bump 228 (#7393 )

Added param to return only a structured json from the get_format_instructions method (#5848 )
I just added a parameter to the method get_format_instructions, to return directly the JSON instructions without the leading instruction sentence. I'm planning to use it to define the structure of a JSON object passed in input, the get_format_instructions(). --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
2026-02-07 01:30:24 +00:00 · 2023-07-08 03:05:20 -04:00 · 2023-07-08 02:57:26 -04:00 · 2023-07-08 02:41:54 -04:00 · 2023-07-08 02:17:04 -04:00 · 2023-07-07 23:06:49 -07:00
348 changed files with 14299 additions and 2245 deletions
--- a/docs/api_reference/api_reference.rst
+++ b/docs/api_reference/api_reference.rst
@@ -165,28 +165,35 @@ Classes
    callbacks.aim_callback.AimCallbackHandler
    callbacks.argilla_callback.ArgillaCallbackHandler
    callbacks.arize_callback.ArizeCallbackHandler
+    callbacks.arthur_callback.ArthurCallbackHandler
    callbacks.base.AsyncCallbackHandler
    callbacks.base.BaseCallbackHandler
    callbacks.base.BaseCallbackManager
    callbacks.clearml_callback.ClearMLCallbackHandler
    callbacks.comet_ml_callback.CometCallbackHandler
    callbacks.file.FileCallbackHandler
+    callbacks.flyte_callback.FlyteCallbackHandler
    callbacks.human.HumanApprovalCallbackHandler
    callbacks.human.HumanRejectedException
    callbacks.infino_callback.InfinoCallbackHandler
    callbacks.manager.AsyncCallbackManager
    callbacks.manager.AsyncCallbackManagerForChainRun
    callbacks.manager.AsyncCallbackManagerForLLMRun
+    callbacks.manager.AsyncCallbackManagerForRetrieverRun
    callbacks.manager.AsyncCallbackManagerForToolRun
+    callbacks.manager.AsyncParentRunManager
    callbacks.manager.AsyncRunManager
    callbacks.manager.BaseRunManager
    callbacks.manager.CallbackManager
    callbacks.manager.CallbackManagerForChainRun
    callbacks.manager.CallbackManagerForLLMRun
+    callbacks.manager.CallbackManagerForRetrieverRun
    callbacks.manager.CallbackManagerForToolRun
+    callbacks.manager.ParentRunManager
    callbacks.manager.RunManager
    callbacks.mlflow_callback.MlflowCallbackHandler
    callbacks.openai_info.OpenAICallbackHandler
+    callbacks.promptlayer_callback.PromptLayerCallbackHandler
    callbacks.stdout.StdOutCallbackHandler
    callbacks.streaming_aiter.AsyncIteratorCallbackHandler
    callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler
@@ -229,6 +236,8 @@ Functions
    callbacks.aim_callback.import_aim
    callbacks.clearml_callback.import_clearml
    callbacks.comet_ml_callback.import_comet_ml
+    callbacks.flyte_callback.analyze_text
+    callbacks.flyte_callback.import_flytekit
    callbacks.infino_callback.import_infino
    callbacks.manager.env_var_is_set
    callbacks.manager.get_openai_callback
@@ -283,9 +292,11 @@ Classes
    chains.base.Chain
    chains.combine_documents.base.AnalyzeDocumentChain
    chains.combine_documents.base.BaseCombineDocumentsChain
-    chains.combine_documents.map_reduce.CombineDocsProtocol
    chains.combine_documents.map_reduce.MapReduceDocumentsChain
    chains.combine_documents.map_rerank.MapRerankDocumentsChain
+    chains.combine_documents.reduce.AsyncCombineDocsProtocol
+    chains.combine_documents.reduce.CombineDocsProtocol
+    chains.combine_documents.reduce.ReduceDocumentsChain
    chains.combine_documents.refine.RefineDocumentsChain
    chains.combine_documents.stuff.StuffDocumentsChain
    chains.constitutional_ai.base.ConstitutionalChain
@@ -299,8 +310,10 @@ Classes
    chains.flare.prompts.FinishedOutputParser
    chains.graph_qa.base.GraphQAChain
    chains.graph_qa.cypher.GraphCypherQAChain
+    chains.graph_qa.hugegraph.HugeGraphQAChain
    chains.graph_qa.kuzu.KuzuQAChain
    chains.graph_qa.nebulagraph.NebulaGraphQAChain
+    chains.graph_qa.sparql.GraphSparqlQAChain
    chains.hyde.base.HypotheticalDocumentEmbedder
    chains.llm.LLMChain
    chains.llm_bash.base.LLMBashChain
@@ -363,7 +376,6 @@ Functions
 .. autosummary::
    :toctree: chains

-    chains.combine_documents.base.format_document
    chains.graph_qa.cypher.extract_cypher
    chains.loading.load_chain
    chains.loading.load_chain_from_config
@@ -415,6 +427,7 @@ Classes
    chat_models.fake.FakeListChatModel
    chat_models.google_palm.ChatGooglePalm
    chat_models.google_palm.ChatGooglePalmError
+    chat_models.human.HumanInputChatModel
    chat_models.openai.ChatOpenAI
    chat_models.promptlayer_openai.PromptLayerChatOpenAI
    chat_models.vertexai.ChatVertexAI
@@ -513,6 +526,7 @@ Classes
    document_loaders.blob_loaders.youtube_audio.YoutubeAudioLoader
    document_loaders.blockchain.BlockchainDocumentLoader
    document_loaders.blockchain.BlockchainType
+    document_loaders.brave_search.BraveSearchLoader
    document_loaders.chatgpt.ChatGPTLoader
    document_loaders.college_confidential.CollegeConfidentialLoader
    document_loaders.confluence.ConfluenceLoader
@@ -520,6 +534,7 @@ Classes
    document_loaders.conllu.CoNLLULoader
    document_loaders.csv_loader.CSVLoader
    document_loaders.csv_loader.UnstructuredCSVLoader
+    document_loaders.cube_semantic.CubeSemanticLoader
    document_loaders.dataframe.DataFrameLoader
    document_loaders.diffbot.DiffbotLoader
    document_loaders.directory.DirectoryLoader
@@ -736,6 +751,7 @@ Classes
    embeddings.self_hosted.SelfHostedEmbeddings
    embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceEmbeddings
    embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceInstructEmbeddings
+    embeddings.spacy_embeddings.SpacyEmbeddings
    embeddings.tensorflow_hub.TensorflowHubEmbeddings
    embeddings.vertexai.VertexAIEmbeddings

@@ -790,6 +806,9 @@ Classes
    evaluation.comparison.eval_chain.PairwiseStringResultOutputParser
    evaluation.criteria.eval_chain.CriteriaEvalChain
    evaluation.criteria.eval_chain.CriteriaResultOutputParser
+    evaluation.embedding_distance.base.EmbeddingDistance
+    evaluation.embedding_distance.base.EmbeddingDistanceEvalChain
+    evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain
    evaluation.qa.eval_chain.ContextQAEvalChain
    evaluation.qa.eval_chain.CotQAEvalChain
    evaluation.qa.eval_chain.QAEvalChain
@@ -799,10 +818,16 @@ Classes
    evaluation.run_evaluators.implementations.ChoicesOutputParser
    evaluation.run_evaluators.implementations.CriteriaOutputParser
    evaluation.run_evaluators.implementations.StringRunEvaluatorInputMapper
-    evaluation.run_evaluators.implementations.TrajectoryEvalOutputParser
    evaluation.run_evaluators.implementations.TrajectoryInputMapper
+    evaluation.run_evaluators.implementations.TrajectoryRunEvalOutputParser
+    evaluation.schema.AgentTrajectoryEvaluator
+    evaluation.schema.EvaluatorType
+    evaluation.schema.LLMEvalChain
    evaluation.schema.PairwiseStringEvaluator
    evaluation.schema.StringEvaluator
+    evaluation.string_distance.base.PairwiseStringDistanceEvalChain
+    evaluation.string_distance.base.StringDistance
+    evaluation.string_distance.base.StringDistanceEvalChain

 Functions
 --------------
@@ -812,6 +837,8 @@ Functions
    :toctree: evaluation

    evaluation.loading.load_dataset
+    evaluation.loading.load_evaluator
+    evaluation.loading.load_evaluators
    evaluation.run_evaluators.implementations.get_criteria_evaluator
    evaluation.run_evaluators.implementations.get_qa_evaluator
    evaluation.run_evaluators.implementations.get_trajectory_evaluator
@@ -1057,6 +1084,7 @@ Functions

    llms.aviary.get_completions
    llms.aviary.get_models
+    llms.base.create_base_retry_decorator
    llms.base.get_prompts
    llms.base.update_cache
    llms.cohere.completion_with_retry
@@ -1069,6 +1097,7 @@ Functions
    llms.openai.completion_with_retry
    llms.openai.update_token_usage
    llms.utils.enforce_stop_tokens
+    llms.vertexai.completion_with_retry
    llms.vertexai.is_codey_model

 :mod:`langchain.load`: Load
@@ -1241,7 +1270,6 @@ Classes
    :toctree: prompts
    :template: class.rst

-    prompts.base.BasePromptTemplate
    prompts.base.StringPromptTemplate
    prompts.base.StringPromptValue
    prompts.chat.AIMessagePromptTemplate
@@ -1316,7 +1344,7 @@ Classes
    retrievers.azure_cognitive_search.AzureCognitiveSearchRetriever
    retrievers.chatgpt_plugin_retriever.ChatGPTPluginRetriever
    retrievers.contextual_compression.ContextualCompressionRetriever
-    retrievers.databerry.DataberryRetriever
+    retrievers.chaindesk.ChaindeskRetriever
    retrievers.docarray.DocArrayRetriever
    retrievers.docarray.SearchType
    retrievers.document_compressors.base.BaseDocumentCompressor
@@ -1348,7 +1376,7 @@ Classes
    retrievers.multi_query.LineListOutputParser
    retrievers.multi_query.MultiQueryRetriever
    retrievers.pinecone_hybrid_search.PineconeHybridSearchRetriever
-    retrievers.pupmed.PubMedRetriever
+    retrievers.pubmed.PubMedRetriever
    retrievers.remote_retriever.RemoteLangChainRetriever
    retrievers.self_query.base.SelfQueryRetriever
    retrievers.self_query.chroma.ChromaTranslator
@@ -1400,28 +1428,29 @@ Classes
    :toctree: schema
    :template: class.rst

-    schema.AIMessage
-    schema.AgentFinish
-    schema.BaseChatMessageHistory
-    schema.BaseDocumentTransformer
-    schema.BaseLLMOutputParser
-    schema.BaseMemory
-    schema.BaseMessage
-    schema.BaseOutputParser
-    schema.BaseRetriever
-    schema.ChatGeneration
-    schema.ChatMessage
-    schema.ChatResult
-    schema.Document
-    schema.FunctionMessage
-    schema.Generation
-    schema.HumanMessage
-    schema.LLMResult
-    schema.NoOpOutputParser
-    schema.OutputParserException
-    schema.PromptValue
-    schema.RunInfo
-    schema.SystemMessage
+    schema.agent.AgentFinish
+    schema.document.BaseDocumentTransformer
+    schema.document.Document
+    schema.memory.BaseChatMessageHistory
+    schema.memory.BaseMemory
+    schema.messages.AIMessage
+    schema.messages.BaseMessage
+    schema.messages.ChatMessage
+    schema.messages.FunctionMessage
+    schema.messages.HumanMessage
+    schema.messages.SystemMessage
+    schema.output.ChatGeneration
+    schema.output.ChatResult
+    schema.output.Generation
+    schema.output.LLMResult
+    schema.output.RunInfo
+    schema.output_parser.BaseLLMOutputParser
+    schema.output_parser.BaseOutputParser
+    schema.output_parser.NoOpOutputParser
+    schema.output_parser.OutputParserException
+    schema.prompt.PromptValue
+    schema.prompt_template.BasePromptTemplate
+    schema.retriever.BaseRetriever

 Functions
 --------------
@@ -1430,9 +1459,10 @@ Functions
 .. autosummary::
    :toctree: schema

-    schema.get_buffer_string
-    schema.messages_from_dict
-    schema.messages_to_dict
+    schema.messages.get_buffer_string
+    schema.messages.messages_from_dict
+    schema.messages.messages_to_dict
+    schema.prompt_template.format_document

 :mod:`langchain.server`: Server
 ================================
@@ -1535,6 +1565,8 @@ Classes
    tools.bing_search.tool.BingSearchRun
    tools.brave_search.tool.BraveSearch
    tools.convert_to_openai.FunctionDescription
+    tools.dataforseo_api_search.tool.DataForSeoAPISearchResults
+    tools.dataforseo_api_search.tool.DataForSeoAPISearchRun
    tools.ddg_search.tool.DuckDuckGoSearchResults
    tools.ddg_search.tool.DuckDuckGoSearchRun
    tools.file_management.copy.CopyFileTool
@@ -1708,6 +1740,7 @@ Classes
    utilities.bibtex.BibtexparserWrapper
    utilities.bing_search.BingSearchAPIWrapper
    utilities.brave_search.BraveSearchWrapper
+    utilities.dataforseo_api_search.DataForSeoAPIWrapper
    utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper
    utilities.google_places_api.GooglePlacesAPIWrapper
    utilities.google_search.GoogleSearchAPIWrapper
@@ -1805,12 +1838,17 @@ Classes
    vectorstores.faiss.FAISS
    vectorstores.hologres.Hologres
    vectorstores.lancedb.LanceDB
+    vectorstores.marqo.Marqo
    vectorstores.matching_engine.MatchingEngine
    vectorstores.milvus.Milvus
    vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch
    vectorstores.myscale.MyScale
    vectorstores.myscale.MyScaleSettings
    vectorstores.opensearch_vector_search.OpenSearchVectorSearch
+    vectorstores.pgembedding.BaseModel
+    vectorstores.pgembedding.CollectionStore
+    vectorstores.pgembedding.EmbeddingStore
+    vectorstores.pgembedding.PGEmbedding
    vectorstores.pgvector.BaseModel
    vectorstores.pgvector.CollectionStore
    vectorstores.pgvector.DistanceStrategy
--- a/docs/api_reference/themes/scikit-learn-modern/nav.html
+++ b/docs/api_reference/themes/scikit-learn-modern/nav.html
@@ -16,22 +16,6 @@
  {%- set development_attrs = '' %}
 {%- endif %}

-{# title, link, link_attrs #}
-{%- set drop_down_navigation = [
-  ('Getting Started', pathto('getting_started'), ''),
-  ('Tutorial', pathto('tutorial/index'), ''),
-  ("What's new", pathto('whats_new/v' + version), ''),
-  ('Glossary', pathto('glossary'), ''),
-  ('Development', development_link, development_attrs),
-  ('FAQ', pathto('faq'), ''),
-  ('Support', pathto('support'), ''),
-  ('Related packages', pathto('related_projects'), ''),
-  ('Roadmap', pathto('roadmap'), ''),
-  ('Governance', pathto('governance'), ''),
-  ('About us', pathto('about'), ''),
-  ('GitHub', 'https://github.com/scikit-learn/scikit-learn', ''),
-  ('Other Versions and Download', 'https://scikit-learn.org/dev/versions.html', '')]
-%}

 <nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
  <div class="container-fluid {{ top_container_cls }} px-0">
--- a/docs/docs_skeleton/docs/_static/ChaindeskDashboard.png
+++ b/docs/docs_skeleton/docs/_static/ChaindeskDashboard.png
--- a/docs/docs_skeleton/docs/get_started/tutorials.mdx
+++ b/docs/docs_skeleton/docs/get_started/tutorials.mdx
@@ -0,0 +1,124 @@
+# Tutorials
+
+
+⛓ icon marks a new addition [last update 2023-07-05]
+
+---------------------
+
+### DeepLearning.AI courses
+ by [Harrison Chase](https://github.com/hwchase17) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
+- [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain)
+- ⛓ [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data)
+
+### Handbook
+[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
+
+### Short Tutorials
+[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
+
+[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
+
+[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+
+
+##  Tutorials
+
+### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs)
+- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
+- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
+- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
+- [LangChain Data Loaders, Tokenizers, Chunking, and Datasets - Data Prep 101](https://youtu.be/eqOfr4AGLk8)
+- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
+- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
+- #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
+- #7 [LangChain Agents Deep Dive with `GPT 3.5`](https://youtu.be/jSP-gSEyVeI)
+- #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
+- #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
+- [Using NEW `MPT-7B` in Hugging Face and LangChain](https://youtu.be/DXpk9K7DgMo)
+- ⛓ [`MPT-30B` Chatbot with LangChain](https://youtu.be/pnem-EhT6VI)
+
+
+### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent)
+- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
+- [Quickstart Guide](https://youtu.be/kYRB-vJFy38)
+- [Beginner Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
+- [Beginner Guide To 9 Use Cases](https://youtu.be/vGP4pQdCocw)
+- [Agents Overview + Google Searches](https://youtu.be/Jq9Sf68ozk0)
+- [`OpenAI` + `Wolfram Alpha`](https://youtu.be/UijbzCIJ99g)
+- [Ask Questions On Your Custom (or Private) Files](https://youtu.be/EnT-ZTrcPrg)
+- [Connect `Google Drive Files` To `OpenAI`](https://youtu.be/IqqHqDcXLww)
+- [`YouTube Transcripts` + `OpenAI`](https://youtu.be/pNcQ5XXMgH4)
+- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
+- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
+- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
+- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
+- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
+- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
+- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
+- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
+- [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
+- [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
+- [Control Tone & Writing Style Of Your LLM Output](https://youtu.be/miBG-a3FuhU)
+- [Build Your Own `AI Twitter Bot` Using LLMs](https://youtu.be/yLWLDjT01q8)
+- [ChatGPT made my interview questions for me (`Streamlit` + LangChain)](https://youtu.be/zvoAMx0WKkw)
+- [Function Calling via ChatGPT API - First Look With LangChain](https://youtu.be/0-zlUy7VUjg)
+- ⛓ [Extract Topics From Video/Audio With LLMs (Topic Modeling w/ LangChain)](https://youtu.be/pEkxRQFNAs4)
+
+
+### [LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai)
+- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
+- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
+- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
+- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
+- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
+- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
+- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
+- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
+- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
+- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
+- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
+- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
+- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
+- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
+- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
+- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
+- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
+- [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
+- [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
+- [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
+- [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
+- [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
+- [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
+- [`Camel` + LangChain for Synthetic Data & Market Research](https://youtu.be/GldMMK6-_-g)
+- [Information Extraction with LangChain & `Kor`](https://youtu.be/SW1ZdqH0rRQ)
+- [Converting a LangChain App from OpenAI to OpenSource](https://youtu.be/KUDn7bVyIfc)
+- [Using LangChain `Output Parsers` to get what you want out of LLMs](https://youtu.be/UVn2NroKQCw)
+- [Building a LangChain Custom Medical Agent with Memory](https://youtu.be/6UFtRwWnHws)
+- [Understanding `ReACT` with LangChain](https://youtu.be/Eug2clsLtFs)
+- [`OpenAI Functions` + LangChain : Building a Multi Tool Agent](https://youtu.be/4KXK6c6TVXQ)
+- [What can you do with 16K tokens in LangChain?](https://youtu.be/z2aCZBAtWXs)
+- [Tagging and Extraction - Classification using `OpenAI Functions`](https://youtu.be/a8hMgIcUEnE)
+- ⛓ [HOW to Make Conversational Form with LangChain](https://youtu.be/IT93On2LB5k)
+
+
+### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
+- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
+- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
+- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
+- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
+- [Langchain: PDF Chat App (GUI) | ChatGPT for Your PDF FILES](https://youtu.be/RIWbalZ7sTo)
+- [LangFlow: Build Chatbots without Writing Code](https://youtu.be/KJ-ux3hre4s)
+- [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY)
+- [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY)
+
+
+### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
+- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
+- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
+- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
+- [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
+- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI)
+
+
+---------------------
+⛓ icon marks a new addition [last update 2023-07-05]
--- a/docs/docs_skeleton/vercel.json
+++ b/docs/docs_skeleton/vercel.json
@@ -138,7 +138,11 @@
    },
    {
      "source": "/en/latest/integrations/databerry.html",
-      "destination": "/docs/ecosystem/integrations/databerry"
+      "destination": "/docs/ecosystem/integrations/chaindesk"
+    },
+    {
+      "source": "/docs/ecosystem/integrations/databerry",
+      "destination": "/docs/ecosystem/integrations/chaindesk"
    },
    {
      "source": "/en/latest/integrations/databricks/databricks.html",
@@ -1330,7 +1334,11 @@
    },
    {
      "source": "/en/latest/modules/indexes/retrievers/examples/databerry.html",
-      "destination": "/docs/modules/data_connection/retrievers/integrations/databerry"
+      "destination": "/docs/modules/data_connection/retrievers/integrations/chaindesk"
+    },
+    {
+      "source": "/docs/modules/data_connection/retrievers/integrations/databerry",
+      "destination": "/docs/modules/data_connection/retrievers/integrations/chaindesk"
    },
    {
      "source": "/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html",
@@ -1864,6 +1872,14 @@
      "source": "/en/latest/modules/models/llms/integrations/writer.html",
      "destination": "/docs/modules/model_io/models/llms/integrations/writer"
    },
+    {
+      "source": "/en/latest/modules/prompts/output_parsers.html",
+      "destination": "/docs/modules/model_io/output_parsers/"
+    },
+    {
+      "source": "/docs/modules/prompts/output_parsers.html",
+      "destination": "/docs/modules/model_io/output_parsers/"
+    },
    {
      "source": "/en/latest/modules/prompts/output_parsers/examples/datetime.html",
      "destination": "/docs/modules/model_io/output_parsers/datetime"
@@ -2117,4 +2133,4 @@
      "destination": "/docs/:path*"
    }
  ]
-}
+}
--- a/docs/extras/additional_resources/youtube.mdx
+++ b/docs/extras/additional_resources/youtube.mdx
@@ -1,6 +1,6 @@
-# YouTube tutorials
+# YouTube videos

-This is a collection of `LangChain` videos on `YouTube`.
+⛓ icon marks a new addition [last update 2023-06-20]

 ### [Official LangChain YouTube channel](https://www.youtube.com/@LangChain)

@@ -9,7 +9,6 @@ This is a collection of `LangChain` videos on `YouTube`.
 - [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
 - [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
 - [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
- ⛓️ [LangChain "Agents in Production" Webinar](https://youtu.be/k8GNCCs16F4) by [LangChain](https://www.youtube.com/@LangChain)

 ## Videos (sorted by views)

@@ -31,6 +30,9 @@ This is a collection of `LangChain` videos on `YouTube`.
 - [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
 - [Langchain Overview — How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
 - [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
+- [LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
+  - [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
+  - [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI)
 - [Custom langchain Agent & Tools with memory. Turn any `Python function` into langchain tool with Gpt 3](https://youtu.be/NIG8lXk0ULg) by [echohive](https://www.youtube.com/@echohive)
 - [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
 - [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
@@ -46,154 +48,68 @@ This is a collection of `LangChain` videos on `YouTube`.
 - [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld)
 - [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
 - [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
- ⛓️ [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
- ⛓️ [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
- ⛓️ [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
- ⛓️ [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
- ⛓️ [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
- ⛓️ [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
- ⛓️ [KI schreibt krasses Youtube Skript 😲😳 | LangChain Tutorial Deutsch](https://youtu.be/QpTiXyK1jus) by [SimpleKI](https://www.youtube.com/@simpleki)
- ⛓️ [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
- ⛓️ [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
- ⛓️ [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
- ⛓️ [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
- ⛓️ [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
- ⛓️ [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
- ⛓️ [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
- ⛓️ [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
- ⛓️ [9 LangChain UseCases | Beginner's Guide | 2023](https://youtu.be/zS8_qosHNMw) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
- ⛓️ [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
- ⛓️ [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
- ⛓️ [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
- ⛓️ [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
- ⛓️ [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
- ⛓️ [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
- ⛓️ [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
- ⛓️ [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
- ⛓️ [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
- ⛓️ [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
- ⛓️ [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- ⛓️ [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
- ⛓️ [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
- ⛓️ [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
- ⛓️ [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- ⛓️ [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- ⛓️ [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
- ⛓️ [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
- ⛓️ [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
- ⛓️ [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
- ⛓️ [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
- ⛓️ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
- [LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
- [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
- [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
+- [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
+- [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
+- [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
+- [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
+- [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
+- [KI schreibt krasses Youtube Skript 😲😳 | LangChain Tutorial Deutsch](https://youtu.be/QpTiXyK1jus) by [SimpleKI](https://www.youtube.com/@simpleki)
+- [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
+- [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
+- [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
+- [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
+- [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
+- [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
+- [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
+- [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
+- [9 LangChain UseCases | Beginner's Guide | 2023](https://youtu.be/zS8_qosHNMw) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
+- [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
+- [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
+- [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
+- [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
+- [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
+- [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
+- [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
+- [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
+- [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
+- [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
+- [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
+- [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
+- [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
+- [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
+- ⛓ [Build AI chatbot with custom knowledge base using OpenAI API and GPT Index](https://youtu.be/vDZAZuaXf48) by [Irina Nik](https://www.youtube.com/@irina_nik)
+- ⛓ [Build Your Own Auto-GPT Apps with LangChain (Python Tutorial)](https://youtu.be/NYSWn1ipbgg) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
+- ⛓ [Chat with Multiple `PDFs` | LangChain App Tutorial in Python (Free LLMs and Embeddings)](https://youtu.be/dXxQ0LR-3Hg) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- ⛓ [Chat with a `CSV` | `LangChain Agents` Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
+- ⛓ [Create Your Own ChatGPT with `PDF` Data in 5 Minutes (LangChain Tutorial)](https://youtu.be/au2WVVGUvc8) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
+- ⛓ [Using ChatGPT with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
+- ⛓ [Build a Custom Chatbot with OpenAI: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU) by [Fabrikod](https://www.youtube.com/@fabrikod)
+- ⛓ [`Flowise` is an open source no-code UI visual tool to build 🦜🔗LangChain applications](https://youtu.be/CovAPtQPU0k) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
+- ⛓ [LangChain & GPT 4 For Data Analysis: The `Pandas` Dataframe Agent](https://youtu.be/rFQ5Kmkd4jc) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
+- ⛓ [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw) by [Toolfinder AI](https://www.youtube.com/@toolfinderai)
+- ⛓ [`PrivateGPT`: Chat to your FILES OFFLINE and FREE [Installation and Tutorial]](https://youtu.be/G7iLllmx4qc) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
+- ⛓ [How to build with Langchain 10x easier | ⛓️ LangFlow & `Flowise`](https://youtu.be/Ya1oGL7ZTvU) by [AI Jason](https://www.youtube.com/@AIJasonZ)
+- ⛓ [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg) by [Krish Naik](https://www.youtube.com/@krishnaik06)


-## Tutorial Series

-
-⛓ icon marks a new addition [last update 2023-05-15]
-
-### DeepLearning.AI course
-⛓[LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain) by Harrison Chase presented by [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
-
-### Handbook
-[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
-
-### Tutorials
-[LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
- ⛓ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
- ⛓ [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI)
-
-[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
-
-
-[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
-
-
-[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
-
-
-### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs):
- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
- ⛓ #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
- ⛓ #7 [LangChain Agents Deep Dive with GPT 3.5](https://youtu.be/jSP-gSEyVeI)
- ⛓ #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
- ⛓ #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
-
-
-### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Data Independent](https://www.youtube.com/@DataIndependent):
- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
- [Quickstart Guide](https://youtu.be/kYRB-vJFy38)
- [Beginner Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
- [`OpenAI` + `Wolfram Alpha`](https://youtu.be/UijbzCIJ99g)
- [Ask Questions On Your Custom (or Private) Files](https://youtu.be/EnT-ZTrcPrg)
- [Connect `Google Drive Files` To `OpenAI`](https://youtu.be/IqqHqDcXLww)
- [`YouTube Transcripts` + `OpenAI`](https://youtu.be/pNcQ5XXMgH4)
- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
- ⛓ [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
- ⛓ [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
-
-
-### [LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai):
- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
- ⛓ [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
- ⛓ [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
- ⛓ [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
- ⛓ [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
- ⛓ [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
- ⛓ [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
-
-
-### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt):
- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
- ⛓️ [CHATGPT For WEBSITES: Custom ChatBOT](https://youtu.be/RBnuhhmD21U)
-
-
-### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
- ⛓ [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
-
-
-### [Get SH\*T Done with Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
+### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
 - [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
 - [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
 - [LangChain Models: `ChatGPT`, `Flan Alpaca`, `OpenAI Embeddings`, Prompt Templates & Streaming](https://www.youtube.com/watch?v=zy6LiK5F5-s)
 - [LangChain Chains: Use `ChatGPT` to Build Conversational Agents, Summaries and Q&A on Text With LLMs](https://www.youtube.com/watch?v=h1tJZQPcimM)
 - [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
- ⛓ [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
+- [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
+

 ---------------------
-⛓ icon marks a new addition [last update 2023-05-15]
+⛓ icon marks a new addition [last update 2023-06-20]
--- a/docs/extras/ecosystem/dependents.mdx
+++ b/docs/extras/ecosystem/dependents.mdx
@@ -2,188 +2,261 @@

 Dependents stats for `hwchase17/langchain`

-[![](https://img.shields.io/static/v1?label=Used%20by&message=5152&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=172&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=4980&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=17239&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by&message=9941&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=244&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=9697&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=19827&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)

-[update: 2023-05-17; only dependent repositories with Stars > 100]
+
+[update: 2023-07-07; only dependent repositories with Stars > 100]


 | Repository | Stars  |
 | :--------  | -----: |
-|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 35401 |
-|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 32861 |
-|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 32766 |
-|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 29560 |
-|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 22315 |
-|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 17474 |
-|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 16923 |
-|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 16112 |
-|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 15407 |
-|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 14345 |
-|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 10372 |
-|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 9919 |
-|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 8177 |
-|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 6807 |
-|[imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) | 6087 |
-|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 5292 |
-|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 4622 |
-|[nsarrazin/serge](https://github.com/nsarrazin/serge) | 4076 |
-|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 3952 |
-|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 3952 |
-|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 3762 |
-|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 3388 |
-|[mmabrouk/chatgpt-wrapper](https://github.com/mmabrouk/chatgpt-wrapper) | 3243 |
-|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 3189 |
-|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 3050 |
-|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 2930 |
-|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 2710 |
-|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 2545 |
-|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2479 |
-|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2399 |
-|[langgenius/dify](https://github.com/langgenius/dify) | 2344 |
-|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2283 |
-|[hwchase17/chat-langchain](https://github.com/hwchase17/chat-langchain) | 2266 |
-|[guangzhengli/ChatFiles](https://github.com/guangzhengli/ChatFiles) | 1903 |
-|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 1884 |
-|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 1860 |
-|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1813 |
-|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 1571 |
-|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 1480 |
-|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1464 |
-|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1419 |
-|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 1410 |
-|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1363 |
-|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1344 |
-|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 1330 |
-|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1318 |
-|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1286 |
-|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1156 |
-|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 1141 |
-|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1106 |
-|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 1072 |
-|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1064 |
-|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1057 |
-|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1003 |
-|[greshake/llm-security](https://github.com/greshake/llm-security) | 1002 |
-|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 957 |
-|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 918 |
-|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 886 |
-|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 867 |
-|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 850 |
-|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 837 |
-|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 826 |
-|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 782 |
-|[hashintel/hash](https://github.com/hashintel/hash) | 778 |
-|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 773 |
-|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 738 |
-|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 737 |
-|[ai-sidekick/sidekick](https://github.com/ai-sidekick/sidekick) | 717 |
-|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 703 |
-|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 689 |
-|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 666 |
-|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 608 |
-|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 559 |
-|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 544 |
-|[pieroit/cheshire-cat](https://github.com/pieroit/cheshire-cat) | 520 |
-|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 514 |
-|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 481 |
-|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 462 |
-|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 452 |
-|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 439 |
-|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 437 |
-|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 433 |
-|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 427 |
-|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 425 |
-|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 422 |
-|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 421 |
-|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 407 |
-|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 395 |
-|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 383 |
-|[akshata29/chatpdf](https://github.com/akshata29/chatpdf) | 374 |
-|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 368 |
-|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 358 |
-|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 357 |
-|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 354 |
-|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 343 |
-|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 334 |
-|[showlab/VLog](https://github.com/showlab/VLog) | 330 |
-|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 324 |
-|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 323 |
-|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 320 |
-|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 308 |
-|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 301 |
-|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 300 |
-|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 299 |
-|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 287 |
-|[itamargol/openai](https://github.com/itamargol/openai) | 273 |
-|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 267 |
-|[momegas/megabots](https://github.com/momegas/megabots) | 259 |
-|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 238 |
-|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 232 |
-|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 227 |
-|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 227 |
-|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 226 |
-|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 218 |
-|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 218 |
-|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 215 |
-|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 213 |
-|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 209 |
-|[JohnSnowLabs/nlptest](https://github.com/JohnSnowLabs/nlptest) | 208 |
-|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 197 |
-|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 195 |
-|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 195 |
-|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 192 |
-|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 189 |
-|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 187 |
-|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 184 |
-|[Anil-matcha/Website-to-Chatbot](https://github.com/Anil-matcha/Website-to-Chatbot) | 183 |
-|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 180 |
-|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 166 |
-|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 166 |
-|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 161 |
-|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 160 |
-|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 153 |
-|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 153 |
-|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 152 |
-|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 149 |
-|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 149 |
-|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 147 |
-|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 144 |
-|[homanp/superagent](https://github.com/homanp/superagent) | 143 |
-|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 141 |
-|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 141 |
-|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 139 |
-|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 138 |
-|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 136 |
-|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 135 |
-|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 134 |
-|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 130 |
-|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 130 |
-|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 128 |
-|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 128 |
-|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 127 |
-|[steamship-core/vercel-examples](https://github.com/steamship-core/vercel-examples) | 127 |
-|[yasyf/summ](https://github.com/yasyf/summ) | 127 |
-|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 126 |
-|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 125 |
-|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 124 |
-|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 124 |
-|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 124 |
-|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 123 |
-|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 123 |
-|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 123 |
-|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 115 |
-|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 113 |
-|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 113 |
-|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 112 |
-|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 111 |
-|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 109 |
-|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 108 |
-|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 104 |
-|[enhancedocs/enhancedocs](https://github.com/enhancedocs/enhancedocs) | 102 |
-|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 101 |
+|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 41047 |
+|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 33983 |
+|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 33375 |
+|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 31114 |
+|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 30369 |
+|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 24116 |
+|[OpenBB-finance/OpenBBTerminal](https://github.com/OpenBB-finance/OpenBBTerminal) | 22565 |
+|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 18375 |
+|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 17723 |
+|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 16958 |
+|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 14632 |
+|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 11273 |
+|[openai/evals](https://github.com/openai/evals) | 10745 |
+|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10298 |
+|[imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) | 9838 |
+|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 9247 |
+|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 8768 |
+|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 8651 |
+|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 8119 |
+|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 7418 |
+|[gventuri/pandas-ai](https://github.com/gventuri/pandas-ai) | 7301 |
+|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 6636 |
+|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 5849 |
+|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 5129 |
+|[langgenius/dify](https://github.com/langgenius/dify) | 4804 |
+|[serge-chat/serge](https://github.com/serge-chat/serge) | 4448 |
+|[csunny/DB-GPT](https://github.com/csunny/DB-GPT) | 4350 |
+|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 4268 |
+|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 4244 |
+|[intitni/CopilotForXcode](https://github.com/intitni/CopilotForXcode) | 4232 |
+|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 4154 |
+|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4080 |
+|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 3949 |
+|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 3920 |
+|[bentoml/OpenLLM](https://github.com/bentoml/OpenLLM) | 3481 |
+|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 3453 |
+|[mmabrouk/chatgpt-wrapper](https://github.com/mmabrouk/chatgpt-wrapper) | 3355 |
+|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 3328 |
+|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3100 |
+|[kyegomez/tree-of-thoughts](https://github.com/kyegomez/tree-of-thoughts) | 3049 |
+|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 2844 |
+|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2833 |
+|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 2809 |
+|[hwchase17/chat-langchain](https://github.com/hwchase17/chat-langchain) | 2809 |
+|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2664 |
+|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 2650 |
+|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2525 |
+|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2372 |
+|[ParisNeo/lollms-webui](https://github.com/ParisNeo/lollms-webui) | 2287 |
+|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2265 |
+|[SamurAIGPT/privateGPT](https://github.com/SamurAIGPT/privateGPT) | 2084 |
+|[Chainlit/chainlit](https://github.com/Chainlit/chainlit) | 1912 |
+|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1869 |
+|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 1864 |
+|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 1849 |
+|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 1766 |
+|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 1745 |
+|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1732 |
+|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1716 |
+|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1619 |
+|[pinterest/querybook](https://github.com/pinterest/querybook) | 1468 |
+|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1446 |
+|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 1430 |
+|[Mintplex-Labs/anything-llm](https://github.com/Mintplex-Labs/anything-llm) | 1419 |
+|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1416 |
+|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1327 |
+|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1307 |
+|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1242 |
+|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1239 |
+|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1203 |
+|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1179 |
+|[keephq/keep](https://github.com/keephq/keep) | 1169 |
+|[greshake/llm-security](https://github.com/greshake/llm-security) | 1156 |
+|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1090 |
+|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 1088 |
+|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 1074 |
+|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1057 |
+|[noahshinn024/reflexion](https://github.com/noahshinn024/reflexion) | 1045 |
+|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 1036 |
+|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 999 |
+|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 989 |
+|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 974 |
+|[homanp/superagent](https://github.com/homanp/superagent) | 970 |
+|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 941 |
+|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 896 |
+|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 856 |
+|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 840 |
+|[chatarena/chatarena](https://github.com/chatarena/chatarena) | 829 |
+|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 816 |
+|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 816 |
+|[hashintel/hash](https://github.com/hashintel/hash) | 806 |
+|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 790 |
+|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 752 |
+|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 713 |
+|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 686 |
+|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 685 |
+|[refuel-ai/autolabel](https://github.com/refuel-ai/autolabel) | 673 |
+|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 617 |
+|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 616 |
+|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 609 |
+|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 592 |
+|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 581 |
+|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 574 |
+|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 572 |
+|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 564 |
+|[akshata29/chatpdf](https://github.com/akshata29/chatpdf) | 540 |
+|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 540 |
+|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 537 |
+|[khoj-ai/khoj](https://github.com/khoj-ai/khoj) | 531 |
+|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 528 |
+|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 526 |
+|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 515 |
+|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 494 |
+|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 483 |
+|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 472 |
+|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 465 |
+|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 464 |
+|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 464 |
+|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 455 |
+|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 455 |
+|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 450 |
+|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 446 |
+|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 445 |
+|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 426 |
+|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 426 |
+|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 418 |
+|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 416 |
+|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 401 |
+|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 400 |
+|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 386 |
+|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 382 |
+|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 368 |
+|[showlab/VLog](https://github.com/showlab/VLog) | 363 |
+|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 363 |
+|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 361 |
+|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 360 |
+|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 355 |
+|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 351 |
+|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 348 |
+|[alejandro-ao/ask-multiple-pdfs](https://github.com/alejandro-ao/ask-multiple-pdfs) | 321 |
+|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 314 |
+|[mosaicml/examples](https://github.com/mosaicml/examples) | 313 |
+|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 306 |
+|[itamargol/openai](https://github.com/itamargol/openai) | 304 |
+|[Anil-matcha/Website-to-Chatbot](https://github.com/Anil-matcha/Website-to-Chatbot) | 299 |
+|[momegas/megabots](https://github.com/momegas/megabots) | 299 |
+|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 289 |
+|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 283 |
+|[wandb/weave](https://github.com/wandb/weave) | 279 |
+|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 273 |
+|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 271 |
+|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 270 |
+|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 269 |
+|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 259 |
+|[Azure-Samples/openai](https://github.com/Azure-Samples/openai) | 252 |
+|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 248 |
+|[hnawaz007/pythondataanalysis](https://github.com/hnawaz007/pythondataanalysis) | 247 |
+|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 243 |
+|[truera/trulens](https://github.com/truera/trulens) | 239 |
+|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 238 |
+|[intel/intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers) | 237 |
+|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 236 |
+|[wandb/edu](https://github.com/wandb/edu) | 231 |
+|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 229 |
+|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 223 |
+|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 221 |
+|[JohnSnowLabs/nlptest](https://github.com/JohnSnowLabs/nlptest) | 220 |
+|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 219 |
+|[Safiullah-Rahu/CSV-AI](https://github.com/Safiullah-Rahu/CSV-AI) | 215 |
+|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 215 |
+|[steamship-packages/langchain-agent-production-starter](https://github.com/steamship-packages/langchain-agent-production-starter) | 214 |
+|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 213 |
+|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 211 |
+|[marella/chatdocs](https://github.com/marella/chatdocs) | 207 |
+|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 200 |
+|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 195 |
+|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 189 |
+|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 186 |
+|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 185 |
+|[huchenxucs/ChatDB](https://github.com/huchenxucs/ChatDB) | 179 |
+|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 178 |
+|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 178 |
+|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 177 |
+|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 176 |
+|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 174 |
+|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 174 |
+|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 172 |
+|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 171 |
+|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 165 |
+|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 164 |
+|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 163 |
+|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 161 |
+|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 161 |
+|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 160 |
+|[jondurbin/airoboros](https://github.com/jondurbin/airoboros) | 157 |
+|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 157 |
+|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 156 |
+|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 155 |
+|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 155 |
+|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 154 |
+|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 153 |
+|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 150 |
+|[onlyphantom/llm-python](https://github.com/onlyphantom/llm-python) | 148 |
+|[Azure-Samples/azure-search-power-skills](https://github.com/Azure-Samples/azure-search-power-skills) | 146 |
+|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 144 |
+|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 144 |
+|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 143 |
+|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 142 |
+|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 141 |
+|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 140 |
+|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 140 |
+|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 139 |
+|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 139 |
+|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 138 |
+|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 137 |
+|[steamship-core/vercel-examples](https://github.com/steamship-core/vercel-examples) | 137 |
+|[deeppavlov/dream](https://github.com/deeppavlov/dream) | 136 |
+|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 135 |
+|[sugarforever/LangChain-Tutorials](https://github.com/sugarforever/LangChain-Tutorials) | 135 |
+|[yasyf/summ](https://github.com/yasyf/summ) | 135 |
+|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 134 |
+|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 132 |
+|[ju-bezdek/langchain-decorators](https://github.com/ju-bezdek/langchain-decorators) | 130 |
+|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 128 |
+|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 127 |
+|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 125 |
+|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 122 |
+|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 122 |
+|[Aggregate-Intellect/practical-llms](https://github.com/Aggregate-Intellect/practical-llms) | 120 |
+|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 120 |
+|[Azure-Samples/azure-search-openai-demo-csharp](https://github.com/Azure-Samples/azure-search-openai-demo-csharp) | 119 |
+|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 117 |
+|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 117 |
+|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 116 |
+|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 114 |
+|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 112 |
+|[RedisVentures/redis-openai-qna](https://github.com/RedisVentures/redis-openai-qna) | 111 |
+|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 111 |
+|[kulltc/chatgpt-sql](https://github.com/kulltc/chatgpt-sql) | 109 |
+|[summarizepaper/summarizepaper](https://github.com/summarizepaper/summarizepaper) | 109 |
+|[Azure-Samples/miyagi](https://github.com/Azure-Samples/miyagi) | 106 |
+|[ssheng/BentoChain](https://github.com/ssheng/BentoChain) | 106 |
+|[voxel51/voxelgpt](https://github.com/voxel51/voxelgpt) | 105 |
+|[mallahyari/drqa](https://github.com/mallahyari/drqa) | 103 |



--- a/docs/extras/ecosystem/integrations/chaindesk.mdx
+++ b/docs/extras/ecosystem/integrations/chaindesk.mdx
@@ -1,17 +1,17 @@
-# Databerry
+# Chaindesk

->[Databerry](https://databerry.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
+>[Chaindesk](https://chaindesk.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.


 ## Installation and Setup

-We need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url. 
-We need the [API Key](https://docs.databerry.ai/api-reference/authentication).
+We need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. 
+We need the [API Key](https://docs.chaindesk.ai/api-reference/authentication).

 ## Retriever

-See a [usage example](/docs/modules/data_connection/retrievers/integrations/databerry.html).
+See a [usage example](/docs/modules/data_connection/retrievers/integrations/chaindesk.html).

 ```python
-from langchain.retrievers import DataberryRetriever
+from langchain.retrievers import ChaindeskRetriever
 ```
--- a/docs/extras/ecosystem/integrations/clarifai.mdx
+++ b/docs/extras/ecosystem/integrations/clarifai.mdx
@@ -0,0 +1,52 @@
+# Clarifai
+
+>[Clarifai](https://clarifai.com) is one of first deep learning platforms having been founded in 2013. Clarifai provides an AI platform with the full AI lifecycle for data exploration, data labeling, model training, evaluation and inference around images, video, text and audio data. In the LangChain ecosystem, as far as we're aware, Clarifai is the only provider that supports LLMs, embeddings and a vector store in one production scale platform, making it an excellent choice to operationalize your LangChain implementations.
+
+## Installation and Setup
+- Install the Python SDK:
+```bash
+pip install clarifai
+```
+[Sign-up](https://clarifai.com/signup) for a Clarifai account, then get a personal access token to access the Clarifai API from your [security settings](https://clarifai.com/settings/security) and set it as an environment variable (`CLARIFAI_PAT`).
+
+
+## Models
+
+Clarifai provides 1,000s of AI models for many different use cases. You can [explore them here](https://clarifai.com/explore) to find the one most suited for your use case. These models include those created by other providers such as OpenAI, Anthropic, Cohere, AI21, etc. as well as state of the art from open source such as Falcon, InstructorXL, etc. so that you build the best in AI into your products. You'll find these organized by the creator's user_id and into projects we call applications denoted by their app_id. Those IDs will be needed in additional to the model_id and optionally the version_id, so make note of all these IDs once you found the best model for your use case!
+
+Also note that given there are many models for images, video, text and audio understanding, you can build some interested AI agents that utilize the variety of AI models as experts to understand those data types.
+
+### LLMs
+
+To find the selection of LLMs in the Clarifai platform you can select the text to text model type [here](https://clarifai.com/explore/models?filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-to-text%22%5D%7D%5D&page=1&perPage=24).
+
+```python
+from langchain.llms import Clarifai
+llm = Clarifai(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
+```
+
+For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/modules/model_io/models/llms/integrations/clarifai.html).
+
+
+### Text Embedding Models
+
+To find the selection of text embeddings models in the Clarifai platform you can select the text to embedding model type [here](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-embedder%22%5D%7D%5D).
+
+There is a Clarifai Embedding model in LangChain, which you can access with:
+```python
+from langchain.embeddings import ClarifaiEmbeddings
+embeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
+```
+For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walthrough](/docs/modules/data_connection/text_embedding/integrations/clarifai.html).
+
+## Vectorstore
+
+Clarifai's vector DB was launched in 2016 and has been optimized to support live search queries. With workflows in the Clarifai platform, you data is automatically indexed by am embedding model and optionally other models as well to index that information in the DB for search. You can query the DB not only via the vectors but also filter by metadata matches, other AI predicted concepts, and even do geo-coordinate search. Simply create an application, select the appropriate base workflow for your type of data, and upload it (through the API as [documented here](https://docs.clarifai.com/api-guide/data/create-get-update-delete) or the UIs at clarifai.com).
+
+You an also add data directly from LangChain as well, and the auto-indexing will take place for you. You'll notice this is a little different than other vectorstores where you need to provde an embedding model in their constructor and have LangChain coordinate getting the embeddings from text and writing those to the index. Not only is it more convenient, but it's much more scalable to use Clarifai's distributed cloud to do all the index in the background.
+
+```python
+from langchain.vectorstores import Clarifai
+clarifai_vector_db = Clarifai.from_texts(user_id=USER_ID, app_id=APP_ID, texts=texts, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS, metadatas = metadatas)
+```
+For more details, the docs on the Clarifai vector store provide a [detailed walthrough](/docs/modules/data_connection/text_embedding/integrations/clarifai.html).
--- a/docs/extras/ecosystem/integrations/cnosdb.mdx
+++ b/docs/extras/ecosystem/integrations/cnosdb.mdx
@@ -0,0 +1,108 @@
+# CnosDB
+> [CnosDB](https://github.com/cnosdb/cnosdb) is an open source distributed time series database with high performance, high compression rate and high ease of use.
+
+## Installation and Setup
+
+```python
+pip install cnos-connector
+```
+
+## Connecting to CnosDB
+You can connect to CnosDB using the SQLDatabase.from_cnosdb() method.
+### Syntax
+```python
+def SQLDatabase.from_cnosdb(url: str = "127.0.0.1:8902",
+                              user: str = "root",
+                              password: str = "",
+                              tenant: str = "cnosdb",
+                              database: str = "public")
+```
+Args:
+1. url (str): The HTTP connection host name and port number of the CnosDB
+                service, excluding "http://" or "https://", with a default value
+                of "127.0.0.1:8902".
+2. user (str): The username used to connect to the CnosDB service, with a
+                default value of "root".
+3. password (str): The password of the user connecting to the CnosDB service,
+                with a default value of "".
+4. tenant (str): The name of the tenant used to connect to the CnosDB service,
+                with a default value of "cnosdb".
+5. database (str): The name of the database in the CnosDB tenant.
+## Examples
+```python
+# Connecting to CnosDB with SQLDatabase Wrapper
+from cnosdb_connector import make_cnosdb_langchain_uri
+from langchain import SQLDatabase
+
+db = SQLDatabase.from_cnosdb()
+```
+```python
+# Creating a OpenAI Chat LLM Wrapper
+from langchain.chat_models import ChatOpenAI
+
+llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
+```
+
+### SQL Chain
+This example demonstrates the use of the SQL Chain for answering a question over a CnosDB.
+```python
+from langchain import SQLDatabaseChain
+
+db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
+
+db_chain.run(
+  "What is the average fa of test table that time between November 3,2022 and November 4, 2022?"
+)
+```
+```shell
+> Entering new  chain...
+What is the average fa of test table that time between November 3, 2022 and November 4, 2022?
+SQLQuery:SELECT AVG(fa) FROM test WHERE time >= '2022-11-03' AND time < '2022-11-04'
+SQLResult: [(2.0,)]
+Answer:The average fa of the test table between November 3, 2022, and November 4, 2022, is 2.0.
+> Finished chain.
+```
+### SQL Database Agent
+This example demonstrates the use of the SQL Database Agent for answering questions over a CnosDB.
+```python
+from langchain.agents import create_sql_agent
+from langchain.agents.agent_toolkits import SQLDatabaseToolkit
+
+toolkit = SQLDatabaseToolkit(db=db, llm=llm)
+agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)
+```
+```python
+agent.run(
+  "What is the average fa of test table that time between November 3, 2022 and November 4, 2022?"
+)
+```
+```shell
+> Entering new  chain...
+Action: sql_db_list_tables
+Action Input: ""
+Observation: test
+Thought:The relevant table is "test". I should query the schema of this table to see the column names.
+Action: sql_db_schema
+Action Input: "test"
+Observation:
+CREATE TABLE test (
+	time TIMESTAMP,
+	fa BIGINT
+)
+
+/*
+3 rows from test table:
+fa	time
+1	2022-11-03T06:20:11
+2	2022-11-03T06:20:11.000000001
+3	2022-11-03T06:20:11.000000002
+*/
+Thought:The relevant column is "fa" in the "test" table. I can now construct the query to calculate the average "fa" between the specified time range.
+Action: sql_db_query
+Action Input: "SELECT AVG(fa) FROM test WHERE time >= '2022-11-03' AND time < '2022-11-04'"
+Observation: [(2.0,)]
+Thought:The average "fa" of the "test" table between November 3, 2022 and November 4, 2022 is 2.0.
+Final Answer: 2.0
+
+> Finished chain.
+```
--- a/docs/extras/ecosystem/integrations/dataforseo.mdx
+++ b/docs/extras/ecosystem/integrations/dataforseo.mdx
@@ -0,0 +1,51 @@
+# DataForSEO
+
+This page provides instructions on how to use the DataForSEO search APIs within LangChain.
+
+## Installation and Setup
+
+- Get a DataForSEO API Access login and password, and set them as environment variables (`DATAFORSEO_LOGIN` and `DATAFORSEO_PASSWORD` respectively). You can find it in your dashboard.
+
+## Wrappers
+
+### Utility
+
+The DataForSEO utility wraps the API. To import this utility, use:
+
+```python
+from langchain.utilities import DataForSeoAPIWrapper
+```
+
+For a detailed walkthrough of this wrapper, see [this notebook](/docs/modules/agents/tools/integrations/dataforseo.ipynb).
+
+### Tool
+
+You can also load this wrapper as a Tool to use with an Agent:
+
+```python
+from langchain.agents import load_tools
+tools = load_tools(["dataforseo-api-search"])
+```
+
+## Example usage
+
+```python
+dataforseo = DataForSeoAPIWrapper(api_login="your_login", api_password="your_password")
+result = dataforseo.run("Bill Gates")
+print(result)
+```
+
+## Environment Variables
+
+You can store your DataForSEO API Access login and password as environment variables. The wrapper will automatically check for these environment variables if no values are provided:
+
+```python
+import os
+
+os.environ["DATAFORSEO_LOGIN"] = "your_login"
+os.environ["DATAFORSEO_PASSWORD"] = "your_password"
+
+dataforseo = DataForSeoAPIWrapper()
+result = dataforseo.run("weather in Los Angeles")
+print(result)
+```
--- a/docs/extras/ecosystem/integrations/marqo.md
+++ b/docs/extras/ecosystem/integrations/marqo.md
@@ -0,0 +1,31 @@
+# Marqo
+
+This page covers how to use the Marqo ecosystem within LangChain.
+
+### **What is Marqo?**
+
+Marqo is a tensor search engine that uses embeddings stored in in-memory HNSW indexes to achieve cutting edge search speeds. Marqo can scale to hundred-million document indexes with horizontal index sharding and allows for async and non-blocking data upload and search. Marqo uses the latest machine learning models from PyTorch, Huggingface, OpenAI and more. You can start with a pre-configured model or bring your own. The built in ONNX support and conversion allows for faster inference and higher throughput on both CPU and GPU.
+
+Because Marqo include its own inference your documents can have a mix of text and images, you can bring Marqo indexes with data from your other systems into the langchain ecosystem without having to worry about your embeddings being compatible. 
+
+Deployment of Marqo is flexible, you can get started yourself with our docker image or [contact us about our managed cloud offering!](https://www.marqo.ai/pricing)
+
+To run Marqo locally with our docker image, [see our getting started.](https://docs.marqo.ai/latest/)
+
+## Installation and Setup
+- Install the Python SDK with `pip install marqo`
+
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around Marqo indexes, allowing you to use them within the vectorstore framework. Marqo lets you select from a range of models for generating embeddings and exposes some preprocessing configurations.
+
+The Marqo vectorstore can also work with existing multimodel indexes where your documents have a mix of images and text, for more information refer to [our documentation](https://docs.marqo.ai/latest/#multi-modal-and-cross-modal-search). Note that instaniating the Marqo vectorstore with an existing multimodal index will disable the ability to add any new documents to it via the langchain vectorstore `add_texts` method.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Marqo
+```
+
+For a more detailed walkthrough of the Marqo wrapper and some of its unique features, see [this notebook](../modules/data_connection/vectorstores/integrations/marqo.ipynb)
--- a/docs/extras/ecosystem/integrations/trulens.mdx
+++ b/docs/extras/ecosystem/integrations/trulens.mdx
@@ -0,0 +1,56 @@
+# TruLens
+
+This page covers how to use [TruLens](https://trulens.org) to evaluate and track LLM apps built on langchain.
+
+## What is TruLens?
+
+TruLens is an [opensource](https://github.com/truera/trulens) package that provides instrumentation and evaluation tools for large language model (LLM) based applications.
+
+## Quick start
+
+Once you've created your LLM chain, you can use TruLens for evaluation and tracking. TruLens has a number of [out-of-the-box Feedback Functions](https://www.trulens.org/trulens_eval/feedback_functions/), and is also an extensible framework for LLM evaluation.
+
+```python
+# create a feedback function
+
+from trulens_eval.feedback import Feedback, Huggingface, OpenAI
+# Initialize HuggingFace-based feedback function collection class:
+hugs = Huggingface()
+openai = OpenAI()
+
+# Define a language match feedback function using HuggingFace.
+lang_match = Feedback(hugs.language_match).on_input_output()
+# By default this will check language match on the main app input and main app
+# output.
+
+# Question/answer relevance between overall question and answer.
+qa_relevance = Feedback(openai.relevance).on_input_output()
+# By default this will evaluate feedback on main app input and main app output.
+
+# Toxicity of input
+toxicity = Feedback(openai.toxicity).on_input()
+
+```
+
+After you've set up Feedback Function(s) for evaluating your LLM, you can wrap your application with TruChain to get detailed tracing, logging and evaluation of your LLM app.
+
+```python
+# wrap your chain with TruChain
+truchain = TruChain(
+    chain,
+    app_id='Chain1_ChatApplication',
+    feedbacks=[lang_match, qa_relevance, toxicity]
+)
+# Note: any `feedbacks` specified here will be evaluated and logged whenever the chain is used.
+truchain("que hora es?")
+```
+
+Now you can explore your LLM-based application!
+
+Doing so will help you understand how your LLM application is performing at a glance. As you iterate new versions of your LLM application, you can compare their performance across all of the different quality metrics you've set up. You'll also be able to view evaluations at a record level, and explore the chain metadata for each record.
+
+```python
+tru.run_dashboard() # open a Streamlit app to explore
+```
+
+For more information on TruLens, visit [trulens.org](https://www.trulens.org/)
--- a/docs/extras/guides/deployments/template_repos.mdx
+++ b/docs/extras/guides/deployments/template_repos.mdx
@@ -51,6 +51,10 @@ A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using

 A minimal example on how to deploy LangChain to DigitalOcean App Platform.

+## [CI/CD Google Cloud Build + Dockerfile + Serverless Google Cloud Run](https://github.com/g-emarco/github-assistant)
+
+Boilerplate LangChain project on how to deploy to Google Cloud Run using Docker with Cloud Build CI/CD pipeline
+
 ## [Google Cloud Run](https://github.com/homanp/gcp-langchain)

 A minimal example on how to deploy LangChain to Google Cloud Run.
--- a/docs/extras/modules/agents/toolkits/sql_database.ipynb
+++ b/docs/extras/modules/agents/toolkits/sql_database.ipynb
@@ -7,7 +7,7 @@
   "source": [
    "# SQL Database Agent\n",
    "\n",
-    "This notebook showcases an agent designed to interact with a sql databases. The agent builds off of [SQLDatabaseChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/sqlite.html) and is designed to answer more general questions about a database, as well as recover from errors.\n",
+    "This notebook showcases an agent designed to interact with a sql databases. The agent builds off of [SQLDatabaseChain](https://python.langchain.com/docs/modules/chains/popular/sqlite) and is designed to answer more general questions about a database, as well as recover from errors.\n",
    "\n",
    "Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your database given certain questions. Be careful running it on sensitive data!\n",
    "\n",
--- a/docs/extras/modules/agents/tools/integrations/dataforseo.ipynb
+++ b/docs/extras/modules/agents/tools/integrations/dataforseo.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DataForSeo API Wrapper\n",
+    "This notebook demonstrates how to use the DataForSeo API wrapper to obtain search engine results. The DataForSeo API allows users to retrieve SERP from most popular search engines like Google, Bing, Yahoo. It also allows to get SERPs from different search engine types like Maps, News, Events, etc.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.utilities import DataForSeoAPIWrapper"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up the API wrapper with your credentials\n",
+    "You can obtain your API credentials by registering on the DataForSeo website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"DATAFORSEO_LOGIN\"] = \"your_api_access_username\"\n",
+    "os.environ[\"DATAFORSEO_PASSWORD\"] = \"your_api_access_password\"\n",
+    "\n",
+    "wrapper = DataForSeoAPIWrapper()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The run method will return the first result snippet from one of the following elements: answer_box, knowledge_graph, featured_snippet, shopping, organic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wrapper.run(\"Weather in Los Angeles\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Difference Between `run` and `results`\n",
+    "`run` and `results` are two methods provided by the `DataForSeoAPIWrapper` class.\n",
+    "\n",
+    "The `run` method executes the search and returns the first result snippet from the answer box, knowledge graph, featured snippet, shopping, or organic results. These elements are sorted by priority from highest to lowest.\n",
+    "\n",
+    "The `results` method returns a JSON response configured according to the parameters set in the wrapper. This allows for more flexibility in terms of what data you want to return from the API."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting Results as JSON\n",
+    "You can customize the result types and fields you want to return in the JSON response. You can also set a maximum count for the number of top results to return."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "json_wrapper = DataForSeoAPIWrapper(\n",
+    "    json_result_types=[\"organic\", \"knowledge_graph\", \"answer_box\"],\n",
+    "    json_result_fields=[\"type\", \"title\", \"description\", \"text\"],\n",
+    "    top_count=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "json_wrapper.results(\"Bill Gates\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customizing Location and Language\n",
+    "You can specify the location and language of your search results by passing additional parameters to the API wrapper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customized_wrapper = DataForSeoAPIWrapper(\n",
+    "    top_count=10,\n",
+    "    json_result_types=[\"organic\", \"local_pack\"],\n",
+    "    json_result_fields=[\"title\", \"description\", \"type\"],\n",
+    "    params={\"location_name\": \"Germany\", \"language_code\": \"en\"})\n",
+    "customized_wrapper.results(\"coffee near me\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customizing the Search Engine\n",
+    "You can also specify the search engine you want to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "customized_wrapper = DataForSeoAPIWrapper(\n",
+    "    top_count=10,\n",
+    "    json_result_types=[\"organic\", \"local_pack\"],\n",
+    "    json_result_fields=[\"title\", \"description\", \"type\"],\n",
+    "    params={\"location_name\": \"Germany\", \"language_code\": \"en\", \"se_name\": \"bing\"})\n",
+    "customized_wrapper.results(\"coffee near me\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customizing the Search Type\n",
+    "The API wrapper also allows you to specify the type of search you want to perform. For example, you can perform a maps search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maps_search = DataForSeoAPIWrapper(\n",
+    "    top_count=10,\n",
+    "    json_result_fields=[\"title\", \"value\", \"address\", \"rating\", \"type\"],\n",
+    "    params={\"location_coordinate\": \"52.512,13.36,12z\", \"language_code\": \"en\", \"se_type\": \"maps\"})\n",
+    "maps_search.results(\"coffee near me\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integration with Langchain Agents\n",
+    "You can use the `Tool` class from the `langchain.agents` module to integrate the `DataForSeoAPIWrapper` with a langchain agent. The `Tool` class encapsulates a function that the agent can call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import Tool\n",
+    "search = DataForSeoAPIWrapper(\n",
+    "    top_count=3,\n",
+    "    json_result_types=[\"organic\"],\n",
+    "    json_result_fields=[\"title\", \"description\", \"type\"])\n",
+    "tool = Tool(\n",
+    "    name=\"google-search-answer\",\n",
+    "    description=\"My new answer tool\",\n",
+    "    func=search.run,\n",
+    ")\n",
+    "json_tool = Tool(\n",
+    "    name=\"google-search-json\",\n",
+    "    description=\"My new json tool\",\n",
+    "    func=search.results,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/docs/extras/modules/agents/tools/integrations/wikipedia.ipynb
+++ b/docs/extras/modules/agents/tools/integrations/wikipedia.ipynb
--- a/docs/extras/modules/callbacks/integrations/context.ipynb
+++ b/docs/extras/modules/callbacks/integrations/context.ipynb
@@ -0,0 +1,220 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Context\n",
+    "\n",
+    "![Context - Product Analytics for AI Chatbots](https://go.getcontext.ai/langchain.png)\n",
+    "\n",
+    "[Context](https://getcontext.ai/) provides product analytics for AI chatbots.\n",
+    "\n",
+    "Context helps you understand how users are interacting with your AI chat products.\n",
+    "Gain critical insights, optimise poor experiences, and minimise brand risks.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this guide we will show you how to integrate with Context."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Installation and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "$ pip install context-python --upgrade"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Getting API Credentials\n",
+    "\n",
+    "To get your Context API token:\n",
+    "\n",
+    "1. Go to the settings page within your Context account (https://go.getcontext.ai/settings).\n",
+    "2. Generate a new API Token.\n",
+    "3. Store this token somewhere secure."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup Context\n",
+    "\n",
+    "To use the `ContextCallbackHandler`, import the handler from Langchain and instantiate it with your Context API token.\n",
+    "\n",
+    "Ensure you have installed the `context-python` package before using the handler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain.callbacks import ContextCallbackHandler\n",
+    "\n",
+    "token = os.environ[\"CONTEXT_API_TOKEN\"]\n",
+    "\n",
+    "context_callback = ContextCallbackHandler(token)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "### Using the Context callback within a Chat Model\n",
+    "\n",
+    "The Context callback handler can be used to directly record transcripts between users and AI assistants.\n",
+    "\n",
+    "#### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema import (\n",
+    "    SystemMessage,\n",
+    "    HumanMessage,\n",
+    ")\n",
+    "from langchain.callbacks import ContextCallbackHandler\n",
+    "\n",
+    "token = os.environ[\"CONTEXT_API_TOKEN\"]\n",
+    "\n",
+    "chat = ChatOpenAI(\n",
+    "    headers={\"user_id\": \"123\"}, temperature=0, callbacks=[ContextCallbackHandler(token)]\n",
+    ")\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful assistant that translates English to French.\"\n",
+    "    ),\n",
+    "    HumanMessage(content=\"I love programming.\"),\n",
+    "]\n",
+    "\n",
+    "print(chat(messages))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the Context callback within Chains\n",
+    "\n",
+    "The Context callback handler can also be used to record the inputs and outputs of chains. Note that intermediate steps of the chain are not recorded - only the starting inputs and final outputs.\n",
+    "\n",
+    "__Note:__ Ensure that you pass the same context object to the chat model and the chain.\n",
+    "\n",
+    "Wrong:\n",
+    "> ```python\n",
+    "> chat = ChatOpenAI(temperature=0.9, callbacks=[ContextCallbackHandler(token)])\n",
+    "> chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[ContextCallbackHandler(token)])\n",
+    "> ```\n",
+    "\n",
+    "Correct:\n",
+    ">```python\n",
+    ">handler = ContextCallbackHandler(token)\n",
+    ">chat = ChatOpenAI(temperature=0.9, callbacks=[callback])\n",
+    ">chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback])\n",
+    ">```\n",
+    "\n",
+    "#### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain import LLMChain\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.callbacks import ContextCallbackHandler\n",
+    "\n",
+    "token = os.environ[\"CONTEXT_API_TOKEN\"]\n",
+    "\n",
+    "human_message_prompt = HumanMessagePromptTemplate(\n",
+    "    prompt=PromptTemplate(\n",
+    "        template=\"What is a good name for a company that makes {product}?\",\n",
+    "        input_variables=[\"product\"],\n",
+    "    )\n",
+    ")\n",
+    "chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
+    "callback = ContextCallbackHandler(token)\n",
+    "chat = ChatOpenAI(temperature=0.9, callbacks=[callback])\n",
+    "chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback])\n",
+    "print(chain.run(\"colorful socks\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a53ebf4a859167383b364e7e7521d0add3c2dbbdecce4edf676e8c4634ff3fbb"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/docs/extras/modules/callbacks/integrations/streamlit.md
+++ b/docs/extras/modules/callbacks/integrations/streamlit.md
@@ -9,7 +9,7 @@
 In this guide we will demonstrate how to use `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an
 interactive Streamlit app. Try it out with the running app below using the [MRKL agent](/docs/modules/agents/how_to/mrkl/):

-<iframe loading="lazy" src="https://mrkl-minimal.streamlit.app/?embed=true&embed_options=light_theme"
+<iframe loading="lazy" src="https://langchain-mrkl.streamlit.app/?embed=true&embed_options=light_theme"
    style={{ width: 100 + '%', border: 'none', marginBottom: 1 + 'rem', height: 600 }}
    allow="camera;clipboard-read;clipboard-write;"
 ></iframe>
@@ -35,7 +35,7 @@ st_callback = StreamlitCallbackHandler(st.container())
 ```

 Additional keyword arguments to customize the display behavior are described in the
-[API reference](https://api.python.langchain.com/en/latest/modules/callbacks.html#langchain.callbacks.StreamlitCallbackHandler).
+[API reference](https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html).

 ### Scenario 1: Using an Agent with Tools

--- a/docs/extras/modules/chains/additional/flare.ipynb
+++ b/docs/extras/modules/chains/additional/flare.ipynb
@@ -56,7 +56,8 @@
   "source": [
    "import os\n",
    "\n",
-    "os.environ[\"SERPER_API_KEY\"] = \"\""
+    "os.environ[\"SERPER_API_KEY\"] = \"\"",
+    "os.environ[\"OPENAI_API_KEY\"] = \"\""
   ]
  },
  {
@@ -77,7 +78,7 @@
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.schema import Document\n",
-    "from typing import Any"
+    "from typing import Any, List"
   ]
  },
  {
@@ -96,8 +97,8 @@
   "outputs": [],
   "source": [
    "class SerperSearchRetriever(BaseRetriever):\n",
-    "    def __init__(self, search):\n",
-    "        self.search = search\n",
+    "\n",
+    "    search: GoogleSerperAPIWrapper = None\n",
    "\n",
    "    def _get_relevant_documents(self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any) -> List[Document]:\n",
    "        return [Document(page_content=self.search.run(query))]\n",
@@ -111,7 +112,7 @@
    "        raise NotImplementedError()\n",
    "\n",
    "\n",
-    "retriever = SerperSearchRetriever(GoogleSerperAPIWrapper())"
+    "retriever = SerperSearchRetriever(search=GoogleSerperAPIWrapper())"
   ]
  },
  {
--- a/docs/extras/modules/chains/additional/graph_sparql_qa.ipynb
+++ b/docs/extras/modules/chains/additional/graph_sparql_qa.ipynb
@@ -0,0 +1,300 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c94240f5",
+   "metadata": {},
+   "source": [
+    "# GraphSparqlQAChain\n",
+    "\n",
+    "Graph databases are an excellent choice for applications based on network-like models. To standardize the syntax and semantics of such graphs, the W3C recommends Semantic Web Technologies, cp. [Semantic Web](https://www.w3.org/standards/semanticweb/). [SPARQL](https://www.w3.org/TR/sparql11-query/) serves as a query language analogously to SQL or Cypher for these graphs. This notebook demonstrates the application of LLMs as a natural language interface to a graph database by generating SPARQL.\\\n",
+    "Disclaimer: To date, SPARQL query generation via LLMs is still a bit unstable. Be especially careful with UPDATE queries, which alter the graph."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbc0ee68",
+   "metadata": {},
+   "source": [
+    "There are several sources you can run queries against, including files on the web, files you have available locally, SPARQL endpoints, e.g., [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page), and [triple stores](https://www.w3.org/wiki/LargeTripleStores)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "62812aad",
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import GraphSparqlQAChain\n",
+    "from langchain.graphs import RdfGraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0928915d",
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "graph = RdfGraph(\n",
+    "    source_file=\"http://www.w3.org/People/Berners-Lee/card\",\n",
+    "    standard=\"rdf\",\n",
+    "    local_copy=\"test.ttl\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Note that providing a `local_file` is necessary for storing changes locally if the source is read-only."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58c1a8ea",
+   "metadata": {},
+   "source": [
+    "## Refresh graph schema information\n",
+    "If the schema of the database changes, you can refresh the schema information needed to generate SPARQL queries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4e3de44f",
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "graph.load_schema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1fe76ccd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In the following, each IRI is followed by the local name and optionally its description in parentheses. \n",
+      "The RDF graph supports the following node types:\n",
+      "<http://xmlns.com/foaf/0.1/PersonalProfileDocument> (PersonalProfileDocument, None), <http://www.w3.org/ns/auth/cert#RSAPublicKey> (RSAPublicKey, None), <http://www.w3.org/2000/10/swap/pim/contact#Male> (Male, None), <http://xmlns.com/foaf/0.1/Person> (Person, None), <http://www.w3.org/2006/vcard/ns#Work> (Work, None)\n",
+      "The RDF graph supports the following relationships:\n",
+      "<http://www.w3.org/2000/01/rdf-schema#seeAlso> (seeAlso, None), <http://purl.org/dc/elements/1.1/title> (title, None), <http://xmlns.com/foaf/0.1/mbox_sha1sum> (mbox_sha1sum, None), <http://xmlns.com/foaf/0.1/maker> (maker, None), <http://www.w3.org/ns/solid/terms#oidcIssuer> (oidcIssuer, None), <http://www.w3.org/2000/10/swap/pim/contact#publicHomePage> (publicHomePage, None), <http://xmlns.com/foaf/0.1/openid> (openid, None), <http://www.w3.org/ns/pim/space#storage> (storage, None), <http://xmlns.com/foaf/0.1/name> (name, None), <http://www.w3.org/2000/10/swap/pim/contact#country> (country, None), <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> (type, None), <http://www.w3.org/ns/solid/terms#profileHighlightColor> (profileHighlightColor, None), <http://www.w3.org/ns/pim/space#preferencesFile> (preferencesFile, None), <http://www.w3.org/2000/01/rdf-schema#label> (label, None), <http://www.w3.org/ns/auth/cert#modulus> (modulus, None), <http://www.w3.org/2000/10/swap/pim/contact#participant> (participant, None), <http://www.w3.org/2000/10/swap/pim/contact#street2> (street2, None), <http://www.w3.org/2006/vcard/ns#locality> (locality, None), <http://xmlns.com/foaf/0.1/nick> (nick, None), <http://xmlns.com/foaf/0.1/homepage> (homepage, None), <http://creativecommons.org/ns#license> (license, None), <http://xmlns.com/foaf/0.1/givenname> (givenname, None), <http://www.w3.org/2006/vcard/ns#street-address> (street-address, None), <http://www.w3.org/2006/vcard/ns#postal-code> (postal-code, None), <http://www.w3.org/2000/10/swap/pim/contact#street> (street, None), <http://www.w3.org/2003/01/geo/wgs84_pos#lat> (lat, None), <http://xmlns.com/foaf/0.1/primaryTopic> (primaryTopic, None), <http://www.w3.org/2006/vcard/ns#fn> (fn, None), <http://www.w3.org/2003/01/geo/wgs84_pos#location> (location, None), <http://usefulinc.com/ns/doap#developer> (developer, None), <http://www.w3.org/2000/10/swap/pim/contact#city> (city, None), <http://www.w3.org/2006/vcard/ns#region> (region, None), <http://xmlns.com/foaf/0.1/member> (member, None), <http://www.w3.org/2003/01/geo/wgs84_pos#long> (long, None), <http://www.w3.org/2000/10/swap/pim/contact#address> (address, None), <http://xmlns.com/foaf/0.1/family_name> (family_name, None), <http://xmlns.com/foaf/0.1/account> (account, None), <http://xmlns.com/foaf/0.1/workplaceHomepage> (workplaceHomepage, None), <http://purl.org/dc/terms/title> (title, None), <http://www.w3.org/ns/solid/terms#publicTypeIndex> (publicTypeIndex, None), <http://www.w3.org/2000/10/swap/pim/contact#office> (office, None), <http://www.w3.org/2000/10/swap/pim/contact#homePage> (homePage, None), <http://xmlns.com/foaf/0.1/mbox> (mbox, None), <http://www.w3.org/2000/10/swap/pim/contact#preferredURI> (preferredURI, None), <http://www.w3.org/ns/solid/terms#profileBackgroundColor> (profileBackgroundColor, None), <http://schema.org/owns> (owns, None), <http://xmlns.com/foaf/0.1/based_near> (based_near, None), <http://www.w3.org/2006/vcard/ns#hasAddress> (hasAddress, None), <http://xmlns.com/foaf/0.1/img> (img, None), <http://www.w3.org/2000/10/swap/pim/contact#assistant> (assistant, None), <http://xmlns.com/foaf/0.1/title> (title, None), <http://www.w3.org/ns/auth/cert#key> (key, None), <http://www.w3.org/ns/ldp#inbox> (inbox, None), <http://www.w3.org/ns/solid/terms#editableProfile> (editableProfile, None), <http://www.w3.org/2000/10/swap/pim/contact#postalCode> (postalCode, None), <http://xmlns.com/foaf/0.1/weblog> (weblog, None), <http://www.w3.org/ns/auth/cert#exponent> (exponent, None), <http://rdfs.org/sioc/ns#avatar> (avatar, None)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "graph.get_schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68a3c677",
+   "metadata": {},
+   "source": [
+    "## Querying the graph\n",
+    "\n",
+    "Now, you can use the graph SPARQL QA chain to ask questions about the graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7476ce98",
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "chain = GraphSparqlQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ef8ee27b",
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001B[1m> Entering new GraphSparqlQAChain chain...\u001B[0m\n",
+      "Identified intent:\n",
+      "\u001B[32;1m\u001B[1;3mSELECT\u001B[0m\n",
+      "Generated SPARQL:\n",
+      "\u001B[32;1m\u001B[1;3mPREFIX foaf: <http://xmlns.com/foaf/0.1/>\n",
+      "SELECT ?homepage\n",
+      "WHERE {\n",
+      "    ?person foaf:name \"Tim Berners-Lee\" .\n",
+      "    ?person foaf:workplaceHomepage ?homepage .\n",
+      "}\u001B[0m\n",
+      "Full Context:\n",
+      "\u001B[32;1m\u001B[1;3m[]\u001B[0m\n",
+      "\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Tim Berners-Lee's work homepage is http://www.w3.org/People/Berners-Lee/.\""
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"What is Tim Berners-Lee's work homepage?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af4b3294",
+   "metadata": {},
+   "source": [
+    "## Updating the graph\n",
+    "\n",
+    "Analogously, you can update the graph, i.e., insert triples, using natural language."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "fdf38841",
+   "metadata": {
+    "pycharm": {
+     "is_executing": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001B[1m> Entering new GraphSparqlQAChain chain...\u001B[0m\n",
+      "Identified intent:\n",
+      "\u001B[32;1m\u001B[1;3mUPDATE\u001B[0m\n",
+      "Generated SPARQL:\n",
+      "\u001B[32;1m\u001B[1;3mPREFIX foaf: <http://xmlns.com/foaf/0.1/>\n",
+      "INSERT {\n",
+      "    ?person foaf:workplaceHomepage <http://www.w3.org/foo/bar/> .\n",
+      "}\n",
+      "WHERE {\n",
+      "    ?person foaf:name \"Timothy Berners-Lee\" .\n",
+      "}\u001B[0m\n",
+      "\n",
+      "\u001B[1m> Finished chain.\u001B[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Successfully inserted triples into the graph.'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"Save that the person with the name 'Timothy Berners-Lee' has a work homepage at 'http://www.w3.org/foo/bar/'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e0f7fc1",
+   "metadata": {},
+   "source": [
+    "Let's verify the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f874171b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(rdflib.term.URIRef('https://www.w3.org/'),),\n",
+       " (rdflib.term.URIRef('http://www.w3.org/foo/bar/'),)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = (\n",
+    "    \"\"\"PREFIX foaf: <http://xmlns.com/foaf/0.1/>\\n\"\"\"\n",
+    "    \"\"\"SELECT ?hp\\n\"\"\"\n",
+    "    \"\"\"WHERE {\\n\"\"\"\n",
+    "    \"\"\"    ?person foaf:name \"Timothy Berners-Lee\" . \\n\"\"\"\n",
+    "    \"\"\"    ?person foaf:workplaceHomepage ?hp .\\n\"\"\"\n",
+    "    \"\"\"}\"\"\"\n",
+    ")\n",
+    "graph.query(query)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lc",
+   "language": "python",
+   "name": "lc"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/extras/modules/chains/how_to/custom_chain.ipynb
+++ b/docs/extras/modules/chains/how_to/custom_chain.ipynb
@@ -28,7 +28,7 @@
    "\n",
    "from pydantic import Extra\n",
    "\n",
-    "from langchain.base_language import BaseLanguageModel\n",
+    "from langchain.schemea import BaseLanguageModel\n",
    "from langchain.callbacks.manager import (\n",
    "    AsyncCallbackManagerForChainRun,\n",
    "    CallbackManagerForChainRun,\n",
--- a/docs/extras/modules/chains/popular/openai_functions.ipynb
+++ b/docs/extras/modules/chains/popular/openai_functions.ipynb
@@ -0,0 +1,518 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "54ccb772",
+   "metadata": {},
+   "source": [
+    "# Using OpenAI functions\n",
+    "This walkthrough demonstrates how to incorporate OpenAI function-calling API's in a chain. We'll go over: \n",
+    "1. How to use functions to get structured outputs from ChatOpenAI\n",
+    "2. How to create a generic chain that uses (multiple) functions\n",
+    "3. How to create a chain that actually executes the chosen function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "767ac575",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional\n",
+    "\n",
+    "from langchain.chains.openai_functions import (\n",
+    "    create_openai_fn_chain, create_structured_output_chain\n",
+    ")\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate\n",
+    "from langchain.schema import HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "976b6496",
+   "metadata": {},
+   "source": [
+    "## Getting structured outputs\n",
+    "We can take advantage of OpenAI functions to try and force the model to return a particular kind of structured output. We'll use the `create_structured_output_chain` to create our chain, which takes the desired structured output either as a Pydantic class or as JsonSchema.\n",
+    "\n",
+    "See here for relevant [reference docs](https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.base.create_structured_output_chain.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e052faae",
+   "metadata": {},
+   "source": [
+    "### Using Pydantic classes\n",
+    "When passing in Pydantic classes to structure our text, we need to make sure to have a docstring description for the class. It also helps to have descriptions for each of the classes attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0e085c99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "class Person(BaseModel):\n",
+    "    \"\"\"Identifying information about a person.\"\"\"\n",
+    "    name: str = Field(..., description=\"The person's name\")\n",
+    "    age: int = Field(..., description=\"The person's age\")\n",
+    "    fav_food: Optional[str] = Field(None, description=\"The person's favorite food\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b459a33e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
+      "Human: Use the given format to extract information from the following input:\n",
+      "Human: Sally is 13\n",
+      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Sally', 'age': 13}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If we pass in a model explicitly, we need to make sure it supports the OpenAI function-calling API.\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0613\", temperature=0)\n",
+    "\n",
+    "prompt_msgs = [\n",
+    "        SystemMessage(\n",
+    "            content=\"You are a world class algorithm for extracting information in structured formats.\"\n",
+    "        ),\n",
+    "        HumanMessage(content=\"Use the given format to extract information from the following input:\"),\n",
+    "        HumanMessagePromptTemplate.from_template(\"{input}\"),\n",
+    "        HumanMessage(content=\"Tips: Make sure to answer in the correct format\"),\n",
+    "    ]\n",
+    "prompt = ChatPromptTemplate(messages=prompt_msgs)\n",
+    "\n",
+    "chain = create_structured_output_chain(Person, llm, prompt, verbose=True)\n",
+    "chain.run(\"Sally is 13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3539936",
+   "metadata": {},
+   "source": [
+    "To extract arbitrarily many structured outputs of a given format, we can just create a wrapper Pydantic class  that takes a sequence of the original class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4d8ea815",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
+      "Human: Use the given format to extract information from the following input:\n",
+      "Human: Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally, so she's 23.\n",
+      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'people': [{'name': 'Sally', 'age': 13, 'fav_food': ''},\n",
+       "  {'name': 'Joey', 'age': 12, 'fav_food': 'spinach'},\n",
+       "  {'name': 'Caroline', 'age': 23, 'fav_food': ''}]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Sequence\n",
+    "\n",
+    "class People(BaseModel):\n",
+    "    \"\"\"Identifying information about all people in a text.\"\"\"\n",
+    "    people: Sequence[Person] = Field(..., description=\"The people in the text\")\n",
+    "        \n",
+    "chain = create_structured_output_chain(People, llm, prompt, verbose=True)\n",
+    "chain.run(\"Sally is 13, Joey just turned 12 and loves spinach. Caroline is 10 years older than Sally, so she's 23.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea66e10e",
+   "metadata": {},
+   "source": [
+    "### Using JsonSchema\n",
+    "\n",
+    "We can also pass in JsonSchema instead of Pydantic classes to specify the desired structure. When we do this, our chain will output json corresponding to the properties described in the JsonSchema, instead of a Pydantic class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3484415e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "json_schema = {\n",
+    "    \"title\": \"Person\",\n",
+    "    \"description\": \"Identifying information about a person.\",\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "      \"name\": {\n",
+    "        \"title\": \"Name\",\n",
+    "        \"description\": \"The person's name\",\n",
+    "        \"type\": \"string\"\n",
+    "      },\n",
+    "      \"age\": {\n",
+    "        \"title\": \"Age\",\n",
+    "        \"description\": \"The person's age\",\n",
+    "        \"type\": \"integer\"\n",
+    "      },\n",
+    "      \"fav_food\": {\n",
+    "        \"title\": \"Fav Food\",\n",
+    "        \"description\": \"The person's favorite food\",\n",
+    "        \"type\": \"string\"\n",
+    "      }\n",
+    "    },\n",
+    "    \"required\": [\n",
+    "      \"name\",\n",
+    "      \"age\"\n",
+    "    ]\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "be9b76b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for extracting information in structured formats.\n",
+      "Human: Use the given format to extract information from the following input:\n",
+      "Human: Sally is 13\n",
+      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Sally', 'age': 13}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain = create_structured_output_chain(json_schema, llm, prompt, verbose=True)\n",
+    "chain.run(\"Sally is 13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12394696",
+   "metadata": {},
+   "source": [
+    "## Creating a generic OpenAI functions chain\n",
+    "To create a generic OpenAI functions chain, we can use the `create_openai_fn_chain` method. This is the same as `create_structured_output_chain` except that instead of taking a single output schema, it takes a sequence of function definitions.\n",
+    "\n",
+    "Functions can be passed in as:\n",
+    "- dicts conforming to OpenAI functions spec,\n",
+    "- Pydantic classes, in which case they should have docstring descriptions of the function they represent and descriptions for each of the parameters,\n",
+    "- Python functions, in which case they should have docstring descriptions of the function and args, along with type hints.\n",
+    "\n",
+    "See here for relevant [reference docs](https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.base.create_openai_fn_chain.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff19be25",
+   "metadata": {},
+   "source": [
+    "### Using Pydantic classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "17f52508",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RecordPerson(BaseModel):\n",
+    "    \"\"\"Record some identifying information about a pe.\"\"\"\n",
+    "    name: str = Field(..., description=\"The person's name\")\n",
+    "    age: int = Field(..., description=\"The person's age\")\n",
+    "    fav_food: Optional[str] = Field(None, description=\"The person's favorite food\")\n",
+    "\n",
+    "        \n",
+    "class RecordDog(BaseModel):\n",
+    "    \"\"\"Record some identifying information about a dog.\"\"\"\n",
+    "    name: str = Field(..., description=\"The dog's name\")\n",
+    "    color: str = Field(..., description=\"The dog's color\")\n",
+    "    fav_food: Optional[str] = Field(None, description=\"The dog's favorite food\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4658ad8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
+      "Human: Make calls to the relevant function to record the entities in the following input:\n",
+      "Human: Harry was a chubby brown beagle who loved chicken\n",
+      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "RecordDog(name='Harry', color='brown', fav_food='chicken')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt_msgs = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a world class algorithm for recording entities\"\n",
+    "    ),\n",
+    "    HumanMessage(content=\"Make calls to the relevant function to record the entities in the following input:\"),\n",
+    "    HumanMessagePromptTemplate.from_template(\"{input}\"),\n",
+    "    HumanMessage(content=\"Tips: Make sure to answer in the correct format\"),\n",
+    "]\n",
+    "prompt = ChatPromptTemplate(messages=prompt_msgs)\n",
+    "\n",
+    "chain = create_openai_fn_chain([RecordPerson, RecordDog], llm, prompt, verbose=True)\n",
+    "chain.run(\"Harry was a chubby brown beagle who loved chicken\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df6d9147",
+   "metadata": {},
+   "source": [
+    "### Using Python functions\n",
+    "We can pass in functions as Pydantic classes, directly as OpenAI function dicts, or Python functions. To pass Python function in directly, we'll want to make sure our parameters have type hints, we have a docstring, and we use [Google Python style docstrings](https://google.github.io/styleguide/pyguide.html#doc-function-args) to describe the parameters.\n",
+    "\n",
+    "**NOTE**: To use Python functions, make sure the function arguments are of primitive types (str, float, int, bool) or that they are Pydantic objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "95ac5825",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
+      "Human: Make calls to the relevant function to record the entities in the following input:\n",
+      "Human: The most important thing to remember about Tommy, my 12 year old, is that he'll do anything for apple pie.\n",
+      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Tommy', 'age': 12, 'fav_food': {'food': 'apple pie'}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class OptionalFavFood(BaseModel):\n",
+    "    \"\"\"Either a food or null.\"\"\"\n",
+    "    food: Optional[str] = Field(None, description=\"Either the name of a food or null. Should be null if the food isn't known.\")\n",
+    "\n",
+    "def record_person(name: str, age: int, fav_food: OptionalFavFood) -> str:\n",
+    "    \"\"\"Record some basic identifying information about a person.\n",
+    "    \n",
+    "    Args:\n",
+    "        name: The person's name.\n",
+    "        age: The person's age in years.\n",
+    "        fav_food: An OptionalFavFood object that either contains the person's favorite food or a null value. Food should be null if it's not known.\n",
+    "    \"\"\"\n",
+    "    return f\"Recording person {name} of age {age} with favorite food {fav_food.food}!\"\n",
+    "\n",
+    "    \n",
+    "chain = create_openai_fn_chain([record_person], llm, prompt, verbose=True)\n",
+    "chain.run(\"The most important thing to remember about Tommy, my 12 year old, is that he'll do anything for apple pie.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403ea5dd",
+   "metadata": {},
+   "source": [
+    "If we pass in multiple Python functions or OpenAI functions, then the returned output will be of the form\n",
+    "```python\n",
+    "{\"name\": \"<<function_name>>\", \"arguments\": {<<function_arguments>>}}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8b0d11de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "Prompt after formatting:\n",
+      "\u001b[32;1m\u001b[1;3mSystem: You are a world class algorithm for recording entities\n",
+      "Human: Make calls to the relevant function to record the entities in the following input:\n",
+      "Human: I can't find my dog Henry anywhere, he's a small brown beagle. Could you send a message about him?\n",
+      "Human: Tips: Make sure to answer in the correct format\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'record_dog',\n",
+       " 'arguments': {'name': 'Henry', 'color': 'brown', 'fav_food': {'food': None}}}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def record_dog(name: str, color: str, fav_food: OptionalFavFood) -> str:\n",
+    "    \"\"\"Record some basic identifying information about a dog.\n",
+    "    \n",
+    "    Args:\n",
+    "        name: The dog's name.\n",
+    "        color: The dog's color.\n",
+    "        fav_food: An OptionalFavFood object that either contains the dog's favorite food or a null value. Food should be null if it's not known.\n",
+    "    \"\"\"\n",
+    "    return f\"Recording dog {name} of color {color} with favorite food {fav_food}!\"\n",
+    "\n",
+    "\n",
+    "chain = create_openai_fn_chain([record_person, record_dog], llm, prompt, verbose=True)\n",
+    "chain.run(\"I can't find my dog Henry anywhere, he's a small brown beagle. Could you send a message about him?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f93686b",
+   "metadata": {},
+   "source": [
+    "## Other Chains using OpenAI  functions\n",
+    "\n",
+    "There are a number of more specific chains that use OpenAI functions.\n",
+    "- [Extraction](/docs/modules/chains/additional/extraction): very similar to structured output chain, intended for information/entity extraction specifically.\n",
+    "- [Tagging](/docs/modules/chains/additional/tagging): tag inputs.\n",
+    "- [OpenAPI](/docs/modules/chains/additional/openapi_openai): take an OpenAPI spec and create + execute valid requests against the API, using OpenAI functions under the hood.\n",
+    "- [QA with citations](/docs/modules/chains/additional/qa_citations): use OpenAI functions ability to extract citations from text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93425c66",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/extras/modules/data_connection/document_loaders/integrations/cube_semantic.ipynb
+++ b/docs/extras/modules/data_connection/document_loaders/integrations/cube_semantic.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cube Semantic Layer"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates the process of retrieving Cube's data model metadata in a format suitable for passing to LLMs as embeddings, thereby enhancing contextual information."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### About Cube"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Cube](https://cube.dev/) is the Semantic Layer for building data apps. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cube’s data model provides structure and definitions that are used as a context for LLM to understand data and generate correct queries. LLM doesn’t need to navigate complex joins and metrics calculations because Cube abstracts those and provides a simple interface that operates on the business-level terminology, instead of SQL table and column names. This simplification helps LLM to be less error-prone and avoid hallucinations."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Cube Semantic Loader` requires 2 arguments:\n",
+    "| Input Parameter | Description |\n",
+    "| --- | --- |\n",
+    "| `cube_api_url` | The URL of your Cube's deployment REST API. Please refer to the [Cube documentation](https://cube.dev/docs/http-api/rest#configuration-base-path) for more information on configuring the base path. |\n",
+    "| `cube_api_token` | The authentication token generated based on your Cube's API secret. Please refer to the [Cube documentation](https://cube.dev/docs/security#generating-json-web-tokens-jwt) for instructions on generating JSON Web Tokens (JWT). |\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jwt\n",
+    "from langchain.document_loaders import CubeSemanticLoader\n",
+    "\n",
+    "api_url = \"https://api-example.gcp-us-central1.cubecloudapp.dev/cubejs-api/v1/meta\"\n",
+    "cubejs_api_secret = \"api-secret-here\"\n",
+    "security_context = {}\n",
+    "# Read more about security context here: https://cube.dev/docs/security\n",
+    "api_token = jwt.encode(security_context, cubejs_api_secret, algorithm=\"HS256\")\n",
+    "\n",
+    "loader = CubeSemanticLoader(api_url, api_token)\n",
+    "\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Returns:\n",
+    "\n",
+    "A list of documents with the following attributes:\n",
+    "\n",
+    "- `page_content`\n",
+    "- `metadata`\n",
+    "  - `table_name`\n",
+    "  - `column_name`\n",
+    "  - `column_data_type`\n",
+    "  - `column_title`\n",
+    "  - `column_description`"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> page_content='table name: orders_view, column name: orders_view.total_amount, column data type: number, column title: Orders View Total Amount, column description: None' metadata={'table_name': 'orders_view', 'column_name': 'orders_view.total_amount', 'column_data_type': 'number', 'column_title': 'Orders View Total Amount', 'column_description': 'None'}"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/docs/extras/modules/data_connection/retrievers/how_to/self_query/pinecone.ipynb
+++ b/docs/extras/modules/data_connection/retrievers/how_to/self_query/pinecone.ipynb
@@ -18,7 +18,7 @@
    "## Creating a Pinecone index\n",
    "First we'll want to create a `Pinecone` VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
    "\n",
-    "To use Pinecone, you to have `pinecone` package installed and you must have an API key and an Environment. Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).\n",
+    "To use Pinecone, you have to have `pinecone` package installed and you must have an API key and an Environment. Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).\n",
    "\n",
    "NOTE: The self-query retriever requires you to have `lark` package installed."
   ]
--- a/docs/extras/modules/data_connection/retrievers/integrations/amazon_kendra_retriever.ipynb
+++ b/docs/extras/modules/data_connection/retrievers/integrations/amazon_kendra_retriever.ipynb
@@ -5,9 +5,9 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# AWS Kendra\n",
+    "# Amazon Kendra\n",
    "\n",
-    "> AWS Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.\n",
+    "> Amazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.\n",
    "\n",
    "> With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results."
   ]
@@ -17,7 +17,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Using the AWS Kendra Index Retriever"
+    "## Using the Amazon Kendra Index Retriever"
   ]
  },
  {
--- a/docs/extras/modules/data_connection/retrievers/integrations/chaindesk.ipynb
+++ b/docs/extras/modules/data_connection/retrievers/integrations/chaindesk.ipynb
@@ -1,21 +1,31 @@
 {
 "cells": [
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "9fc6205b",
   "metadata": {},
   "source": [
-    "# Databerry\n",
+    "# Chaindesk\n",
    "\n",
-    ">[Databerry platform](https://docs.databerry.ai/introduction) brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).\n",
-    "Then your Datastores can be connected to ChatGPT via Plugins or any other Large Langue Model (LLM) via the `Databerry API`.\n",
+    ">[Chaindesk platform](https://docs.chaindesk.ai/introduction) brings data from anywhere (Datsources: Text, PDF, Word, PowerPpoint, Excel, Notion, Airtable, Google Sheets, etc..) into Datastores (container of multiple Datasources).\n",
+    "Then your Datastores can be connected to ChatGPT via Plugins or any other Large Langue Model (LLM) via the `Chaindesk API`.\n",
    "\n",
-    "This notebook shows how to use [Databerry's](https://www.databerry.ai/) retriever.\n",
+    "This notebook shows how to use [Chaindesk's](https://www.chaindesk.ai/) retriever.\n",
    "\n",
-    "First, you will need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url. You need the [API Key](https://docs.databerry.ai/api-reference/authentication)."
+    "First, you will need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. You need the [API Key](https://docs.chaindesk.ai/api-reference/authentication)."
   ]
  },
  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3697b9fd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "944e172b",
   "metadata": {},
@@ -34,7 +44,7 @@
   },
   "outputs": [],
   "source": [
-    "from langchain.retrievers import DataberryRetriever"
+    "from langchain.retrievers import ChaindeskRetriever"
   ]
  },
  {
@@ -46,9 +56,9 @@
   },
   "outputs": [],
   "source": [
-    "retriever = DataberryRetriever(\n",
-    "    datastore_url=\"https://clg1xg2h80000l708dymr0fxc.databerry.ai/query\",\n",
-    "    # api_key=\"DATABERRY_API_KEY\", # optional if datastore is public\n",
+    "retriever = ChaindeskRetriever(\n",
+    "    datastore_url=\"https://clg1xg2h80000l708dymr0fxc.chaindesk.ai/query\",\n",
+    "    # api_key=\"CHAINDESK_API_KEY\", # optional if datastore is public\n",
    "    # top_k=10 # optional\n",
    ")"
   ]
--- a/docs/extras/modules/data_connection/retrievers/integrations/merger_retriever.ipynb
+++ b/docs/extras/modules/data_connection/retrievers/integrations/merger_retriever.ipynb
@@ -1,6 +1,7 @@
 {
 "cells": [
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "fc0db1bc",
   "metadata": {},
@@ -25,7 +26,7 @@
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.document_transformers import EmbeddingsRedundantFilter\n",
+    "from langchain.document_transformers import EmbeddingsRedundantFilter,EmbeddingsClusteringFilter\n",
    "from langchain.retrievers.document_compressors import DocumentCompressorPipeline\n",
    "from langchain.retrievers import ContextualCompressionRetriever\n",
    "\n",
@@ -70,6 +71,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "c152339d",
   "metadata": {},
@@ -92,6 +94,46 @@
    "    base_compressor=pipeline, base_retriever=lotr\n",
    ")"
   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c10022fa",
+   "metadata": {},
+   "source": [
+    "## Pick a representative sample of documents from the merged retrievers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3885482",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This filter will divide the documents vectors into clusters or \"centers\" of meaning.\n",
+    "# Then it will pick the closest document to that center for the final results.\n",
+    "# By default the result document will be ordered/grouped by clusters.\n",
+    "filter_ordered_cluster = EmbeddingsClusteringFilter(\n",
+    "            embeddings=filter_embeddings,\n",
+    "            num_clusters=10,\n",
+    "            num_closest=1,\n",
+    "        )\n",
+    "\n",
+    "# If you want the final document to be ordered by the original retriever scores\n",
+    "# you need to add the \"sorted\" parameter.\n",
+    "filter_ordered_by_retriever = EmbeddingsClusteringFilter(\n",
+    "            embeddings=filter_embeddings,\n",
+    "            num_clusters=10,\n",
+    "            num_closest=1,\n",
+    "            sorted = True,\n",
+    "        )\n",
+    "\n",
+    "pipeline = DocumentCompressorPipeline(transformers=[filter_ordered_by_retriever])\n",
+    "compression_retriever = ContextualCompressionRetriever(\n",
+    "    base_compressor=pipeline, base_retriever=lotr\n",
+    ")\n"
+   ]
  }
 ],
 "metadata": {
--- a/docs/extras/modules/data_connection/text_embedding/integrations/clarifai.ipynb
+++ b/docs/extras/modules/data_connection/text_embedding/integrations/clarifai.ipynb
@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "9597802c",
+   "metadata": {},
+   "source": [
+    "# Clarifai\n",
+    "\n",
+    ">[Clarifai](https://www.clarifai.com/) is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference.\n",
+    "\n",
+    "This example goes over how to use LangChain to interact with `Clarifai` [models](https://clarifai.com/explore/models). Text embedding models in particular can be found [here](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22model_type_id%22%2C%22value%22%3A%5B%22text-embedder%22%5D%7D%5D).\n",
+    "\n",
+    "To use Clarifai, you must have an account and a Personal Access Token (PAT) key. \n",
+    "[Check here](https://clarifai.com/settings/security) to get or create a PAT."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2a773d8d",
+   "metadata": {},
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91ea14ce-831d-409a-a88f-30353acdabd1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Install required dependencies\n",
+    "!pip install clarifai"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "426f1156",
+   "metadata": {},
+   "source": [
+    "# Imports\n",
+    "Here we will be setting the personal access token. You can find your PAT under [settings/security](https://clarifai.com/settings/security) in your Clarifai account."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3f5dc9d7-65e3-4b5b-9086-3327d016cfe0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Please login and get your API key from  https://clarifai.com/settings/security \n",
+    "from getpass import getpass\n",
+    "\n",
+    "CLARIFAI_PAT = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fb585dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Import the required modules\n",
+    "from langchain.embeddings import ClarifaiEmbeddings\n",
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "16521ed2",
+   "metadata": {},
+   "source": [
+    "# Input\n",
+    "Create a prompt template to be used with the LLM Chain:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "035dea0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c8905eac",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "Set the user id and app id to the application in which the model resides. You can find a list of public models on https://clarifai.com/explore/models\n",
+    "\n",
+    "You will have to also initialize the model id and if needed, the model version id. Some models have many versions, you can choose the one appropriate for your task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1fe9bf15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "USER_ID = 'openai'\n",
+    "APP_ID = 'embed'\n",
+    "MODEL_ID = 'text-embedding-ada'\n",
+    "\n",
+    "# You can provide a specific model version as the model_version_id arg.\n",
+    "# MODEL_VERSION_ID = \"MODEL_VERSION_ID\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3f3458d9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize a Clarifai embedding model\n",
+    "embeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a641dbd9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "32b4d5f4-2b8e-4681-856f-19a3dd141ae4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "47076457-1880-48ac-970f-872ead6f0d94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_result = embeddings.embed_documents([text])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/extras/modules/data_connection/text_embedding/integrations/elasticsearch.ipynb
+++ b/docs/extras/modules/data_connection/text_embedding/integrations/elasticsearch.ipynb
@@ -9,7 +9,7 @@
    "# Elasticsearch\n",
    "Walkthrough of how to generate embeddings using a hosted embedding model in Elasticsearch\n",
    "\n",
-    "The easiest way to instantiate the `ElasticsearchEmebddings` class it either\n",
+    "The easiest way to instantiate the `ElasticsearchEmbeddings` class it either\n",
    "- using the `from_credentials` constructor if you are using Elastic Cloud\n",
    "- or using the `from_es_connection` constructor with any Elasticsearch cluster"
   ],
--- a/docs/extras/modules/data_connection/vectorstores/integrations/alibabacloud_opensearch.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/alibabacloud_opensearch.ipynb
@@ -10,14 +10,32 @@
    "\n",
    ">`OpenSearch` helps you develop high quality, maintenance-free, and high performance intelligent search services to provide your users with high search efficiency and accuracy.\n",
    "\n",
-    ">`OpenSearch` provides the vector search feature. In specific scenarios, especially test question search and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results. This topic describes the syntax and usage notes of vector indexes.\n",
+    ">`OpenSearch` provides the vector search feature. In specific scenarios, especially test question search and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results.\n",
    "\n",
    "This notebook shows how to use functionality related to the `Alibaba Cloud OpenSearch Vector Search Edition`.\n",
    "To run, you should have an [OpenSearch Vector Search Edition](https://opensearch.console.aliyun.com) instance up and running:\n",
    "\n",
-    "Read the [help document](https://www.alibabacloud.com/help/en/opensearch/latest/vector-search) to quickly familiarize and configure OpenSearch Vector Search Edition instance.\n"
+    "Read the [help document](https://www.alibabacloud.com/help/en/opensearch/latest/vector-search) to quickly familiarize and configure OpenSearch Vector Search Edition instance."
   ]
  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "After the instance is up and running, follow these steps to split documents, get embeddings, connect to the alibaba cloud opensearch instance, index documents, and perform vector retrieval."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "We need to install the following Python packages first."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
  {
   "cell_type": "code",
   "execution_count": 1,
@@ -29,10 +47,29 @@
  },
  {
   "cell_type": "markdown",
-   "metadata": {},
   "source": [
-    "After completing the configuration, follow these steps to connect to the instance, index documents, and perform vector retrieval."
-   ]
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
  },
  {
   "cell_type": "code",
@@ -60,7 +97,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Split documents and get embeddings by call OpenAI API"
+    "Split documents and get embeddings."
   ]
  },
  {
@@ -123,7 +160,7 @@
    "        \"id\": \"id\",  # The id field name mapping of index document.\n",
    "        \"document\": \"document\",  # The text field name mapping of index document.\n",
    "        \"embedding\": \"embedding\",  # The embedding field name mapping of index document.\n",
-    "        \"metadata_x\": \"metadata_x,=\",  # The metadata field name mapping of index document, could specify multiple, The value field contains mapping name and operator, the operator would be used when executing metadata filter query.\n",
+    "        \"name_of_the_metadata_specified_during_search\": \"opensearch_metadata_field_name,=\",  # The metadata field name mapping of index document, could specify multiple, The value field contains mapping name and operator, the operator would be used when executing metadata filter query.\n",
    "    },\n",
    ")\n",
    "\n",
@@ -139,7 +176,10 @@
    "#         \"id\": \"id\",\n",
    "#         \"document\": \"document\",\n",
    "#         \"embedding\": \"embedding\",\n",
-    "#         \"metadata\": \"metadata,=\" #The value field contains mapping name and operator, the operator would be used when executing metadata filter query\n",
+    "#         \"metadata_a\": \"metadata_a,=\" #The value field contains mapping name and operator, the operator would be used when executing metadata filter query\n",
+    "#         \"metadata_b\": \"metadata_b,>\"\n",
+    "#         \"metadata_c\": \"metadata_c,<\"\n",
+    "#         \"metadata_else\": \"metadata_else,=\"\n",
    "#     })"
   ]
  },
@@ -251,7 +291,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Query and retrieve data with metadata\n"
+    "Query and retrieve data with metadata.\n"
   ]
  },
  {
@@ -307,4 +347,4 @@
 },
 "nbformat": 4,
 "nbformat_minor": 4
-}
+}
--- a/docs/extras/modules/data_connection/vectorstores/integrations/clarifai.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/clarifai.ipynb
@@ -8,12 +8,12 @@
   "source": [
    "# Clarifai\n",
    "\n",
-    ">[Clarifai](https://www.clarifai.com/) is a AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model building and inference. A Clarifai application can be used as a vector database after uploading inputs. \n",
+    ">[Clarifai](https://www.clarifai.com/) is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. \n",
    "\n",
    "This notebook shows how to use functionality related to the `Clarifai` vector database.\n",
    "\n",
-    "To use Clarifai, you must have an account and a Personal Access Token key. \n",
-    "Here are the [installation instructions](https://clarifai.com/settings/security )."
+    "To use Clarifai, you must have an account and a Personal Access Token (PAT) key. \n",
+    "[Check here](https://clarifai.com/settings/security) to get or create a PAT."
   ]
  },
  {
@@ -58,7 +58,7 @@
    "# Please login and get your API key from  https://clarifai.com/settings/security \n",
    "from getpass import getpass\n",
    "\n",
-    "CLARIFAI_PAT_KEY = getpass()"
+    "CLARIFAI_PAT = getpass()"
   ]
  },
  {
@@ -92,7 +92,7 @@
   "metadata": {},
   "source": [
    "# Setup\n",
-    "Setup the user id and app id where the text data will be uploaded. \n",
+    "Setup the user id and app id where the text data will be uploaded. Note: when creating that application please select an appropriate base workflow for indexing your text documents such as the Language-Understanding workflow.\n",
    "\n",
    "You will have to first create an account on [Clarifai](https://clarifai.com/login) and then create an application."
   ]
@@ -139,7 +139,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "clarifai_vector_db = Clarifai.from_texts(user_id=USER_ID, app_id=APP_ID, texts=texts, pat=CLARIFAI_PAT_KEY, number_of_docs=NUMBER_OF_DOCS, metadatas = metadatas)"
+    "clarifai_vector_db = Clarifai.from_texts(user_id=USER_ID, app_id=APP_ID, texts=texts, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS, metadatas = metadatas)"
   ]
  },
  {
--- a/docs/extras/modules/data_connection/vectorstores/integrations/marqo.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/marqo.ipynb
@@ -0,0 +1,575 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "683953b3",
+   "metadata": {},
+   "source": [
+    "# Marqo\n",
+    "\n",
+    "This notebook shows how to use functionality related to the Marqo vectorstore.\n",
+    "\n",
+    ">[Marqo](https://www.marqo.ai/) is an open-source vector search engine. Marqo allows you to store and query multimodal data such as text and images. Marqo creates the vectors for you using a huge selection of opensource models, you can also provide your own finetuned models and Marqo will handle the loading and inference for you.\n",
+    "\n",
+    "To run this notebook with our docker image please run the following commands first to get Marqo:\n",
+    "\n",
+    "```\n",
+    "docker pull marqoai/marqo:latest\n",
+    "docker rm -f marqo\n",
+    "docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "aac9563e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install marqo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5d1489ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import Marqo\n",
+    "from langchain.document_loaders import TextLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a3c3999a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import TextLoader\n",
+    "loader = TextLoader('../../../state_of_the_union.txt')\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6e104aee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index langchain-demo exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import marqo \n",
+    "\n",
+    "# initialize marqo\n",
+    "marqo_url = \"http://localhost:8882\" # if using marqo cloud replace with your endpoint (console.marqo.ai)\n",
+    "marqo_api_key = \"\" # if using marqo cloud replace with your api key (console.marqo.ai)\n",
+    "\n",
+    "client = marqo.Client(url=marqo_url, api_key=marqo_api_key)\n",
+    "\n",
+    "index_name = \"langchain-demo\"\n",
+    "\n",
+    "docsearch = Marqo.from_documents(docs, index_name=index_name)\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result_docs = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9c608226",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result_docs[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "98704b27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
+      "\n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
+      "\n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
+      "\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
+      "0.68647254\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_docs = docsearch.similarity_search_with_score(query)\n",
+    "print(result_docs[0][0].page_content, result_docs[0][1], sep=\"\\n\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "eb3395b6",
+   "metadata": {},
+   "source": [
+    "## Additional features\n",
+    "\n",
+    "One of the powerful features of Marqo as a vectorstore is that you can use indexes created externally. For example:\n",
+    "\n",
+    "+ If you had a database of image and text pairs from another application, you can simply just use it in langchain with the Marqo vectorstore. Note that bringing your own multimodal indexes will disable the `add_texts` method.\n",
+    "\n",
+    "+ If you had a database of text documents, you can bring it into the langchain framework and add more texts through `add_texts`.\n",
+    "\n",
+    "The documents that are returned are customised by passing your own function to the `page_content_builder` callback in the search methods."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "35b99fef",
+   "metadata": {},
+   "source": [
+    "#### Multimodal Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a359ed74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'errors': False,\n",
+       " 'processingTimeMs': 2090.2822139996715,\n",
+       " 'index_name': 'langchain-multimodal-demo',\n",
+       " 'items': [{'_id': 'aa92fc1c-1fb2-4d86-b027-feb507c419f7',\n",
+       "   'result': 'created',\n",
+       "   'status': 201},\n",
+       "  {'_id': '5142c258-ef9f-4bf2-a1a6-2307280173a0',\n",
+       "   'result': 'created',\n",
+       "   'status': 201}]}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# use a new index\n",
+    "index_name = \"langchain-multimodal-demo\"\n",
+    "\n",
+    "# incase the demo is re-run\n",
+    "try:\n",
+    "    client.delete_index(index_name)\n",
+    "except Exception:\n",
+    "    print(f\"Creating {index_name}\")\n",
+    "    \n",
+    "# This index could have been created by another system\n",
+    "settings = {\"treat_urls_and_pointers_as_images\": True, \"model\": \"ViT-L/14\"}\n",
+    "client.create_index(index_name, **settings)\n",
+    "client.index(index_name).add_documents(\n",
+    "    [   \n",
+    "        # image of a bus\n",
+    "        {\n",
+    "            \"caption\": \"Bus\",\n",
+    "            \"image\": \"https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image4.jpg\"\n",
+    "        },\n",
+    "        # image of a plane\n",
+    "        {   \n",
+    "            \"caption\": \"Plane\", \n",
+    "            \"image\": \"https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image2.jpg\"\n",
+    "        }\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "368d1fab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_content(res):\n",
+    "    \"\"\"Helper to format Marqo's documents into text to be used as page_content\"\"\"\n",
+    "    return f\"{res['caption']}: {res['image']}\"\n",
+    "\n",
+    "docsearch = Marqo(client, index_name, page_content_builder=get_content)\n",
+    "\n",
+    "\n",
+    "query = \"vehicles that fly\"\n",
+    "doc_results = docsearch.similarity_search(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eef4edf9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plane: https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image2.jpg\n",
+      "Bus: https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image4.jpg\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doc in doc_results:\n",
+    "    print(doc.page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c255f603",
+   "metadata": {},
+   "source": [
+    "#### Text only example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9e9a2b20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'errors': False,\n",
+       " 'processingTimeMs': 139.2144540004665,\n",
+       " 'index_name': 'langchain-byo-index-demo',\n",
+       " 'items': [{'_id': '27c05a1c-b8a9-49a5-ae73-fbf1eb51dc3f',\n",
+       "   'result': 'created',\n",
+       "   'status': 201},\n",
+       "  {'_id': '6889afe0-e600-43c1-aa3b-1d91bf6db274',\n",
+       "   'result': 'created',\n",
+       "   'status': 201}]}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# use a new index\n",
+    "index_name = \"langchain-byo-index-demo\"\n",
+    "\n",
+    "# incase the demo is re-run\n",
+    "try:\n",
+    "    client.delete_index(index_name)\n",
+    "except Exception:\n",
+    "    print(f\"Creating {index_name}\")\n",
+    "\n",
+    "# This index could have been created by another system\n",
+    "client.create_index(index_name)\n",
+    "client.index(index_name).add_documents(\n",
+    "    [   \n",
+    "        {\n",
+    "            \"Title\": \"Smartphone\",\n",
+    "            \"Description\": \"A smartphone is a portable computer device that combines mobile telephone \"\n",
+    "            \"functions and computing functions into one unit.\",\n",
+    "        },\n",
+    "        {   \n",
+    "            \"Title\": \"Telephone\",\n",
+    "            \"Description\": \"A telephone is a telecommunications device that permits two or more users to\"\n",
+    "            \"conduct a conversation when they are too far apart to be easily heard directly.\",\n",
+    "        }\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b2943ea9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['9986cc72-adcd-4080-9d74-265c173a9ec3']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Note text indexes retain the ability to use add_texts despite different field names in documents\n",
+    "# this is because the page_content_builder callback lets you handle these document fields as required\n",
+    "\n",
+    "def get_content(res):\n",
+    "    \"\"\"Helper to format Marqo's documents into text to be used as page_content\"\"\"\n",
+    "    if 'text' in res:\n",
+    "        return res['text']\n",
+    "    return res['Description']\n",
+    "\n",
+    "\n",
+    "docsearch = Marqo(client, index_name, page_content_builder=get_content)\n",
+    "\n",
+    "docsearch.add_texts([\"This is a document that is about elephants\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "851450e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A smartphone is a portable computer device that combines mobile telephone functions and computing functions into one unit.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"modern communications devices\"\n",
+    "doc_results = docsearch.similarity_search(query)\n",
+    "\n",
+    "print(doc_results[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9a438fec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a document that is about elephants\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"elephants\"\n",
+    "doc_results = docsearch.similarity_search(query, page_content_builder=get_content)\n",
+    "\n",
+    "print(doc_results[0].page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "0d04c9d4",
+   "metadata": {},
+   "source": [
+    "## Weighted Queries\n",
+    "\n",
+    "We also expose marqos weighted queries which are a powerful way to compose complex semantic searches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d42ba0d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A smartphone is a portable computer device that combines mobile telephone functions and computing functions into one unit.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = {\"communications devices\" : 1.0}\n",
+    "doc_results = docsearch.similarity_search(query)\n",
+    "print(doc_results[0].page_content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b5918a16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A telephone is a telecommunications device that permits two or more users toconduct a conversation when they are too far apart to be easily heard directly.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = {\"communications devices\" : 1.0, \"technology post 2000\": -1.0}\n",
+    "doc_results = docsearch.similarity_search(query)\n",
+    "print(doc_results[0].page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2d026aa0",
+   "metadata": {},
+   "source": [
+    "# Question Answering with Sources\n",
+    "\n",
+    "This section shows how to use Marqo as part of a `RetrievalQAWithSourcesChain`. Marqo will perform the searches for information in the sources."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e4ca223c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key:········\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chains import RetrievalQAWithSourcesChain\n",
+    "from langchain import OpenAI\n",
+    "\n",
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5c6e45f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"../../../state_of_the_union.txt\") as f:\n",
+    "    state_of_the_union = f.read()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "texts = text_splitter.split_text(state_of_the_union)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "70a7f320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index langchain-qa-with-retrieval exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "index_name = \"langchain-qa-with-retrieval\"\n",
+    "docsearch = Marqo.from_documents(docs, index_name=index_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b3b008a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
+    "    OpenAI(temperature=0), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e1457716",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': ' The president honored Justice Breyer, thanking him for his service and noting that he is a retiring Justice of the United States Supreme Court.\\n',\n",
+       " 'sources': '../../../state_of_the_union.txt'}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain(\n",
+    "    {\"question\": \"What did the president say about Justice Breyer\"},\n",
+    "    return_only_outputs=True,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/extras/modules/data_connection/vectorstores/integrations/mongodb_atlas.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/mongodb_atlas.ipynb
@@ -1,6 +1,7 @@
 {
 "cells": [
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "683953b3",
   "metadata": {},
@@ -44,6 +45,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "457ace44-1d95-4001-9dd5-78811ab208ad",
   "metadata": {},
@@ -63,6 +65,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "1f3ecc42",
   "metadata": {},
@@ -130,7 +133,7 @@
    "# initialize MongoDB python client\n",
    "client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)\n",
    "\n",
-    "db_name = \"lanchain_db\"\n",
+    "db_name = \"langchain_db\"\n",
    "collection_name = \"langchain_col\"\n",
    "collection = client[db_name][collection_name]\n",
    "index_name = \"langchain_demo\"\n",
@@ -156,6 +159,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "851a2ec9-9390-49a4-8412-3e132c9f789d",
   "metadata": {},
@@ -183,14 +187,14 @@
    "db_name = \"langchain_db\"\n",
    "collection_name = \"langchain_col\"\n",
    "collection = client[db_name][collection_name]\n",
-    "index_name = \"langchain_index\"\n",
+    "index_name = \"langchain_demo\"\n",
    "\n",
    "# initialize vector store\n",
    "vectorStore = MongoDBAtlasVectorSearch(\n",
    "    collection, OpenAIEmbeddings(), index_name=index_name\n",
    ")\n",
    "\n",
-    "# perform a similarity search between the embedding of the query and the embeddings of the documents\n",
+    "# perform a similarity search between a query and the ingested documents\n",
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = vectorStore.similarity_search(query)\n",
    "\n",
--- a/docs/extras/modules/data_connection/vectorstores/integrations/pgembedding.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/pgembedding.ipynb
@@ -0,0 +1,338 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "1292f057",
+   "metadata": {},
+   "source": [
+    "# pg_hnsw\n",
+    "\n",
+    "> [pg_embedding](https://github.com/knizhnik/hnsw) is an open-source vector similarity search for `Postgres` that uses  Hierarchical Navigable Small Worlds for approximate nearest neighbor search.\n",
+    "\n",
+    "It supports:\n",
+    "- exact and approximate nearest neighbor search using HNSW\n",
+    "- L2 distance\n",
+    "\n",
+    "This notebook shows how to use the Postgres vector database (`PGEmbedding`).\n",
+    "\n",
+    "> The PGEmbedding integration creates the pg_embedding extension for you, but you run the following Postgres query to add it:\n",
+    "```sql\n",
+    "CREATE EXTENSION embedding;\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6214221",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pip install necessary package\n",
+    "!pip install openai\n",
+    "!pip install psycopg2-binary\n",
+    "!pip install tiktoken"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "b2e49694",
+   "metadata": {},
+   "source": [
+    "Add the OpenAI API Key to the environment variables to use `OpenAIEmbeddings`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1dcc8d99",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key:········\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9719ea68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Loading Environment Variables\n",
+    "from typing import List, Tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfd1f38d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.vectorstores import PGEmbedding\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.docstore.document import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8fab8cc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Database Url:········\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.environ[\"DATABASE_URL\"] = getpass.getpass(\"Database Url:\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bef17115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = TextLoader(\"state_of_the_union.txt\")\n",
+    "documents = loader.load()\n",
+    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "docs = text_splitter.split_documents(documents)\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "connection_string = os.environ.get(\"DATABASE_URL\")\n",
+    "collection_name = \"state_of_the_union\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "743abfaa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = PGEmbedding.from_documents(\n",
+    "    embedding=embeddings,\n",
+    "    documents=docs,\n",
+    "    collection_name=collection_name,\n",
+    "    connection_string=connection_string,\n",
+    ")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41ce4c4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for doc, score in docs_with_score:\n",
+    "    print(\"-\" * 80)\n",
+    "    print(\"Score: \", score)\n",
+    "    print(doc.page_content)\n",
+    "    print(\"-\" * 80)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7ef7b052",
+   "metadata": {},
+   "source": [
+    "## Working with vectorstore in Postgres"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "939151f7",
+   "metadata": {},
+   "source": [
+    "### Uploading a vectorstore in PG "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "595ac511",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = PGEmbedding.from_documents(\n",
+    "    embedding=embeddings,\n",
+    "    documents=docs,\n",
+    "    collection_name=collection_name,\n",
+    "    connection_string=connection_string,\n",
+    "    pre_delete_collection=False,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "f9510e6b",
+   "metadata": {},
+   "source": [
+    "### Create HNSW Index\n",
+    "By default, the extension performs a sequential scan search, with 100% recall. You might consider creating an HNSW index for approximate nearest neighbor (ANN) search to speed up `similarity_search_with_score` execution time. To create the HNSW index on your vector column, use a `create_hnsw_index` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d1981fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PGEmbedding.create_hnsw_index(\n",
+    "    max_elements=10000, dims=1536, m=8, ef_construction=16, ef_search=16\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7adacf29",
+   "metadata": {},
+   "source": [
+    "The function above is equivalent to running the below SQL query:\n",
+    "```sql\n",
+    "CREATE INDEX ON vectors USING hnsw(vec) WITH (maxelements=10000, dims=1536, m=3, efconstruction=16, efsearch=16);\n",
+    "```\n",
+    "The HNSW index options used in the statement above include:\n",
+    "\n",
+    "- maxelements: Defines the maximum number of elements indexed. This is a required parameter. The example shown above has a value of 3. A real-world example would have a much large value, such as 1000000. An \"element\" refers to a data point (a vector) in the dataset, which is represented as a node in the HNSW graph. Typically, you would set this option to a value able to accommodate the number of rows in your in your dataset.\n",
+    "- dims: Defines the number of dimensions in your vector data. This is a required parameter. A small value is used in the example above. If you are storing data generated using OpenAI's text-embedding-ada-002 model, which supports 1536 dimensions, you would define a value of 1536, for example.\n",
+    "- m: Defines the maximum number of bi-directional links (also referred to as \"edges\") created for each node during graph construction.\n",
+    "The following additional index options are supported:\n",
+    "\n",
+    "- efConstruction: Defines the number of nearest neighbors considered during index construction. The default value is 32.\n",
+    "- efsearch: Defines the number of nearest neighbors considered during index search. The default value is 32.\n",
+    "For information about how you can configure these options to influence the HNSW algorithm, refer to [Tuning the HNSW algorithm](https://neon-next-git-dprice-hnsw-extension-neondatabase.vercel.app/docs/extensions/hnsw#tuning-the-hnsw-algorithm)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "528893fb",
+   "metadata": {},
+   "source": [
+    "### Retrieving a vectorstore in PG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b6162b1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "store = PGEmbedding(\n",
+    "    connection_string=connection_string,\n",
+    "    embedding_function=embeddings,\n",
+    "    collection_name=collection_name,\n",
+    ")\n",
+    "\n",
+    "retriever = store.as_retriever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1a5fedb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VectorStoreRetriever(vectorstore=<langchain.vectorstores.pghnsw.HNSWVectoreStore object at 0x121d3c8b0>, search_type='similarity', search_kwargs={})"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "0cefc938",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db1 = PGEmbedding.from_existing_index(\n",
+    "    embedding=embeddings,\n",
+    "    collection_name=collection_name,\n",
+    "    pre_delete_collection=False,\n",
+    "    connection_string=connection_string,\n",
+    ")\n",
+    "\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85cde495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for doc, score in docs_with_score:\n",
+    "    print(\"-\" * 80)\n",
+    "    print(\"Score: \", score)\n",
+    "    print(doc.page_content)\n",
+    "    print(\"-\" * 80)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/extras/modules/data_connection/vectorstores/integrations/pgvector.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/pgvector.ipynb
@@ -123,7 +123,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 1,
   "metadata": {
    "tags": []
   },
@@ -138,7 +138,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -152,49 +152,25 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
-    "## PGVector needs the connection string to the database.\n",
-    "## We will load it from the environment variables.\n",
-    "import os\n",
+    "# PGVector needs the connection string to the database.\n",
+    "CONNECTION_STRING = \"postgresql+psycopg2://harrisonchase@localhost:5432/test3\"\n",
    "\n",
-    "CONNECTION_STRING = PGVector.connection_string_from_db_params(\n",
-    "    driver=os.environ.get(\"PGVECTOR_DRIVER\", \"psycopg2\"),\n",
-    "    host=os.environ.get(\"PGVECTOR_HOST\", \"localhost\"),\n",
-    "    port=int(os.environ.get(\"PGVECTOR_PORT\", \"5432\")),\n",
-    "    database=os.environ.get(\"PGVECTOR_DATABASE\", \"postgres\"),\n",
-    "    user=os.environ.get(\"PGVECTOR_USER\", \"postgres\"),\n",
-    "    password=os.environ.get(\"PGVECTOR_PASSWORD\", \"postgres\"),\n",
-    ")\n",
-    "\n",
-    "\n",
-    "## Example\n",
-    "# postgresql+psycopg2://username:password@localhost:5432/database_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ## PGVector needs the connection string to the database.\n",
-    "# ## We will load it from the environment variables.\n",
+    "# # Alternatively, you can create it from enviornment variables.\n",
    "# import os\n",
+    "\n",
    "# CONNECTION_STRING = PGVector.connection_string_from_db_params(\n",
    "#     driver=os.environ.get(\"PGVECTOR_DRIVER\", \"psycopg2\"),\n",
    "#     host=os.environ.get(\"PGVECTOR_HOST\", \"localhost\"),\n",
    "#     port=int(os.environ.get(\"PGVECTOR_PORT\", \"5432\")),\n",
-    "#     database=os.environ.get(\"PGVECTOR_DATABASE\", \"rd-embeddings\"),\n",
-    "#     user=os.environ.get(\"PGVECTOR_USER\", \"admin\"),\n",
-    "#     password=os.environ.get(\"PGVECTOR_PASSWORD\", \"password\"),\n",
+    "#     database=os.environ.get(\"PGVECTOR_DATABASE\", \"postgres\"),\n",
+    "#     user=os.environ.get(\"PGVECTOR_USER\", \"postgres\"),\n",
+    "#     password=os.environ.get(\"PGVECTOR_PASSWORD\", \"postgres\"),\n",
    "# )\n",
-    "\n",
-    "\n",
-    "# ## Example\n",
-    "# # postgresql+psycopg2://username:password@localhost:5432/database_name"
+    "\n"
   ]
  },
  {
@@ -206,27 +182,36 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
-    "# The PGVector Module will try to create a table with the name of the collection. So, make sure that the collection name is unique and the user has the\n",
-    "# permission to create a table.\n",
+    "# The PGVector Module will try to create a table with the name of the collection. \n",
+    "# So, make sure that the collection name is unique and the user has the permission to create a table.\n",
+    "\n",
+    "COLLECTION_NAME = \"state_of_the_union_test\"\n",
    "\n",
    "db = PGVector.from_documents(\n",
    "    embedding=embeddings,\n",
    "    documents=docs,\n",
-    "    collection_name=\"state_of_the_union\",\n",
+    "    collection_name=COLLECTION_NAME,\n",
    "    connection_string=CONNECTION_STRING,\n",
-    ")\n",
-    "\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)"
+    ")"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "docs_with_score = db.similarity_search_with_score(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
@@ -234,7 +219,7 @@
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
-      "Score:  0.6076804864602984\n",
+      "Score:  0.18460171628856903\n",
      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
@@ -244,7 +229,7 @@
      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
-      "Score:  0.6076804864602984\n",
+      "Score:  0.18460171628856903\n",
      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
@@ -254,21 +239,17 @@
      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
-      "Score:  0.659062774389974\n",
-      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
+      "Score:  0.18470284560586236\n",
+      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
+      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
+      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
-      "\n",
-      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
+      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
      "--------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------\n",
-      "Score:  0.659062774389974\n",
+      "Score:  0.21730864082247825\n",
      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
      "\n",
      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
@@ -296,183 +277,189 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "## Working with vectorstore"
+    "## Working with vectorstore\n",
+    "\n",
+    "Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.\n",
+    "In order to do that, we can initialize it directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "store = PGVector(\n",
+    "    collection_name=COLLECTION_NAME,\n",
+    "    connection_string=CONNECTION_STRING,\n",
+    "    embedding_function=embeddings,\n",
+    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Uploading a vectorstore"
+    "### Add documents\n",
+    "We can add documents to the existing vectorstore."
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['048c2e14-1cf3-11ee-8777-e65801318980']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "store.add_documents([Document(page_content=\"foo\")])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs_with_score = db.similarity_search_with_score(\"foo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='foo', metadata={}), 3.3203430005457335e-09)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_with_score[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
+       " 0.2404395365581814)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_with_score[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Overriding a vectorstore\n",
+    "\n",
+    "If you have an existing collection, you override it by doing `from_documents` and setting `pre_delete_collection` = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
-    "data = docs\n",
-    "api_key = os.environ[\"OPENAI_API_KEY\"]\n",
    "db = PGVector.from_documents(\n",
    "    documents=docs,\n",
    "    embedding=embeddings,\n",
-    "    collection_name=collection_name,\n",
-    "    connection_string=connection_string,\n",
-    "    distance_strategy=DistanceStrategy.COSINE,\n",
-    "    openai_api_key=api_key,\n",
-    "    pre_delete_collection=False,\n",
+    "    collection_name=COLLECTION_NAME,\n",
+    "    connection_string=CONNECTION_STRING,\n",
+    "    pre_delete_collection=True,\n",
    ")"
   ]
  },
  {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 24,
   "metadata": {},
+   "outputs": [],
   "source": [
-    "### Retrieving a vectorstore"
+    "docs_with_score = db.similarity_search_with_score(\"foo\")"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}),\n",
+       " 0.2404115088144465)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "docs_with_score[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using a VectorStore as a Retriever"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
-    "connection_string = CONNECTION_STRING\n",
-    "embedding = embeddings\n",
-    "collection_name = \"state_of_the_union\"\n",
-    "from langchain.vectorstores.pgvector import DistanceStrategy\n",
-    "\n",
-    "store = PGVector(\n",
-    "    connection_string=connection_string,\n",
-    "    embedding_function=embedding,\n",
-    "    collection_name=collection_name,\n",
-    "    distance_strategy=DistanceStrategy.COSINE,\n",
-    ")\n",
-    "\n",
    "retriever = store.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "vectorstore=<langchain.vectorstores.pgvector.PGVector object at 0x7fe9a1b1c670> search_type='similarity' search_kwargs={}\n"
+      "tags=None metadata=None vectorstore=<langchain.vectorstores.pgvector.PGVector object at 0x29f94f880> search_type='similarity' search_kwargs={}\n"
     ]
    }
   ],
   "source": [
    "print(retriever)"
   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}), 0.6075870262188066), (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}), 0.6075870262188066), (Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}), 0.6589478388546668), (Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}), 0.6589478388546668)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# When we have an existing PG VEctor\n",
-    "DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN\n",
-    "db1 = PGVector.from_existing_index(\n",
-    "    embedding=embeddings,\n",
-    "    collection_name=\"state_of_the_union\",\n",
-    "    distance_strategy=DEFAULT_DISTANCE_STRATEGY,\n",
-    "    pre_delete_collection=False,\n",
-    "    connection_string=CONNECTION_STRING,\n",
-    ")\n",
-    "\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)\n",
-    "print(docs_with_score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------------------------------------\n",
-      "Score:  0.6075870262188066\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "--------------------------------------------------------------------------------\n",
-      "--------------------------------------------------------------------------------\n",
-      "Score:  0.6075870262188066\n",
-      "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
-      "\n",
-      "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
-      "\n",
-      "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
-      "\n",
-      "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
-      "--------------------------------------------------------------------------------\n",
-      "--------------------------------------------------------------------------------\n",
-      "Score:  0.6589478388546668\n",
-      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
-      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
-      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
-      "\n",
-      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
-      "--------------------------------------------------------------------------------\n",
-      "--------------------------------------------------------------------------------\n",
-      "Score:  0.6589478388546668\n",
-      "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n",
-      "\n",
-      "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n",
-      "\n",
-      "We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n",
-      "\n",
-      "We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n",
-      "\n",
-      "We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n",
-      "\n",
-      "We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "for doc, score in docs_with_score:\n",
-    "    print(\"-\" * 80)\n",
-    "    print(\"Score: \", score)\n",
-    "    print(doc.page_content)\n",
-    "    print(\"-\" * 80)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
  }
 ],
 "metadata": {
@@ -491,7 +478,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.1"
  }
 },
 "nbformat": 4,
--- a/docs/extras/modules/data_connection/vectorstores/integrations/pinecone.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/pinecone.ipynb
@@ -1,6 +1,7 @@
 {
 "cells": [
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "683953b3",
   "metadata": {},
@@ -51,6 +52,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "id": "320af802-9271-46ee-948f-d2453933d44b",
   "metadata": {},
@@ -136,6 +138,30 @@
    "print(docs[0].page_content)"
   ]
  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "86a4b96b",
+   "metadata": {},
+   "source": [
+    "### Adding More Text to an Existing Index\n",
+    "\n",
+    "More text can embedded and upserted to an existing Pinecone index using the `add_texts` function\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38a7a60e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "index = pinecone.Index(\"langchain-demo\")\n",
+    "vectorstore = Pinecone(index, embeddings.embed_query, \"text\")\n",
+    "\n",
+    "vectorstore.add_texts(\"More text!\")"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
--- a/docs/extras/modules/data_connection/vectorstores/integrations/supabase.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/supabase.ipynb
@@ -43,7 +43,7 @@
    "\n",
    "       CREATE FUNCTION match_documents(query_embedding vector(1536), match_count int)\n",
    "           RETURNS TABLE(\n",
-    "               id bigint,\n",
+    "               id uuid,\n",
    "               content text,\n",
    "               metadata jsonb,\n",
    "               -- we return matched vectors to enable maximal marginal relevance searches\n",
--- a/docs/extras/modules/model_io/models/chat/how_to/human_input_chat_model.ipynb
+++ b/docs/extras/modules/model_io/models/chat/how_to/human_input_chat_model.ipynb
@@ -0,0 +1,212 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Human input Chat Model\n",
+    "\n",
+    "Along with HumanInputLLM, LangChain also provides a pseudo Chat Model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the Chat Model and simulate how a human would respond if they received the messages.\n",
+    "\n",
+    "In this notebook, we go over how to use this.\n",
+    "\n",
+    "We start this with using the HumanInputChatModel in an agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models.human import HumanInputChatModel"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we will use the `WikipediaQueryRun` tool in this notebook, you might need to install the `wikipedia` package if you haven't done so already."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/mskim58/dev/research/chatbot/github/langchain/.venv/bin/python: No module named pip\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install wikipedia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.agents import load_tools\n",
+    "from langchain.agents import initialize_agent\n",
+    "from langchain.agents import AgentType"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = load_tools([\"wikipedia\"])\n",
+    "llm = HumanInputChatModel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent = initialize_agent(\n",
+    "    tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "\n",
+      " ======= start of message ======= \n",
+      "\n",
+      "\n",
+      "type: system\n",
+      "data:\n",
+      "  content: \"Answer the following questions as best you can. You have access to the following tools:\\n\\nWikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.\\n\\nThe way you use the tools is by specifying a json blob.\\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\\n\\nThe only values that should be in the \\\"action\\\" field are: Wikipedia\\n\\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\\n\\n```\\n{\\n  \\\"action\\\": $TOOL_NAME,\\n  \\\"action_input\\\": $INPUT\\n}\\n```\\n\\nALWAYS use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction:\\n```\\n$JSON_BLOB\\n```\\nObservation: the result of the action\\n... (this Thought/Action/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin! Reminder to always use the exact characters `Final Answer` when responding.\"\n",
+      "  additional_kwargs: {}\n",
+      "\n",
+      "======= end of message ======= \n",
+      "\n",
+      "\n",
+      "\n",
+      " ======= start of message ======= \n",
+      "\n",
+      "\n",
+      "type: human\n",
+      "data:\n",
+      "  content: 'What is Bocchi the Rock?\n",
+      "\n",
+      "\n",
+      "    '\n",
+      "  additional_kwargs: {}\n",
+      "  example: false\n",
+      "\n",
+      "======= end of message ======= \n",
+      "\n",
+      "\n",
+      "\u001b[32;1m\u001b[1;3mAction:\n",
+      "```\n",
+      "{\n",
+      "  \"action\": \"Wikipedia\",\n",
+      "  \"action_input\": \"What is Bocchi the Rock?\"\n",
+      "}\n",
+      "```\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3mPage: Bocchi the Rock!\n",
+      "Summary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Botchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōbon volumes as of November 2022.\n",
+      "An anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.\n",
+      "\n",
+      "Page: Hitori Bocchi no Marumaru Seikatsu\n",
+      "Summary: Hitori Bocchi no Marumaru Seikatsu (Japanese: ひとりぼっちの○○生活, lit. \"Bocchi Hitori's ____ Life\" or \"The ____ Life of Being Alone\") is a Japanese yonkoma manga series written and illustrated by Katsuwo. It was serialized in ASCII Media Works' Comic Dengeki Daioh \"g\" magazine from September 2013 to April 2021. Eight tankōbon volumes have been released. An anime television series adaptation by C2C aired from April to June 2019.\n",
+      "\n",
+      "Page: Kessoku Band (album)\n",
+      "Summary: Kessoku Band (Japanese: 結束バンド, Hepburn: Kessoku Bando) is the debut studio album by Kessoku Band, a fictional musical group from the anime television series Bocchi the Rock!, released digitally on December 25, 2022, and physically on CD on December 28 by Aniplex. Featuring vocals from voice actresses Yoshino Aoyama, Sayumi Suzushiro, Saku Mizuno, and Ikumi Hasegawa, the album consists of 14 tracks previously heard in the anime, including a cover of Asian Kung-Fu Generation's \"Rockn' Roll, Morning Light Falls on You\", as well as newly recorded songs; nine singles preceded the album's physical release. Commercially, Kessoku Band peaked at number one on the Billboard Japan Hot Albums Chart and Oricon Albums Chart, and was certified gold by the Recording Industry Association of Japan.\n",
+      "\n",
+      "\u001b[0m\n",
+      "Thought:\n",
+      " ======= start of message ======= \n",
+      "\n",
+      "\n",
+      "type: system\n",
+      "data:\n",
+      "  content: \"Answer the following questions as best you can. You have access to the following tools:\\n\\nWikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.\\n\\nThe way you use the tools is by specifying a json blob.\\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\\n\\nThe only values that should be in the \\\"action\\\" field are: Wikipedia\\n\\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\\n\\n```\\n{\\n  \\\"action\\\": $TOOL_NAME,\\n  \\\"action_input\\\": $INPUT\\n}\\n```\\n\\nALWAYS use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction:\\n```\\n$JSON_BLOB\\n```\\nObservation: the result of the action\\n... (this Thought/Action/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin! Reminder to always use the exact characters `Final Answer` when responding.\"\n",
+      "  additional_kwargs: {}\n",
+      "\n",
+      "======= end of message ======= \n",
+      "\n",
+      "\n",
+      "\n",
+      " ======= start of message ======= \n",
+      "\n",
+      "\n",
+      "type: human\n",
+      "data:\n",
+      "  content: \"What is Bocchi the Rock?\\n\\nThis was your previous work (but I haven't seen any of it! I only see what you return as final answer):\\nAction:\\n```\\n{\\n  \\\"action\\\": \\\"Wikipedia\\\",\\n  \\\"action_input\\\": \\\"What is Bocchi the Rock?\\\"\\n}\\n```\\nObservation: Page: Bocchi the Rock!\\nSummary: Bocchi the Rock! (ぼっち・ざ・ろっく!, Botchi Za Rokku!) is a Japanese four-panel manga series written and illustrated by Aki Hamaji. It has been serialized in Houbunsha's seinen manga magazine Manga Time Kirara Max since December 2017. Its chapters have been collected in five tankōbon volumes as of November 2022.\\nAn anime television series adaptation produced by CloverWorks aired from October to December 2022. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.\\n\\nPage: Hitori Bocchi no Marumaru Seikatsu\\nSummary: Hitori Bocchi no Marumaru Seikatsu (Japanese: ひとりぼっちの○○生活, lit. \\\"Bocchi Hitori's ____ Life\\\" or \\\"The ____ Life of Being Alone\\\") is a Japanese yonkoma manga series written and illustrated by Katsuwo. It was serialized in ASCII Media Works' Comic Dengeki Daioh \\\"g\\\" magazine from September 2013 to April 2021. Eight tankōbon volumes have been released. An anime television series adaptation by C2C aired from April to June 2019.\\n\\nPage: Kessoku Band (album)\\nSummary: Kessoku Band (Japanese: 結束バンド, Hepburn: Kessoku Bando) is the debut studio album by Kessoku Band, a fictional musical group from the anime television series Bocchi the Rock!, released digitally on December 25, 2022, and physically on CD on December 28 by Aniplex. Featuring vocals from voice actresses Yoshino Aoyama, Sayumi Suzushiro, Saku Mizuno, and Ikumi Hasegawa, the album consists of 14 tracks previously heard in the anime, including a cover of Asian Kung-Fu Generation's \\\"Rockn' Roll, Morning Light Falls on You\\\", as well as newly recorded songs; nine singles preceded the album's physical release. Commercially, Kessoku Band peaked at number one on the Billboard Japan Hot Albums Chart and Oricon Albums Chart, and was certified gold by the Recording Industry Association of Japan.\\n\\n\\nThought:\"\n",
+      "  additional_kwargs: {}\n",
+      "  example: false\n",
+      "\n",
+      "======= end of message ======= \n",
+      "\n",
+      "\n",
+      "\u001b[32;1m\u001b[1;3mThis finally works.\n",
+      "Final Answer: Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'input': 'What is Bocchi the Rock?',\n",
+       " 'output': \"Bocchi the Rock! is a four-panel manga series and anime television series. The series has been praised for its writing, comedy, characters, and depiction of social anxiety, with the anime's visual creativity receiving acclaim.\"}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agent(\"What is Bocchi the Rock?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/docs/extras/modules/model_io/models/chat/integrations/anthropic.ipynb
+++ b/docs/extras/modules/model_io/models/chat/integrations/anthropic.ipynb
@@ -52,7 +52,7 @@
    {
     "data": {
      "text/plain": [
-       "AIMessage(content=\" J'aime programmer. \", additional_kwargs={})"
+       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 3,
@@ -101,7 +101,7 @@
    {
     "data": {
      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\" J'aime la programmation.\", generation_info=None, message=AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}))]], llm_output={})"
+       "LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
      ]
     },
     "execution_count": 5,
@@ -125,13 +125,13 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      " J'adore programmer."
+      " J'aime la programmation."
     ]
    },
    {
     "data": {
      "text/plain": [
-       "AIMessage(content=\" J'adore programmer.\", additional_kwargs={})"
+       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 6,
@@ -151,7 +151,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "df45f59f",
+   "id": "c253883f",
   "metadata": {},
   "outputs": [],
   "source": []
@@ -173,7 +173,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
  }
 },
 "nbformat": 4,
--- a/docs/extras/modules/model_io/models/chat/integrations/jinachat.ipynb
+++ b/docs/extras/modules/model_io/models/chat/integrations/jinachat.ipynb
@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# JinaChat\n",
+    "\n",
+    "This notebook covers how to get started with JinaChat chat models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "522686de",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import JinaChat\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    AIMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.schema import AIMessage, HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat = JinaChat(temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful assistant that translates English to French.\"\n",
+    "    ),\n",
+    "    HumanMessage(\n",
+    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
+    "    ),\n",
+    "]\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
+   "metadata": {},
+   "source": [
+    "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
+    "\n",
+    "For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "180c5cc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template = (\n",
+    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
+    ")\n",
+    "system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
+    "human_template = \"{text}\"\n",
+    "human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fbb043e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [system_message_prompt, human_message_prompt]\n",
+    ")\n",
+    "\n",
+    "# get a chat completion from the formatted messages\n",
+    "chat(\n",
+    "    chat_prompt.format_prompt(\n",
+    "        input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
+    "    ).to_messages()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c095285d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/extras/modules/model_io/models/llms/integrations/clarifai.ipynb
+++ b/docs/extras/modules/model_io/models/llms/integrations/clarifai.ipynb
@@ -8,9 +8,12 @@
   "source": [
    "# Clarifai\n",
    "\n",
-    ">[Clarifai](https://www.clarifai.com/) is a AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model building and inference.\n",
+    ">[Clarifai](https://www.clarifai.com/) is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference.\n",
    "\n",
-    "This example goes over how to use LangChain to interact with `Clarifai` [models](https://clarifai.com/explore/models)."
+    "This example goes over how to use LangChain to interact with `Clarifai` [models](https://clarifai.com/explore/models). \n",
+    "\n",
+    "To use Clarifai, you must have an account and a Personal Access Token (PAT) key. \n",
+    "[Check here](https://clarifai.com/settings/security) to get or create a PAT."
   ]
  },
  {
@@ -42,7 +45,7 @@
   "metadata": {},
   "source": [
    "# Imports\n",
-    "Here we will be setting the personal access token. You can find your PAT under settings/security on the platform."
+    "Here we will be setting the personal access token. You can find your PAT under [settings/security](https://clarifai.com/settings/security) in your Clarifai account."
   ]
  },
  {
@@ -52,17 +55,25 @@
   "metadata": {
    "tags": []
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
   "source": [
    "# Please login and get your API key from  https://clarifai.com/settings/security \n",
    "from getpass import getpass\n",
    "\n",
-    "CLARIFAI_PAT_KEY = getpass()"
+    "CLARIFAI_PAT = getpass()"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
   "id": "6fb585dd",
   "metadata": {
    "tags": []
@@ -81,12 +92,12 @@
   "metadata": {},
   "source": [
    "# Input\n",
-    "Create a prompt template to be used with the LLM Chain"
+    "Create a prompt template to be used with the LLM Chain:"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
   "id": "035dea0f",
   "metadata": {
    "tags": []
@@ -121,10 +132,10 @@
   "source": [
    "USER_ID = 'openai'\n",
    "APP_ID = 'chat-completion'\n",
-    "MODEL_ID = 'chatgpt-3_5-turbo'\n",
+    "MODEL_ID = 'GPT-3_5-turbo'\n",
    "\n",
-    "# You can provide a specific model version\n",
-    "# model_version_id = \"MODEL_VERSION_ID\""
+    "# You can provide a specific model version as the model_version_id arg.\n",
+    "# MODEL_VERSION_ID = \"MODEL_VERSION_ID\""
   ]
  },
  {
@@ -137,7 +148,7 @@
   "outputs": [],
   "source": [
    "# Initialize a Clarifai LLM\n",
-    "clarifai_llm = Clarifai(clarifai_pat_key=CLARIFAI_PAT_KEY, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)"
+    "clarifai_llm = Clarifai(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)"
   ]
  },
  {
@@ -171,7 +182,7 @@
    {
     "data": {
      "text/plain": [
-       "'Justin Bieber was born on March 1, 1994. So, we need to look at the Super Bowl that was played in the year 1994. \\n\\nThe Super Bowl in 1994 was Super Bowl XXVIII (28). It was played on January 30, 1994, between the Dallas Cowboys and the Buffalo Bills. \\n\\nThe Dallas Cowboys won the Super Bowl in 1994, defeating the Buffalo Bills by a score of 30-13. \\n\\nTherefore, the Dallas Cowboys are the NFL team that won the Super Bowl in the year Justin Bieber was born.'"
+       "'Justin Bieber was born on March 1, 1994. So, we need to figure out the Super Bowl winner for the 1994 season. The NFL season spans two calendar years, so the Super Bowl for the 1994 season would have taken place in early 1995. \\n\\nThe Super Bowl in question is Super Bowl XXIX, which was played on January 29, 1995. The game was won by the San Francisco 49ers, who defeated the San Diego Chargers by a score of 49-26. Therefore, the San Francisco 49ers won the Super Bowl in the year Justin Bieber was born.'"
      ]
     },
     "execution_count": 7,
--- a/docs/extras/modules/model_io/models/llms/integrations/huggingface_hub.ipynb
+++ b/docs/extras/modules/model_io/models/llms/integrations/huggingface_hub.ipynb
@@ -1,20 +1,26 @@
 {
 "cells": [
  {
-   "attachments": {},
   "cell_type": "markdown",
   "id": "959300d4",
   "metadata": {},
   "source": [
    "# Hugging Face Hub\n",
    "\n",
-    "The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
+    ">The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
    "\n",
-    "This example showcases how to connect to the Hugging Face Hub."
+    "This example showcases how to connect to the `Hugging Face Hub` and use different models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ddafc6d-7d7c-48fa-838f-0e7f50895ce3",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup"
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
   "id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
   "metadata": {
@@ -26,22 +32,30 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "id": "d772b637-de00-4663-bd77-9bc96d798db2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
-    "!pip install huggingface_hub > /dev/null"
+    "!pip install huggingface_hub"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "id": "d597a792-354c-4ca5-b483-5965eec5d63d",
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
   "source": [
    "# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token\n",
    "\n",
@@ -52,7 +66,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
   "id": "b8c5b88c-e4b8-4d0d-9a35-6e8f106452c2",
   "metadata": {},
   "outputs": [],
@@ -63,108 +77,101 @@
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
   "id": "84dd44c1-c428-41f3-a911-520281386c94",
   "metadata": {},
   "source": [
-    "**Select a Model**"
+    "## Prepare Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "39c7eeac-01c4-486b-9480-e828a9e73e78",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain import HuggingFaceHub\n",
-    "\n",
-    "repo_id = \"google/flan-t5-xl\"  # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n",
-    "\n",
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0, \"max_length\": 64})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3acf0069",
+   "id": "3fe7d1d1-241d-426a-acff-e208f1088871",
   "metadata": {},
   "outputs": [],
   "source": [
-    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain import HuggingFaceHub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6620f39b-3d32-4840-8931-ff7d2c3e47e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate, LLMChain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "44adc1a0-9c0a-4f1e-af5a-fe04222e78d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
-    "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
-    "\n",
-    "print(llm_chain.run(question))"
+    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
   "id": "ddaa06cf-95ec-48ce-b0ab-d892a7909693",
   "metadata": {},
   "source": [
    "## Examples\n",
    "\n",
-    "Below are some examples of models you can access through the Hugging Face Hub integration."
+    "Below are some examples of models you can access through the `Hugging Face Hub` integration."
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
-   "id": "4fa9337e-ccb5-4c52-9b7c-1653148bc256",
+   "id": "4c16fded-70d1-42af-8bfa-6ddda9f0bc63",
   "metadata": {},
   "source": [
-    "### StableLM, by Stability AI\n",
-    "\n",
-    "See [Stability AI's](https://huggingface.co/stabilityai) organization page for a list of available models."
+    "### Flan, by Google"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
-   "id": "36a1ce01-bd46-451f-8ee6-61c8f4bd665a",
-   "metadata": {},
+   "execution_count": 5,
+   "id": "39c7eeac-01c4-486b-9480-e828a9e73e78",
+   "metadata": {
+    "tags": []
+   },
   "outputs": [],
   "source": [
-    "repo_id = \"stabilityai/stablelm-tuned-alpha-3b\"\n",
-    "# Others include stabilityai/stablelm-base-alpha-3b\n",
-    "# as well as 7B parameter versions"
+    "repo_id = \"google/flan-t5-xxl\"  # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
-   "id": "b5654cea-60b0-4f40-ab34-06ba1eca810d",
+   "execution_count": 8,
+   "id": "3acf0069",
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The FIFA World Cup was held in the year 1994. West Germany won the FIFA World Cup in 1994\n"
+     ]
+    }
+   ],
   "source": [
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0, \"max_length\": 64})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2f19d0dc-c987-433f-a8d6-b1214e8ee067",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reuse the prompt and question from above.\n",
+    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64})\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "\n",
    "print(llm_chain.run(question))"
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
   "id": "1a5c97af-89bc-4e59-95c1-223742a9160b",
   "metadata": {},
@@ -176,34 +183,40 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
   "id": "521fcd2b-8e38-4920-b407-5c7d330411c9",
   "metadata": {},
   "outputs": [],
   "source": [
-    "from langchain import HuggingFaceHub\n",
-    "\n",
-    "repo_id = \"databricks/dolly-v2-3b\"\n",
-    "\n",
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0, \"max_length\": 64})"
+    "repo_id = \"databricks/dolly-v2-3b\""
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
   "id": "9907ec3a-fe0c-4543-81c4-d42f9453f16c",
   "metadata": {
    "tags": []
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " First of all, the world cup was won by the Germany. Then the Argentina won the world cup in 2022. So, the Argentina won the world cup in 1994.\n",
+      "\n",
+      "\n",
+      "Question: Who\n"
+     ]
+    }
+   ],
   "source": [
-    "# Reuse the prompt and question from above.\n",
+    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64})\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "print(llm_chain.run(question))"
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
   "id": "03f6ae52-b5f9-4de6-832c-551cb3fa11ae",
   "metadata": {},
@@ -215,17 +228,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
   "id": "257a091d-750b-4910-ac08-fe1c7b3fd98b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
-    "from langchain import HuggingFaceHub\n",
-    "\n",
-    "repo_id = \"Writer/camel-5b-hf\"  # See https://huggingface.co/Writer for other options\n",
-    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0, \"max_length\": 64})"
+    "repo_id = \"Writer/camel-5b-hf\"  # See https://huggingface.co/Writer for other options"
   ]
  },
  {
@@ -235,27 +245,74 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "# Reuse the prompt and question from above.\n",
+    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64})\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "print(llm_chain.run(question))"
   ]
  },
  {
-   "attachments": {},
   "cell_type": "markdown",
   "id": "2bf838eb-1083-402f-b099-b07c452418c8",
   "metadata": {},
   "source": [
-    "**And many more!**"
+    "### XGen, by Salesforce\n",
+    "\n",
+    "See [more information](https://github.com/salesforce/xgen)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "18c78880-65d7-41d0-9722-18090efb60e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "repo_id = \"Salesforce/xgen-7b-8k-base\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "18c78880-65d7-41d0-9722-18090efb60e9",
+   "id": "1b1150b4-ec30-4674-849e-6a41b085aa2b",
   "metadata": {},
   "outputs": [],
-   "source": []
+   "source": [
+    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64})\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "print(llm_chain.run(question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aca9f9e-f333-449c-97b2-10d1dbf17e75",
+   "metadata": {},
+   "source": [
+    "### Falcon, by Technology Innovation Institute (TII)\n",
+    "\n",
+    "See [more information](https://huggingface.co/tiiuae/falcon-40b)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "496b35ac-5ee2-4b68-a6ce-232608f56c03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "repo_id = \"tiiuae/falcon-40b\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff2541ad-e394-4179-93c2-7ae9c4ca2a25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64})\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "print(llm_chain.run(question))"
+   ]
  }
 ],
 "metadata": {
@@ -274,7 +331,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.6"
  }
 },
 "nbformat": 4,
--- a/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx
+++ b/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx
@@ -253,7 +253,7 @@ html_text = """

 ```python
 html_splitter = RecursiveCharacterTextSplitter.from_language(
-    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
+    language=Language.HTML, chunk_size=60, chunk_overlap=0
 )
 html_docs = html_splitter.create_documents([html_text])
 html_docs
@@ -262,19 +262,18 @@ html_docs
 <CodeOutputBlock lang="python">

 ```
-    [Document(page_content='<!DOCTYPE html>\n<html>\n    <head>', metadata={}),
-     Document(page_content='<title>🦜️🔗 LangChain</title>\n        <style>', metadata={}),
-     Document(page_content='body {', metadata={}),
-     Document(page_content='font-family: Arial, sans-serif;', metadata={}),
-     Document(page_content='}\n            h1 {', metadata={}),
-     Document(page_content='color: darkblue;\n            }', metadata={}),
-     Document(page_content='</style>\n    </head>\n    <body>\n        <div>', metadata={}),
-     Document(page_content='<h1>🦜️🔗 LangChain</h1>', metadata={}),
-     Document(page_content='<p>⚡ Building applications with LLMs through', metadata={}),
-     Document(page_content='composability ⚡</p>', metadata={}),
-     Document(page_content='</div>\n        <div>', metadata={}),
-     Document(page_content='As an open source project in a rapidly', metadata={}),
-     Document(page_content='developing field, we are extremely open to contributions.', metadata={}),
+    [Document(page_content='<!DOCTYPE html>\n<html>', metadata={}),
+     Document(page_content='<head>\n        <title>🦜️🔗 LangChain</title>', metadata={}),
+     Document(page_content='<style>\n            body {\n                font-family: Aria', metadata={}),
+     Document(page_content='l, sans-serif;\n            }\n            h1 {', metadata={}),
+     Document(page_content='color: darkblue;\n            }\n        </style>\n    </head', metadata={}),
+     Document(page_content='>', metadata={}),
+     Document(page_content='<body>', metadata={}),
+     Document(page_content='<div>\n            <h1>🦜️🔗 LangChain</h1>', metadata={}),
+     Document(page_content='<p>⚡ Building applications with LLMs through composability ⚡', metadata={}),
+     Document(page_content='</p>\n        </div>', metadata={}),
+     Document(page_content='<div>\n            As an open source project in a rapidly dev', metadata={}),
+     Document(page_content='eloping field, we are extremely open to contributions.', metadata={}),
     Document(page_content='</div>\n    </body>\n</html>', metadata={})]
 ```

@@ -310,4 +309,4 @@ sol_docs
 ]
 ```

- </CodeOutputBlock>
+ </CodeOutputBlock>
--- a/langchain/agents/agent.py
+++ b/langchain/agents/agent.py
@@ -14,7 +14,6 @@ from pydantic import BaseModel, root_validator

 from langchain.agents.agent_types import AgentType
 from langchain.agents.tools import InvalidTool
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
@@ -35,6 +34,7 @@ from langchain.schema import (
    BasePromptTemplate,
    OutputParserException,
 )
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage
 from langchain.tools.base import BaseTool
 from langchain.utilities.asyncio import asyncio_timeout
--- a/langchain/agents/agent_toolkits/init.py
+++ b/langchain/agents/agent_toolkits/init.py
@@ -12,6 +12,7 @@ from langchain.agents.agent_toolkits.jira.toolkit import JiraToolkit
 from langchain.agents.agent_toolkits.json.base import create_json_agent
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
 from langchain.agents.agent_toolkits.nla.toolkit import NLAToolkit
+from langchain.agents.agent_toolkits.office365.toolkit import O365Toolkit
 from langchain.agents.agent_toolkits.openapi.base import create_openapi_agent
 from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
@@ -64,4 +65,5 @@ __all__ = [
    "FileManagementToolkit",
    "PlayWrightBrowserToolkit",
    "AzureCognitiveServicesToolkit",
+    "O365Toolkit",
 ]
--- a/langchain/agents/agent_toolkits/base.py
+++ b/langchain/agents/agent_toolkits/base.py
@@ -1,5 +1,5 @@
 """Toolkits for agents."""
-from abc import abstractmethod
+from abc import ABC, abstractmethod
 from typing import List

 from pydantic import BaseModel
@@ -7,8 +7,8 @@ from pydantic import BaseModel
 from langchain.tools import BaseTool


-class BaseToolkit(BaseModel):
-    """Class responsible for defining a collection of related tools."""
+class BaseToolkit(BaseModel, ABC):
+    """Class representing a collection of related tools."""

    @abstractmethod
    def get_tools(self) -> List[BaseTool]:
--- a/langchain/agents/agent_toolkits/csv/base.py
+++ b/langchain/agents/agent_toolkits/csv/base.py
@@ -1,9 +1,9 @@
-"""Agent for working with csvs."""
+"""Agent for working with csv files."""
 from typing import Any, List, Optional, Union

 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
-from langchain.base_language import BaseLanguageModel
+from langchain.schema.language_model import BaseLanguageModel


 def create_csv_agent(
--- a/langchain/agents/agent_toolkits/json/base.py
+++ b/langchain/agents/agent_toolkits/json/base.py
@@ -6,9 +6,9 @@ from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
+from langchain.schema.language_model import BaseLanguageModel


 def create_json_agent(
--- a/langchain/agents/agent_toolkits/nla/tool.py
+++ b/langchain/agents/agent_toolkits/nla/tool.py
@@ -4,9 +4,9 @@
 from typing import Any, Optional

 from langchain.agents.tools import Tool
-from langchain.base_language import BaseLanguageModel
 from langchain.chains.api.openapi.chain import OpenAPIEndpointChain
 from langchain.requests import Requests
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.openapi.utils.api_models import APIOperation
 from langchain.tools.openapi.utils.openapi_utils import OpenAPISpec

--- a/langchain/agents/agent_toolkits/nla/toolkit.py
+++ b/langchain/agents/agent_toolkits/nla/toolkit.py
@@ -7,8 +7,8 @@ from pydantic import Field

 from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.agents.agent_toolkits.nla.tool import NLATool
-from langchain.base_language import BaseLanguageModel
 from langchain.requests import Requests
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
 from langchain.tools.openapi.utils.openapi_utils import OpenAPISpec
 from langchain.tools.plugin import AIPlugin
--- a/langchain/agents/agent_toolkits/office365/init.py
+++ b/langchain/agents/agent_toolkits/office365/init.py
@@ -1 +1 @@
-"""Gmail toolkit."""
+"""Office365 toolkit."""
--- a/langchain/agents/agent_toolkits/office365/toolkit.py
+++ b/langchain/agents/agent_toolkits/office365/toolkit.py
@@ -30,9 +30,9 @@ class O365Toolkit(BaseToolkit):
    def get_tools(self) -> List[BaseTool]:
        """Get the tools in the toolkit."""
        return [
-            O365SearchEvents(account=self.account),
-            O365CreateDraftMessage(account=self.account),
-            O365SearchEmails(account=self.account),
-            O365SendEvent(account=self.account),
-            O365SendMessage(account=self.account),
+            O365SearchEvents(),
+            O365CreateDraftMessage(),
+            O365SearchEmails(),
+            O365SendEvent(),
+            O365SendMessage(),
        ]
--- a/langchain/agents/agent_toolkits/openapi/base.py
+++ b/langchain/agents/agent_toolkits/openapi/base.py
@@ -9,9 +9,9 @@ from langchain.agents.agent_toolkits.openapi.prompt import (
 from langchain.agents.agent_toolkits.openapi.toolkit import OpenAPIToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
+from langchain.schema.language_model import BaseLanguageModel


 def create_openapi_agent(
--- a/langchain/agents/agent_toolkits/openapi/planner.py
+++ b/langchain/agents/agent_toolkits/openapi/planner.py
@@ -28,7 +28,6 @@ from langchain.agents.agent_toolkits.openapi.planner_prompt import (
 from langchain.agents.agent_toolkits.openapi.spec import ReducedOpenAPISpec
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.tools import Tool
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
 from langchain.llms.openai import OpenAI
@@ -36,6 +35,7 @@ from langchain.memory import ReadOnlySharedMemory
 from langchain.prompts import PromptTemplate
 from langchain.requests import RequestsWrapper
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
 from langchain.tools.requests.tool import BaseRequestsTool

--- a/langchain/agents/agent_toolkits/openapi/toolkit.py
+++ b/langchain/agents/agent_toolkits/openapi/toolkit.py
@@ -9,8 +9,8 @@ from langchain.agents.agent_toolkits.json.base import create_json_agent
 from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
 from langchain.agents.agent_toolkits.openapi.prompt import DESCRIPTION
 from langchain.agents.tools import Tool
-from langchain.base_language import BaseLanguageModel
 from langchain.requests import TextRequestsWrapper
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.json.tool import JsonSpec
 from langchain.tools.requests.tool import (
@@ -39,7 +39,7 @@ class RequestsToolkit(BaseToolkit):


 class OpenAPIToolkit(BaseToolkit):
-    """Toolkit for interacting with a OpenAPI api."""
+    """Toolkit for interacting with an OpenAPI API."""

    json_agent: AgentExecutor
    requests_wrapper: TextRequestsWrapper
--- a/langchain/agents/agent_toolkits/pandas/base.py
+++ b/langchain/agents/agent_toolkits/pandas/base.py
@@ -16,10 +16,10 @@ from langchain.agents.agent_toolkits.pandas.prompt import (
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
 from langchain.agents.types import AgentType
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import SystemMessage
 from langchain.tools.python.tool import PythonAstREPLTool

@@ -30,6 +30,7 @@ def _get_multi_prompt(
    suffix: Optional[str] = None,
    input_variables: Optional[List[str]] = None,
    include_df_in_prompt: Optional[bool] = True,
+    number_of_head_rows: int = 5,
 ) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    num_dfs = len(dfs)
    if suffix is not None:
@@ -60,7 +61,7 @@ def _get_multi_prompt(

    partial_prompt = prompt.partial()
    if "dfs_head" in input_variables:
-        dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
+        dfs_head = "\n\n".join([d.head(number_of_head_rows).to_markdown() for d in dfs])
        partial_prompt = partial_prompt.partial(num_dfs=str(num_dfs), dfs_head=dfs_head)
    if "num_dfs" in input_variables:
        partial_prompt = partial_prompt.partial(num_dfs=str(num_dfs))
@@ -73,6 +74,7 @@ def _get_single_prompt(
    suffix: Optional[str] = None,
    input_variables: Optional[List[str]] = None,
    include_df_in_prompt: Optional[bool] = True,
+    number_of_head_rows: int = 5,
 ) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    if suffix is not None:
        suffix_to_use = suffix
@@ -100,7 +102,9 @@ def _get_single_prompt(

    partial_prompt = prompt.partial()
    if "df_head" in input_variables:
-        partial_prompt = partial_prompt.partial(df_head=str(df.head().to_markdown()))
+        partial_prompt = partial_prompt.partial(
+            df_head=str(df.head(number_of_head_rows).to_markdown())
+        )
    return partial_prompt, tools


@@ -110,6 +114,7 @@ def _get_prompt_and_tools(
    suffix: Optional[str] = None,
    input_variables: Optional[List[str]] = None,
    include_df_in_prompt: Optional[bool] = True,
+    number_of_head_rows: int = 5,
 ) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    try:
        import pandas as pd
@@ -131,6 +136,7 @@ def _get_prompt_and_tools(
            suffix=suffix,
            input_variables=input_variables,
            include_df_in_prompt=include_df_in_prompt,
+            number_of_head_rows=number_of_head_rows,
        )
    else:
        if not isinstance(df, pd.DataFrame):
@@ -141,6 +147,7 @@ def _get_prompt_and_tools(
            suffix=suffix,
            input_variables=input_variables,
            include_df_in_prompt=include_df_in_prompt,
+            number_of_head_rows=number_of_head_rows,
        )


@@ -149,13 +156,18 @@ def _get_functions_single_prompt(
    prefix: Optional[str] = None,
    suffix: Optional[str] = None,
    include_df_in_prompt: Optional[bool] = True,
+    number_of_head_rows: int = 5,
 ) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    if suffix is not None:
        suffix_to_use = suffix
        if include_df_in_prompt:
-            suffix_to_use = suffix_to_use.format(df_head=str(df.head().to_markdown()))
+            suffix_to_use = suffix_to_use.format(
+                df_head=str(df.head(number_of_head_rows).to_markdown())
+            )
    elif include_df_in_prompt:
-        suffix_to_use = FUNCTIONS_WITH_DF.format(df_head=str(df.head().to_markdown()))
+        suffix_to_use = FUNCTIONS_WITH_DF.format(
+            df_head=str(df.head(number_of_head_rows).to_markdown())
+        )
    else:
        suffix_to_use = ""

@@ -173,16 +185,19 @@ def _get_functions_multi_prompt(
    prefix: Optional[str] = None,
    suffix: Optional[str] = None,
    include_df_in_prompt: Optional[bool] = True,
+    number_of_head_rows: int = 5,
 ) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    if suffix is not None:
        suffix_to_use = suffix
        if include_df_in_prompt:
-            dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
+            dfs_head = "\n\n".join(
+                [d.head(number_of_head_rows).to_markdown() for d in dfs]
+            )
            suffix_to_use = suffix_to_use.format(
                dfs_head=dfs_head,
            )
    elif include_df_in_prompt:
-        dfs_head = "\n\n".join([d.head().to_markdown() for d in dfs])
+        dfs_head = "\n\n".join([d.head(number_of_head_rows).to_markdown() for d in dfs])
        suffix_to_use = FUNCTIONS_WITH_MULTI_DF.format(
            dfs_head=dfs_head,
        )
@@ -208,6 +223,7 @@ def _get_functions_prompt_and_tools(
    suffix: Optional[str] = None,
    input_variables: Optional[List[str]] = None,
    include_df_in_prompt: Optional[bool] = True,
+    number_of_head_rows: int = 5,
 ) -> Tuple[BasePromptTemplate, List[PythonAstREPLTool]]:
    try:
        import pandas as pd
@@ -230,6 +246,7 @@ def _get_functions_prompt_and_tools(
            prefix=prefix,
            suffix=suffix,
            include_df_in_prompt=include_df_in_prompt,
+            number_of_head_rows=number_of_head_rows,
        )
    else:
        if not isinstance(df, pd.DataFrame):
@@ -239,6 +256,7 @@ def _get_functions_prompt_and_tools(
            prefix=prefix,
            suffix=suffix,
            include_df_in_prompt=include_df_in_prompt,
+            number_of_head_rows=number_of_head_rows,
        )


@@ -257,6 +275,7 @@ def create_pandas_dataframe_agent(
    early_stopping_method: str = "force",
    agent_executor_kwargs: Optional[Dict[str, Any]] = None,
    include_df_in_prompt: Optional[bool] = True,
+    number_of_head_rows: int = 5,
    **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
    """Construct a pandas agent from an LLM and dataframe."""
@@ -268,6 +287,7 @@ def create_pandas_dataframe_agent(
            suffix=suffix,
            input_variables=input_variables,
            include_df_in_prompt=include_df_in_prompt,
+            number_of_head_rows=number_of_head_rows,
        )
        llm_chain = LLMChain(
            llm=llm,
@@ -288,6 +308,7 @@ def create_pandas_dataframe_agent(
            suffix=suffix,
            input_variables=input_variables,
            include_df_in_prompt=include_df_in_prompt,
+            number_of_head_rows=number_of_head_rows,
        )
        agent = OpenAIFunctionsAgent(
            llm=llm,
--- a/langchain/agents/agent_toolkits/powerbi/base.py
+++ b/langchain/agents/agent_toolkits/powerbi/base.py
@@ -9,15 +9,15 @@ from langchain.agents.agent_toolkits.powerbi.prompt import (
 from langchain.agents.agent_toolkits.powerbi.toolkit import PowerBIToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities.powerbi import PowerBIDataset


 def create_pbi_agent(
    llm: BaseLanguageModel,
-    toolkit: Optional[PowerBIToolkit],
+    toolkit: Optional[PowerBIToolkit] = None,
    powerbi: Optional[PowerBIDataset] = None,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = POWERBI_PREFIX,
@@ -36,13 +36,13 @@ def create_pbi_agent(
            raise ValueError("Must provide either a toolkit or powerbi dataset")
        toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
    tools = toolkit.get_tools()
-
+    tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
    agent = ZeroShotAgent(
        llm_chain=LLMChain(
            llm=llm,
            prompt=ZeroShotAgent.create_prompt(
                tools,
-                prefix=prefix.format(top_k=top_k),
+                prefix=prefix.format(top_k=top_k).format(tables=tables),
                suffix=suffix,
                format_instructions=format_instructions,
                input_variables=input_variables,
--- a/langchain/agents/agent_toolkits/powerbi/chat_base.py
+++ b/langchain/agents/agent_toolkits/powerbi/chat_base.py
@@ -18,7 +18,7 @@ from langchain.utilities.powerbi import PowerBIDataset

 def create_pbi_chat_agent(
    llm: BaseChatModel,
-    toolkit: Optional[PowerBIToolkit],
+    toolkit: Optional[PowerBIToolkit] = None,
    powerbi: Optional[PowerBIDataset] = None,
    callback_manager: Optional[BaseCallbackManager] = None,
    output_parser: Optional[AgentOutputParser] = None,
@@ -32,19 +32,20 @@ def create_pbi_chat_agent(
    agent_executor_kwargs: Optional[Dict[str, Any]] = None,
    **kwargs: Dict[str, Any],
 ) -> AgentExecutor:
-    """Construct a pbi agent from an Chat LLM and tools.
+    """Construct a Power BI agent from a Chat LLM and tools.

-    If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
+    If you supply only a toolkit and no Power BI dataset, the same LLM is used for both.
    """
    if toolkit is None:
        if powerbi is None:
            raise ValueError("Must provide either a toolkit or powerbi dataset")
        toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
    tools = toolkit.get_tools()
+    tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
    agent = ConversationalChatAgent.from_llm_and_tools(
        llm=llm,
        tools=tools,
-        system_message=prefix.format(top_k=top_k),
+        system_message=prefix.format(top_k=top_k).format(tables=tables),
        human_message=suffix,
        input_variables=input_variables,
        callback_manager=callback_manager,
--- a/langchain/agents/agent_toolkits/powerbi/prompt.py
+++ b/langchain/agents/agent_toolkits/powerbi/prompt.py
@@ -4,7 +4,7 @@

 POWERBI_PREFIX = """You are an agent designed to help users interact with a PowerBI Dataset.

-Agent has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, just return "This does not appear to be part of this dataset." as the answer.
+Agent has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, return "This does not appear to be part of this dataset." as the answer.

 Given an input question, ask to run the questions against the dataset, then look at the results and return the answer, the answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that in a easily readable format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.
 """
@@ -17,9 +17,9 @@ Thought: I can first ask which tables I have, then how each table is defined and

 POWERBI_CHAT_PREFIX = """Assistant is a large language model built to help users interact with a PowerBI Dataset.

-Assistant has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, just return "This does not appear to be part of this dataset." as the answer.
+Assistant should try to create a correct and complete answer to the question from the user. If the user asks a question not related to the dataset it should return "This does not appear to be part of this dataset." as the answer. The user might make a mistake with the spelling of certain values, if you think that is the case, ask the user to confirm the spelling of the value and then run the query again. Unless the user specifies a specific number of examples they wish to obtain, and the results are too large, limit your query to at most {top_k} results, but make it clear when answering which field was used for the filtering. The user has access to these tables: {{tables}}.

-Given an input question, ask to run the questions against the dataset, then look at the results and return the answer, the answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that in a easily readable format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.
+The answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that in a easily readable format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. 
 """

 POWERBI_CHAT_SUFFIX = """TOOLS
--- a/langchain/agents/agent_toolkits/powerbi/toolkit.py
+++ b/langchain/agents/agent_toolkits/powerbi/toolkit.py
@@ -1,15 +1,25 @@
 """Toolkit for interacting with a Power BI dataset."""
-from typing import List, Optional
+from typing import List, Optional, Union

 from pydantic import Field

 from langchain.agents.agent_toolkits.base import BaseToolkit
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
+from langchain.chat_models.base import BaseChatModel
 from langchain.prompts import PromptTemplate
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+)
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
-from langchain.tools.powerbi.prompt import QUESTION_TO_QUERY
+from langchain.tools.powerbi.prompt import (
+    QUESTION_TO_QUERY_BASE,
+    SINGLE_QUESTION_TO_QUERY,
+    USER_INPUT,
+)
 from langchain.tools.powerbi.tool import (
    InfoPowerBITool,
    ListPowerBITool,
@@ -22,10 +32,12 @@ class PowerBIToolkit(BaseToolkit):
    """Toolkit for interacting with PowerBI dataset."""

    powerbi: PowerBIDataset = Field(exclude=True)
-    llm: BaseLanguageModel = Field(exclude=True)
+    llm: Union[BaseLanguageModel, BaseChatModel] = Field(exclude=True)
    examples: Optional[str] = None
    max_iterations: int = 5
    callback_manager: Optional[BaseCallbackManager] = None
+    output_token_limit: Optional[int] = None
+    tiktoken_model_name: Optional[str] = None

    class Config:
        """Configuration for this pydantic object."""
@@ -34,30 +46,47 @@ class PowerBIToolkit(BaseToolkit):

    def get_tools(self) -> List[BaseTool]:
        """Get the tools in the toolkit."""
-        if self.callback_manager:
-            chain = LLMChain(
-                llm=self.llm,
-                callback_manager=self.callback_manager,
-                prompt=PromptTemplate(
-                    template=QUESTION_TO_QUERY,
-                    input_variables=["tool_input", "tables", "schemas", "examples"],
-                ),
-            )
-        else:
-            chain = LLMChain(
-                llm=self.llm,
-                prompt=PromptTemplate(
-                    template=QUESTION_TO_QUERY,
-                    input_variables=["tool_input", "tables", "schemas", "examples"],
-                ),
-            )
        return [
            QueryPowerBITool(
-                llm_chain=chain,
+                llm_chain=self._get_chain(),
                powerbi=self.powerbi,
                examples=self.examples,
                max_iterations=self.max_iterations,
+                output_token_limit=self.output_token_limit,
+                tiktoken_model_name=self.tiktoken_model_name,
            ),
            InfoPowerBITool(powerbi=self.powerbi),
            ListPowerBITool(powerbi=self.powerbi),
        ]
+
+    def _get_chain(self) -> LLMChain:
+        """Construct the chain based on the callback manager and model type."""
+        if isinstance(self.llm, BaseLanguageModel):
+            return LLMChain(
+                llm=self.llm,
+                callback_manager=self.callback_manager
+                if self.callback_manager
+                else None,
+                prompt=PromptTemplate(
+                    template=SINGLE_QUESTION_TO_QUERY,
+                    input_variables=["tool_input", "tables", "schemas", "examples"],
+                ),
+            )
+
+        system_prompt = SystemMessagePromptTemplate(
+            prompt=PromptTemplate(
+                template=QUESTION_TO_QUERY_BASE,
+                input_variables=["tables", "schemas", "examples"],
+            )
+        )
+        human_prompt = HumanMessagePromptTemplate(
+            prompt=PromptTemplate(
+                template=USER_INPUT,
+                input_variables=["tool_input"],
+            )
+        )
+        return LLMChain(
+            llm=self.llm,
+            callback_manager=self.callback_manager if self.callback_manager else None,
+            prompt=ChatPromptTemplate.from_messages([system_prompt, human_prompt]),
+        )
--- a/langchain/agents/agent_toolkits/python/base.py
+++ b/langchain/agents/agent_toolkits/python/base.py
@@ -7,9 +7,9 @@ from langchain.agents.agent_toolkits.python.prompt import PREFIX
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
 from langchain.agents.types import AgentType
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import SystemMessage
 from langchain.tools.python.tool import PythonREPLTool

--- a/langchain/agents/agent_toolkits/spark_sql/base.py
+++ b/langchain/agents/agent_toolkits/spark_sql/base.py
@@ -6,9 +6,9 @@ from langchain.agents.agent_toolkits.spark_sql.prompt import SQL_PREFIX, SQL_SUF
 from langchain.agents.agent_toolkits.spark_sql.toolkit import SparkSQLToolkit
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
+from langchain.schema.language_model import BaseLanguageModel


 def create_spark_sql_agent(
--- a/langchain/agents/agent_toolkits/spark_sql/toolkit.py
+++ b/langchain/agents/agent_toolkits/spark_sql/toolkit.py
@@ -4,7 +4,7 @@ from typing import List
 from pydantic import Field

 from langchain.agents.agent_toolkits.base import BaseToolkit
-from langchain.base_language import BaseLanguageModel
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.spark_sql.tool import (
    InfoSparkSQLTool,
--- a/langchain/agents/agent_toolkits/sql/base.py
+++ b/langchain/agents/agent_toolkits/sql/base.py
@@ -12,7 +12,6 @@ from langchain.agents.agent_types import AgentType
 from langchain.agents.mrkl.base import ZeroShotAgent
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
 from langchain.prompts.chat import (
@@ -20,6 +19,7 @@ from langchain.prompts.chat import (
    HumanMessagePromptTemplate,
    MessagesPlaceholder,
 )
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import AIMessage, SystemMessage


--- a/langchain/agents/agent_toolkits/sql/toolkit.py
+++ b/langchain/agents/agent_toolkits/sql/toolkit.py
@@ -4,7 +4,7 @@ from typing import List
 from pydantic import Field

 from langchain.agents.agent_toolkits.base import BaseToolkit
-from langchain.base_language import BaseLanguageModel
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.sql_database import SQLDatabase
 from langchain.tools import BaseTool
 from langchain.tools.sql_database.tool import (
--- a/langchain/agents/agent_toolkits/vectorstore/base.py
+++ b/langchain/agents/agent_toolkits/vectorstore/base.py
@@ -8,9 +8,9 @@ from langchain.agents.agent_toolkits.vectorstore.toolkit import (
    VectorStoreToolkit,
 )
 from langchain.agents.mrkl.base import ZeroShotAgent
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
+from langchain.schema.language_model import BaseLanguageModel


 def create_vectorstore_agent(
--- a/langchain/agents/agent_toolkits/vectorstore/toolkit.py
+++ b/langchain/agents/agent_toolkits/vectorstore/toolkit.py
@@ -4,8 +4,8 @@ from typing import List
 from pydantic import BaseModel, Field

 from langchain.agents.agent_toolkits.base import BaseToolkit
-from langchain.base_language import BaseLanguageModel
 from langchain.llms.openai import OpenAI
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.vectorstore.tool import (
    VectorStoreQATool,
--- a/langchain/agents/chat/base.py
+++ b/langchain/agents/chat/base.py
@@ -11,7 +11,6 @@ from langchain.agents.chat.prompt import (
    SYSTEM_MESSAGE_SUFFIX,
 )
 from langchain.agents.utils import validate_tools_single_input
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
 from langchain.prompts.chat import (
@@ -20,6 +19,7 @@ from langchain.prompts.chat import (
    SystemMessagePromptTemplate,
 )
 from langchain.schema import AgentAction, BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool


--- a/langchain/agents/chat/output_parser.py
+++ b/langchain/agents/chat/output_parser.py
@@ -30,9 +30,8 @@ class ChatOutputParser(AgentOutputParser):
        except Exception:
            if not includes_answer:
                raise OutputParserException(f"Could not parse LLM output: {text}")
-            return AgentFinish(
-                {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
-            )
+            output = text.split(FINAL_ANSWER_ACTION)[-1].strip()
+            return AgentFinish({"output": output}, text)

    @property
    def _type(self) -> str:
--- a/langchain/agents/conversational/base.py
+++ b/langchain/agents/conversational/base.py
@@ -10,10 +10,10 @@ from langchain.agents.agent_types import AgentType
 from langchain.agents.conversational.output_parser import ConvoOutputParser
 from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
 from langchain.agents.utils import validate_tools_single_input
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool


--- a/langchain/agents/conversational_chat/base.py
+++ b/langchain/agents/conversational_chat/base.py
@@ -13,7 +13,6 @@ from langchain.agents.conversational_chat.prompt import (
    TEMPLATE_TOOL_RESPONSE,
 )
 from langchain.agents.utils import validate_tools_single_input
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains import LLMChain
 from langchain.prompts.chat import (
@@ -23,6 +22,7 @@ from langchain.prompts.chat import (
    SystemMessagePromptTemplate,
 )
 from langchain.schema import AgentAction, BaseOutputParser, BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
 from langchain.tools.base import BaseTool

--- a/langchain/agents/initialize.py
+++ b/langchain/agents/initialize.py
@@ -4,8 +4,8 @@ from typing import Any, Optional, Sequence
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_types import AgentType
 from langchain.agents.loading import AGENT_TO_CLASS, load_agent
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool


--- a/langchain/agents/load_tools.py
+++ b/langchain/agents/load_tools.py
@@ -5,7 +5,7 @@ from typing import Any, Dict, List, Optional, Callable, Tuple
 from mypy_extensions import Arg, KwArg

 from langchain.agents.tools import Tool
-from langchain.base_language import BaseLanguageModel
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
@@ -38,6 +38,8 @@ from langchain.tools.sleep.tool import SleepTool
 from langchain.tools.wikipedia.tool import WikipediaQueryRun
 from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
 from langchain.tools.openweathermap.tool import OpenWeatherMapQueryRun
+from langchain.tools.dataforseo_api_search import DataForSeoAPISearchRun
+from langchain.tools.dataforseo_api_search import DataForSeoAPISearchResults
 from langchain.utilities import ArxivAPIWrapper
 from langchain.utilities import PubMedAPIWrapper
 from langchain.utilities.bing_search import BingSearchAPIWrapper
@@ -53,6 +55,7 @@ from langchain.utilities.twilio import TwilioAPIWrapper
 from langchain.utilities.wikipedia import WikipediaAPIWrapper
 from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper
+from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper


 def _get_python_repl() -> BaseTool:
@@ -278,6 +281,14 @@ def _get_openweathermap(**kwargs: Any) -> BaseTool:
    return OpenWeatherMapQueryRun(api_wrapper=OpenWeatherMapAPIWrapper(**kwargs))


+def _get_dataforseo_api_search(**kwargs: Any) -> BaseTool:
+    return DataForSeoAPISearchRun(api_wrapper=DataForSeoAPIWrapper(**kwargs))
+
+
+def _get_dataforseo_api_search_json(**kwargs: Any) -> BaseTool:
+    return DataForSeoAPISearchResults(api_wrapper=DataForSeoAPIWrapper(**kwargs))
+
+
 _EXTRA_LLM_TOOLS: Dict[
    str,
    Tuple[Callable[[Arg(BaseLanguageModel, "llm"), KwArg(Any)], BaseTool], List[str]],
@@ -326,6 +337,14 @@ _EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[st
    "sceneXplain": (_get_scenexplain, []),
    "graphql": (_get_graphql_tool, ["graphql_endpoint"]),
    "openweathermap-api": (_get_openweathermap, ["openweathermap_api_key"]),
+    "dataforseo-api-search": (
+        _get_dataforseo_api_search,
+        ["api_login", "api_password", "aiosession"],
+    ),
+    "dataforseo-api-search-json": (
+        _get_dataforseo_api_search_json,
+        ["api_login", "api_password", "aiosession"],
+    ),
 }


--- a/langchain/agents/loading.py
+++ b/langchain/agents/loading.py
@@ -9,8 +9,8 @@ import yaml
 from langchain.agents.agent import BaseMultiActionAgent, BaseSingleActionAgent
 from langchain.agents.tools import Tool
 from langchain.agents.types import AGENT_TO_CLASS
-from langchain.base_language import BaseLanguageModel
 from langchain.chains.loading import load_chain, load_chain_from_config
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities.loading import try_load_from_hub

 logger = logging.getLogger(__file__)
--- a/langchain/agents/mrkl/base.py
+++ b/langchain/agents/mrkl/base.py
@@ -11,10 +11,10 @@ from langchain.agents.mrkl.output_parser import MRKLOutputParser
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
 from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool


--- a/langchain/agents/openai_functions_agent/base.py
+++ b/langchain/agents/openai_functions_agent/base.py
@@ -7,7 +7,6 @@ from typing import Any, List, Optional, Sequence, Tuple, Union
 from pydantic import root_validator

 from langchain.agents import BaseSingleActionAgent
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.chat_models.openai import ChatOpenAI
@@ -23,6 +22,7 @@ from langchain.schema import (
    BasePromptTemplate,
    OutputParserException,
 )
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import (
    AIMessage,
    BaseMessage,
@@ -108,7 +108,6 @@ def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
    function_call = message.additional_kwargs.get("function_call", {})

    if function_call:
-        function_call = message.additional_kwargs["function_call"]
        function_name = function_call["name"]
        try:
            _tool_input = json.loads(function_call["arguments"])
--- a/langchain/agents/openai_functions_multi_agent/base.py
+++ b/langchain/agents/openai_functions_multi_agent/base.py
@@ -7,7 +7,6 @@ from typing import Any, List, Optional, Sequence, Tuple, Union
 from pydantic import root_validator

 from langchain.agents import BaseMultiActionAgent
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.chat_models.openai import ChatOpenAI
@@ -23,6 +22,7 @@ from langchain.schema import (
    BasePromptTemplate,
    OutputParserException,
 )
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import (
    AIMessage,
    BaseMessage,
@@ -107,7 +107,6 @@ def _parse_ai_message(message: BaseMessage) -> Union[List[AgentAction], AgentFin
    function_call = message.additional_kwargs.get("function_call", {})

    if function_call:
-        function_call = message.additional_kwargs["function_call"]
        try:
            tools = json.loads(function_call["arguments"])["actions"]
        except JSONDecodeError:
--- a/langchain/agents/react/base.py
+++ b/langchain/agents/react/base.py
@@ -10,10 +10,10 @@ from langchain.agents.react.textworld_prompt import TEXTWORLD_PROMPT
 from langchain.agents.react.wiki_prompt import WIKI_PROMPT
 from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
-from langchain.base_language import BaseLanguageModel
 from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool


--- a/langchain/agents/self_ask_with_search/base.py
+++ b/langchain/agents/self_ask_with_search/base.py
@@ -1,4 +1,4 @@
-"""Chain that does self ask with search."""
+"""Chain that does self-ask with search."""
 from typing import Any, Sequence, Union

 from pydantic import Field
@@ -9,8 +9,8 @@ from langchain.agents.self_ask_with_search.output_parser import SelfAskOutputPar
 from langchain.agents.self_ask_with_search.prompt import PROMPT
 from langchain.agents.tools import Tool
 from langchain.agents.utils import validate_tools_single_input
-from langchain.base_language import BaseLanguageModel
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
 from langchain.utilities.google_serper import GoogleSerperAPIWrapper
 from langchain.utilities.serpapi import SerpAPIWrapper
@@ -59,7 +59,7 @@ class SelfAskWithSearchAgent(Agent):


 class SelfAskWithSearchChain(AgentExecutor):
-    """Chain that does self ask with search.
+    """Chain that does self-ask with search.

    Example:
        .. code-block:: python
--- a/langchain/agents/structured_chat/base.py
+++ b/langchain/agents/structured_chat/base.py
@@ -8,7 +8,6 @@ from langchain.agents.structured_chat.output_parser import (
    StructuredChatOutputParserWithRetries,
 )
 from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.chains.llm import LLMChain
 from langchain.prompts.chat import (
@@ -17,6 +16,7 @@ from langchain.prompts.chat import (
    SystemMessagePromptTemplate,
 )
 from langchain.schema import AgentAction, BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool

 HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
--- a/langchain/agents/structured_chat/output_parser.py
+++ b/langchain/agents/structured_chat/output_parser.py
@@ -9,9 +9,9 @@ from pydantic import Field

 from langchain.agents.agent import AgentOutputParser
 from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS
-from langchain.base_language import BaseLanguageModel
 from langchain.output_parsers import OutputFixingParser
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
+from langchain.schema.language_model import BaseLanguageModel

 logger = logging.getLogger(__name__)

--- a/langchain/base_language.py
+++ b/langchain/base_language.py
@@ -1,104 +1,6 @@
-"""Base class for all language models."""
+"""Deprecated module for BaseLanguageModel class, kept for backwards compatibility."""
 from __future__ import annotations

-from abc import ABC, abstractmethod
-from typing import Any, List, Optional, Sequence, Set
+from langchain.schema.language_model import BaseLanguageModel

-from langchain.callbacks.manager import Callbacks
-from langchain.load.serializable import Serializable
-from langchain.schema import LLMResult, PromptValue
-from langchain.schema.messages import BaseMessage, get_buffer_string
-
-
-def _get_token_ids_default_method(text: str) -> List[int]:
-    """Encode the text into token IDs."""
-    # TODO: this method may not be exact.
-    # TODO: this method may differ based on model (eg codex).
-    try:
-        from transformers import GPT2TokenizerFast
-    except ImportError:
-        raise ValueError(
-            "Could not import transformers python package. "
-            "This is needed in order to calculate get_token_ids. "
-            "Please install it with `pip install transformers`."
-        )
-    # create a GPT-2 tokenizer instance
-    tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
-
-    # tokenize the text using the GPT-2 tokenizer
-    return tokenizer.encode(text)
-
-
-class BaseLanguageModel(Serializable, ABC):
-    @abstractmethod
-    def generate_prompt(
-        self,
-        prompts: List[PromptValue],
-        stop: Optional[List[str]] = None,
-        callbacks: Callbacks = None,
-        **kwargs: Any,
-    ) -> LLMResult:
-        """Take in a list of prompt values and return an LLMResult."""
-
-    @abstractmethod
-    async def agenerate_prompt(
-        self,
-        prompts: List[PromptValue],
-        stop: Optional[List[str]] = None,
-        callbacks: Callbacks = None,
-        **kwargs: Any,
-    ) -> LLMResult:
-        """Take in a list of prompt values and return an LLMResult."""
-
-    @abstractmethod
-    def predict(
-        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
-    ) -> str:
-        """Predict text from text."""
-
-    @abstractmethod
-    def predict_messages(
-        self,
-        messages: List[BaseMessage],
-        *,
-        stop: Optional[Sequence[str]] = None,
-        **kwargs: Any,
-    ) -> BaseMessage:
-        """Predict message from messages."""
-
-    @abstractmethod
-    async def apredict(
-        self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
-    ) -> str:
-        """Predict text from text."""
-
-    @abstractmethod
-    async def apredict_messages(
-        self,
-        messages: List[BaseMessage],
-        *,
-        stop: Optional[Sequence[str]] = None,
-        **kwargs: Any,
-    ) -> BaseMessage:
-        """Predict message from messages."""
-
-    def get_token_ids(self, text: str) -> List[int]:
-        """Get the token present in the text."""
-        return _get_token_ids_default_method(text)
-
-    def get_num_tokens(self, text: str) -> int:
-        """Get the number of tokens present in the text."""
-        return len(self.get_token_ids(text))
-
-    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
-        """Get the number of tokens in the message."""
-        return sum([self.get_num_tokens(get_buffer_string([m])) for m in messages])
-
-    @classmethod
-    def all_required_field_names(cls) -> Set:
-        all_required_field_names = set()
-        for field in cls.__fields__.values():
-            all_required_field_names.add(field.name)
-            if field.has_alias:
-                all_required_field_names.add(field.alias)
-        return all_required_field_names
+__all__ = ["BaseLanguageModel"]
--- a/langchain/callbacks/init.py
+++ b/langchain/callbacks/init.py
@@ -6,6 +6,7 @@ from langchain.callbacks.arize_callback import ArizeCallbackHandler
 from langchain.callbacks.arthur_callback import ArthurCallbackHandler
 from langchain.callbacks.clearml_callback import ClearMLCallbackHandler
 from langchain.callbacks.comet_ml_callback import CometCallbackHandler
+from langchain.callbacks.context_callback import ContextCallbackHandler
 from langchain.callbacks.file import FileCallbackHandler
 from langchain.callbacks.flyte_callback import FlyteCallbackHandler
 from langchain.callbacks.human import HumanApprovalCallbackHandler
@@ -36,6 +37,7 @@ __all__ = [
    "ArthurCallbackHandler",
    "ClearMLCallbackHandler",
    "CometCallbackHandler",
+    "ContextCallbackHandler",
    "FileCallbackHandler",
    "HumanApprovalCallbackHandler",
    "InfinoCallbackHandler",
--- a/langchain/callbacks/base.py
+++ b/langchain/callbacks/base.py
@@ -147,6 +147,7 @@ class CallbackManagerMixin:
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run when LLM starts running."""
@@ -159,6 +160,7 @@ class CallbackManagerMixin:
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run when a chat model starts running."""
@@ -168,10 +170,13 @@ class CallbackManagerMixin:

    def on_retriever_start(
        self,
+        serialized: Dict[str, Any],
        query: str,
        *,
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run when Retriever starts running."""
@@ -184,6 +189,7 @@ class CallbackManagerMixin:
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run when chain starts running."""
@@ -196,6 +202,7 @@ class CallbackManagerMixin:
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run when tool starts running."""
@@ -266,6 +273,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Run when LLM starts running."""
@@ -278,6 +286,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> Any:
        """Run when a chat model starts running."""
@@ -326,6 +335,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Run when chain starts running."""
@@ -360,6 +370,7 @@ class AsyncCallbackHandler(BaseCallbackHandler):
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Run when tool starts running."""
@@ -421,11 +432,13 @@ class AsyncCallbackHandler(BaseCallbackHandler):

    async def on_retriever_start(
        self,
+        serialized: Dict[str, Any],
        query: str,
        *,
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Run on retriever start."""
@@ -464,6 +477,8 @@ class BaseCallbackManager(CallbackManagerMixin):
        *,
        tags: Optional[List[str]] = None,
        inheritable_tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        inheritable_metadata: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Initialize callback manager."""
        self.handlers: List[BaseCallbackHandler] = handlers
@@ -473,6 +488,8 @@ class BaseCallbackManager(CallbackManagerMixin):
        self.parent_run_id: Optional[UUID] = parent_run_id
        self.tags = tags or []
        self.inheritable_tags = inheritable_tags or []
+        self.metadata = metadata or {}
+        self.inheritable_metadata = inheritable_metadata or {}

    @property
    def is_async(self) -> bool:
@@ -515,3 +532,13 @@ class BaseCallbackManager(CallbackManagerMixin):
        for tag in tags:
            self.tags.remove(tag)
            self.inheritable_tags.remove(tag)
+
+    def add_metadata(self, metadata: Dict[str, Any], inherit: bool = True) -> None:
+        self.metadata.update(metadata)
+        if inherit:
+            self.inheritable_metadata.update(metadata)
+
+    def remove_metadata(self, keys: List[str]) -> None:
+        for key in keys:
+            self.metadata.pop(key)
+            self.inheritable_metadata.pop(key)
--- a/langchain/callbacks/comet_ml_callback.py
+++ b/langchain/callbacks/comet_ml_callback.py
@@ -18,6 +18,7 @@ LANGCHAIN_MODEL_NAME = "langchain-model"


 def import_comet_ml() -> Any:
+    """Import comet_ml and raise an error if it is not installed."""
    try:
        import comet_ml  # noqa: F401
    except ImportError:
--- a/langchain/callbacks/context_callback.py
+++ b/langchain/callbacks/context_callback.py
@@ -0,0 +1,193 @@
+"""Callback handler for Context AI"""
+import os
+from typing import Any, Dict, List
+from uuid import UUID
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.schema import (
+    BaseMessage,
+    LLMResult,
+)
+
+
+def import_context() -> Any:
+    try:
+        import getcontext  # noqa: F401
+        from getcontext.generated.models import (
+            Conversation,
+            Message,
+            MessageRole,
+            Rating,
+        )
+        from getcontext.token import Credential  # noqa: F401
+    except ImportError:
+        raise ImportError(
+            "To use the context callback manager you need to have the "
+            "`getcontext` python package installed (version >=0.3.0). "
+            "Please install it with `pip install --upgrade python-context`"
+        )
+    return getcontext, Credential, Conversation, Message, MessageRole, Rating
+
+
+class ContextCallbackHandler(BaseCallbackHandler):
+    """Callback Handler that records transcripts to Context (https://getcontext.ai).
+
+    Keyword Args:
+        token (optional): The token with which to authenticate requests to Context.
+            Visit https://go.getcontext.ai/settings to generate a token.
+            If not provided, the value of the `CONTEXT_TOKEN` environment
+            variable will be used.
+
+    Raises:
+        ImportError: if the `context-python` package is not installed.
+
+    Chat Example:
+        >>> from langchain.llms import ChatOpenAI
+        >>> from langchain.callbacks import ContextCallbackHandler
+        >>> context_callback = ContextCallbackHandler(
+        ...     token="<CONTEXT_TOKEN_HERE>",
+        ... )
+        >>> chat = ChatOpenAI(
+        ...     temperature=0,
+        ...     headers={"user_id": "123"},
+        ...     callbacks=[context_callback],
+        ...     openai_api_key="API_KEY_HERE",
+        ... )
+        >>> messages = [
+        ...     SystemMessage(content="You translate English to French."),
+        ...     HumanMessage(content="I love programming with LangChain."),
+        ... ]
+        >>> chat(messages)
+
+    Chain Example:
+        >>> from langchain import LLMChain
+        >>> from langchain.llms import ChatOpenAI
+        >>> from langchain.callbacks import ContextCallbackHandler
+        >>> context_callback = ContextCallbackHandler(
+        ...     token="<CONTEXT_TOKEN_HERE>",
+        ... )
+        >>> human_message_prompt = HumanMessagePromptTemplate(
+        ...     prompt=PromptTemplate(
+        ...         template="What is a good name for a company that makes {product}?",
+        ...         input_variables=["product"],
+        ...    ),
+        ... )
+        >>> chat_prompt_template = ChatPromptTemplate.from_messages(
+        ...   [human_message_prompt]
+        ... )
+        >>> callback = ContextCallbackHandler(token)
+        >>> # Note: the same callback object must be shared between the
+        ...   LLM and the chain.
+        >>> chat = ChatOpenAI(temperature=0.9, callbacks=[callback])
+        >>> chain = LLMChain(
+        ...   llm=chat,
+        ...   prompt=chat_prompt_template,
+        ...   callbacks=[callback]
+        ... )
+        >>> chain.run("colorful socks")
+    """
+
+    def __init__(self, token: str = "", verbose: bool = False, **kwargs: Any) -> None:
+        (
+            self.context,
+            self.credential,
+            self.conversation_model,
+            self.message_model,
+            self.message_role_model,
+            self.rating_model,
+        ) = import_context()
+
+        token = token or os.environ.get("CONTEXT_TOKEN") or ""
+
+        self.client = self.context.ContextAPI(credential=self.credential(token))
+
+        self.chain_run_id = None
+
+        self.llm_model = None
+
+        self.messages: List[Any] = []
+        self.metadata: Dict[str, str] = {}
+
+    def on_chat_model_start(
+        self,
+        serialized: Dict[str, Any],
+        messages: List[List[BaseMessage]],
+        *,
+        run_id: UUID,
+        **kwargs: Any,
+    ) -> Any:
+        """Run when the chat model is started."""
+        llm_model = kwargs.get("invocation_params", {}).get("model", None)
+        if llm_model is not None:
+            self.metadata["llm_model"] = llm_model
+
+        if len(messages) == 0:
+            return
+
+        for message in messages[0]:
+            role = self.message_role_model.SYSTEM
+            if message.type == "human":
+                role = self.message_role_model.USER
+            elif message.type == "system":
+                role = self.message_role_model.SYSTEM
+            elif message.type == "ai":
+                role = self.message_role_model.ASSISTANT
+
+            self.messages.append(
+                self.message_model(
+                    message=message.content,
+                    role=role,
+                )
+            )
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Run when LLM ends."""
+        if len(response.generations) == 0 or len(response.generations[0]) == 0:
+            return
+
+        if not self.chain_run_id:
+            generation = response.generations[0][0]
+            self.messages.append(
+                self.message_model(
+                    message=generation.text,
+                    role=self.message_role_model.ASSISTANT,
+                )
+            )
+
+            self._log_conversation()
+
+    def on_chain_start(
+        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+    ) -> None:
+        """Run when chain starts."""
+        self.chain_run_id = kwargs.get("run_id", None)
+
+    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+        """Run when chain ends."""
+        self.messages.append(
+            self.message_model(
+                message=outputs["text"],
+                role=self.message_role_model.ASSISTANT,
+            )
+        )
+
+        self._log_conversation()
+
+        self.chain_run_id = None
+
+    def _log_conversation(self) -> None:
+        """Log the conversation to the context API."""
+        if len(self.messages) == 0:
+            return
+
+        self.client.log.conversation_upsert(
+            body={
+                "conversation": self.conversation_model(
+                    messages=self.messages,
+                    metadata=self.metadata,
+                )
+            }
+        )
+
+        self.messages = []
+        self.metadata = {}
--- a/langchain/callbacks/file.py
+++ b/langchain/callbacks/file.py
@@ -39,7 +39,7 @@ class FileCallbackHandler(BaseCallbackHandler):
        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
    ) -> Any:
        """Run on agent action."""
-        print_text(action.log, color=color if color else self.color, file=self.file)
+        print_text(action.log, color=color or self.color, file=self.file)

    def on_tool_end(
        self,
@@ -52,24 +52,18 @@ class FileCallbackHandler(BaseCallbackHandler):
        """If not the final action, print out observation."""
        if observation_prefix is not None:
            print_text(f"\n{observation_prefix}", file=self.file)
-        print_text(output, color=color if color else self.color, file=self.file)
+        print_text(output, color=color or self.color, file=self.file)
        if llm_prefix is not None:
            print_text(f"\n{llm_prefix}", file=self.file)

    def on_text(
-        self,
-        text: str,
-        color: Optional[str] = None,
-        end: str = "",
-        **kwargs: Any,
+        self, text: str, color: Optional[str] = None, end: str = "", **kwargs: Any
    ) -> None:
        """Run when agent ends."""
-        print_text(text, color=color if color else self.color, end=end, file=self.file)
+        print_text(text, color=color or self.color, end=end, file=self.file)

    def on_agent_finish(
        self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
    ) -> None:
        """Run on agent end."""
-        print_text(
-            finish.log, color=color if self.color else color, end="\n", file=self.file
-        )
+        print_text(finish.log, color=color or self.color, end="\n", file=self.file)
--- a/langchain/callbacks/flyte_callback.py
+++ b/langchain/callbacks/flyte_callback.py
@@ -23,6 +23,7 @@ logger = logging.getLogger(__name__)


 def import_flytekit() -> Tuple[flytekit, renderer]:
+    """Import flytekit and flytekitplugins-deck-standard."""
    try:
        import flytekit  # noqa: F401
        from flytekitplugins.deck import renderer  # noqa: F401
@@ -39,6 +40,7 @@ def import_flytekit() -> Tuple[flytekit, renderer]:
 def analyze_text(
    text: str,
    nlp: Any = None,
+    textstat: Any = None,
 ) -> dict:
    """Analyze text using textstat and spacy.

@@ -51,26 +53,26 @@ def analyze_text(
            files serialized to HTML string.
    """
    resp: Dict[str, Any] = {}
-    textstat = import_textstat()
-    text_complexity_metrics = {
-        "flesch_reading_ease": textstat.flesch_reading_ease(text),
-        "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
-        "smog_index": textstat.smog_index(text),
-        "coleman_liau_index": textstat.coleman_liau_index(text),
-        "automated_readability_index": textstat.automated_readability_index(text),
-        "dale_chall_readability_score": textstat.dale_chall_readability_score(text),
-        "difficult_words": textstat.difficult_words(text),
-        "linsear_write_formula": textstat.linsear_write_formula(text),
-        "gunning_fog": textstat.gunning_fog(text),
-        "fernandez_huerta": textstat.fernandez_huerta(text),
-        "szigriszt_pazos": textstat.szigriszt_pazos(text),
-        "gutierrez_polini": textstat.gutierrez_polini(text),
-        "crawford": textstat.crawford(text),
-        "gulpease_index": textstat.gulpease_index(text),
-        "osman": textstat.osman(text),
-    }
-    resp.update({"text_complexity_metrics": text_complexity_metrics})
-    resp.update(text_complexity_metrics)
+    if textstat is not None:
+        text_complexity_metrics = {
+            "flesch_reading_ease": textstat.flesch_reading_ease(text),
+            "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
+            "smog_index": textstat.smog_index(text),
+            "coleman_liau_index": textstat.coleman_liau_index(text),
+            "automated_readability_index": textstat.automated_readability_index(text),
+            "dale_chall_readability_score": textstat.dale_chall_readability_score(text),
+            "difficult_words": textstat.difficult_words(text),
+            "linsear_write_formula": textstat.linsear_write_formula(text),
+            "gunning_fog": textstat.gunning_fog(text),
+            "fernandez_huerta": textstat.fernandez_huerta(text),
+            "szigriszt_pazos": textstat.szigriszt_pazos(text),
+            "gutierrez_polini": textstat.gutierrez_polini(text),
+            "crawford": textstat.crawford(text),
+            "gulpease_index": textstat.gulpease_index(text),
+            "osman": textstat.osman(text),
+        }
+        resp.update({"text_complexity_metrics": text_complexity_metrics})
+        resp.update(text_complexity_metrics)

    if nlp is not None:
        spacy = import_spacy()
@@ -78,16 +80,13 @@ def analyze_text(
        dep_out = spacy.displacy.render(  # type: ignore
            doc, style="dep", jupyter=False, page=True
        )
-
        ent_out = spacy.displacy.render(  # type: ignore
            doc, style="ent", jupyter=False, page=True
        )
-
        text_visualizations = {
            "dependency_tree": dep_out,
            "entities": ent_out,
        }
-
        resp.update(text_visualizations)

    return resp
@@ -98,10 +97,19 @@ class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):

    def __init__(self) -> None:
        """Initialize callback handler."""
-        import_textstat()  # Raise error since it is required
        flytekit, renderer = import_flytekit()
        self.pandas = import_pandas()

+        self.textstat = None
+        try:
+            self.textstat = import_textstat()
+        except ImportError:
+            logger.warning(
+                "Textstat library is not installed. \
+                It may result in the inability to log \
+                certain metrics that can be captured with Textstat."
+            )
+
        spacy = None
        try:
            spacy = import_spacy()
@@ -123,7 +131,7 @@ class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
                    "FlyteCallbackHandler uses spacy's en_core_web_sm model"
                    " for certain metrics. To download,"
                    " run the following command in your terminal:"
-                    " `python -m spacy download en_core_web_sm` command."
+                    " `python -m spacy download en_core_web_sm`"
                )

        self.table_renderer = renderer.TableRenderer
@@ -180,11 +188,10 @@ class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
            for generation in generations:
                generation_resp = deepcopy(resp)
                generation_resp.update(flatten_dict(generation.dict()))
-                if self.nlp:
+                if self.nlp or self.textstat:
                    generation_resp.update(
                        analyze_text(
-                            generation.text,
-                            nlp=self.nlp,
+                            generation.text, nlp=self.nlp, textstat=self.textstat
                        )
                    )

--- a/langchain/callbacks/infino_callback.py
+++ b/langchain/callbacks/infino_callback.py
@@ -6,6 +6,7 @@ from langchain.schema import AgentAction, AgentFinish, LLMResult


 def import_infino() -> Any:
+    """Import the infino client."""
    try:
        from infinopy import InfinoClient
    except ImportError:
--- a/langchain/callbacks/manager.py
+++ b/langchain/callbacks/manager.py
@@ -144,6 +144,7 @@ def tracing_v2_enabled(
    project_name: Optional[str] = None,
    *,
    example_id: Optional[Union[str, UUID]] = None,
+    tags: Optional[List[str]] = None,
 ) -> Generator[None, None, None]:
    """Instruct LangChain to log all runs in context to LangSmith.

@@ -152,6 +153,8 @@ def tracing_v2_enabled(
            Defaults to "default".
        example_id (str or UUID, optional): The ID of the example.
            Defaults to None.
+        tags (List[str], optional): The tags to add to the run.
+            Defaults to None.

    Returns:
        None
@@ -170,6 +173,7 @@ def tracing_v2_enabled(
    cb = LangChainTracer(
        example_id=example_id,
        project_name=project_name,
+        tags=tags,
    )
    tracing_v2_callback_var.set(cb)
    yield
@@ -383,6 +387,8 @@ class BaseRunManager(RunManagerMixin):
        parent_run_id: Optional[UUID] = None,
        tags: Optional[List[str]] = None,
        inheritable_tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        inheritable_metadata: Optional[Dict[str, Any]] = None,
    ) -> None:
        """Initialize the run manager.

@@ -395,6 +401,8 @@ class BaseRunManager(RunManagerMixin):
                Defaults to None.
            tags (Optional[List[str]]): The list of tags.
            inheritable_tags (Optional[List[str]]): The list of inheritable tags.
+            metadata (Optional[Dict[str, Any]]): The metadata.
+            inheritable_metadata (Optional[Dict[str, Any]]): The inheritable metadata.
        """
        self.run_id = run_id
        self.handlers = handlers
@@ -402,6 +410,8 @@ class BaseRunManager(RunManagerMixin):
        self.parent_run_id = parent_run_id
        self.tags = tags or []
        self.inheritable_tags = inheritable_tags or []
+        self.metadata = metadata or {}
+        self.inheritable_metadata = inheritable_metadata or {}

    @classmethod
    def get_noop_manager(cls: Type[BRM]) -> BRM:
@@ -416,6 +426,8 @@ class BaseRunManager(RunManagerMixin):
            inheritable_handlers=[],
            tags=[],
            inheritable_tags=[],
+            metadata={},
+            inheritable_metadata={},
        )


@@ -447,6 +459,28 @@ class RunManager(BaseRunManager):
        )


+class ParentRunManager(RunManager):
+    """Sync Parent Run Manager."""
+
+    def get_child(self, tag: Optional[str] = None) -> CallbackManager:
+        """Get a child callback manager.
+
+        Args:
+            tag (str, optional): The tag for the child callback manager.
+                Defaults to None.
+
+        Returns:
+            CallbackManager: The child callback manager.
+        """
+        manager = CallbackManager(handlers=[], parent_run_id=self.run_id)
+        manager.set_handlers(self.inheritable_handlers)
+        manager.add_tags(self.inheritable_tags)
+        manager.add_metadata(self.inheritable_metadata)
+        if tag is not None:
+            manager.add_tags([tag], False)
+        return manager
+
+
 class AsyncRunManager(BaseRunManager):
    """Async Run Manager."""

@@ -475,6 +509,28 @@ class AsyncRunManager(BaseRunManager):
        )


+class AsyncParentRunManager(AsyncRunManager):
+    """Async Parent Run Manager."""
+
+    def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
+        """Get a child callback manager.
+
+        Args:
+            tag (str, optional): The tag for the child callback manager.
+                Defaults to None.
+
+        Returns:
+            AsyncCallbackManager: The child callback manager.
+        """
+        manager = AsyncCallbackManager(handlers=[], parent_run_id=self.run_id)
+        manager.set_handlers(self.inheritable_handlers)
+        manager.add_tags(self.inheritable_tags)
+        manager.add_metadata(self.inheritable_metadata)
+        if tag is not None:
+            manager.add_tags([tag], False)
+        return manager
+
+
 class CallbackManagerForLLMRun(RunManager, LLMManagerMixin):
    """Callback manager for LLM run."""

@@ -601,26 +657,9 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):
        )


-class CallbackManagerForChainRun(RunManager, ChainManagerMixin):
+class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin):
    """Callback manager for chain run."""

-    def get_child(self, tag: Optional[str] = None) -> CallbackManager:
-        """Get a child callback manager.
-
-        Args:
-            tag (str, optional): The tag for the child callback manager.
-                Defaults to None.
-
-        Returns:
-            CallbackManager: The child callback manager.
-        """
-        manager = CallbackManager(handlers=[], parent_run_id=self.run_id)
-        manager.set_handlers(self.inheritable_handlers)
-        manager.add_tags(self.inheritable_tags)
-        if tag is not None:
-            manager.add_tags([tag], False)
-        return manager
-
    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
        """Run when chain ends running.

@@ -700,26 +739,9 @@ class CallbackManagerForChainRun(RunManager, ChainManagerMixin):
        )


-class AsyncCallbackManagerForChainRun(AsyncRunManager, ChainManagerMixin):
+class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin):
    """Async callback manager for chain run."""

-    def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
-        """Get a child callback manager.
-
-        Args:
-            tag (str, optional): The tag for the child callback manager.
-                Defaults to None.
-
-        Returns:
-            AsyncCallbackManager: The child callback manager.
-        """
-        manager = AsyncCallbackManager(handlers=[], parent_run_id=self.run_id)
-        manager.set_handlers(self.inheritable_handlers)
-        manager.add_tags(self.inheritable_tags)
-        if tag is not None:
-            manager.add_tags([tag], False)
-        return manager
-
    async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
        """Run when chain ends running.

@@ -799,26 +821,9 @@ class AsyncCallbackManagerForChainRun(AsyncRunManager, ChainManagerMixin):
        )


-class CallbackManagerForToolRun(RunManager, ToolManagerMixin):
+class CallbackManagerForToolRun(ParentRunManager, ToolManagerMixin):
    """Callback manager for tool run."""

-    def get_child(self, tag: Optional[str] = None) -> CallbackManager:
-        """Get a child callback manager.
-
-        Args:
-            tag (str, optional): The tag for the child callback manager.
-                Defaults to None.
-
-        Returns:
-            CallbackManager: The child callback manager.
-        """
-        manager = CallbackManager(handlers=[], parent_run_id=self.run_id)
-        manager.set_handlers(self.inheritable_handlers)
-        manager.add_tags(self.inheritable_tags)
-        if tag is not None:
-            manager.add_tags([tag], False)
-        return manager
-
    def on_tool_end(
        self,
        output: str,
@@ -862,26 +867,9 @@ class CallbackManagerForToolRun(RunManager, ToolManagerMixin):
        )


-class AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):
+class AsyncCallbackManagerForToolRun(AsyncParentRunManager, ToolManagerMixin):
    """Async callback manager for tool run."""

-    def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
-        """Get a child callback manager.
-
-        Args:
-            tag (str, optional): The tag to add to the child
-                 callback manager. Defaults to None.
-
-        Returns:
-            AsyncCallbackManager: The child callback manager.
-        """
-        manager = AsyncCallbackManager(handlers=[], parent_run_id=self.run_id)
-        manager.set_handlers(self.inheritable_handlers)
-        manager.add_tags(self.inheritable_tags)
-        if tag is not None:
-            manager.add_tags([tag], False)
-        return manager
-
    async def on_tool_end(self, output: str, **kwargs: Any) -> None:
        """Run when tool ends running.

@@ -921,18 +909,9 @@ class AsyncCallbackManagerForToolRun(AsyncRunManager, ToolManagerMixin):
        )


-class CallbackManagerForRetrieverRun(RunManager, RetrieverManagerMixin):
+class CallbackManagerForRetrieverRun(ParentRunManager, RetrieverManagerMixin):
    """Callback manager for retriever run."""

-    def get_child(self, tag: Optional[str] = None) -> CallbackManager:
-        """Get a child callback manager."""
-        manager = CallbackManager([], parent_run_id=self.run_id)
-        manager.set_handlers(self.inheritable_handlers)
-        manager.add_tags(self.inheritable_tags)
-        if tag is not None:
-            manager.add_tags([tag], False)
-        return manager
-
    def on_retriever_end(
        self,
        documents: Sequence[Document],
@@ -969,20 +948,11 @@ class CallbackManagerForRetrieverRun(RunManager, RetrieverManagerMixin):


 class AsyncCallbackManagerForRetrieverRun(
-    AsyncRunManager,
+    AsyncParentRunManager,
    RetrieverManagerMixin,
 ):
    """Async callback manager for retriever run."""

-    def get_child(self, tag: Optional[str] = None) -> AsyncCallbackManager:
-        """Get a child callback manager."""
-        manager = AsyncCallbackManager([], parent_run_id=self.run_id)
-        manager.set_handlers(self.inheritable_handlers)
-        manager.add_tags(self.inheritable_tags)
-        if tag is not None:
-            manager.add_tags([tag], False)
-        return manager
-
    async def on_retriever_end(
        self, documents: Sequence[Document], **kwargs: Any
    ) -> None:
@@ -1048,6 +1018,7 @@ class CallbackManager(BaseCallbackManager):
                run_id=run_id_,
                parent_run_id=self.parent_run_id,
                tags=self.tags,
+                metadata=self.metadata,
                **kwargs,
            )

@@ -1059,6 +1030,8 @@ class CallbackManager(BaseCallbackManager):
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
+                    metadata=self.metadata,
+                    inheritable_metadata=self.inheritable_metadata,
                )
            )

@@ -1094,6 +1067,7 @@ class CallbackManager(BaseCallbackManager):
                run_id=run_id_,
                parent_run_id=self.parent_run_id,
                tags=self.tags,
+                metadata=self.metadata,
                **kwargs,
            )

@@ -1105,6 +1079,8 @@ class CallbackManager(BaseCallbackManager):
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
+                    metadata=self.metadata,
+                    inheritable_metadata=self.inheritable_metadata,
                )
            )

@@ -1139,6 +1115,7 @@ class CallbackManager(BaseCallbackManager):
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
+            metadata=self.metadata,
            **kwargs,
        )

@@ -1149,6 +1126,8 @@ class CallbackManager(BaseCallbackManager):
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
+            metadata=self.metadata,
+            inheritable_metadata=self.inheritable_metadata,
        )

    def on_tool_start(
@@ -1182,6 +1161,7 @@ class CallbackManager(BaseCallbackManager):
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
+            metadata=self.metadata,
            **kwargs,
        )

@@ -1192,10 +1172,13 @@ class CallbackManager(BaseCallbackManager):
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
+            metadata=self.metadata,
+            inheritable_metadata=self.inheritable_metadata,
        )

    def on_retriever_start(
        self,
+        serialized: Dict[str, Any],
        query: str,
        run_id: Optional[UUID] = None,
        parent_run_id: Optional[UUID] = None,
@@ -1209,10 +1192,12 @@ class CallbackManager(BaseCallbackManager):
            self.handlers,
            "on_retriever_start",
            "ignore_retriever",
+            serialized,
            query,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
+            metadata=self.metadata,
            **kwargs,
        )

@@ -1223,6 +1208,8 @@ class CallbackManager(BaseCallbackManager):
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
+            metadata=self.metadata,
+            inheritable_metadata=self.inheritable_metadata,
        )

    @classmethod
@@ -1233,6 +1220,8 @@ class CallbackManager(BaseCallbackManager):
        verbose: bool = False,
        inheritable_tags: Optional[List[str]] = None,
        local_tags: Optional[List[str]] = None,
+        inheritable_metadata: Optional[Dict[str, Any]] = None,
+        local_metadata: Optional[Dict[str, Any]] = None,
    ) -> CallbackManager:
        """Configure the callback manager.

@@ -1246,6 +1235,10 @@ class CallbackManager(BaseCallbackManager):
                Defaults to None.
            local_tags (Optional[List[str]], optional): The local tags.
                Defaults to None.
+            inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
+                metadata. Defaults to None.
+            local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
+                Defaults to None.

        Returns:
            CallbackManager: The configured callback manager.
@@ -1257,6 +1250,8 @@ class CallbackManager(BaseCallbackManager):
            verbose,
            inheritable_tags,
            local_tags,
+            inheritable_metadata,
+            local_metadata,
        )


@@ -1303,6 +1298,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                    run_id=run_id_,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
+                    metadata=self.metadata,
                    **kwargs,
                )
            )
@@ -1315,6 +1311,8 @@ class AsyncCallbackManager(BaseCallbackManager):
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
+                    metadata=self.metadata,
+                    inheritable_metadata=self.inheritable_metadata,
                )
            )

@@ -1356,6 +1354,7 @@ class AsyncCallbackManager(BaseCallbackManager):
                    run_id=run_id_,
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
+                    metadata=self.metadata,
                    **kwargs,
                )
            )
@@ -1368,6 +1367,8 @@ class AsyncCallbackManager(BaseCallbackManager):
                    parent_run_id=self.parent_run_id,
                    tags=self.tags,
                    inheritable_tags=self.inheritable_tags,
+                    metadata=self.metadata,
+                    inheritable_metadata=self.inheritable_metadata,
                )
            )

@@ -1404,6 +1405,7 @@ class AsyncCallbackManager(BaseCallbackManager):
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
+            metadata=self.metadata,
            **kwargs,
        )

@@ -1414,6 +1416,8 @@ class AsyncCallbackManager(BaseCallbackManager):
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
+            metadata=self.metadata,
+            inheritable_metadata=self.inheritable_metadata,
        )

    async def on_tool_start(
@@ -1449,6 +1453,7 @@ class AsyncCallbackManager(BaseCallbackManager):
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
+            metadata=self.metadata,
            **kwargs,
        )

@@ -1459,10 +1464,13 @@ class AsyncCallbackManager(BaseCallbackManager):
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
+            metadata=self.metadata,
+            inheritable_metadata=self.inheritable_metadata,
        )

    async def on_retriever_start(
        self,
+        serialized: Dict[str, Any],
        query: str,
        run_id: Optional[UUID] = None,
        parent_run_id: Optional[UUID] = None,
@@ -1476,10 +1484,12 @@ class AsyncCallbackManager(BaseCallbackManager):
            self.handlers,
            "on_retriever_start",
            "ignore_retriever",
+            serialized,
            query,
            run_id=run_id,
            parent_run_id=self.parent_run_id,
            tags=self.tags,
+            metadata=self.metadata,
            **kwargs,
        )

@@ -1490,6 +1500,8 @@ class AsyncCallbackManager(BaseCallbackManager):
            parent_run_id=self.parent_run_id,
            tags=self.tags,
            inheritable_tags=self.inheritable_tags,
+            metadata=self.metadata,
+            inheritable_metadata=self.inheritable_metadata,
        )

    @classmethod
@@ -1500,6 +1512,8 @@ class AsyncCallbackManager(BaseCallbackManager):
        verbose: bool = False,
        inheritable_tags: Optional[List[str]] = None,
        local_tags: Optional[List[str]] = None,
+        inheritable_metadata: Optional[Dict[str, Any]] = None,
+        local_metadata: Optional[Dict[str, Any]] = None,
    ) -> AsyncCallbackManager:
        """Configure the async callback manager.

@@ -1513,6 +1527,10 @@ class AsyncCallbackManager(BaseCallbackManager):
                Defaults to None.
            local_tags (Optional[List[str]], optional): The local tags.
                Defaults to None.
+            inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
+                metadata. Defaults to None.
+            local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
+                Defaults to None.

        Returns:
            AsyncCallbackManager: The configured async callback manager.
@@ -1524,6 +1542,8 @@ class AsyncCallbackManager(BaseCallbackManager):
            verbose,
            inheritable_tags,
            local_tags,
+            inheritable_metadata,
+            local_metadata,
        )


@@ -1554,6 +1574,8 @@ def _configure(
    verbose: bool = False,
    inheritable_tags: Optional[List[str]] = None,
    local_tags: Optional[List[str]] = None,
+    inheritable_metadata: Optional[Dict[str, Any]] = None,
+    local_metadata: Optional[Dict[str, Any]] = None,
 ) -> T:
    """Configure the callback manager.

@@ -1567,6 +1589,10 @@ def _configure(
        inheritable_tags (Optional[List[str]], optional): The inheritable tags.
            Defaults to None.
        local_tags (Optional[List[str]], optional): The local tags. Defaults to None.
+        inheritable_metadata (Optional[Dict[str, Any]], optional): The inheritable
+            metadata. Defaults to None.
+        local_metadata (Optional[Dict[str, Any]], optional): The local metadata.
+            Defaults to None.

    Returns:
        T: The configured callback manager.
@@ -1586,6 +1612,8 @@ def _configure(
                parent_run_id=inheritable_callbacks.parent_run_id,
                tags=inheritable_callbacks.tags,
                inheritable_tags=inheritable_callbacks.inheritable_tags,
+                metadata=inheritable_callbacks.metadata,
+                inheritable_metadata=inheritable_callbacks.inheritable_metadata,
            )
        local_handlers_ = (
            local_callbacks
@@ -1597,6 +1625,9 @@ def _configure(
    if inheritable_tags or local_tags:
        callback_manager.add_tags(inheritable_tags or [])
        callback_manager.add_tags(local_tags or [], False)
+    if inheritable_metadata or local_metadata:
+        callback_manager.add_metadata(inheritable_metadata or {})
+        callback_manager.add_metadata(local_metadata or {}, False)

    tracer = tracing_callback_var.get()
    wandb_tracer = wandb_tracing_callback_var.get()
--- a/langchain/callbacks/mlflow_callback.py
+++ b/langchain/callbacks/mlflow_callback.py
@@ -551,8 +551,18 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
        on_llm_start_records_df = pd.DataFrame(self.records["on_llm_start_records"])
        on_llm_end_records_df = pd.DataFrame(self.records["on_llm_end_records"])

+        llm_input_columns = ["step", "prompt"]
+        if "name" in on_llm_start_records_df.columns:
+            llm_input_columns.append("name")
+        elif "id" in on_llm_start_records_df.columns:
+            # id is llm class's full import path. For example:
+            # ["langchain", "llms", "openai", "AzureOpenAI"]
+            on_llm_start_records_df["name"] = on_llm_start_records_df["id"].apply(
+                lambda id_: id_[-1]
+            )
+            llm_input_columns.append("name")
        llm_input_prompts_df = (
-            on_llm_start_records_df[["step", "prompt", "name"]]
+            on_llm_start_records_df[llm_input_columns]
            .dropna(axis=1)
            .rename({"step": "prompt_step"}, axis=1)
        )
--- a/langchain/callbacks/stdout.py
+++ b/langchain/callbacks/stdout.py
@@ -63,7 +63,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
    ) -> Any:
        """Run on agent action."""
-        print_text(action.log, color=color if color else self.color)
+        print_text(action.log, color=color or self.color)

    def on_tool_end(
        self,
@@ -76,7 +76,7 @@ class StdOutCallbackHandler(BaseCallbackHandler):
        """If not the final action, print out observation."""
        if observation_prefix is not None:
            print_text(f"\n{observation_prefix}")
-        print_text(output, color=color if color else self.color)
+        print_text(output, color=color or self.color)
        if llm_prefix is not None:
            print_text(f"\n{llm_prefix}")

@@ -94,10 +94,10 @@ class StdOutCallbackHandler(BaseCallbackHandler):
        **kwargs: Any,
    ) -> None:
        """Run when agent ends."""
-        print_text(text, color=color if color else self.color, end=end)
+        print_text(text, color=color or self.color, end=end)

    def on_agent_finish(
        self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
    ) -> None:
        """Run on agent end."""
-        print_text(finish.log, color=color if self.color else color, end="\n")
+        print_text(finish.log, color=color or self.color, end="\n")
--- a/langchain/callbacks/streaming_aiter.py
+++ b/langchain/callbacks/streaming_aiter.py
@@ -31,7 +31,8 @@ class AsyncIteratorCallbackHandler(AsyncCallbackHandler):
        self.done.clear()

    async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        self.queue.put_nowait(token)
+        if token is not None and token != "":
+            self.queue.put_nowait(token)

    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        self.done.set()
--- a/langchain/callbacks/streaming_stdout_final_only.py
+++ b/langchain/callbacks/streaming_stdout_final_only.py
@@ -37,7 +37,7 @@ class FinalStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):
        """Instantiate FinalStreamingStdOutCallbackHandler.

        Args:
-            answer_prefix_tokens: Token sequence that prefixes the anwer.
+            answer_prefix_tokens: Token sequence that prefixes the answer.
                Default is ["Final", "Answer", ":"]
            strip_tokens: Ignore white spaces and new lines when comparing
                answer_prefix_tokens to last tokens? (to determine if answer has been
--- a/langchain/callbacks/streamlit/mutable_expander.py
+++ b/langchain/callbacks/streamlit/mutable_expander.py
@@ -9,11 +9,15 @@ if TYPE_CHECKING:


 class ChildType(Enum):
+    """The enumerator of the child type."""
+
    MARKDOWN = "MARKDOWN"
    EXCEPTION = "EXCEPTION"


 class ChildRecord(NamedTuple):
+    """The child record as a NamedTuple."""
+
    type: ChildType
    kwargs: Dict[str, Any]
    dg: DeltaGenerator
--- a/langchain/callbacks/streamlit/streamlit_callback_handler.py
+++ b/langchain/callbacks/streamlit/streamlit_callback_handler.py
@@ -27,6 +27,8 @@ EXCEPTION_EMOJI = "⚠️"


 class LLMThoughtState(Enum):
+    """Enumerator of the LLMThought state."""
+
    # The LLM is thinking about what to do next. We don't know which tool we'll run.
    THINKING = "THINKING"
    # The LLM has decided to run a tool. We don't have results from the tool yet.
@@ -36,6 +38,8 @@ class LLMThoughtState(Enum):


 class ToolRecord(NamedTuple):
+    """The tool record as a NamedTuple."""
+
    name: str
    input_str: str

@@ -100,6 +104,8 @@ class LLMThoughtLabeler:


 class LLMThought:
+    """A thought in the LLM's thought stream."""
+
    def __init__(
        self,
        parent_container: DeltaGenerator,
@@ -107,6 +113,14 @@ class LLMThought:
        expanded: bool,
        collapse_on_complete: bool,
    ):
+        """Initialize the LLMThought.
+
+        Args:
+            parent_container: The container we're writing into.
+            labeler: The labeler to use for this thought.
+            expanded: Whether the thought should be expanded by default.
+            collapse_on_complete: Whether the thought should be collapsed.
+        """
        self._container = MutableExpander(
            parent_container=parent_container,
            label=labeler.get_initial_label(),
@@ -213,6 +227,8 @@ class LLMThought:


 class StreamlitCallbackHandler(BaseCallbackHandler):
+    """A callback handler that writes to a Streamlit app."""
+
    def __init__(
        self,
        parent_container: DeltaGenerator,
--- a/langchain/callbacks/tracers/base.py
+++ b/langchain/callbacks/tracers/base.py
@@ -89,12 +89,15 @@ class BaseTracer(BaseCallbackHandler, ABC):
        run_id: UUID,
        tags: Optional[List[str]] = None,
        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Start a trace for an LLM run."""
        parent_run_id_ = str(parent_run_id) if parent_run_id else None
        execution_order = self._get_execution_order(parent_run_id_)
        start_time = datetime.utcnow()
+        if metadata:
+            kwargs.update({"metadata": metadata})
        llm_run = Run(
            id=run_id,
            parent_run_id=parent_run_id,
@@ -186,12 +189,15 @@ class BaseTracer(BaseCallbackHandler, ABC):
        run_id: UUID,
        tags: Optional[List[str]] = None,
        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Start a trace for a chain run."""
        parent_run_id_ = str(parent_run_id) if parent_run_id else None
        execution_order = self._get_execution_order(parent_run_id_)
        start_time = datetime.utcnow()
+        if metadata:
+            kwargs.update({"metadata": metadata})
        chain_run = Run(
            id=run_id,
            parent_run_id=parent_run_id,
@@ -253,12 +259,15 @@ class BaseTracer(BaseCallbackHandler, ABC):
        run_id: UUID,
        tags: Optional[List[str]] = None,
        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Start a trace for a tool run."""
        parent_run_id_ = str(parent_run_id) if parent_run_id else None
        execution_order = self._get_execution_order(parent_run_id_)
        start_time = datetime.utcnow()
+        if metadata:
+            kwargs.update({"metadata": metadata})
        tool_run = Run(
            id=run_id,
            parent_run_id=parent_run_id,
@@ -312,20 +321,25 @@ class BaseTracer(BaseCallbackHandler, ABC):

    def on_retriever_start(
        self,
+        serialized: Dict[str, Any],
        query: str,
        *,
        run_id: UUID,
        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Run when Retriever starts running."""
        parent_run_id_ = str(parent_run_id) if parent_run_id else None
        execution_order = self._get_execution_order(parent_run_id_)
        start_time = datetime.utcnow()
+        if metadata:
+            kwargs.update({"metadata": metadata})
        retrieval_run = Run(
            id=run_id,
            name="Retriever",
            parent_run_id=parent_run_id,
+            serialized=serialized,
            inputs={"query": query},
            extra=kwargs,
            events=[{"name": "start", "time": start_time}],
--- a/langchain/callbacks/tracers/evaluation.py
+++ b/langchain/callbacks/tracers/evaluation.py
@@ -6,6 +6,7 @@ from uuid import UUID

 from langchainplus_sdk import LangChainPlusClient, RunEvaluator

+from langchain.callbacks.manager import tracing_v2_enabled
 from langchain.callbacks.tracers.base import BaseTracer
 from langchain.callbacks.tracers.schemas import Run

@@ -27,6 +28,8 @@ class EvaluatorCallbackHandler(BaseTracer):
        If not specified, a new instance will be created.
    example_id : Union[UUID, str], optional
        The example ID to be associated with the runs.
+    project_name : str, optional
+        The LangSmith project name to be organize eval chain runs under.

    Attributes
    ----------
@@ -40,6 +43,11 @@ class EvaluatorCallbackHandler(BaseTracer):
        The thread pool executor used for running the evaluators.
    futures : Set[Future]
        The set of futures representing the running evaluators.
+    skip_unfinished : bool
+        Whether to skip runs that are not finished or raised
+        an error.
+    project_name : Optional[str]
+        The LangSmith project name to be organize eval chain runs under.
    """

    name = "evaluator_callback_handler"
@@ -50,6 +58,8 @@ class EvaluatorCallbackHandler(BaseTracer):
        max_workers: Optional[int] = None,
        client: Optional[LangChainPlusClient] = None,
        example_id: Optional[Union[UUID, str]] = None,
+        skip_unfinished: bool = True,
+        project_name: Optional[str] = None,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
@@ -62,10 +72,25 @@ class EvaluatorCallbackHandler(BaseTracer):
            max_workers=max(max_workers or len(evaluators), 1)
        )
        self.futures: Set[Future] = set()
+        self.skip_unfinished = skip_unfinished
+        self.project_name = project_name

-    def _evaluate_run(self, run: Run, evaluator: RunEvaluator) -> None:
+    def _evaluate_in_project(self, run: Run, evaluator: RunEvaluator) -> None:
+        """Evaluate the run in the project.
+
+        Parameters
+        ----------
+        run : Run
+            The run to be evaluated.
+        evaluator : RunEvaluator
+            The evaluator to use for evaluating the run.
+
+        """
        try:
-            self.client.evaluate_run(run, evaluator)
+            if self.project_name is None:
+                self.client.evaluate_run(run, evaluator)
+            with tracing_v2_enabled(project_name=self.project_name, tags=["eval"]):
+                self.client.evaluate_run(run, evaluator)
        except Exception as e:
            logger.error(
                f"Error evaluating run {run.id} with "
@@ -83,10 +108,15 @@ class EvaluatorCallbackHandler(BaseTracer):
            The run to be evaluated.

        """
+        if self.skip_unfinished and not run.outputs:
+            logger.debug(f"Skipping unfinished run {run.id}")
+            return
        run_ = run.copy()
        run_.reference_example_id = self.example_id
        for evaluator in self.evaluators:
-            self.futures.add(self.executor.submit(self._evaluate_run, run_, evaluator))
+            self.futures.add(
+                self.executor.submit(self._evaluate_in_project, run_, evaluator)
+            )

    def wait_for_futures(self) -> None:
        """Wait for all futures to complete."""
--- a/langchain/callbacks/tracers/langchain.py
+++ b/langchain/callbacks/tracers/langchain.py
@@ -31,6 +31,7 @@ def log_error_once(method: str, exception: Exception) -> None:


 def wait_for_all_tracers() -> None:
+    """Wait for all tracers to finish."""
    global _TRACERS
    for tracer in _TRACERS:
        tracer.wait_for_futures()
@@ -44,6 +45,7 @@ class LangChainTracer(BaseTracer):
        example_id: Optional[Union[UUID, str]] = None,
        project_name: Optional[str] = None,
        client: Optional[LangChainPlusClient] = None,
+        tags: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> None:
        """Initialize the LangChain tracer."""
@@ -59,6 +61,7 @@ class LangChainTracer(BaseTracer):
        self.executor = ThreadPoolExecutor(max_workers=1)
        self.client = client or LangChainPlusClient()
        self._futures: Set[Future] = set()
+        self.tags = tags or []
        global _TRACERS
        _TRACERS.append(self)

@@ -70,12 +73,15 @@ class LangChainTracer(BaseTracer):
        run_id: UUID,
        tags: Optional[List[str]] = None,
        parent_run_id: Optional[UUID] = None,
+        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> None:
        """Start a trace for an LLM run."""
        parent_run_id_ = str(parent_run_id) if parent_run_id else None
        execution_order = self._get_execution_order(parent_run_id_)
        start_time = datetime.utcnow()
+        if metadata:
+            kwargs.update({"metadata": metadata})
        chat_model_run = Run(
            id=run_id,
            parent_run_id=parent_run_id,
@@ -95,11 +101,16 @@ class LangChainTracer(BaseTracer):
    def _persist_run(self, run: Run) -> None:
        """The Langchain Tracer uses Post/Patch rather than persist."""

+    def _get_tags(self, run: Run) -> List[str]:
+        """Get combined tags for a run."""
+        tags = set(run.tags or [])
+        tags.update(self.tags or [])
+        return list(tags)
+
    def _persist_run_single(self, run: Run) -> None:
        """Persist a run."""
-        if run.parent_run_id is None:
-            run.reference_example_id = self.example_id
        run_dict = run.dict(exclude={"child_runs"})
+        run_dict["tags"] = self._get_tags(run)
        extra = run_dict.get("extra", {})
        extra["runtime"] = get_runtime_environment()
        run_dict["extra"] = extra
@@ -113,7 +124,9 @@ class LangChainTracer(BaseTracer):
    def _update_run_single(self, run: Run) -> None:
        """Update a run."""
        try:
-            self.client.update_run(run.id, **run.dict())
+            run_dict = run.dict()
+            run_dict["tags"] = self._get_tags(run)
+            self.client.update_run(run.id, **run_dict)
        except Exception as e:
            # Errors are swallowed by the thread executor so we need to log them here
            log_error_once("patch", e)
@@ -121,12 +134,16 @@ class LangChainTracer(BaseTracer):

    def _on_llm_start(self, run: Run) -> None:
        """Persist an LLM run."""
+        if run.parent_run_id is None:
+            run.reference_example_id = self.example_id
        self._futures.add(
            self.executor.submit(self._persist_run_single, run.copy(deep=True))
        )

    def _on_chat_model_start(self, run: Run) -> None:
        """Persist an LLM run."""
+        if run.parent_run_id is None:
+            run.reference_example_id = self.example_id
        self._futures.add(
            self.executor.submit(self._persist_run_single, run.copy(deep=True))
        )
@@ -145,6 +162,8 @@ class LangChainTracer(BaseTracer):

    def _on_chain_start(self, run: Run) -> None:
        """Process the Chain Run upon start."""
+        if run.parent_run_id is None:
+            run.reference_example_id = self.example_id
        self._futures.add(
            self.executor.submit(self._persist_run_single, run.copy(deep=True))
        )
@@ -163,6 +182,8 @@ class LangChainTracer(BaseTracer):

    def _on_tool_start(self, run: Run) -> None:
        """Process the Tool Run upon start."""
+        if run.parent_run_id is None:
+            run.reference_example_id = self.example_id
        self._futures.add(
            self.executor.submit(self._persist_run_single, run.copy(deep=True))
        )
@@ -181,6 +202,8 @@ class LangChainTracer(BaseTracer):

    def _on_retriever_start(self, run: Run) -> None:
        """Process the Retriever Run upon start."""
+        if run.parent_run_id is None:
+            run.reference_example_id = self.example_id
        self._futures.add(
            self.executor.submit(self._persist_run_single, run.copy(deep=True))
        )
--- a/Show More
+++ b/Show More