- [create_stuff_documents_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) specifies how retrieved context is fed into a prompt and LLM. In this case, we will \"stuff\" the contents into the prompt -- i.e., we will include all retrieved context without any summarization or other processing. It largely implements our above `rag_chain`, with input keys `context` and `input`-- it generates an answer using retrieved context and query.\n",
+    "- [create_retrieval_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html) adds the retrieval step and propagates the retrieved context through the chain, providing it alongside the final answer. It has input key `input`, and includes `input`, `context`, and `answer` in its output."
    ]
   },
   {
@@ -851,13 +851,13 @@
     "\n",
     "- [Docs](/docs/how_to#chat-models)\n",
     "- [Integrations](/docs/integrations/chat/): 25+ integrations to choose from.\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html): API reference for the base interface.\n",
+    "- [Interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.chat_models.BaseChatModel.html): API reference for the base interface.\n",
     "\n",
     "`LLM`: A text-in-text-out LLM. Takes in a string and returns a string.\n",
     "\n",
     "- [Docs](/docs/how_to#llms)\n",
     "- [Integrations](/docs/integrations/llms): 75+ integrations to choose from.\n",
-    "- [Interface](https://python.langchain.com/v0.2/api_reference/core/language_models/langchain_core.language_models.llms.BaseLLM.html): API reference for the base interface.\n",
+    "- [Interface](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.llms.BaseLLM.html): API reference for the base interface.\n",
     "\n",
     "See a guide on RAG with locally-running models\n",
     "[here](/docs/tutorials/local_rag).\n",
diff --git a/docs/docs/tutorials/retrievers.ipynb b/docs/docs/tutorials/retrievers.ipynb
index 721a20c3334..34d12c65cd3 100644
--- a/docs/docs/tutorials/retrievers.ipynb
+++ b/docs/docs/tutorials/retrievers.ipynb
@@ -71,7 +71,7 @@
     "\n",
     "## Documents\n",
     "\n",
-    "LangChain implements a [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html) abstraction, which is intended to represent a unit of text and associated metadata. It has two attributes:\n",
+    "LangChain implements a [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) abstraction, which is intended to represent a unit of text and associated metadata. It has two attributes:\n",
     "\n",
     "- `page_content`: a string representing the content;\n",
     "- `metadata`: a dict containing arbitrary metadata.\n",
@@ -125,7 +125,7 @@
     "\n",
     "Vector search is a common way to store and search over unstructured data (such as unstructured text). The idea is to store numeric vectors that are associated with the text. Given a query, we can [embed](/docs/concepts#embedding-models) it as a vector of the same dimension and use vector similarity metrics to identify related data in the store.\n",
     "\n",
-    "LangChain [VectorStore](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) objects contain methods for adding text and `Document` objects to the store, and querying them using various similarity metrics. They are often initialized with [embedding](/docs/how_to/embed_text) models, which determine how text data is translated to numeric vectors.\n",
+    "LangChain [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) objects contain methods for adding text and `Document` objects to the store, and querying them using various similarity metrics. They are often initialized with [embedding](/docs/how_to/embed_text) models, which determine how text data is translated to numeric vectors.\n",
     "\n",
     "LangChain includes a suite of [integrations](/docs/integrations/vectorstores) with different vector store technologies. Some vector stores are hosted by a provider (e.g., various cloud providers) and require specific credentials to use; some (such as [Postgres](/docs/integrations/vectorstores/pgvector)) run in separate infrastructure that can be run locally or via a third-party; others can run in-memory for lightweight workloads. Here we will demonstrate usage of LangChain VectorStores using [Chroma](/docs/integrations/vectorstores/chroma), which includes an in-memory implementation.\n",
     "\n",
@@ -153,15 +153,15 @@
    "id": "ff0f0b43-e5b8-4c79-b782-a02f17345487",
    "metadata": {},
    "source": [
-    "Calling `.from_documents` here will add the documents to the vector store. [VectorStore](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) implements methods for adding documents that can also be called after the object is instantiated. Most implementations will allow you to connect to an existing vector store--  e.g., by providing a client, index name, or other information. See the documentation for a specific [integration](/docs/integrations/vectorstores) for more detail.\n",
+    "Calling `.from_documents` here will add the documents to the vector store. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) implements methods for adding documents that can also be called after the object is instantiated. Most implementations will allow you to connect to an existing vector store--  e.g., by providing a client, index name, or other information. See the documentation for a specific [integration](/docs/integrations/vectorstores) for more detail.\n",
     "\n",
-    "Once we've instantiated a `VectorStore` that contains documents, we can query it. [VectorStore](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) includes methods for querying:\n",
+    "Once we've instantiated a `VectorStore` that contains documents, we can query it. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) includes methods for querying:\n",
     "- Synchronously and asynchronously;\n",
     "- By string query and by vector;\n",
     "- With and without returning similarity scores;\n",
-    "- By similarity and [maximum marginal relevance](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search) (to balance similarity with query to diversity in retrieved results).\n",
+    "- By similarity and [maximum marginal relevance](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search) (to balance similarity with query to diversity in retrieved results).\n",
     "\n",
-    "The methods will generally include a list of [Document](https://python.langchain.com/v0.2/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects in their outputs.\n",
+    "The methods will generally include a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects in their outputs.\n",
     "\n",
     "### Examples\n",
     "\n",
@@ -305,15 +305,15 @@
    "source": [
     "Learn more:\n",
     "\n",
-    "- [API reference](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html)\n",
+    "- [API reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html)\n",
     "- [How-to guide](/docs/how_to/vectorstores)\n",
     "- [Integration-specific docs](/docs/integrations/vectorstores)\n",
     "\n",
     "## Retrievers\n",
     "\n",
-    "LangChain `VectorStore` objects do not subclass [Runnable](https://python.langchain.com/v0.2/api_reference/core/index.html#module-langchain_core.runnables), and so cannot immediately be integrated into LangChain Expression Language [chains](/docs/concepts/#langchain-expression-language-lcel).\n",
+    "LangChain `VectorStore` objects do not subclass [Runnable](https://python.langchain.com/api_reference/core/index.html#module-langchain_core.runnables), and so cannot immediately be integrated into LangChain Expression Language [chains](/docs/concepts/#langchain-expression-language-lcel).\n",
     "\n",
-    "LangChain [Retrievers](https://python.langchain.com/v0.2/api_reference/core/index.html#module-langchain_core.retrievers) are Runnables, so they implement a standard set of methods (e.g., synchronous and asynchronous `invoke` and `batch` operations) and are designed to be incorporated in LCEL chains.\n",
+    "LangChain [Retrievers](https://python.langchain.com/api_reference/core/index.html#module-langchain_core.retrievers) are Runnables, so they implement a standard set of methods (e.g., synchronous and asynchronous `invoke` and `batch` operations) and are designed to be incorporated in LCEL chains.\n",
     "\n",
     "We can create a simple version of this ourselves, without subclassing `Retriever`. If we choose what method we wish to use to retrieve documents, we can create a runnable easily. Below we will build one around the `similarity_search` method:"
    ]
@@ -350,7 +350,7 @@
    "id": "a36d3f64-a8bc-4baa-b2ea-07e324a0143e",
    "metadata": {},
    "source": [
-    "Vectorstores implement an `as_retriever` method that will generate a Retriever, specifically a [VectorStoreRetriever](https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html). These retrievers include specific `search_type` and `search_kwargs` attributes that identify what methods of the underlying vector store to call, and how to parameterize them. For instance, we can replicate the above with the following:"
+    "Vectorstores implement an `as_retriever` method that will generate a Retriever, specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html). These retrievers include specific `search_type` and `search_kwargs` attributes that identify what methods of the underlying vector store to call, and how to parameterize them. For instance, we can replicate the above with the following:"
    ]
   },
   {
@@ -472,7 +472,7 @@
     "\n",
     "The [retrievers](/docs/how_to#retrievers) section of the how-to guides covers these and other built-in retrieval strategies.\n",
     "\n",
-    "It is also straightforward to extend the [BaseRetriever](https://python.langchain.com/v0.2/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html) class in order to implement custom retrievers. See our how-to guide [here](/docs/how_to/custom_retriever)."
+    "It is also straightforward to extend the [BaseRetriever](https://python.langchain.com/api_reference/core/retrievers/langchain_core.retrievers.BaseRetriever.html) class in order to implement custom retrievers. See our how-to guide [here](/docs/how_to/custom_retriever)."
    ]
   }
  ],
diff --git a/docs/docs/tutorials/sql_qa.ipynb b/docs/docs/tutorials/sql_qa.ipynb
index 726ffa7136b..cc94b5522bf 100644
--- a/docs/docs/tutorials/sql_qa.ipynb
+++ b/docs/docs/tutorials/sql_qa.ipynb
@@ -140,7 +140,7 @@
     "\n",
     "### Convert question to SQL query\n",
     "\n",
-    "The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. LangChain comes with a built-in chain for this: [create_sql_query_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html)."
+    "The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. LangChain comes with a built-in chain for this: [create_sql_query_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.sql_database.query.create_sql_query_chain.html)."
    ]
   },
   {
diff --git a/docs/docs/tutorials/summarization.ipynb b/docs/docs/tutorials/summarization.ipynb
index 36375989b2b..47ddee04145 100644
--- a/docs/docs/tutorials/summarization.ipynb
+++ b/docs/docs/tutorials/summarization.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "This tutorial demonstrates text summarization using built-in chains and [LangGraph](https://langchain-ai.github.io/langgraph/).\n",
     "\n",
-    "A [previous version](https://python.langchain.com/v0.1/docs/use_cases/summarization/) of this page showcased the legacy chains [StuffDocumentsChain](/docs/versions/migrating_chains/stuff_docs_chain/), [MapReduceDocumentsChain](/docs/versions/migrating_chains/map_reduce_chain/), and [RefineDocumentsChain](https://python.langchain.com/v0.2/docs/versions/migrating_chains/refine_docs_chain/). See [here](/docs/versions/migrating_chains/) for information on using those abstractions and a comparison with the methods demonstrated in this tutorial.\n",
+    "A [previous version](https://python.langchain.com/v0.1/docs/use_cases/summarization/) of this page showcased the legacy chains [StuffDocumentsChain](/docs/versions/migrating_chains/stuff_docs_chain/), [MapReduceDocumentsChain](/docs/versions/migrating_chains/map_reduce_chain/), and [RefineDocumentsChain](https://python.langchain.com/docs/versions/migrating_chains/refine_docs_chain/). See [here](/docs/versions/migrating_chains/) for information on using those abstractions and a comparison with the methods demonstrated in this tutorial.\n",
     "\n",
     ":::\n",
     "\n",
@@ -54,7 +54,7 @@
     "\n",
     "- Using [language models](/docs/concepts/#chat-models).\n",
     "\n",
-    "- Using [document loaders](/docs/concepts/#document-loaders), specifically the [WebBaseLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load content from an HTML webpage.\n",
+    "- Using [document loaders](/docs/concepts/#document-loaders), specifically the [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load content from an HTML webpage.\n",
     "\n",
     "- Two ways to summarize or otherwise combine documents.\n",
     "  1. [Stuff](/docs/tutorials/summarization#stuff), which simply concatenates documents into a prompt;\n",
@@ -128,7 +128,7 @@
     "\n",
     "1. `Stuff`: Simply \"stuff\" all your documents into a single prompt. This is the simplest approach (see [here](/docs/tutorials/rag#built-in-chains) for more on the `create_stuff_documents_chain` constructor, which is used for this method).\n",
     "\n",
-    "2. `Map-reduce`: Summarize each document on its own in a \"map\" step and then \"reduce\" the summaries into a final summary (see [here](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain.html) for more on the `MapReduceDocumentsChain`, which is used for this method).\n",
+    "2. `Map-reduce`: Summarize each document on its own in a \"map\" step and then \"reduce\" the summaries into a final summary (see [here](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain.html) for more on the `MapReduceDocumentsChain`, which is used for this method).\n",
     "\n",
     "Note that map-reduce is especially effective when understanding of a sub-document does not rely on preceding context. For example, when summarizing a corpus of many, shorter documents. In other cases, such as summarizing a novel or body of text with an inherent sequence, [iterative refinement](/docs/how_to/summarize_refine) may be more effective."
    ]
@@ -183,7 +183,7 @@
    "id": "21541329-f883-42ca-bc94-ab9793951dfa",
    "metadata": {},
    "source": [
-    "First we load in our documents. We will use [WebBaseLoader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load a blog post:"
+    "First we load in our documents. We will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load a blog post:"
    ]
   },
   {
@@ -237,7 +237,7 @@
    "source": [
     "## Stuff: summarize in a single LLM call {#stuff}\n",
     "\n",
-    "We can use [create_stuff_documents_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html), especially if using larger context window models such as:\n",
+    "We can use [create_stuff_documents_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html), especially if using larger context window models such as:\n",
     "\n",
     "* 128k token OpenAI `gpt-4o` \n",
     "* 200k token Anthropic `claude-3-5-sonnet-20240620`\n",
diff --git a/docs/docs/versions/migrating_chains/constitutional_chain.ipynb b/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
index 66db7669b4a..06adc77c9b9 100644
--- a/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/constitutional_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from ConstitutionalChain\n",
     "\n",
-    "[ConstitutionalChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html) allowed for a LLM to critique and revise generations based on [principles](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html), structured as combinations of critique and revision requests. For example, a principle might include a request to identify harmful content, and a request to rewrite the content.\n",
+    "[ConstitutionalChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html) allowed for a LLM to critique and revise generations based on [principles](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html), structured as combinations of critique and revision requests. For example, a principle might include a request to identify harmful content, and a request to rewrite the content.\n",
     "\n",
     "`Constitutional AI principles` are based on the [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/pdf/2212.08073) paper.\n",
     "\n",
diff --git a/docs/docs/versions/migrating_chains/conversation_chain.ipynb b/docs/docs/versions/migrating_chains/conversation_chain.ipynb
index fe778f1bd08..9f5eb1ba175 100644
--- a/docs/docs/versions/migrating_chains/conversation_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/conversation_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from ConversationalChain\n",
     "\n",
-    "[`ConversationChain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.conversation.base.ConversationChain.html) incorporated a memory of previous messages to sustain a stateful conversation.\n",
+    "[`ConversationChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversation.base.ConversationChain.html) incorporated a memory of previous messages to sustain a stateful conversation.\n",
     "\n",
     "Some advantages of switching to the LCEL implementation are:\n",
     "\n",
@@ -15,7 +15,7 @@
     "- More explicit parameters. `ConversationChain` contains a hidden default prompt, which can cause confusion.\n",
     "- Streaming support. `ConversationChain` only supports streaming via callbacks.\n",
     "\n",
-    "`RunnableWithMessageHistory` implements sessions via configuration parameters. It should be instantiated with a callable that returns a [chat message history](https://python.langchain.com/v0.2/api_reference/core/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html). By default, it expects this function to take a single argument `session_id`."
+    "`RunnableWithMessageHistory` implements sessions via configuration parameters. It should be instantiated with a callable that returns a [chat message history](https://python.langchain.com/api_reference/core/chat_history/langchain_core.chat_history.BaseChatMessageHistory.html). By default, it expects this function to take a single argument `session_id`."
    ]
   },
   {
@@ -219,7 +219,7 @@
     "\n",
     "## Next steps\n",
     "\n",
-    "See [this tutorial](/docs/tutorials/chatbot) for a more end-to-end guide on building with [`RunnableWithMessageHistory`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html).\n",
+    "See [this tutorial](/docs/tutorials/chatbot) for a more end-to-end guide on building with [`RunnableWithMessageHistory`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html).\n",
     "\n",
     "Check out the [LCEL conceptual docs](/docs/concepts/#langchain-expression-language-lcel) for more background information."
    ]
diff --git a/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb b/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb
index 99b12fb7c35..31930082875 100644
--- a/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/conversation_retrieval_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from ConversationalRetrievalChain\n",
     "\n",
-    "The [`ConversationalRetrievalChain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html) was an all-in one way that combined retrieval-augmented generation with chat history, allowing you to \"chat with\" your documents.\n",
+    "The [`ConversationalRetrievalChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html) was an all-in one way that combined retrieval-augmented generation with chat history, allowing you to \"chat with\" your documents.\n",
     "\n",
     "Advantages of switching to the LCEL implementation are similar to the [`RetrievalQA` migration guide](./retrieval_qa.ipynb):\n",
     "\n",
diff --git a/docs/docs/versions/migrating_chains/llm_chain.ipynb b/docs/docs/versions/migrating_chains/llm_chain.ipynb
index 3e1492f6059..a4addea4a4c 100644
--- a/docs/docs/versions/migrating_chains/llm_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/llm_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from LLMChain\n",
     "\n",
-    "[`LLMChain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) combined a prompt template, LLM, and output parser into a class.\n",
+    "[`LLMChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) combined a prompt template, LLM, and output parser into a class.\n",
     "\n",
     "Some advantages of switching to the LCEL implementation are:\n",
     "\n",
@@ -160,7 +160,7 @@
    "id": "3c0b0513-77b8-4371-a20e-3e487cec7e7f",
    "metadata": {},
    "source": [
-    "If you'd like to mimic the `dict` packaging of input and output in `LLMChain`, you can use a [`RunnablePassthrough.assign`](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html) like:"
+    "If you'd like to mimic the `dict` packaging of input and output in `LLMChain`, you can use a [`RunnablePassthrough.assign`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html) like:"
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/llm_math_chain.ipynb b/docs/docs/versions/migrating_chains/llm_math_chain.ipynb
index 71dc8afab9e..2697d02fd17 100644
--- a/docs/docs/versions/migrating_chains/llm_math_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/llm_math_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from LLMMathChain\n",
     "\n",
-    "[`LLMMathChain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.llm_math.base.LLMMathChain.html) enabled the evaluation of mathematical expressions generated by a LLM. Instructions for generating the expressions were formatted into the prompt, and the expressions were parsed out of the string response before evaluation using the [numexpr](https://numexpr.readthedocs.io/en/latest/user_guide.html) library.\n",
+    "[`LLMMathChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm_math.base.LLMMathChain.html) enabled the evaluation of mathematical expressions generated by a LLM. Instructions for generating the expressions were formatted into the prompt, and the expressions were parsed out of the string response before evaluation using the [numexpr](https://numexpr.readthedocs.io/en/latest/user_guide.html) library.\n",
     "\n",
     "This is more naturally achieved via [tool calling](/docs/concepts/#functiontool-calling). We can equip a chat model with a simple calculator tool leveraging `numexpr` and construct a simple chain around it using [LangGraph](https://langchain-ai.github.io/langgraph/). Some advantages of this approach include:\n",
     "\n",
diff --git a/docs/docs/versions/migrating_chains/llm_router_chain.ipynb b/docs/docs/versions/migrating_chains/llm_router_chain.ipynb
index fb0cfc03757..5e37f2874c8 100644
--- a/docs/docs/versions/migrating_chains/llm_router_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/llm_router_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from LLMRouterChain\n",
     "\n",
-    "The [`LLMRouterChain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.router.llm_router.LLMRouterChain.html) routed an input query to one of multiple destinations-- that is, given an input query, it used a LLM to select from a list of destination chains, and passed its inputs to the selected chain.\n",
+    "The [`LLMRouterChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.router.llm_router.LLMRouterChain.html) routed an input query to one of multiple destinations-- that is, given an input query, it used a LLM to select from a list of destination chains, and passed its inputs to the selected chain.\n",
     "\n",
     "`LLMRouterChain` does not support common [chat model](/docs/concepts/#chat-models) features, such as message roles and [tool calling](/docs/concepts/#functiontool-calling). Under the hood, `LLMRouterChain` routes a query by instructing the LLM to generate JSON-formatted text, and parsing out the intended destination.\n",
     "\n",
diff --git a/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb b/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb
index 52fad8721ea..40ca0040082 100644
--- a/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/map_reduce_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from MapReduceDocumentsChain\n",
     "\n",
-    "[MapReduceDocumentsChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain.html) implements a map-reduce strategy over (potentially long) texts. The strategy is as follows:\n",
+    "[MapReduceDocumentsChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain.html) implements a map-reduce strategy over (potentially long) texts. The strategy is as follows:\n",
     "\n",
     "- Split a text into smaller documents;\n",
     "- Map a process onto the smaller documents;\n",
@@ -588,7 +588,7 @@
    "source": [
     "As before, we can stream the graph to observe its sequence of steps. Below, we will simply print out the name of the step.\n",
     "\n",
-    "Note that because we have a loop in the graph, it can be helpful to specify a [recursion_limit](https://langchain-ai.github.io/langgraph/reference/errors/#langgraph.errors.GraphRecursionError) on its execution. This is analogous to [ReduceDocumentsChain.token_max](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.reduce.ReduceDocumentsChain.html#langchain.chains.combine_documents.reduce.ReduceDocumentsChain.token_max) to will raise a specific error when the specified limit is exceeded."
+    "Note that because we have a loop in the graph, it can be helpful to specify a [recursion_limit](https://langchain-ai.github.io/langgraph/reference/errors/#langgraph.errors.GraphRecursionError) on its execution. This is analogous to [ReduceDocumentsChain.token_max](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.reduce.ReduceDocumentsChain.html#langchain.chains.combine_documents.reduce.ReduceDocumentsChain.token_max) to will raise a specific error when the specified limit is exceeded."
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb b/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb
index 03e58842c32..6b979eeaed9 100644
--- a/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/map_rerank_docs_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from MapRerankDocumentsChain\n",
     "\n",
-    "[MapRerankDocumentsChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html) implements a strategy for analyzing long texts. The strategy is as follows:\n",
+    "[MapRerankDocumentsChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html) implements a strategy for analyzing long texts. The strategy is as follows:\n",
     "\n",
     "- Split a text into smaller documents;\n",
     "- Map a process to the set of documents, where the process includes generating a score;\n",
@@ -55,7 +55,7 @@
     "\n",
     "<details open>\n",
     "\n",
-    "Below we show an implementation with `MapRerankDocumentsChain`. We define the prompt template for a question-answering task and instantiate a [LLMChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) object for this purpose. We define how documents are formatted into the prompt and ensure consistency among the keys in the various prompts."
+    "Below we show an implementation with `MapRerankDocumentsChain`. We define the prompt template for a question-answering task and instantiate a [LLMChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) object for this purpose. We define how documents are formatted into the prompt and ensure consistency among the keys in the various prompts."
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb b/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb
index b49c1708efe..6c987880357 100644
--- a/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/multi_prompt_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from MultiPromptChain\n",
     "\n",
-    "The [`MultiPromptChain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html) routed an input query to one of multiple LLMChains-- that is, given an input query, it used a LLM to select from a list of prompts, formatted the query into the prompt, and generated a response.\n",
+    "The [`MultiPromptChain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html) routed an input query to one of multiple LLMChains-- that is, given an input query, it used a LLM to select from a list of prompts, formatted the query into the prompt, and generated a response.\n",
     "\n",
     "`MultiPromptChain` does not support common [chat model](/docs/concepts/#chat-models) features, such as message roles and [tool calling](/docs/concepts/#functiontool-calling).\n",
     "\n",
diff --git a/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb b/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb
index 6461e604bc8..80ee5afbac0 100644
--- a/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/refine_docs_chain.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from RefineDocumentsChain\n",
     "\n",
-    "[RefineDocumentsChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html) implements a strategy for analyzing long texts. The strategy is as follows:\n",
+    "[RefineDocumentsChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html) implements a strategy for analyzing long texts. The strategy is as follows:\n",
     "\n",
     "- Split a text into smaller documents;\n",
     "- Apply a process to the first document;\n",
@@ -81,7 +81,7 @@
     "\n",
     "<details open>\n",
     "\n",
-    "Below we show an implementation with `RefineDocumentsChain`. We define the prompt templates for the initial summarization and successive refinements, instantiate separate [LLMChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) objects for these two purposes, and instantiate `RefineDocumentsChain` with these components."
+    "Below we show an implementation with `RefineDocumentsChain`. We define the prompt templates for the initial summarization and successive refinements, instantiate separate [LLMChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) objects for these two purposes, and instantiate `RefineDocumentsChain` with these components."
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/retrieval_qa.ipynb b/docs/docs/versions/migrating_chains/retrieval_qa.ipynb
index 4bcbeb6a810..fec2a75f1b4 100644
--- a/docs/docs/versions/migrating_chains/retrieval_qa.ipynb
+++ b/docs/docs/versions/migrating_chains/retrieval_qa.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Migrating from RetrievalQA\n",
     "\n",
-    "The [`RetrievalQA` chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html) performed natural-language question answering over a data source using retrieval-augmented generation.\n",
+    "The [`RetrievalQA` chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html) performed natural-language question answering over a data source using retrieval-augmented generation.\n",
     "\n",
     "Some advantages of switching to the LCEL implementation are:\n",
     "\n",
@@ -178,7 +178,7 @@
    "id": "d6f44fe8",
    "metadata": {},
    "source": [
-    "The LCEL implementation exposes the internals of what's happening around retrieving, formatting documents, and passing them through a prompt to the LLM, but it is more verbose. You can customize and wrap this composition logic in a helper function, or use the higher-level [`create_retrieval_chain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html) and [`create_stuff_documents_chain`](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) helper method:"
+    "The LCEL implementation exposes the internals of what's happening around retrieving, formatting documents, and passing them through a prompt to the LLM, but it is more verbose. You can customize and wrap this composition logic in a helper function, or use the higher-level [`create_retrieval_chain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html) and [`create_stuff_documents_chain`](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) helper method:"
    ]
   },
   {
diff --git a/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb b/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb
index 2d28e1f2d58..c09b0279b65 100644
--- a/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb
+++ b/docs/docs/versions/migrating_chains/stuff_docs_chain.ipynb
@@ -7,9 +7,9 @@
    "source": [
     "# Migrating from StuffDocumentsChain\n",
     "\n",
-    "[StuffDocumentsChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html) combines documents by concatenating them into a single context window. It is a straightforward and effective strategy for combining documents for question-answering, summarization, and other purposes.\n",
+    "[StuffDocumentsChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html) combines documents by concatenating them into a single context window. It is a straightforward and effective strategy for combining documents for question-answering, summarization, and other purposes.\n",
     "\n",
-    "[create_stuff_documents_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) is the recommended alternative. It functions the same as `StuffDocumentsChain`, with better support for streaming and batch functionality. Because it is a simple combination of [LCEL primitives](/docs/concepts/#langchain-expression-language-lcel), it is also easier to extend and incorporate into other LangChain applications.\n",
+    "[create_stuff_documents_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html) is the recommended alternative. It functions the same as `StuffDocumentsChain`, with better support for streaming and batch functionality. Because it is a simple combination of [LCEL primitives](/docs/concepts/#langchain-expression-language-lcel), it is also easier to extend and incorporate into other LangChain applications.\n",
     "\n",
     "Below we will go through both `StuffDocumentsChain` and `create_stuff_documents_chain` on a simple example for illustrative purposes.\n",
     "\n",
@@ -70,7 +70,7 @@
     "\n",
     "<details open>\n",
     "\n",
-    "Below we show an implementation with `StuffDocumentsChain`. We define the prompt template for a summarization task and instantiate a [LLMChain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) object for this purpose. We define how documents are formatted into the prompt and ensure consistency among the keys in the various prompts."
+    "Below we show an implementation with `StuffDocumentsChain`. We define the prompt template for a summarization task and instantiate a [LLMChain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html) object for this purpose. We define how documents are formatted into the prompt and ensure consistency among the keys in the various prompts."
    ]
   },
   {
diff --git a/docs/docs/versions/v0_2/deprecations.mdx b/docs/docs/versions/v0_2/deprecations.mdx
index b97c27888f4..2cad3a76648 100644
--- a/docs/docs/versions/v0_2/deprecations.mdx
+++ b/docs/docs/versions/v0_2/deprecations.mdx
@@ -755,7 +755,7 @@ Deprecated: 0.1.17
 Removal: 0.3.0
 
 
-Alternative: [create_retrieval_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html#langchain-chains-retrieval-create-retrieval-chain)
+Alternative: [create_retrieval_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html#langchain-chains-retrieval-create-retrieval-chain)
 This [migration guide](/docs/versions/migrating_chains/retrieval_qa) has a side-by-side comparison.
 
 
@@ -822,7 +822,7 @@ Deprecated: 0.1.17
 Removal: 0.3.0
 
 
-Alternative: [create_history_aware_retriever](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) together with [create_retrieval_chain](https://python.langchain.com/v0.2/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html#langchain-chains-retrieval-create-retrieval-chain) (see example in docstring)
+Alternative: [create_history_aware_retriever](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html) together with [create_retrieval_chain](https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html#langchain-chains-retrieval-create-retrieval-chain) (see example in docstring)
 This [migration guide](/docs/versions/migrating_chains/conversation_retrieval_chain) has a side-by-side comparison.
 
 
diff --git a/docs/docs/versions/v0_2/migrating_astream_events.mdx b/docs/docs/versions/v0_2/migrating_astream_events.mdx
index 0498f1f26fd..522b2f6c6aa 100644
--- a/docs/docs/versions/v0_2/migrating_astream_events.mdx
+++ b/docs/docs/versions/v0_2/migrating_astream_events.mdx
@@ -111,7 +111,7 @@ If you're filtering by event names, check if you need to update your filters.
 
 ### RunnableRetry
 
-Usage of [RunnableRetry](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.retry.RunnableRetry.html)
+Usage of [RunnableRetry](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.retry.RunnableRetry.html)
 within an LCEL chain being streamed generated an incorrect `on_chain_end` event in `v1` corresponding
 to the failed runnable invocation that was being retried. This event has been removed in `v2`.
 
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index 714e217cfad..8df4f8712d1 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -9,7 +9,7 @@ require("dotenv").config();
 const baseLightCodeBlockTheme = require("prism-react-renderer/themes/vsLight");
 const baseDarkCodeBlockTheme = require("prism-react-renderer/themes/vsDark");
 
-const baseUrl = "/v0.2/";
+const baseUrl = "/";
 
 /** @type {import('@docusaurus/types').Config} */
 const config = {
@@ -174,7 +174,7 @@ const config = {
             items: [
               {
                 label: "Latest",
-                to: "https://python.langchain.com/v0.2/api_reference/reference.html",
+                to: "https://python.langchain.com/api_reference/reference.html",
               },
               {
                 label: "Legacy",
@@ -219,13 +219,17 @@ const config = {
           },
           {
             type: "dropdown",
-            label: "v0.2",
+            label: "v0.3",
             position: "right",
             items: [
               {
-                label: "v0.2",
+                label: "v0.3",
                 href: "/docs/introduction"
               },
+              {
+                label: "v0.2",
+                href: "https://python.langchain.com/v0.2/docs/introduction"
+              },
               {
                 label: "v0.1",
                 href: "https://python.langchain.com/v0.1/docs/get_started/introduction"
diff --git a/docs/scripts/arxiv_references.py b/docs/scripts/arxiv_references.py
index ff65c39cd5c..be8ba975df1 100644
--- a/docs/scripts/arxiv_references.py
+++ b/docs/scripts/arxiv_references.py
@@ -378,18 +378,18 @@ class ArxivAPIWrapper(BaseModel):
                 abstract=result.summary,
                 url=result.entry_id,
                 published_date=str(result.published.date()),
-                referencing_doc2url=type2key2urls["docs"]
-                if "docs" in type2key2urls
-                else {},
-                referencing_api_ref2url=type2key2urls["apis"]
-                if "apis" in type2key2urls
-                else {},
-                referencing_template2url=type2key2urls["templates"]
-                if "templates" in type2key2urls
-                else {},
-                referencing_cookbook2url=type2key2urls["cookbooks"]
-                if "cookbooks" in type2key2urls
-                else {},
+                referencing_doc2url=(
+                    type2key2urls["docs"] if "docs" in type2key2urls else {}
+                ),
+                referencing_api_ref2url=(
+                    type2key2urls["apis"] if "apis" in type2key2urls else {}
+                ),
+                referencing_template2url=(
+                    type2key2urls["templates"] if "templates" in type2key2urls else {}
+                ),
+                referencing_cookbook2url=(
+                    type2key2urls["cookbooks"] if "cookbooks" in type2key2urls else {}
+                ),
             )
             for result, type2key2urls in zip(results, arxiv_id2type2key2urls.values())
         ]
@@ -397,7 +397,7 @@ class ArxivAPIWrapper(BaseModel):
 
 
 def _format_doc_url(doc_path: str) -> str:
-    return f"https://{LANGCHAIN_PYTHON_URL}/v0.2/{doc_path}"
+    return f"https://{LANGCHAIN_PYTHON_URL}/{doc_path}"
 
 
 def _format_api_ref_url(doc_path: str, compact: bool = False) -> str:
diff --git a/docs/scripts/generate_api_reference_links.py b/docs/scripts/generate_api_reference_links.py
index 08ed1562364..de0f873f6aa 100644
--- a/docs/scripts/generate_api_reference_links.py
+++ b/docs/scripts/generate_api_reference_links.py
@@ -10,7 +10,7 @@ from pathlib import Path
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 # Base URL for all class documentation
-_BASE_URL = "https://python.langchain.com/v0.2/api_reference/"
+_BASE_URL = "https://python.langchain.com/api_reference/"
 
 # Regular expression to match Python code blocks
 code_block_re = re.compile(r"^(```\s?python\n)(.*?)(```)", re.DOTALL | re.MULTILINE)
@@ -85,7 +85,7 @@ def main():
                 .replace(".md", "/")
             )
 
-            doc_url = f"https://python.langchain.com/v0.2/docs/{relative_path}"
+            doc_url = f"https://python.langchain.com/docs/{relative_path}"
             for import_info in file_imports:
                 doc_title = import_info["title"]
                 class_name = import_info["imported"]
diff --git a/docs/scripts/kv_store_feat_table.py b/docs/scripts/kv_store_feat_table.py
index acfe4094b1f..55adb2ac87a 100644
--- a/docs/scripts/kv_store_feat_table.py
+++ b/docs/scripts/kv_store_feat_table.py
@@ -33,45 +33,45 @@ The following table shows information on all available key-value stores.
 
 KV_STORE_FEAT_TABLE = {
     "AstraDBByteStore": {
-        "class": "[AstraDBByteStore](https://python.langchain.com/v0.2/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html)",
+        "class": "[AstraDBByteStore](https://python.langchain.com/api_reference/astradb/storage/langchain_astradb.storage.AstraDBByteStore.html)",
         "local": False,
-        "package": "[langchain_astradb](https://python.langchain.com/v0.2/api_reference/astradb/)",
+        "package": "[langchain_astradb](https://python.langchain.com/api_reference/astradb/)",
         "downloads": "![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_astradb?style=flat-square&label=%20)",
     },
     "CassandraByteStore": {
-        "class": "[CassandraByteStore](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html)",
+        "class": "[CassandraByteStore](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.cassandra.CassandraByteStore.html)",
         "local": False,
-        "package": "[langchain_community](https://python.langchain.com/v0.2/api_reference/community/)",
+        "package": "[langchain_community](https://python.langchain.com/api_reference/community/)",
         "downloads": "![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20)",
     },
     "ElasticsearchEmbeddingsCache": {
-        "class": "[ElasticsearchEmbeddingsCache](https://python.langchain.com/v0.2/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html)",
+        "class": "[ElasticsearchEmbeddingsCache](https://python.langchain.com/api_reference/elasticsearch/cache/langchain_elasticsearch.cache.ElasticsearchEmbeddingsCache.html)",
         "local": True,
-        "package": "[langchain_elasticsearch](https://python.langchain.com/v0.2/api_reference/elasticsearch/)",
+        "package": "[langchain_elasticsearch](https://python.langchain.com/api_reference/elasticsearch/)",
         "downloads": "![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_elasticsearch?style=flat-square&label=%20)",
     },
     "LocalFileStore": {
-        "class": "[LocalFileStore](https://python.langchain.com/v0.2/api_reference/storage/langchain.storage.file_system.LocalFileStore.html)",
+        "class": "[LocalFileStore](https://python.langchain.com/api_reference/storage/langchain.storage.file_system.LocalFileStore.html)",
         "local": True,
-        "package": "[langchain](https://python.langchain.com/v0.2/api_reference/langchain/)",
+        "package": "[langchain](https://python.langchain.com/api_reference/langchain/)",
         "downloads": "![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain?style=flat-square&label=%20)",
     },
     "InMemoryByteStore": {
-        "class": "[InMemoryByteStore](https://python.langchain.com/v0.2/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html)",
+        "class": "[InMemoryByteStore](https://python.langchain.com/api_reference/core/stores/langchain_core.stores.InMemoryByteStore.html)",
         "local": True,
-        "package": "[langchain_core](https://python.langchain.com/v0.2/api_reference/core/)",
+        "package": "[langchain_core](https://python.langchain.com/api_reference/core/)",
         "downloads": "![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_core?style=flat-square&label=%20)",
     },
     "RedisStore": {
-        "class": "[RedisStore](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html)",
+        "class": "[RedisStore](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.redis.RedisStore.html)",
         "local": True,
-        "package": "[langchain_community](https://python.langchain.com/v0.2/api_reference/community/)",
+        "package": "[langchain_community](https://python.langchain.com/api_reference/community/)",
         "downloads": "![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20)",
     },
     "UpstashRedisByteStore": {
-        "class": "[UpstashRedisByteStore](https://python.langchain.com/v0.2/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html)",
+        "class": "[UpstashRedisByteStore](https://python.langchain.com/api_reference/community/storage/langchain_community.storage.upstash_redis.UpstashRedisByteStore.html)",
         "local": False,
-        "package": "[langchain_community](https://python.langchain.com/v0.2/api_reference/community/)",
+        "package": "[langchain_community](https://python.langchain.com/api_reference/community/)",
         "downloads": "![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_community?style=flat-square&label=%20)",
     },
 }
diff --git a/docs/scripts/partner_pkg_table.py b/docs/scripts/partner_pkg_table.py
index a9a303e7296..4b733e59fcf 100644
--- a/docs/scripts/partner_pkg_table.py
+++ b/docs/scripts/partner_pkg_table.py
@@ -100,11 +100,11 @@ def package_row(name: str) -> str:
         "db", "DB"
     ).replace("Db", "DB").replace("ai", "AI").replace("Ai", "AI")
     provider = f"[{title}]({link})" if link else title
-    return f"| {provider} | [langchain-{name}](https://python.langchain.com/v0.2/api_reference/{name.replace('-', '_')}/) | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-{name}?style=flat-square&label=%20&color=blue) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-{name}?style=flat-square&label=%20&color=orange) | {js} |"
+    return f"| {provider} | [langchain-{name}](https://python.langchain.com/api_reference/{name.replace('-', '_')}/) | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-{name}?style=flat-square&label=%20&color=blue) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-{name}?style=flat-square&label=%20&color=orange) | {js} |"
 
 
 def table() -> str:
-    header = """| Provider | Package | Downloads | Latest | [JS](https://js.langchain.com/v0.2/docs/integrations/platforms/) |
+    header = """| Provider | Package | Downloads | Latest | [JS](https://js.langchain.com/docs/integrations/platforms/) |
 | :--- | :---: | :---: | :---: | :---: |
 """
     return header + "\n".join(package_row(name) for name in sorted(ALL_PACKAGES))
diff --git a/docs/src/theme/FeatureTables.js b/docs/src/theme/FeatureTables.js
index 3b4857a1453..07688324669 100644
--- a/docs/src/theme/FeatureTables.js
+++ b/docs/src/theme/FeatureTables.js
@@ -26,7 +26,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": true,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html"
+                "apiLink": "https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html"
                 
             },
             {
@@ -38,7 +38,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html"
+                "apiLink": "https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html"
             },
             {
                 "name": "ChatFireworks",
@@ -49,7 +49,7 @@ const FEATURE_TABLES = {
                 "json_mode": true,
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
+                "apiLink": "https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html"
             },
             {
                 "name": "AzureChatOpenAI",
@@ -60,7 +60,7 @@ const FEATURE_TABLES = {
                 "json_mode": true,
                 "multimodal": true,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html"
+                "apiLink": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html"
             },
             {
                 "name": "ChatOpenAI",
@@ -71,7 +71,7 @@ const FEATURE_TABLES = {
                 "json_mode": true,
                 "multimodal": true,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html"
+                "apiLink": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html"
             },
             {
                 "name": "ChatTogether",
@@ -82,7 +82,7 @@ const FEATURE_TABLES = {
                 "json_mode": true,
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
+                "apiLink": "https://python.langchain.com/api_reference/together/chat_models/langchain_together.chat_models.ChatTogether.html"
             },
             {
                 "name": "ChatVertexAI",
@@ -93,7 +93,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": true,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
+                "apiLink": "https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html"
             },
             {
                 "name": "ChatGoogleGenerativeAI",
@@ -104,7 +104,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": true,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html"
+                "apiLink": "https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html"
             },
             {
                 "name": "ChatGroq",
@@ -115,7 +115,7 @@ const FEATURE_TABLES = {
                 "json_mode": true,
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
+                "apiLink": "https://python.langchain.com/api_reference/groq/chat_models/langchain_groq.chat_models.ChatGroq.html"
             },
             {
                 "name": "ChatCohere",
@@ -126,7 +126,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/cohere/chat_models/langchain_cohere.chat_models.ChatCohere.html"
+                "apiLink": "https://python.langchain.com/api_reference/cohere/chat_models/langchain_cohere.chat_models.ChatCohere.html"
             },
             {
                 "name": "ChatBedrock",
@@ -137,7 +137,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html"
+                "apiLink": "https://python.langchain.com/api_reference/aws/chat_models/langchain_aws.chat_models.bedrock.ChatBedrock.html"
             },
             {
                 "name": "ChatHuggingFace",
@@ -148,7 +148,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": false,
                 "local": true,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html",
+                "apiLink": "https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html",
             },
             {
                 "name": "ChatNVIDIA",
@@ -159,7 +159,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": false,
                 "local": true,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html"
+                "apiLink": "https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html"
             },
             {
                 "name": "ChatOllama",
@@ -170,7 +170,7 @@ const FEATURE_TABLES = {
                 "json_mode": true,
                 "multimodal": false,
                 "local": true,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html"
+                "apiLink": "https://python.langchain.com/api_reference/ollama/chat_models/langchain_ollama.chat_models.ChatOllama.html"
             },
             {
                 "name": "ChatLlamaCpp",
@@ -181,7 +181,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": false,
                 "local": true,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html"
+                "apiLink": "https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.llamacpp.ChatLlamaCpp.html"
             },
             {
                 "name": "ChatAI21",
@@ -192,7 +192,7 @@ const FEATURE_TABLES = {
                 "json_mode": false,
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html"
+                "apiLink": "https://python.langchain.com/api_reference/ai21/chat_models/langchain_ai21.chat_models.ChatAI21.html"
             },
             {
                 "name": "ChatUpstage",
@@ -203,7 +203,7 @@ const FEATURE_TABLES = {
                 "json_mode": false, 
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/upstage/chat_models/langchain_upstage.chat_models.ChatUpstage.html"
+                "apiLink": "https://python.langchain.com/api_reference/upstage/chat_models/langchain_upstage.chat_models.ChatUpstage.html"
             },
             {
                 "name": "ChatDatabricks",
@@ -214,7 +214,7 @@ const FEATURE_TABLES = {
                 "json_mode": false, 
                 "multimodal": false,
                 "local": false,
-                "apiLink": "https://python.langchain.com/v0.2/api_reference/upstage/chat_models/langchain_databricks.chat_models.ChatDatabricks.html"
+                "apiLink": "https://python.langchain.com/api_reference/upstage/chat_models/langchain_databricks.chat_models.ChatDatabricks.html"
             }
         ],
     },
@@ -233,61 +233,61 @@ const FEATURE_TABLES = {
                 name: "AI21LLM",
                 link: "ai21",
                 package: "langchain-ai21",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/ai21/llms/langchain_ai21.llms.AI21LLM.html"
+                apiLink: "https://python.langchain.com/api_reference/ai21/llms/langchain_ai21.llms.AI21LLM.html"
             },
             {
                 name: "AnthropicLLM",
                 link: "anthropic",
                 package: "langchain-anthropic",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/anthropic/llms/langchain_anthropic.llms.AnthropicLLM.html"
+                apiLink: "https://python.langchain.com/api_reference/anthropic/llms/langchain_anthropic.llms.AnthropicLLM.html"
             },
             {
                 name: "AzureOpenAI",
                 link: "azure_openai",
                 package: "langchain-openai",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/openai/llms/langchain_openai.llms.azure.AzureOpenAI.html"
+                apiLink: "https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.azure.AzureOpenAI.html"
             },
             {
                 name: "BedrockLLM",
                 link: "bedrock",
                 package: "langchain-aws",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/aws/llms/langchain_aws.llms.bedrock.BedrockLLM.html"
+                apiLink: "https://python.langchain.com/api_reference/aws/llms/langchain_aws.llms.bedrock.BedrockLLM.html"
             },
             {
                 name: "CohereLLM",
                 link: "cohere",
                 package: "langchain-cohere",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/cohere/llms/langchain_cohere.llms.Cohere.html"
+                apiLink: "https://python.langchain.com/api_reference/cohere/llms/langchain_cohere.llms.Cohere.html"
             },
             {
                 name: "FireworksLLM",
                 link: "fireworks",
                 package: "langchain-fireworks",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html"
+                apiLink: "https://python.langchain.com/api_reference/fireworks/llms/langchain_fireworks.llms.Fireworks.html"
             },
             {
                 name: "OllamaLLM",
                 link: "ollama",
                 package: "langchain-ollama",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/ollama/llms/langchain_ollama.llms.OllamaLLM.html"
+                apiLink: "https://python.langchain.com/api_reference/ollama/llms/langchain_ollama.llms.OllamaLLM.html"
             },
             {
                 name: "OpenAILLM",
                 link: "openai",
                 package: "langchain-openai",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
+                apiLink: "https://python.langchain.com/api_reference/openai/llms/langchain_openai.llms.base.OpenAI.html"
             },
             {
                 name: "TogetherLLM",
                 link: "together",
                 package: "langchain-together",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/together/llms/langchain_together.llms.Together.html"
+                apiLink: "https://python.langchain.com/api_reference/together/llms/langchain_together.llms.Together.html"
             },
             {
                 name: "VertexAILLM",
                 link: "google_vertexai",
                 package: "langchain-google_vertexai",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/google_vertexai/llms/langchain_google_vertexai.llms.VertexAI.html"
+                apiLink: "https://python.langchain.com/api_reference/google_vertexai/llms/langchain_google_vertexai.llms.VertexAI.html"
             },
         ],
     },
@@ -302,67 +302,67 @@ const FEATURE_TABLES = {
                 name: "AzureOpenAI",
                 link: "azureopenai",
                 package: "langchain-openai",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.azure.AzureOpenAIEmbeddings.html"
             },
             {
                 name: "Ollama",
                 link: "ollama",
                 package: "langchain-ollama",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/ollama/embeddings/langchain_ollama.embeddings.OllamaEmbeddings.html"
             },
             {
                 name: "AI21",
                 link: "ai21",
                 package: "langchain-ai21",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html"
             },
             {
                 name: "Fake",
                 link: "fake",
                 package: "langchain-core",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/core/embeddings/langchain_core.embeddings.fake.FakeEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/core/embeddings/langchain_core.embeddings.fake.FakeEmbeddings.html"
             },
             {
                 name: "OpenAI",
                 link: "openai",
                 package: "langchain-openai",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html"
+                apiLink: "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html"
             },
             {
                 name: "Together",
                 link: "together",
                 package: "langchain-together",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/together/embeddings/langchain_together.embeddings.TogetherEmbeddings.html"
             },
             {
                 name: "Fireworks",
                 link: "fireworks",
                 package: "langchain-fireworks",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/fireworks/embeddings/langchain_fireworks.embeddings.FireworksEmbeddings.html"
             },
             {
                 name: "MistralAI",
                 link: "mistralai",
                 package: "langchain-mistralai",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/mistralai/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html"
             },
             {
                 name: "Cohere",
                 link: "cohere",
                 package: "langchain-cohere",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/cohere/embeddings/langchain_cohere.embeddings.CohereEmbeddings.html"
             },
             {
                 name: "Nomic",
                 link: "cohere",
                 package: "langchain-nomic",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/nomic/embeddings/langchain_nomic.embeddings.NomicEmbeddings.html"
             },
             {
                 name: "Databricks",
                 link: "databricks",
                 package: "langchain-databricks",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/nomic/embeddings/langchain_databricks.embeddings.DatabricksEmbeddings.html"
+                apiLink: "https://python.langchain.com/api_reference/nomic/embeddings/langchain_databricks.embeddings.DatabricksEmbeddings.html"
             },
         ]
     },
@@ -380,7 +380,7 @@ const FEATURE_TABLES = {
                 link: "bedrock",
                 selfHost: false,
                 cloudOffering: true,
-                apiLink: "https://python.langchain.com/v0.2/api_reference/aws/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/aws/retrievers/langchain_aws.retrievers.bedrock.AmazonKnowledgeBasesRetriever.html",
                 package: "langchain_aws"
             },
             {
@@ -388,7 +388,7 @@ const FEATURE_TABLES = {
                 link: "azure_ai_search",
                 selfHost: false,
                 cloudOffering: true,
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.azure_ai_search.AzureAISearchRetriever.html",
                 package: "langchain_community"
             },
             {
@@ -396,7 +396,7 @@ const FEATURE_TABLES = {
                 link: "elasticsearch_retriever",
                 selfHost: true,
                 cloudOffering: true,
-                apiLink: "https://python.langchain.com/v0.2/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/elasticsearch/retrievers/langchain_elasticsearch.retrievers.ElasticsearchRetriever.html",
                 package: "langchain_elasticsearch"
             },
             {
@@ -404,7 +404,7 @@ const FEATURE_TABLES = {
                 link: "milvus_hybrid_search",
                 selfHost: true,
                 cloudOffering: false,
-                apiLink: "https://python.langchain.com/v0.2/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/milvus/retrievers/langchain_milvus.retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever.html",
                 package: "langchain_milvus"
             },
             {
@@ -412,7 +412,7 @@ const FEATURE_TABLES = {
                 link: "google_vertex_ai_search",
                 selfHost: false,
                 cloudOffering: true,
-                apiLink: "https://python.langchain.com/v0.2/api_reference/google_community/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/google_community/vertex_ai_search/langchain_google_community.vertex_ai_search.VertexAISearchRetriever.html",
                 package: "langchain_google_community"
             }
         ],
@@ -433,21 +433,21 @@ const FEATURE_TABLES = {
                 name: "ArxivRetriever",
                 link: "arxiv",
                 source: (<>Scholarly articles on <a href="https://arxiv.org/">arxiv.org</a></>),
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.arxiv.ArxivRetriever.html",
                 package: "langchain_community"
             },
             {
                 name: "TavilySearchAPIRetriever",
                 link: "tavily",
                 source: "Internet search",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.tavily_search_api.TavilySearchAPIRetriever.html",
                 package: "langchain_community"
             },
             {
                 name: "WikipediaRetriever",
                 link: "wikipedia",
                 source: (<><a href="https://www.wikipedia.org/">Wikipedia</a> articles</>),
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html",
+                apiLink: "https://python.langchain.com/api_reference/community/retrievers/langchain_community.retrievers.wikipedia.WikipediaRetriever.html",
                 package: "langchain_community"
             }
         ]
@@ -477,7 +477,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from an AWS S3 directory",
                 partnerPackage: false,
                 loaderName: "S3DirectoryLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.s3_directory.S3DirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.s3_directory.S3DirectoryLoader.html"
             },
             {
                 name: "AWS S3 File",
@@ -485,7 +485,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from an AWS S3 file",
                 partnerPackage: false,
                 loaderName: "S3FileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.s3_file.S3FileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.s3_file.S3FileLoader.html"
             },
             {
                 name: "Azure AI Data",
@@ -493,7 +493,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Azure AI services",
                 partnerPackage: false,
                 loaderName: "AzureAIDataLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.azure_ai_data.AzureAIDataLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.azure_ai_data.AzureAIDataLoader.html"
             },
             {
                 name: "Azure Blob Storage Container",
@@ -501,7 +501,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from an Azure Blob Storage container",
                 partnerPackage: false,
                 loaderName: "AzureBlobStorageContainerLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader.html"
             },
             {
                 name: "Azure Blob Storage File",
@@ -509,7 +509,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from an Azure Blob Storage file",
                 partnerPackage: false,
                 loaderName: "AzureBlobStorageFileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.azure_blob_storage_file.AzureBlobStorageFileLoader.html"
             },
             {
                 name: "Dropbox",
@@ -517,7 +517,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Dropbox",
                 partnerPackage: false,
                 loaderName: "DropboxLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.dropbox.DropboxLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.dropbox.DropboxLoader.html"
             },
             {
                 name: "Google Cloud Storage Directory",
@@ -525,7 +525,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from GCS bucket",
                 partnerPackage: true,
                 loaderName: "GCSDirectoryLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/google_community/gcs_directory/langchain_google_community.gcs_directory.GCSDirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/google_community/gcs_directory/langchain_google_community.gcs_directory.GCSDirectoryLoader.html"
             },
             {
                 name: "Google Cloud Storage File",
@@ -533,7 +533,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from GCS file object",
                 partnerPackage: true,
                 loaderName: "GCSFileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/google_community/gcs_file/langchain_google_community.gcs_file.GCSFileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/google_community/gcs_file/langchain_google_community.gcs_file.GCSFileLoader.html"
             },
             {
                 name: "Google Drive",
@@ -541,7 +541,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Google Drive (Google Docs only)",
                 partnerPackage: true,
                 loaderName: "GoogleDriveLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/google_community/drive/langchain_google_community.drive.GoogleDriveLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/google_community/drive/langchain_google_community.drive.GoogleDriveLoader.html"
             },
             {
                 name: "Huawei OBS Directory",
@@ -549,7 +549,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Huawei Object Storage Service Directory",
                 partnerPackage: false,
                 loaderName: "OBSDirectoryLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.obs_directory.OBSDirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.obs_directory.OBSDirectoryLoader.html"
             },
             {
                 name: "Huawei OBS File",
@@ -557,7 +557,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Huawei Object Storage Service File",
                 partnerPackage: false,
                 loaderName: "OBSFileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.obs_file.OBSFileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.obs_file.OBSFileLoader.html"
             },
             {
                 name: "Microsoft OneDrive",
@@ -565,7 +565,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Microsoft OneDrive",
                 partnerPackage: false,
                 loaderName: "OneDriveLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.onedrive.OneDriveLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.onedrive.OneDriveLoader.html"
             },
             {
                 name: "Microsoft SharePoint",
@@ -573,7 +573,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Microsoft SharePoint",
                 partnerPackage: false,
                 loaderName: "SharePointLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.sharepoint.SharePointLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sharepoint.SharePointLoader.html"
                 
             },
             {
@@ -582,7 +582,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Tencent Cloud Object Storage Directory",
                 partnerPackage: false,
                 loaderName: "TencentCOSDirectoryLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.tencent_cos_directory.TencentCOSDirectoryLoader.html"
             },
             {
                 name: "Tencent COS File",
@@ -590,7 +590,7 @@ const FEATURE_TABLES = {
                 source: "Load documents from Tencent Cloud Object Storage File",
                 partnerPackage: false,
                 loaderName: "TencentCOSFileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.tencent_cos_file.TencentCOSFileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.tencent_cos_file.TencentCOSFileLoader.html"
             },
         ]
     },
@@ -609,31 +609,31 @@ const FEATURE_TABLES = {
                 name: "Telegram",
                 link: "telegram",
                 loaderName: "TelegramChatFileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.telegram.TelegramChatFileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.telegram.TelegramChatFileLoader.html"
             },
             {
                 name: "WhatsApp",
                 link: "whatsapp_chat",
                 loaderName: "WhatsAppChatLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/chat_loaders/langchain_community.chat_loaders.whatsapp.WhatsAppChatLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/chat_loaders/langchain_community.chat_loaders.whatsapp.WhatsAppChatLoader.html"
             },
             {
                 name: "Discord",
                 link: "discord",
                 loaderName: "DiscordChatLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.discord.DiscordChatLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.discord.DiscordChatLoader.html"
             },
             {
                 name: "Facebook Chat",
                 link: "facebook_chat",
                 loaderName: "FacebookChatLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.facebook_chat.FacebookChatLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.facebook_chat.FacebookChatLoader.html"
             },
             {
                 name: "Mastodon",
                 link: "mastodon",
                 loaderName: "MastodonTootsLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.mastodon.MastodonTootsLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.mastodon.MastodonTootsLoader.html"
             }
         ]
     },
@@ -652,43 +652,43 @@ const FEATURE_TABLES = {
                 name: "Figma",
                 link: "figma",
                 loaderName: "FigmaFileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.figma.FigmaFileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.figma.FigmaFileLoader.html"
             },
             {
                 name: "Notion",
                 link: "notion",
                 loaderName: "NotionDirectoryLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.notion.NotionDirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.notion.NotionDirectoryLoader.html"
             },
             {
                 name: "Slack",
                 link: "slack",
                 loaderName: "SlackDirectoryLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.slack_directory.SlackDirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.slack_directory.SlackDirectoryLoader.html"
             },
             {
                 name: "Quip",
                 link: "quip",
                 loaderName: "QuipLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.quip.QuipLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.quip.QuipLoader.html"
             },
             {
                 name: "Trello",
                 link: "trello",
                 loaderName: "TrelloLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.trello.TrelloLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.trello.TrelloLoader.html"
             },
             {
                 name: "Roam",
                 link: "roam",
                 loaderName: "RoamLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.roam.RoamLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.roam.RoamLoader.html"
             },
             {
                 name: "GitHub",
                 link: "github",
                 loaderName: "GithubFileLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.github.GithubFileLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.github.GithubFileLoader.html"
             }
         ]
     },
@@ -707,14 +707,14 @@ const FEATURE_TABLES = {
                 name: "Twitter",
                 link: "twitter",
                 loaderName: "TwitterTweetLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.twitter.TwitterTweetLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.twitter.TwitterTweetLoader.html"
                 
             },
             {
                 name: "Reddit",
                 link: "RedditPostsLoader",
                 loaderName: "RedditPostsLoader",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.reddit.RedditPostsLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.reddit.RedditPostsLoader.html"
             },
         ]
     },
@@ -733,28 +733,28 @@ const FEATURE_TABLES = {
                 link: "web_base",
                 source: "Uses urllib and BeautifulSoup to load and parse HTML web pages",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html"
             },
             {
                 name: "RecursiveURL",
                 link: "recursive_url",
                 source: "Recursively scrapes all child links from a root URL",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.recursive_url_loader.RecursiveUrlLoader.html"
             },
             {
                 name: "Sitemap",
                 link: "sitemap",
                 source: "Scrapes all pages on a given sitemap",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.sitemap.SitemapLoader.html"
             },
             {
                 name: "Firecrawl",
                 link: "firecrawl",
                 source: "API service that can be deployed locally, hosted version has free credits.",
                 api: "API",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.firecrawl.FireCrawlLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.firecrawl.FireCrawlLoader.html"
             }
         ]
     },
@@ -773,70 +773,70 @@ const FEATURE_TABLES = {
                 link: "pypdfloader",
                 source: "Uses `pypdf` to load and parse PDFs",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html"
             },
             {
                 name: "Unstructured",
                 link: "unstructured_file",
                 source: "Uses Unstructured's open source library to load PDFs",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html"
             },
             {
                 name: "Amazon Textract",
                 link: "amazon_textract",
                 source: "Uses AWS API to load PDFs",
                 api: "API",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.AmazonTextractPDFLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.AmazonTextractPDFLoader.html"
             },
             {
                 name: "MathPix",
                 link: "mathpix",
                 source: "Uses MathPix to laod PDFs",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.MathpixPDFLoader.html"
             },
             {
                 name: "PDFPlumber",
                 link: "pdfplumber",
                 source: "Load PDF files using PDFPlumber",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFPlumberLoader.html"
             },
             {
                 name: "PyPDFDirectry",
                 link: "pypdfdirectory",
                 source: "Load a directory with PDF files",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFDirectoryLoader.html"
             },
             {
                 name: "PyPDFium2",
                 link: "pypdfium2",
                 source: "Load PDF files using PyPDFium2",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html"
             },
             {
                 name: "UnstructuredPDFLoader",
                 link: "unstructured_pdfloader",
                 source: "Load PDF files using Unstructured",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.UnstructuredPDFLoader.html"
             },
             {
                 name: "PyMuPDF",
                 link: "pymupdf",
                 source: "Load PDF files using PyMuPDF",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyMuPDFLoader.html"
             },
             {
                 name: "PDFMiner",
                 link: "pdfminer",
                 source: "Load PDF files using PDFMiner",
                 api: "Package",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFMinerLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PDFMinerLoader.html"
             }
         ]
     },
@@ -853,43 +853,43 @@ const FEATURE_TABLES = {
                 name: "CSVLoader",
                 link: "csv",
                 source: "CSV files",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.csv_loader.CSVLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.csv_loader.CSVLoader.html"
             },
             {
                 name: "DirectoryLoader",
                 link: "../../how_to/document_loader_directory",
                 source: "All files in a given directory",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.directory.DirectoryLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.directory.DirectoryLoader.html"
             },
             {
                 name: "Unstructured",
                 link: "unstructured_file",
                 source: "All file types",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/unstructured/document_loaders/langchain_unstructured.document_loaders.UnstructuredLoader.html"
             },
             {
                 name: "JSONLoader",
                 link: "json",
                 source: "JSON files",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html"
             },
             {
                 name: "UnstructuredMarkdownLoader",
                 link: "unstructured_markdown",
                 source: "Markdown files",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html"
             },
             {
                 name: "BSHTMLLoader",
                 link: "bshtml",
                 source: "HTML files",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html"
             },
             {
                 name: "UnstrucutredXMLLoader",
                 link: "xml",
                 source: "XML files",
-                apiLink: "https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
+                apiLink: "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.xml.UnstructuredXMLLoader.html"
             }
         ]
     },
@@ -1003,7 +1003,7 @@ const FEATURE_TABLES = {
             },
             {
                 name: "InMemoryVectorStore",
-                link: "https://python.langchain.com/v0.2/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html",
+                link: "https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.in_memory.InMemoryVectorStore.html",
                 deleteById: true,
                 filtering: true,
                 searchByVector: false,
diff --git a/docs/vercel.json b/docs/vercel.json
index 7827704d922..944b5ff35b5 100644
--- a/docs/vercel.json
+++ b/docs/vercel.json
@@ -5,8 +5,8 @@
   "trailingSlash": true,
   "rewrites": [
     {
-      "source": "/v0.2/docs/integrations(/?)",
-      "destination": "/v0.2/docs/integrations/platforms/"
+      "source": "/docs/integrations(/?)",
+      "destination": "/docs/integrations/platforms/"
     },
     {
       "source": "/v0.1",
@@ -17,110 +17,46 @@
       "destination": "https://langchain-v01.vercel.app/v0.1/:path*"
     },
     {
-      "source": "/robots.txt(/?)",
-      "destination": "/v0.2/robots.txt/"
+      "source": "/v0.2",
+      "destination": "https://langchain-v02.vercel.app/v0.2"
     },
     {
-      "source": "/sitemap.xml(/?)",
-      "destination": "/v0.2/sitemap.xml/"
+      "source": "/v0.2/:path(.*/?)*",
+      "destination": "https://langchain-v02.vercel.app/v0.2/:path*"
     }
   ],
   "redirects": [
-    {
-      "source": "/v0.2/docs/versions/packages(/?)",
-      "destination": "/v0.2/docs/versions/release_policy/"
-    },
-    {
-      "source": "/v0.2/docs/integrations/llms/llm_caching(/?)",
-      "destination": "/v0.2/docs/integrations/llm_caching/"
-    },
     {
       "source": "/docs/how_to/tool_calls_multi_modal(/?)",
       "destination": "/docs/how_to/multimodal_inputs/"
     },
     {
-      "source": "/v0.2/docs/langsmith(/?)",
+      "source": "/docs/langsmith(/?)",
       "destination": "https://docs.smith.langchain.com/"
     },
     {
-      "source": "/v0.2/docs/langgraph(/?)",
+      "source": "/docs/langgraph(/?)",
       "destination": "https://langchain-ai.github.io/langgraph"
     },
     {
       "source": "/",
-      "destination": "/v0.2/docs/introduction/"
+      "destination": "/docs/introduction/"
     },
     {
       "source": "/docs(/?)",
-      "destination": "/v0.2/docs/introduction/"
+      "destination": "/docs/introduction/"
     },
     {
       "source": "/docs/get_started/introduction(/?)",
-      "destination": "/v0.2/docs/introduction/"
+      "destination": "/docs/introduction/"
     },
     {
-      "source": "/docs/integrations/:path(.*/?)*",
-      "destination": "/v0.2/docs/integrations/:path*"
-    },
-    {
-      "source": "/docs/:path(.*/?)*",
-      "destination": "/v0.1/docs/:path*"
-    },
-    {
-      "source": "/cookbook(/?)",
-      "destination": "/v0.1/docs/cookbook/"
-    },
-    {
-      "source": "/v0.2/docs/how_to/migrate_chains(/?)",
-      "destination": "/v0.2/docs/versions/migrating_chains"
-    },
-    {
-      "source": "/v0.2/docs/integrations/tools/search_tools/",
-      "destination": "/v0.2/docs/integrations/tools#search"
-    },
-    {
-      "source": "/v0.2/docs/integrations/toolkits/airbyte_structured_qa/",
-      "destination": "/v0.2/docs/integrations/document_loaders/airbyte/"
-    },
-    {
-      "source": "/v0.2/docs/integrations/tools/connery_toolkit/",
-      "destination": "/v0.2/docs/integrations/tools/connery/"
-    },
-    {
-      "source": "/v0.2/docs/integrations/tools/polygon_toolkit/",
-      "destination": "/v0.2/docs/integrations/tools/polygon/"
-    },
-    {
-      "source": "/v0.2/docs/integrations/toolkits/document_comparison_toolkit(/?)",
-      "destination": "/v0.2/docs/tutorials/rag/"
-    },
-    {
-      "source": "/v0.2/docs/integrations/toolkits/:path(.*/?)*",
-      "destination": "/v0.2/docs/integrations/tools/:path*"
-    },
-    {
-      "source": "/v0.2/docs/integrations/toolkits/spark/",
-      "destination": "/v0.2/docs/integrations/tools/spark_sql/"
-    },
-    {
-      "source": "/v0.2/docs/integrations/toolkits/xorbits/",
-      "destination": "/v0.2/docs/integrations/tools#search"
-    },
-    {
-      "source": "/v0.2/docs/integrations/document_loaders/notiondb/",
-      "destination": "/v0.2/docs/integrations/document_loaders/notion/"
-    },
-    {
-      "source": "/v0.2/docs/integrations/chat/ollama_functions/",
-      "destination": "https://python.langchain.com/v0.1/docs/integrations/chat/ollama_functions/"
+      "source": "/docs/how_to/migrate_chains(/?)",
+      "destination": "/docs/versions/migrating_chains"
     },
     {
       "source": "/v0.2/docs/templates/:path(.*/?)*",
       "destination": "https://github.com/langchain-ai/langchain/tree/master/templates/:path*"
-    },
-    {
-      "source": "/v0.2/docs/integrations/text_embedding/nemo/",
-      "destination": "/v0.2/docs/integrations/text_embedding/nvidia_ai_endpoints/"
     }
   ]
 }