Delete deprecated run evaluator loaders

Switch to langsmith
docstrings document_loaders 3 (#6937 )
2026-04-21 19:27:58 +00:00 · 2023-07-10 10:52:19 -07:00 · 2023-07-10 10:49:37 -07:00 · 2023-07-10 08:56:53 -07:00 · 2023-07-10 08:39:47 -07:00 · 2023-07-10 07:04:29 -07:00
274 changed files with 8568 additions and 3312 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
@@ -645,6 +660,7 @@ Classes
    document_loaders.word_document.Docx2txtLoader
    document_loaders.word_document.UnstructuredWordDocumentLoader
    document_loaders.xml.UnstructuredXMLLoader
+    document_loaders.xorbits.XorbitsLoader
    document_loaders.youtube.GoogleApiYoutubeLoader
    document_loaders.youtube.YoutubeLoader

@@ -736,6 +752,7 @@ 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 +807,9 @@ 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 +819,16 @@ 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 +838,8 @@ 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 +1085,7 @@ 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 +1098,7 @@ 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 +1271,6 @@ Classes
    :toctree: prompts
    :template: class.rst

-    prompts.base.BasePromptTemplate
    prompts.base.StringPromptTemplate
    prompts.base.StringPromptValue
    prompts.chat.AIMessagePromptTemplate
@@ -1316,7 +1345,7 @@ 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 +1377,7 @@ 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 +1429,29 @@ 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 +1460,10 @@ 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 +1566,8 @@ 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 +1741,7 @@ 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 +1839,17 @@ 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/docs_skeleton/docs/_static/ChaindeskDashboard.png
+++ b/docs/docs_skeleton/docs/_static/ChaindeskDashboard.png
--- 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",
@@ -2125,4 +2133,4 @@
      "destination": "/docs/:path*"
    }
  ]
-}
+}
--- 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/datadog_logs.mdx
+++ b/docs/extras/ecosystem/integrations/datadog_logs.mdx
@@ -0,0 +1,19 @@
+# Datadog Logs
+
+>[Datadog](https://www.datadoghq.com/) is a monitoring and analytics platform for cloud-scale applications.
+
+## Installation and Setup
+
+```bash
+pip install datadog_api_client
+```
+
+We must initialize the loader with the Datadog API key and APP key, and we need to set up the query to extract the desired logs.
+
+## Document Loader
+
+See a [usage example](/docs/modules/data_connection/document_loaders/integrations/datadog_logs.html).
+
+```python
+from langchain.document_loaders import DatadogLogsLoader
+```
--- a/docs/extras/ecosystem/integrations/youtube.mdx
+++ b/docs/extras/ecosystem/integrations/youtube.mdx
@@ -1,6 +1,6 @@
 # YouTube

->[YouTube](https://www.youtube.com/) is an online video sharing and social media platform created by Google.
+>[YouTube](https://www.youtube.com/) is an online video sharing and social media platform by Google.
 > We download the `YouTube` transcripts and video information.

 ## Installation and Setup
--- a/docs/extras/modules/agents/toolkits/document_comparison_toolkit.ipynb
+++ b/docs/extras/modules/agents/toolkits/document_comparison_toolkit.ipynb
@@ -17,16 +17,7 @@
   "execution_count": 1,
   "id": "8632a37c",
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.5) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "from pydantic import BaseModel, Field\n",
    "\n",
--- 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/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/data_connection/document_loaders/integrations/datadog_logs.ipynb
+++ b/docs/extras/modules/data_connection/document_loaders/integrations/datadog_logs.ipynb
@@ -0,0 +1,96 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Datadog Logs\n",
+    "\n",
+    ">[Datadog](https://www.datadoghq.com/) is a monitoring and analytics platform for cloud-scale applications.\n",
+    "\n",
+    "This loader fetches the logs from your applications in Datadog using the `datadog_api_client` Python package. You must initialize the loader with your `Datadog API key` and `APP key`, and you need to pass in the query to extract the desired logs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import DatadogLogsLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install datadog-api-client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"service:agent status:error\"\n",
+    "\n",
+    "loader = DatadogLogsLoader(\n",
+    "    query=query,\n",
+    "    api_key=DD_API_KEY,\n",
+    "    app_key=DD_APP_KEY,\n",
+    "    from_time=1688732708951, # Optional, timestamp in milliseconds\n",
+    "    to_time=1688736308951, # Optional, timestamp in milliseconds\n",
+    "    limit=100, # Optional, default is 100\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='message: grep: /etc/datadog-agent/system-probe.yaml: No such file or directory', metadata={'id': 'AgAAAYkwpLImvkjRpQAAAAAAAAAYAAAAAEFZa3dwTUFsQUFEWmZfLU5QdElnM3dBWQAAACQAAAAAMDE4OTMwYTQtYzk3OS00MmJjLTlhNDAtOTY4N2EwY2I5ZDdk', 'status': 'error', 'service': 'agent', 'tags': ['accessible-from-goog-gke-node', 'allow-external-ingress-high-ports', 'allow-external-ingress-http', 'allow-external-ingress-https', 'container_id:c7d8ecd27b5b3cfdf3b0df04b8965af6f233f56b7c3c2ffabfab5e3b6ccbd6a5', 'container_name:lab_datadog_1', 'datadog.pipelines:false', 'datadog.submission_auth:private_api_key', 'docker_image:datadog/agent:7.41.1', 'env:dd101-dev', 'hostname:lab-host', 'image_name:datadog/agent', 'image_tag:7.41.1', 'instance-id:7497601202021312403', 'instance-type:custom-1-4096', 'instruqt_aws_accounts:', 'instruqt_azure_subscriptions:', 'instruqt_gcp_projects:', 'internal-hostname:lab-host.d4rjybavkary.svc.cluster.local', 'numeric_project_id:3390740675', 'p-d4rjybavkary', 'project:instruqt-prod', 'service:agent', 'short_image:agent', 'source:agent', 'zone:europe-west1-b'], 'timestamp': datetime.datetime(2023, 7, 7, 13, 57, 27, 206000, tzinfo=tzutc())}),\n",
+       " Document(page_content='message: grep: /etc/datadog-agent/system-probe.yaml: No such file or directory', metadata={'id': 'AgAAAYkwpLImvkjRpgAAAAAAAAAYAAAAAEFZa3dwTUFsQUFEWmZfLU5QdElnM3dBWgAAACQAAAAAMDE4OTMwYTQtYzk3OS00MmJjLTlhNDAtOTY4N2EwY2I5ZDdk', 'status': 'error', 'service': 'agent', 'tags': ['accessible-from-goog-gke-node', 'allow-external-ingress-high-ports', 'allow-external-ingress-http', 'allow-external-ingress-https', 'container_id:c7d8ecd27b5b3cfdf3b0df04b8965af6f233f56b7c3c2ffabfab5e3b6ccbd6a5', 'container_name:lab_datadog_1', 'datadog.pipelines:false', 'datadog.submission_auth:private_api_key', 'docker_image:datadog/agent:7.41.1', 'env:dd101-dev', 'hostname:lab-host', 'image_name:datadog/agent', 'image_tag:7.41.1', 'instance-id:7497601202021312403', 'instance-type:custom-1-4096', 'instruqt_aws_accounts:', 'instruqt_azure_subscriptions:', 'instruqt_gcp_projects:', 'internal-hostname:lab-host.d4rjybavkary.svc.cluster.local', 'numeric_project_id:3390740675', 'p-d4rjybavkary', 'project:instruqt-prod', 'service:agent', 'short_image:agent', 'source:agent', 'zone:europe-west1-b'], 'timestamp': datetime.datetime(2023, 7, 7, 13, 57, 27, 206000, tzinfo=tzutc())})]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents = loader.load()\n",
+    "documents"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.9.11"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/docs/extras/modules/data_connection/document_loaders/integrations/example_data/stanley-cups.tsv
+++ b/docs/extras/modules/data_connection/document_loaders/integrations/example_data/stanley-cups.tsv
@@ -0,0 +1,5 @@
+Stanley Cups		
+Team	Location	Stanley Cups
+Blues	STL	1
+Flyers	PHI	2
+Maple Leafs	TOR	13
--- a/docs/extras/modules/data_connection/document_loaders/integrations/tsv.ipynb
+++ b/docs/extras/modules/data_connection/document_loaders/integrations/tsv.ipynb
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TSV\n",
+    "\n",
+    ">A [tab-separated values (TSV)](https://en.wikipedia.org/wiki/Tab-separated_values) file is a simple, text-based file format for storing tabular data.[3] Records are separated by newlines, and values within a record are separated by tab characters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `UnstructuredTSVLoader`\n",
+    "\n",
+    "You can also load the table using the `UnstructuredTSVLoader`. One advantage of using `UnstructuredTSVLoader` is that if you use it in `\"elements\"` mode, an HTML representation of the table will be available in the metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.tsv import UnstructuredTSVLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = UnstructuredTSVLoader(\n",
+    "    file_path=\"example_data/mlb_teams_2012.csv\", mode=\"elements\"\n",
+    ")\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<table border=\"1\" class=\"dataframe\">\n",
+      "  <tbody>\n",
+      "    <tr>\n",
+      "      <td>Nationals,     81.34, 98</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Reds,          82.20, 97</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Yankees,      197.96, 95</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Giants,       117.62, 94</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Braves,        83.31, 94</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Athletics,     55.37, 94</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Rangers,      120.51, 93</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Orioles,       81.43, 93</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Rays,          64.17, 90</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Angels,       154.49, 89</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Tigers,       132.30, 88</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Cardinals,    110.30, 88</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Dodgers,       95.14, 86</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>White Sox,     96.92, 85</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Brewers,       97.65, 83</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Phillies,     174.54, 81</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Diamondbacks,  74.28, 81</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Pirates,       63.43, 79</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Padres,        55.24, 76</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Mariners,      81.97, 75</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Mets,          93.35, 74</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Blue Jays,     75.48, 73</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Royals,        60.91, 72</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Marlins,      118.07, 69</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Red Sox,      173.18, 69</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Indians,       78.43, 68</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Twins,         94.08, 66</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Rockies,       78.06, 64</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Cubs,          88.19, 61</td>\n",
+      "    </tr>\n",
+      "    <tr>\n",
+      "      <td>Astros,        60.65, 55</td>\n",
+      "    </tr>\n",
+      "  </tbody>\n",
+      "</table>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(docs[0].metadata[\"text_as_html\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/docs/extras/modules/data_connection/document_loaders/integrations/xorbits.ipynb
+++ b/docs/extras/modules/data_connection/document_loaders/integrations/xorbits.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Xorbits Pandas DataFrame\n",
+    "\n",
+    "This notebook goes over how to load data from a [xorbits.pandas](https://doc.xorbits.io/en/latest/reference/pandas/frame.html) DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install xorbits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xorbits.pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"example_data/mlb_teams_2012.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0d1d84e23c04f1296f63b3ea3dd1e5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          |   0.00/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Team</th>\n",
+       "      <th>\"Payroll (millions)\"</th>\n",
+       "      <th>\"Wins\"</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Nationals</td>\n",
+       "      <td>81.34</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Reds</td>\n",
+       "      <td>82.20</td>\n",
+       "      <td>97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Yankees</td>\n",
+       "      <td>197.96</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Giants</td>\n",
+       "      <td>117.62</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Braves</td>\n",
+       "      <td>83.31</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Team   \"Payroll (millions)\"   \"Wins\"\n",
+       "0  Nationals                  81.34       98\n",
+       "1       Reds                  82.20       97\n",
+       "2    Yankees                 197.96       95\n",
+       "3     Giants                 117.62       94\n",
+       "4     Braves                  83.31       94"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import XorbitsLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = XorbitsLoader(df, page_content_column=\"Team\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8c8b67f1aae4a3c9de7734bb6cf738e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          |   0.00/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Nationals', metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}),\n",
+       " Document(page_content='Reds', metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}),\n",
+       " Document(page_content='Yankees', metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}),\n",
+       " Document(page_content='Giants', metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}),\n",
+       " Document(page_content='Braves', metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}),\n",
+       " Document(page_content='Athletics', metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}),\n",
+       " Document(page_content='Rangers', metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}),\n",
+       " Document(page_content='Orioles', metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}),\n",
+       " Document(page_content='Rays', metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}),\n",
+       " Document(page_content='Angels', metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}),\n",
+       " Document(page_content='Tigers', metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}),\n",
+       " Document(page_content='Cardinals', metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}),\n",
+       " Document(page_content='Dodgers', metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}),\n",
+       " Document(page_content='White Sox', metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}),\n",
+       " Document(page_content='Brewers', metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}),\n",
+       " Document(page_content='Phillies', metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}),\n",
+       " Document(page_content='Diamondbacks', metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}),\n",
+       " Document(page_content='Pirates', metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}),\n",
+       " Document(page_content='Padres', metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}),\n",
+       " Document(page_content='Mariners', metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}),\n",
+       " Document(page_content='Mets', metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}),\n",
+       " Document(page_content='Blue Jays', metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}),\n",
+       " Document(page_content='Royals', metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}),\n",
+       " Document(page_content='Marlins', metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}),\n",
+       " Document(page_content='Red Sox', metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}),\n",
+       " Document(page_content='Indians', metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}),\n",
+       " Document(page_content='Twins', metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}),\n",
+       " Document(page_content='Rockies', metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}),\n",
+       " Document(page_content='Cubs', metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}),\n",
+       " Document(page_content='Astros', metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55})]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc85c9f59b3644689d05853159fbd358",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          |   0.00/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='Nationals' metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}\n",
+      "page_content='Reds' metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}\n",
+      "page_content='Yankees' metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}\n",
+      "page_content='Giants' metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}\n",
+      "page_content='Braves' metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}\n",
+      "page_content='Athletics' metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}\n",
+      "page_content='Rangers' metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}\n",
+      "page_content='Orioles' metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}\n",
+      "page_content='Rays' metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}\n",
+      "page_content='Angels' metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}\n",
+      "page_content='Tigers' metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}\n",
+      "page_content='Cardinals' metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}\n",
+      "page_content='Dodgers' metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}\n",
+      "page_content='White Sox' metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}\n",
+      "page_content='Brewers' metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}\n",
+      "page_content='Phillies' metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}\n",
+      "page_content='Diamondbacks' metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}\n",
+      "page_content='Pirates' metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}\n",
+      "page_content='Padres' metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}\n",
+      "page_content='Mariners' metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}\n",
+      "page_content='Mets' metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}\n",
+      "page_content='Blue Jays' metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}\n",
+      "page_content='Royals' metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}\n",
+      "page_content='Marlins' metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}\n",
+      "page_content='Red Sox' metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}\n",
+      "page_content='Indians' metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}\n",
+      "page_content='Twins' metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}\n",
+      "page_content='Rockies' metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}\n",
+      "page_content='Cubs' metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}\n",
+      "page_content='Astros' metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use lazy load for larger table, which won't read the full table into memory\n",
+    "for i in loader.lazy_load():\n",
+    "    print(i)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.13"
+  },
+  "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/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/vectorstores/integrations/chroma.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/chroma.ipynb
@@ -146,11 +146,11 @@
    "# save to disk\n",
    "db2 = Chroma.from_documents(docs, embedding_function, persist_directory=\"./chroma_db\")\n",
    "db2.persist()\n",
-    "docs = db.similarity_search(query)\n",
+    "docs = db2.similarity_search(query)\n",
    "\n",
    "# load from disk\n",
-    "db3 = Chroma(persist_directory=\"./chroma_db\")\n",
-    "docs = db.similarity_search(query)\n",
+    "db3 = Chroma(persist_directory=\"./chroma_db\", embedding_function=embedding_function)\n",
+    "docs = db3.similarity_search(query)\n",
    "print(docs[0].page_content)"
   ]
  },
--- a/docs/extras/modules/data_connection/vectorstores/integrations/deeplake.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/deeplake.ipynb
@@ -1,14 +1,15 @@
 {
 "cells": [
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "#  Deep Lake\n",
+    "#  Activeloop's Deep Lake\n",
    "\n",
-    ">[Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n",
+    ">[Activeloop's Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n",
    "\n",
-    "This notebook showcases basic functionality related to `Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming dataloader to deep learning frameworks.  \n",
+    "This notebook showcases basic functionality related to `Activeloop's Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks.  \n",
    "\n",
    "For more information, please see the Deep Lake [documentation](https://docs.activeloop.ai) or [api reference](https://docs.deeplake.ai)"
   ]
@@ -16,12 +17,10 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
   "outputs": [],
   "source": [
-    "!pip install openai deeplake tiktoken"
+    "!pip install openai 'deeplake[enterprise]' tiktoken"
   ]
  },
  {
@@ -61,7 +60,7 @@
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "\n",
-    "loader = TextLoader(\"docs/modules/state_of_the_union.txt\")\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)\n",
@@ -70,6 +69,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -78,31 +78,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor      htype      shape      dtype  compression\n",
-      "  -------    -------    -------    -------  ------- \n",
-      " embedding  embedding  (42, 1536)  float32   None   \n",
-      "    id        text      (42, 1)      str     None   \n",
-      " metadata     json      (42, 1)      str     None   \n",
-      "   text       text      (42, 1)      str     None   \n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "db = DeepLake(\n",
    "    dataset_path=\"./my_deeplake/\", embedding_function=embeddings, overwrite=True\n",
@@ -116,30 +94,15 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "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"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "print(docs[0].page_content)"
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -148,19 +111,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storage\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "db = DeepLake(\n",
    "    dataset_path=\"./my_deeplake/\", embedding_function=embeddings, read_only=True\n",
@@ -169,6 +122,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -176,6 +130,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -184,20 +139,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/adilkhansarsen/Documents/work/LangChain/langchain/langchain/llms/openai.py:751: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "from langchain.chains import RetrievalQA\n",
    "from langchain.llms import OpenAIChat\n",
@@ -211,28 +155,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The President nominated Ketanji Brown Jackson to serve on the United States Supreme Court and spoke highly of her legal expertise and reputation as a consensus builder.'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "qa.run(query)"
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -240,35 +172,18 @@
   ]
  },
  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "attachments": {},
+   "cell_type": "markdown",
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor      htype      shape     dtype  compression\n",
-      "  -------    -------    -------   -------  ------- \n",
-      " embedding  embedding  (4, 1536)  float32   None   \n",
-      "    id        text      (4, 1)      str     None   \n",
-      " metadata     json      (4, 1)      str     None   \n",
-      "   text       text      (4, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    }
-   ],
+   "source": [
+    "Let's create another vector store containing metadata with the year the documents were created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "import random\n",
    "\n",
@@ -282,29 +197,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 3300.00it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(lc_kwargs={'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': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, 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': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'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': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, 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': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'page_content': 'Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', 'metadata': {'source': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': 'docs/modules/state_of_the_union.txt', 'year': 2013})]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
   "source": [
    "db.similarity_search(\n",
    "    \"What did the president say about Ketanji Brown Jackson\",\n",
@@ -313,6 +208,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -322,23 +218,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(lc_kwargs={'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': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, 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': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'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': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, 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': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'page_content': 'Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', 'metadata': {'source': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'page_content': 'And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', 'metadata': {'source': 'docs/modules/state_of_the_union.txt', 'year': 2012}}, page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': 'docs/modules/state_of_the_union.txt', 'year': 2012})]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
   "source": [
    "db.similarity_search(\n",
    "    \"What did the president say about Ketanji Brown Jackson?\", distance_metric=\"cos\"\n",
@@ -346,6 +228,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -355,23 +238,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(lc_kwargs={'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': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, 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': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'page_content': 'Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', 'metadata': {'source': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave.  \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'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': 'docs/modules/state_of_the_union.txt', 'year': 2013}}, 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': 'docs/modules/state_of_the_union.txt', 'year': 2013}),\n",
-       " Document(lc_kwargs={'page_content': 'And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', 'metadata': {'source': 'docs/modules/state_of_the_union.txt', 'year': 2012}}, page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \\n\\nFirst, beat the opioid epidemic.', metadata={'source': 'docs/modules/state_of_the_union.txt', 'year': 2012})]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
   "source": [
    "db.max_marginal_relevance_search(\n",
    "    \"What did the president say about Ketanji Brown Jackson?\"\n",
@@ -379,6 +248,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -401,6 +271,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -423,11 +294,12 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory\n",
-    "By default deep lake datasets are stored locally, in case you want to store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the [corresponding path to the dataset](https://docs.activeloop.ai/storage-and-credentials/storage-options). You can retrieve your user token from [app.activeloop.ai](https://app.activeloop.ai/)"
+    "By default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the [corresponding path and credentials when creating the vector store](https://docs.activeloop.ai/storage-and-credentials/storage-options). Some paths require registration with Activeloop and creation of an API token that can be [retrieved here](https://app.activeloop.ai/)"
   ]
  },
  {
@@ -439,106 +311,11 @@
    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
   ]
  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Deeplake now supports running the inference in 3 modes. `python` naive way of searching inside of the data, `tensor_db` which is managed database, it runs tql on a remote optimized engine and sends results back, and `compute_engine` which is C++ implementation of search that runs locally."
-   ]
-  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Your Deep Lake dataset has been successfully created!\n",
-      "The dataset is private so make sure you are logged in!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor      htype      shape      dtype  compression\n",
-      "  -------    -------    -------    -------  ------- \n",
-      " embedding  embedding  (42, 1536)  float32   None   \n",
-      "    id        text      (42, 1)      str     None   \n",
-      " metadata     json      (42, 1)      str     None   \n",
-      "   text       text      (42, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['d604b1ac-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b238-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b260-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b27e-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b29c-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b2ba-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b2d8-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b2f6-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b314-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b332-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b350-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b36e-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b38c-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b3a0-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b3be-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b3dc-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b3fa-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b418-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b436-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b454-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b472-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b490-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b4a4-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b4c2-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b4e0-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b4fe-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b51c-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b53a-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b558-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b576-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b594-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b5b2-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b5c6-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b5e4-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b602-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b620-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b63e-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b65c-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b67a-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b698-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b6b6-093c-11ee-bdba-76d8a30504e0',\n",
-       " 'd604b6d4-093c-11ee-bdba-76d8a30504e0']"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Embed and store the texts\n",
    "username = \"<username>\"  # your username on app.activeloop.ai\n",
@@ -553,23 +330,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
   "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"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
    "docs = db.similarity_search(query)\n",
@@ -577,102 +340,30 @@
   ]
  },
  {
-   "cell_type": "code",
-   "execution_count": 20,
+   "attachments": {},
+   "cell_type": "markdown",
   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Your Deep Lake dataset has been successfully created!\n",
-      "The dataset is private so make sure you are logged in!\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "|"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor      htype      shape      dtype  compression\n",
-      "  -------    -------    -------    -------  ------- \n",
-      " embedding  embedding  (42, 1536)  float32   None   \n",
-      "    id        text      (42, 1)      str     None   \n",
-      " metadata     json      (42, 1)      str     None   \n",
-      "   text       text      (42, 1)      str     None   \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " \r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['6584c33a-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c3ee-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c420-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c43e-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c466-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c484-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c4a2-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c4c0-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c4de-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c4fc-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c51a-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c538-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c556-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c574-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c592-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c5b0-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c5ce-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c5f6-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c614-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c632-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c646-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c66e-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c682-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c6a0-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c6be-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c6e6-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c704-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c722-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c740-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c75e-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c77c-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c79a-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c7ae-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c7cc-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c7ea-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c808-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c826-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c844-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c862-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c876-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c894-093d-11ee-bdba-76d8a30504e0',\n",
-       " '6584c8bc-093d-11ee-bdba-76d8a30504e0']"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "source": [
+    "#### `tensor_db` execution option "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to utilize Deep Lake's Managed Tensor Database, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
   "source": [
    "# Embed and store the texts\n",
    "username = \"adilkhan\"  # your username on app.activeloop.ai\n",
-    "dataset_path = f\"hub://{username}/langchain_testing\"  # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
+    "dataset_path = f\"hub://{username}/langchain_testing\"\n",
    "\n",
    "docs = text_splitter.split_documents(documents)\n",
    "\n",
@@ -681,44 +372,13 @@
    "    dataset_path=dataset_path,\n",
    "    embedding_function=embeddings,\n",
    "    overwrite=True,\n",
-    "    exec_option=\"tensor_db\",\n",
+    "    runtime={\"tensor_db\": True},\n",
    ")\n",
    "db.add_documents(docs)"
   ]
  },
  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "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": [
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = db.similarity_search(query, exec_option=\"tensor_db\")\n",
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### The difference will be apparent on a bigger datasets (~10000 rows)"
-   ]
-  },
-  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -726,15 +386,16 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "now we can use tql search with DeepLake"
+    "Furthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL)."
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -743,42 +404,31 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = db.similarity_search(\n",
    "    query=None,\n",
    "    tql_query=f\"SELECT * WHERE id == '{search_id[0]}'\",\n",
-    "    exec_option=\"tensor_db\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(lc_kwargs={'page_content': 'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', 'metadata': {'source': 'docs/modules/state_of_the_union.txt'}}, page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \\n\\nLast year COVID-19 kept us apart. This year we are finally together again. \\n\\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \\n\\nWith a duty to one another to the American people to the Constitution. \\n\\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \\n\\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \\n\\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \\n\\nHe met the Ukrainian people. \\n\\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': 'docs/modules/state_of_the_union.txt'})]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
   "source": [
    "docs"
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "### Creating dataset on AWS S3"
+    "### Creating vector stores on AWS S3"
   ]
  },
  {
@@ -841,11 +491,12 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Lake API\n",
-    "you can access the Deep Lake  dataset at `db.ds`"
+    "you can access the Deep Lake  dataset at `db.vectorstore`"
   ]
  },
  {
@@ -884,6 +535,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
--- a/docs/extras/modules/data_connection/vectorstores/integrations/mongodb_atlas.ipynb
+++ b/docs/extras/modules/data_connection/vectorstores/integrations/mongodb_atlas.ipynb
@@ -1,226 +1,243 @@
 {
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "683953b3",
-   "metadata": {},
-   "source": [
-    "# MongoDB Atlas\n",
-    "\n",
-    ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS , Azure, and GCP.  It now has support for native Vector Search on your MongoDB document data.\n",
-    "\n",
-    "This notebook shows how to use `MongoDB Atlas Vector Search` to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm.\n",
-    "\n",
-    "It uses the [knnBeta Operator](https://www.mongodb.com/docs/atlas/atlas-search/knn-beta) available in MongoDB Atlas Search. This feature is in Public Preview and available for evaluation purposes, to validate functionality, and to gather feedback from public preview users. It is not recommended for production deployments as we may introduce breaking changes.\n",
-    "\n",
-    "To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. \n",
-    "To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4c41cad-08ef-4f72-a545-2151e4598efe",
-   "metadata": {
-    "tags": []
+   "cells":[
+      {
+         "attachments":{
+            
+         },
+         "cell_type":"markdown",
+         "id":"683953b3",
+         "metadata":{
+            
+         },
+         "source":[
+            "# MongoDB Atlas\n",
+            "\n",
+            ">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a fully-managed cloud database available in AWS , Azure, and GCP.  It now has support for native Vector Search on your MongoDB document data.\n",
+            "\n",
+            "This notebook shows how to use `MongoDB Atlas Vector Search` to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm.\n",
+            "\n",
+            "It uses the [knnBeta Operator](https://www.mongodb.com/docs/atlas/atlas-search/knn-beta) available in MongoDB Atlas Search. This feature is in Public Preview and available for evaluation purposes, to validate functionality, and to gather feedback from public preview users. It is not recommended for production deployments as we may introduce breaking changes.\n",
+            "\n",
+            "To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. \n",
+            "To get started head over to Atlas here: [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "id":"b4c41cad-08ef-4f72-a545-2151e4598efe",
+         "metadata":{
+            "tags":[
+               
+            ]
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "!pip install pymongo"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "id":"c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87",
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "import os\n",
+            "import getpass\n",
+            "\n",
+            "MONGODB_ATLAS_CLUSTER_URI = getpass.getpass(\"MongoDB Atlas Cluster URI:\")\n"
+         ]
+      },
+      {
+         "attachments":{
+            
+         },
+         "cell_type":"markdown",
+         "id":"457ace44-1d95-4001-9dd5-78811ab208ad",
+         "metadata":{
+            
+         },
+         "source":[
+            "We want to use `OpenAIEmbeddings` so we need to set up our OpenAI API Key. "
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "id":"2d8f240d",
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n"
+         ]
+      },
+      {
+         "attachments":{
+            
+         },
+         "cell_type":"markdown",
+         "id":"1f3ecc42",
+         "metadata":{
+            
+         },
+         "source":[
+            "Now, let's create a vector search index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/define-field-mappings-for-vector-search) to get more details on how to define an Atlas Vector Search index.\n",
+            "You can name the index `langchain_demo` and create the index on the namespace `lanchain_db.langchain_col`. Finally, write the following definition in the JSON editor on MongoDB Atlas:\n",
+            "\n",
+            "```json\n",
+            "{\n",
+            "  \"mappings\": {\n",
+            "    \"dynamic\": true,\n",
+            "    \"fields\": {\n",
+            "      \"embedding\": {\n",
+            "        \"dimensions\": 1536,\n",
+            "        \"similarity\": \"cosine\",\n",
+            "        \"type\": \"knnVector\"\n",
+            "      }\n",
+            "    }\n",
+            "  }\n",
+            "}\n",
+            "```"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":2,
+         "id":"aac9563e",
+         "metadata":{
+            "tags":[
+               
+            ]
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+            "from langchain.text_splitter import CharacterTextSplitter\n",
+            "from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
+            "from langchain.document_loaders import TextLoader\n",
+            "\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)\n",
+            "\n",
+            "embeddings = OpenAIEmbeddings()"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "id":"6e104aee",
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "from pymongo import MongoClient\n",
+            "\n",
+            "# initialize MongoDB python client\n",
+            "client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)\n",
+            "\n",
+            "db_name = \"langchain_db\"\n",
+            "collection_name = \"langchain_col\"\n",
+            "collection = client[db_name][collection_name]\n",
+            "index_name = \"langchain_demo\"\n",
+            "\n",
+            "# insert the documents in MongoDB Atlas with their embedding\n",
+            "docsearch = MongoDBAtlasVectorSearch.from_documents(\n",
+            "    docs, embeddings, collection=collection, index_name=index_name\n",
+            ")\n",
+            "\n",
+            "# perform a similarity search between the embedding of the query and the embeddings of the documents\n",
+            "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+            "docs = docsearch.similarity_search(query)"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "id":"9c608226",
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "print(docs[0].page_content)"
+         ]
+      },
+      {
+         "attachments":{
+            
+         },
+         "cell_type":"markdown",
+         "id":"851a2ec9-9390-49a4-8412-3e132c9f789d",
+         "metadata":{
+            
+         },
+         "source":[
+            "You can also instantiate the vector store directly and execute a query as follows:"
+         ]
+      },
+      {
+         "cell_type":"code",
+         "execution_count":null,
+         "id":"6336fe79-3e73-48be-b20a-0ff1bb6a4399",
+         "metadata":{
+            
+         },
+         "outputs":[
+            
+         ],
+         "source":[
+            "# initialize vector store\n",
+            "vectorstore = MongoDBAtlasVectorSearch(\n",
+            "    collection, OpenAIEmbeddings(), index_name=index_name\n",
+            ")\n",
+            "\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",
+            "print(docs[0].page_content)"
+         ]
+      }
+   ],
+   "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":"3.10.6"
+      }
   },
-   "outputs": [],
-   "source": [
-    "!pip install pymongo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import getpass\n",
-    "\n",
-    "MONGODB_ATLAS_CLUSTER_URI = getpass.getpass(\"MongoDB Atlas Cluster URI:\")\n",
-    "MONGODB_ATLAS_CLUSTER_URI = os.environ[\"MONGODB_ATLAS_CLUSTER_URI\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "457ace44-1d95-4001-9dd5-78811ab208ad",
-   "metadata": {},
-   "source": [
-    "We want to use `OpenAIEmbeddings` so we need to set up our OpenAI API Key. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2d8f240d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "OPENAI_API_KEY = os.environ[\"OPENAI_API_KEY\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "1f3ecc42",
-   "metadata": {},
-   "source": [
-    "Now, let's create a vector search index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/define-field-mappings-for-vector-search) to get more details on how to define an Atlas Vector Search index.\n",
-    "You can name the index `langchain_demo` and create the index on the namespace `lanchain_db.langchain_col`. Finally, write the following definition in the JSON editor on MongoDB Atlas:\n",
-    "\n",
-    "```json\n",
-    "{\n",
-    "  \"mappings\": {\n",
-    "    \"dynamic\": true,\n",
-    "    \"fields\": {\n",
-    "      \"embedding\": {\n",
-    "        \"dimensions\": 1536,\n",
-    "        \"similarity\": \"cosine\",\n",
-    "        \"type\": \"knnVector\"\n",
-    "      }\n",
-    "    }\n",
-    "  }\n",
-    "}\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "aac9563e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import MongoDBAtlasVectorSearch\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",
-    "\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)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e104aee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pymongo import MongoClient\n",
-    "\n",
-    "# initialize MongoDB python client\n",
-    "client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)\n",
-    "\n",
-    "db_name = \"langchain_db\"\n",
-    "collection_name = \"langchain_col\"\n",
-    "collection = client[db_name][collection_name]\n",
-    "index_name = \"langchain_demo\"\n",
-    "\n",
-    "# insert the documents in MongoDB Atlas with their embedding\n",
-    "docsearch = MongoDBAtlasVectorSearch.from_documents(\n",
-    "    docs, embeddings, collection=collection, index_name=index_name\n",
-    ")\n",
-    "\n",
-    "# perform a similarity search between the embedding of the query and the embeddings of the documents\n",
-    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
-    "docs = docsearch.similarity_search(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9c608226",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(docs[0].page_content)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "851a2ec9-9390-49a4-8412-3e132c9f789d",
-   "metadata": {},
-   "source": [
-    "You can reuse the vector search index you created, make sure the `OPENAI_API_KEY` environment variable is set up, then execute another query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6336fe79-3e73-48be-b20a-0ff1bb6a4399",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pymongo import MongoClient\n",
-    "from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "import os\n",
-    "\n",
-    "MONGODB_ATLAS_URI = os.environ[\"MONGODB_ATLAS_URI\"]\n",
-    "\n",
-    "# initialize MongoDB python client\n",
-    "client = MongoClient(MONGODB_ATLAS_URI)\n",
-    "\n",
-    "db_name = \"langchain_db\"\n",
-    "collection_name = \"langchain_col\"\n",
-    "collection = client[db_name][collection_name]\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 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",
-    "print(docs[0].page_content)"
-   ]
-  }
- ],
- "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+   "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/memory/integrations/zep_memory.ipynb
+++ b/docs/extras/modules/memory/integrations/zep_memory.ipynb
@@ -40,12 +40,15 @@
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
-    "is_executing": true
+    "ExecuteTime": {
+     "end_time": "2023-07-09T19:20:49.003167Z",
+     "start_time": "2023-07-09T19:20:47.446370Z"
+    }
   },
   "outputs": [],
   "source": [
-    "from langchain.memory.chat_message_histories import ZepChatMessageHistory\n",
-    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.memory import ZepMemory\n",
+    "from langchain.retrievers import ZepRetriever\n",
    "from langchain import OpenAI\n",
    "from langchain.schema import HumanMessage, AIMessage\n",
    "from langchain.utilities import WikipediaAPIWrapper\n",
@@ -64,19 +67,11 @@
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
-     "end_time": "2023-05-25T15:09:41.762056Z",
-     "start_time": "2023-05-25T15:09:41.755238Z"
+     "end_time": "2023-07-09T19:23:14.378234Z",
+     "start_time": "2023-07-09T19:20:49.005041Z"
    }
   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "# Provide your OpenAI key\n",
    "import getpass\n",
@@ -87,16 +82,13 @@
  {
   "cell_type": "code",
   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-07-09T19:23:16.329934Z",
+     "start_time": "2023-07-09T19:23:14.345580Z"
    }
-   ],
+   },
+   "outputs": [],
   "source": [
    "# Provide your Zep API key. Note that this is optional. See https://docs.getzep.com/deployment/auth\n",
    "\n",
@@ -116,8 +108,8 @@
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
-     "end_time": "2023-05-25T15:09:41.840440Z",
-     "start_time": "2023-05-25T15:09:41.762277Z"
+     "end_time": "2023-07-09T19:23:16.528212Z",
+     "start_time": "2023-07-09T19:23:16.279045Z"
    }
   },
   "outputs": [],
@@ -132,15 +124,11 @@
    "]\n",
    "\n",
    "# Set up Zep Chat History\n",
-    "zep_chat_history = ZepChatMessageHistory(\n",
+    "memory = ZepMemory(\n",
    "    session_id=session_id,\n",
    "    url=ZEP_API_URL,\n",
-    "    api_key=zep_api_key\n",
-    ")\n",
-    "\n",
-    "# Use a standard ConversationBufferMemory to encapsulate the Zep chat history\n",
-    "memory = ConversationBufferMemory(\n",
-    "    memory_key=\"chat_history\", chat_memory=zep_chat_history\n",
+    "    api_key=zep_api_key,\n",
+    "    memory_key=\"chat_history\",\n",
    ")\n",
    "\n",
    "# Initialize the agent\n",
@@ -167,8 +155,8 @@
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
-     "end_time": "2023-05-25T15:09:41.960661Z",
-     "start_time": "2023-05-25T15:09:41.842656Z"
+     "end_time": "2023-07-09T19:23:16.659484Z",
+     "start_time": "2023-07-09T19:23:16.532090Z"
    }
   },
   "outputs": [],
@@ -230,14 +218,16 @@
    "            \" living in a dystopian future where society has collapsed due to\"\n",
    "            \" environmental disasters, poverty, and violence.\"\n",
    "        ),\n",
+    "        \"metadata\": {\"foo\": \"bar\"},\n",
    "    },\n",
    "]\n",
    "\n",
    "for msg in test_history:\n",
-    "    zep_chat_history.add_message(\n",
+    "    memory.chat_memory.add_message(\n",
    "        HumanMessage(content=msg[\"content\"])\n",
    "        if msg[\"role\"] == \"human\"\n",
-    "        else AIMessage(content=msg[\"content\"])\n",
+    "        else AIMessage(content=msg[\"content\"]),\n",
+    "        metadata=msg.get(\"metadata\", {}),\n",
    "    )"
   ]
  },
@@ -256,8 +246,8 @@
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
-     "end_time": "2023-05-25T15:09:50.485377Z",
-     "start_time": "2023-05-25T15:09:41.962287Z"
+     "end_time": "2023-07-09T19:23:19.348822Z",
+     "start_time": "2023-07-09T19:23:16.660130Z"
    }
   },
   "outputs": [
@@ -269,16 +259,14 @@
      "\n",
      "\u001B[1m> Entering new  chain...\u001B[0m\n",
      "\u001B[32;1m\u001B[1;3mThought: Do I need to use a tool? No\n",
-      "AI: Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.\u001B[0m\n",
+      "AI: Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, inequality, and violence. It is a cautionary tale that warns of the dangers of unchecked greed and the need for individuals to take responsibility for their own lives and the lives of those around them.\u001B[0m\n",
      "\n",
      "\u001B[1m> Finished chain.\u001B[0m\n"
     ]
    },
    {
     "data": {
-      "text/plain": [
-       "'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.'"
-      ]
+      "text/plain": "'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, inequality, and violence. It is a cautionary tale that warns of the dangers of unchecked greed and the need for individuals to take responsibility for their own lives and the lives of those around them.'"
     },
     "execution_count": 6,
     "metadata": {},
@@ -287,7 +275,7 @@
   ],
   "source": [
    "agent_chain.run(\n",
-    "    input=\"WWhat is the book's relevance to the challenges facing contemporary society?\"\n",
+    "    input=\"What is the book's relevance to the challenges facing contemporary society?\",\n",
    ")"
   ]
  },
@@ -305,11 +293,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
-     "end_time": "2023-05-25T15:09:50.493438Z",
-     "start_time": "2023-05-25T15:09:50.479230Z"
+     "end_time": "2023-07-09T19:23:41.042254Z",
+     "start_time": "2023-07-09T19:23:41.016815Z"
    }
   },
   "outputs": [
@@ -317,29 +305,39 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "The human asks about Octavia Butler and the AI identifies her as an American science fiction author. They continue to discuss her works and the fact that the FX series Kindred is based on one of her novels. The AI also lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ as Butler's contemporaries.\n",
+      "The human inquires about Octavia Butler. The AI identifies her as an American science fiction author. The human then asks which books of hers were made into movies. The AI responds by mentioning the FX series Kindred, based on her novel of the same name. The human then asks about her contemporaries, and the AI lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\n",
      "\n",
      "\n",
-      "{'role': 'human', 'content': 'What awards did she win?', 'uuid': 'a4bdc592-71a5-47d0-9c64-230b882aab48', 'created_at': '2023-06-26T23:37:56.383953Z', 'token_count': 8, 'metadata': {'system': {'entities': [], 'intent': 'The subject is asking about the awards someone won, likely referring to a specific individual.'}}}\n",
-      "{'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'uuid': '60cc6e6b-7cd4-4a81-aebc-72ef997286b4', 'created_at': '2023-06-26T23:37:56.389935Z', 'token_count': 21, 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating the accomplishments and awards received by Octavia Butler.'}}}\n",
-      "{'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'uuid': 'b189fc60-1510-4a4b-a503-899481d652de', 'created_at': '2023-06-26T23:37:56.395722Z', 'token_count': 14, 'metadata': {'system': {'entities': [], 'intent': 'The subject is looking for recommendations on women science fiction writers to read.'}}}\n",
-      "{'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'uuid': '4be1ccbb-a915-45d6-9f18-7a0c1cbd9907', 'created_at': '2023-06-26T23:37:56.403596Z', 'token_count': 18, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting reading material and making a literary recommendation.'}}}\n",
-      "{'role': 'human', 'content': \"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", 'uuid': 'ac3c5e3e-26a7-4f3b-aeb0-bba084e22753', 'created_at': '2023-06-26T23:37:56.410662Z', 'token_count': 23, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}], 'intent': 'The subject is asking for a brief overview or summary of the book \"Parable of the Sower\" written by Butler.'}}}\n",
-      "{'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'uuid': '4a463b4c-bcab-473c-bed1-fc56a7a20ae2', 'created_at': '2023-06-26T23:37:56.41764Z', 'token_count': 56, 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}}\n",
-      "{'role': 'human', 'content': \"WWhat is the book's relevance to the challenges facing contemporary society?\", 'uuid': '41bab0c7-5e20-40a4-9303-f82069977c91', 'created_at': '2023-06-26T23:38:03.559642Z', 'token_count': 16, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 5, 'Start': 0, 'Text': 'WWhat'}], 'Name': 'WWhat'}]}}}\n",
-      "{'role': 'ai', 'content': 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.', 'uuid': 'bfd8146a-4632-4c8c-98b6-9468bb624339', 'created_at': '2023-06-26T23:38:03.589312Z', 'token_count': 62, 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}]}}}\n"
+      "system :\n",
+      " {'content': 'The human inquires about Octavia Butler. The AI identifies her as an American science fiction author. The human then asks which books of hers were made into movies. The AI responds by mentioning the FX series Kindred, based on her novel of the same name. The human then asks about her contemporaries, and the AI lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.', 'additional_kwargs': {}}\n",
+      "human :\n",
+      " {'content': 'What awards did she win?', 'additional_kwargs': {'uuid': '6b733f0b-6778-49ae-b3ec-4e077c039f31', 'created_at': '2023-07-09T19:23:16.611232Z', 'token_count': 8, 'metadata': {'system': {'entities': [], 'intent': 'The subject is inquiring about the awards that someone, whose identity is not specified, has won.'}}}, 'example': False}\n",
+      "ai :\n",
+      " {'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'additional_kwargs': {'uuid': '2f6d80c6-3c08-4fd4-8d4e-7bbee341ac90', 'created_at': '2023-07-09T19:23:16.618947Z', 'token_count': 21, 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating that Octavia Butler received the Hugo Award, the Nebula Award, and the MacArthur Fellowship.'}}}, 'example': False}\n",
+      "human :\n",
+      " {'content': 'Which other women sci-fi writers might I want to read?', 'additional_kwargs': {'uuid': 'ccdcc901-ea39-4981-862f-6fe22ab9289b', 'created_at': '2023-07-09T19:23:16.62678Z', 'token_count': 14, 'metadata': {'system': {'entities': [], 'intent': 'The subject is seeking recommendations for additional women science fiction writers to explore.'}}}, 'example': False}\n",
+      "ai :\n",
+      " {'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'additional_kwargs': {'uuid': '7977099a-0c62-4c98-bfff-465bbab6c9c3', 'created_at': '2023-07-09T19:23:16.631721Z', 'token_count': 18, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting that the person should consider reading the works of Ursula K. Le Guin or Joanna Russ.'}}}, 'example': False}\n",
+      "human :\n",
+      " {'content': \"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", 'additional_kwargs': {'uuid': 'e439b7e6-286a-4278-a8cb-dc260fa2e089', 'created_at': '2023-07-09T19:23:16.63623Z', 'token_count': 23, 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}], 'intent': 'The subject is requesting a brief summary or explanation of the book \"Parable of the Sower\" by Butler.'}}}, 'example': False}\n",
+      "ai :\n",
+      " {'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'additional_kwargs': {'uuid': '6760489b-19c9-41aa-8b45-fae6cb1d7ee6', 'created_at': '2023-07-09T19:23:16.647524Z', 'token_count': 56, 'metadata': {'foo': 'bar', 'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}], 'intent': 'The subject is providing information about the novel \"Parable of the Sower\" by Octavia Butler, including its genre, publication date, and a brief summary of the plot.'}}}, 'example': False}\n",
+      "human :\n",
+      " {'content': \"What is the book's relevance to the challenges facing contemporary society?\", 'additional_kwargs': {'uuid': '7dbbbb93-492b-4739-800f-cad2b6e0e764', 'created_at': '2023-07-09T19:23:19.315182Z', 'token_count': 15, 'metadata': {'system': {'entities': [], 'intent': 'The subject is asking about the relevance of a book to the challenges currently faced by society.'}}}, 'example': False}\n",
+      "ai :\n",
+      " {'content': 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, inequality, and violence. It is a cautionary tale that warns of the dangers of unchecked greed and the need for individuals to take responsibility for their own lives and the lives of those around them.', 'additional_kwargs': {'uuid': '3e14ac8f-b7c1-4360-958b-9f3eae1f784f', 'created_at': '2023-07-09T19:23:19.332517Z', 'token_count': 66, 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}], 'intent': 'The subject is providing an analysis and evaluation of the novel \"Parable of the Sower\" and highlighting its relevance to contemporary societal challenges.'}}}, 'example': False}\n"
     ]
    }
   ],
   "source": [
    "def print_messages(messages):\n",
    "    for m in messages:\n",
-    "        print(m.to_dict())\n",
+    "        print(m.type, \":\\n\", m.dict())\n",
    "\n",
    "\n",
-    "print(zep_chat_history.zep_summary)\n",
+    "print(memory.chat_memory.zep_summary)\n",
    "print(\"\\n\")\n",
-    "print_messages(zep_chat_history.zep_messages)"
+    "print_messages(memory.chat_memory.messages)"
   ]
  },
  {
@@ -349,16 +347,18 @@
   "source": [
    "### Vector search over the Zep memory\n",
    "\n",
-    "Zep provides native vector search over historical conversation memory. Embedding happens automatically.\n"
+    "Zep provides native vector search over historical conversation memory via the `ZepRetriever`.\n",
+    "\n",
+    "You can use the `ZepRetriever` with chains that support passing in a Langchain `Retriever` object.\n"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
-     "end_time": "2023-05-25T15:09:50.751203Z",
-     "start_time": "2023-05-25T15:09:50.495050Z"
+     "end_time": "2023-07-09T19:24:30.781893Z",
+     "start_time": "2023-07-09T19:24:30.595650Z"
    }
   },
   "outputs": [
@@ -366,38 +366,36 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "{'uuid': 'b189fc60-1510-4a4b-a503-899481d652de', 'created_at': '2023-06-26T23:37:56.395722Z', 'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is looking for recommendations on women science fiction writers to read.'}}, 'token_count': 14} 0.9119619869747062\n",
-      "{'uuid': '4be1ccbb-a915-45d6-9f18-7a0c1cbd9907', 'created_at': '2023-06-26T23:37:56.403596Z', 'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting reading material and making a literary recommendation.'}}, 'token_count': 18} 0.8534346954749745\n",
-      "{'uuid': '76ec2a3d-b908-4c23-a55d-71ff92865a7a', 'created_at': '2023-06-26T23:37:56.378345Z', 'role': 'ai', 'content': \"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is stating the contemporaries of Octavia Butler, who are also science fiction writers.'}}, 'token_count': 27} 0.8523930955780226\n",
-      "{'uuid': '1feb02c7-63c9-4616-854d-0d97fb590ea5', 'created_at': '2023-06-26T23:37:56.313009Z', 'role': 'human', 'content': 'Who was Octavia Butler?', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject is asking about the identity of Octavia Butler, likely seeking information about her background or accomplishments.'}}, 'token_count': 8} 0.8236355436055457\n",
-      "{'uuid': 'ebe4696d-b5fa-4ca0-88c9-da794d9611ab', 'created_at': '2023-06-26T23:37:56.332247Z', 'role': 'ai', 'content': 'Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 0, 'Text': 'Octavia Estelle Butler'}], 'Name': 'Octavia Estelle Butler'}, {'Label': 'DATE', 'Matches': [{'End': 37, 'Start': 24, 'Text': 'June 22, 1947'}], 'Name': 'June 22, 1947'}, {'Label': 'DATE', 'Matches': [{'End': 57, 'Start': 40, 'Text': 'February 24, 2006'}], 'Name': 'February 24, 2006'}, {'Label': 'NORP', 'Matches': [{'End': 74, 'Start': 66, 'Text': 'American'}], 'Name': 'American'}], 'intent': 'The subject is making a statement about the background and profession of Octavia Estelle Butler, an American author.'}}, 'token_count': 31} 0.8206687242257686\n",
-      "{'uuid': '60cc6e6b-7cd4-4a81-aebc-72ef997286b4', 'created_at': '2023-06-26T23:37:56.389935Z', 'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating the accomplishments and awards received by Octavia Butler.'}}, 'token_count': 21} 0.8194249796585193\n",
-      "{'uuid': '0fa4f336-909d-4880-b01a-8e80e91fa8f2', 'created_at': '2023-06-26T23:37:56.344552Z', 'role': 'human', 'content': 'Which books of hers were made into movies?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is inquiring about which books written by an unknown female author were adapted into movies.'}}, 'token_count': 11} 0.7955105671310818\n",
-      "{'uuid': 'f91de7f2-4b84-4c5a-8a33-a71f38f3a59c', 'created_at': '2023-06-26T23:37:56.368146Z', 'role': 'human', 'content': 'Who were her contemporaries?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is asking about the people who lived during the same time period as a specific individual.'}}, 'token_count': 8} 0.7942358617914813\n",
-      "{'uuid': '4a463b4c-bcab-473c-bed1-fc56a7a20ae2', 'created_at': '2023-06-26T23:37:56.41764Z', 'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}]}}, 'token_count': 56} 0.7816448549236643\n",
-      "{'uuid': '6161d934-a629-4ba2-8bba-0b0996c93964', 'created_at': '2023-06-26T23:37:56.358632Z', 'role': 'ai', 'content': \"The most well-known adaptation of Octavia Butler's work is the FX series Kindred, based on her novel of the same name.\", 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 50, 'Start': 34, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 65, 'Start': 63, 'Text': 'FX'}], 'Name': 'FX'}, {'Label': 'GPE', 'Matches': [{'End': 80, 'Start': 73, 'Text': 'Kindred'}], 'Name': 'Kindred'}], 'intent': \"The subject is discussing Octavia Butler's work being adapted into a TV series called Kindred.\"}}, 'token_count': 29} 0.7815841371388998\n"
+      "{'uuid': 'ccdcc901-ea39-4981-862f-6fe22ab9289b', 'created_at': '2023-07-09T19:23:16.62678Z', 'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'metadata': {'system': {'entities': [], 'intent': 'The subject is seeking recommendations for additional women science fiction writers to explore.'}}, 'token_count': 14} 0.9119619869747062\n",
+      "{'uuid': '7977099a-0c62-4c98-bfff-465bbab6c9c3', 'created_at': '2023-07-09T19:23:16.631721Z', 'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 40, 'Start': 23, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 55, 'Start': 44, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': 'The subject is suggesting that the person should consider reading the works of Ursula K. Le Guin or Joanna Russ.'}}, 'token_count': 18} 0.8534346954749745\n",
+      "{'uuid': 'b05e2eb5-c103-4973-9458-928726f08655', 'created_at': '2023-07-09T19:23:16.603098Z', 'role': 'ai', 'content': \"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\", 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 16, 'Start': 0, 'Text': \"Octavia Butler's\"}], 'Name': \"Octavia Butler's\"}, {'Label': 'ORG', 'Matches': [{'End': 58, 'Start': 41, 'Text': 'Ursula K. Le Guin'}], 'Name': 'Ursula K. Le Guin'}, {'Label': 'PERSON', 'Matches': [{'End': 76, 'Start': 60, 'Text': 'Samuel R. Delany'}], 'Name': 'Samuel R. Delany'}, {'Label': 'PERSON', 'Matches': [{'End': 93, 'Start': 82, 'Text': 'Joanna Russ'}], 'Name': 'Joanna Russ'}], 'intent': \"The subject is stating that Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.\"}}, 'token_count': 27} 0.8523831524040919\n",
+      "{'uuid': 'e346f02b-f854-435d-b6ba-fb394a416b9b', 'created_at': '2023-07-09T19:23:16.556587Z', 'role': 'human', 'content': 'Who was Octavia Butler?', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 8, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}], 'intent': 'The subject is asking for information about the identity or background of Octavia Butler.'}}, 'token_count': 8} 0.8236355436055457\n",
+      "{'uuid': '42ff41d2-c63a-4d5b-b19b-d9a87105cfc3', 'created_at': '2023-07-09T19:23:16.578022Z', 'role': 'ai', 'content': 'Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American science fiction author.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 22, 'Start': 0, 'Text': 'Octavia Estelle Butler'}], 'Name': 'Octavia Estelle Butler'}, {'Label': 'DATE', 'Matches': [{'End': 37, 'Start': 24, 'Text': 'June 22, 1947'}], 'Name': 'June 22, 1947'}, {'Label': 'DATE', 'Matches': [{'End': 57, 'Start': 40, 'Text': 'February 24, 2006'}], 'Name': 'February 24, 2006'}, {'Label': 'NORP', 'Matches': [{'End': 74, 'Start': 66, 'Text': 'American'}], 'Name': 'American'}], 'intent': 'The subject is providing information about Octavia Estelle Butler, who was an American science fiction author.'}}, 'token_count': 31} 0.8206687242257686\n",
+      "{'uuid': '2f6d80c6-3c08-4fd4-8d4e-7bbee341ac90', 'created_at': '2023-07-09T19:23:16.618947Z', 'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'metadata': {'system': {'entities': [{'Label': 'PERSON', 'Matches': [{'End': 14, 'Start': 0, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 33, 'Start': 19, 'Text': 'the Hugo Award'}], 'Name': 'the Hugo Award'}, {'Label': 'EVENT', 'Matches': [{'End': 81, 'Start': 57, 'Text': 'the MacArthur Fellowship'}], 'Name': 'the MacArthur Fellowship'}], 'intent': 'The subject is stating that Octavia Butler received the Hugo Award, the Nebula Award, and the MacArthur Fellowship.'}}, 'token_count': 21} 0.8199012397683285\n"
     ]
    }
   ],
   "source": [
-    "search_results = zep_chat_history.search(\"who are some famous women sci-fi authors?\")\n",
+    "retriever = ZepRetriever(\n",
+    "    session_id=session_id,\n",
+    "    url=ZEP_API_URL,\n",
+    "    api_key=zep_api_key,\n",
+    ")\n",
+    "\n",
+    "search_results = memory.chat_memory.search(\"who are some famous women sci-fi authors?\")\n",
    "for r in search_results:\n",
-    "    print(r.message, r.dist)"
+    "    if r.dist > 0.8:  # Only print results with similarity of 0.8 or higher\n",
+    "        print(r.message, r.dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {},
   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
  }
 ],
 "metadata": {
--- 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/use_cases/code/code-analysis-deeplake.ipynb
+++ b/docs/extras/use_cases/code/code-analysis-deeplake.ipynb
@@ -5,8 +5,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Use LangChain, GPT and Deep Lake to work with code base\n",
-    "In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself. "
+    "# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
+    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
   ]
  },
  {
@@ -60,7 +60,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
@@ -81,19 +81,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
   "metadata": {
    "tags": []
   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
@@ -112,21 +104,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
   "metadata": {
    "tags": []
   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
-    "os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")"
+    "activeloop_token = getpass(\"Activeloop Token:\")\n",
+    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
   ]
  },
  {
@@ -149,19 +134,20 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls \"../../../..\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1147\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "from langchain.document_loaders import TextLoader\n",
    "\n",
@@ -189,180 +175,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Created a chunk of size 1620, which is longer than the specified 1000\n",
-      "Created a chunk of size 1213, which is longer than the specified 1000\n",
-      "Created a chunk of size 1263, which is longer than the specified 1000\n",
-      "Created a chunk of size 1448, which is longer than the specified 1000\n",
-      "Created a chunk of size 1120, which is longer than the specified 1000\n",
-      "Created a chunk of size 1148, which is longer than the specified 1000\n",
-      "Created a chunk of size 1826, which is longer than the specified 1000\n",
-      "Created a chunk of size 1260, which is longer than the specified 1000\n",
-      "Created a chunk of size 1195, which is longer than the specified 1000\n",
-      "Created a chunk of size 2147, which is longer than the specified 1000\n",
-      "Created a chunk of size 1410, which is longer than the specified 1000\n",
-      "Created a chunk of size 1269, which is longer than the specified 1000\n",
-      "Created a chunk of size 1030, which is longer than the specified 1000\n",
-      "Created a chunk of size 1046, which is longer than the specified 1000\n",
-      "Created a chunk of size 1024, which is longer than the specified 1000\n",
-      "Created a chunk of size 1026, which is longer than the specified 1000\n",
-      "Created a chunk of size 1285, which is longer than the specified 1000\n",
-      "Created a chunk of size 1370, which is longer than the specified 1000\n",
-      "Created a chunk of size 1031, which is longer than the specified 1000\n",
-      "Created a chunk of size 1999, which is longer than the specified 1000\n",
-      "Created a chunk of size 1029, which is longer than the specified 1000\n",
-      "Created a chunk of size 1120, which is longer than the specified 1000\n",
-      "Created a chunk of size 1033, which is longer than the specified 1000\n",
-      "Created a chunk of size 1143, which is longer than the specified 1000\n",
-      "Created a chunk of size 1416, which is longer than the specified 1000\n",
-      "Created a chunk of size 2482, which is longer than the specified 1000\n",
-      "Created a chunk of size 1890, which is longer than the specified 1000\n",
-      "Created a chunk of size 1418, which is longer than the specified 1000\n",
-      "Created a chunk of size 1848, which is longer than the specified 1000\n",
-      "Created a chunk of size 1069, which is longer than the specified 1000\n",
-      "Created a chunk of size 2369, which is longer than the specified 1000\n",
-      "Created a chunk of size 1045, which is longer than the specified 1000\n",
-      "Created a chunk of size 1501, which is longer than the specified 1000\n",
-      "Created a chunk of size 1208, which is longer than the specified 1000\n",
-      "Created a chunk of size 1950, which is longer than the specified 1000\n",
-      "Created a chunk of size 1283, which is longer than the specified 1000\n",
-      "Created a chunk of size 1414, which is longer than the specified 1000\n",
-      "Created a chunk of size 1304, which is longer than the specified 1000\n",
-      "Created a chunk of size 1224, which is longer than the specified 1000\n",
-      "Created a chunk of size 1060, which is longer than the specified 1000\n",
-      "Created a chunk of size 2461, which is longer than the specified 1000\n",
-      "Created a chunk of size 1099, which is longer than the specified 1000\n",
-      "Created a chunk of size 1178, which is longer than the specified 1000\n",
-      "Created a chunk of size 1449, which is longer than the specified 1000\n",
-      "Created a chunk of size 1345, which is longer than the specified 1000\n",
-      "Created a chunk of size 3359, which is longer than the specified 1000\n",
-      "Created a chunk of size 2248, which is longer than the specified 1000\n",
-      "Created a chunk of size 1589, which is longer than the specified 1000\n",
-      "Created a chunk of size 2104, which is longer than the specified 1000\n",
-      "Created a chunk of size 1505, which is longer than the specified 1000\n",
-      "Created a chunk of size 1387, which is longer than the specified 1000\n",
-      "Created a chunk of size 1215, which is longer than the specified 1000\n",
-      "Created a chunk of size 1240, which is longer than the specified 1000\n",
-      "Created a chunk of size 1635, which is longer than the specified 1000\n",
-      "Created a chunk of size 1075, which is longer than the specified 1000\n",
-      "Created a chunk of size 2180, which is longer than the specified 1000\n",
-      "Created a chunk of size 1791, which is longer than the specified 1000\n",
-      "Created a chunk of size 1555, which is longer than the specified 1000\n",
-      "Created a chunk of size 1082, which is longer than the specified 1000\n",
-      "Created a chunk of size 1225, which is longer than the specified 1000\n",
-      "Created a chunk of size 1287, which is longer than the specified 1000\n",
-      "Created a chunk of size 1085, which is longer than the specified 1000\n",
-      "Created a chunk of size 1117, which is longer than the specified 1000\n",
-      "Created a chunk of size 1966, which is longer than the specified 1000\n",
-      "Created a chunk of size 1150, which is longer than the specified 1000\n",
-      "Created a chunk of size 1285, which is longer than the specified 1000\n",
-      "Created a chunk of size 1150, which is longer than the specified 1000\n",
-      "Created a chunk of size 1585, which is longer than the specified 1000\n",
-      "Created a chunk of size 1208, which is longer than the specified 1000\n",
-      "Created a chunk of size 1267, which is longer than the specified 1000\n",
-      "Created a chunk of size 1542, which is longer than the specified 1000\n",
-      "Created a chunk of size 1183, which is longer than the specified 1000\n",
-      "Created a chunk of size 2424, which is longer than the specified 1000\n",
-      "Created a chunk of size 1017, which is longer than the specified 1000\n",
-      "Created a chunk of size 1304, which is longer than the specified 1000\n",
-      "Created a chunk of size 1379, which is longer than the specified 1000\n",
-      "Created a chunk of size 1324, which is longer than the specified 1000\n",
-      "Created a chunk of size 1205, which is longer than the specified 1000\n",
-      "Created a chunk of size 1056, which is longer than the specified 1000\n",
-      "Created a chunk of size 1195, which is longer than the specified 1000\n",
-      "Created a chunk of size 3608, which is longer than the specified 1000\n",
-      "Created a chunk of size 1058, which is longer than the specified 1000\n",
-      "Created a chunk of size 1075, which is longer than the specified 1000\n",
-      "Created a chunk of size 1217, which is longer than the specified 1000\n",
-      "Created a chunk of size 1109, which is longer than the specified 1000\n",
-      "Created a chunk of size 1440, which is longer than the specified 1000\n",
-      "Created a chunk of size 1046, which is longer than the specified 1000\n",
-      "Created a chunk of size 1220, which is longer than the specified 1000\n",
-      "Created a chunk of size 1403, which is longer than the specified 1000\n",
-      "Created a chunk of size 1241, which is longer than the specified 1000\n",
-      "Created a chunk of size 1427, which is longer than the specified 1000\n",
-      "Created a chunk of size 1049, which is longer than the specified 1000\n",
-      "Created a chunk of size 1580, which is longer than the specified 1000\n",
-      "Created a chunk of size 1565, which is longer than the specified 1000\n",
-      "Created a chunk of size 1131, which is longer than the specified 1000\n",
-      "Created a chunk of size 1425, which is longer than the specified 1000\n",
-      "Created a chunk of size 1054, which is longer than the specified 1000\n",
-      "Created a chunk of size 1027, which is longer than the specified 1000\n",
-      "Created a chunk of size 2559, which is longer than the specified 1000\n",
-      "Created a chunk of size 1028, which is longer than the specified 1000\n",
-      "Created a chunk of size 1382, which is longer than the specified 1000\n",
-      "Created a chunk of size 1888, which is longer than the specified 1000\n",
-      "Created a chunk of size 1475, which is longer than the specified 1000\n",
-      "Created a chunk of size 1652, which is longer than the specified 1000\n",
-      "Created a chunk of size 1891, which is longer than the specified 1000\n",
-      "Created a chunk of size 1899, which is longer than the specified 1000\n",
-      "Created a chunk of size 1021, which is longer than the specified 1000\n",
-      "Created a chunk of size 1085, which is longer than the specified 1000\n",
-      "Created a chunk of size 1854, which is longer than the specified 1000\n",
-      "Created a chunk of size 1672, which is longer than the specified 1000\n",
-      "Created a chunk of size 2537, which is longer than the specified 1000\n",
-      "Created a chunk of size 1251, which is longer than the specified 1000\n",
-      "Created a chunk of size 1734, which is longer than the specified 1000\n",
-      "Created a chunk of size 1642, which is longer than the specified 1000\n",
-      "Created a chunk of size 1376, which is longer than the specified 1000\n",
-      "Created a chunk of size 1253, which is longer than the specified 1000\n",
-      "Created a chunk of size 1642, which is longer than the specified 1000\n",
-      "Created a chunk of size 1419, which is longer than the specified 1000\n",
-      "Created a chunk of size 1438, which is longer than the specified 1000\n",
-      "Created a chunk of size 1427, which is longer than the specified 1000\n",
-      "Created a chunk of size 1684, which is longer than the specified 1000\n",
-      "Created a chunk of size 1760, which is longer than the specified 1000\n",
-      "Created a chunk of size 1157, which is longer than the specified 1000\n",
-      "Created a chunk of size 2504, which is longer than the specified 1000\n",
-      "Created a chunk of size 1082, which is longer than the specified 1000\n",
-      "Created a chunk of size 2268, which is longer than the specified 1000\n",
-      "Created a chunk of size 1784, which is longer than the specified 1000\n",
-      "Created a chunk of size 1311, which is longer than the specified 1000\n",
-      "Created a chunk of size 2972, which is longer than the specified 1000\n",
-      "Created a chunk of size 1144, which is longer than the specified 1000\n",
-      "Created a chunk of size 1825, which is longer than the specified 1000\n",
-      "Created a chunk of size 1508, which is longer than the specified 1000\n",
-      "Created a chunk of size 2901, which is longer than the specified 1000\n",
-      "Created a chunk of size 1715, which is longer than the specified 1000\n",
-      "Created a chunk of size 1062, which is longer than the specified 1000\n",
-      "Created a chunk of size 1206, which is longer than the specified 1000\n",
-      "Created a chunk of size 1102, which is longer than the specified 1000\n",
-      "Created a chunk of size 1184, which is longer than the specified 1000\n",
-      "Created a chunk of size 1002, which is longer than the specified 1000\n",
-      "Created a chunk of size 1065, which is longer than the specified 1000\n",
-      "Created a chunk of size 1871, which is longer than the specified 1000\n",
-      "Created a chunk of size 1754, which is longer than the specified 1000\n",
-      "Created a chunk of size 2413, which is longer than the specified 1000\n",
-      "Created a chunk of size 1771, which is longer than the specified 1000\n",
-      "Created a chunk of size 2054, which is longer than the specified 1000\n",
-      "Created a chunk of size 2000, which is longer than the specified 1000\n",
-      "Created a chunk of size 2061, which is longer than the specified 1000\n",
-      "Created a chunk of size 1066, which is longer than the specified 1000\n",
-      "Created a chunk of size 1419, which is longer than the specified 1000\n",
-      "Created a chunk of size 1368, which is longer than the specified 1000\n",
-      "Created a chunk of size 1008, which is longer than the specified 1000\n",
-      "Created a chunk of size 1227, which is longer than the specified 1000\n",
-      "Created a chunk of size 1745, which is longer than the specified 1000\n",
-      "Created a chunk of size 2296, which is longer than the specified 1000\n",
-      "Created a chunk of size 1083, which is longer than the specified 1000\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3477\n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
@@ -383,22 +200,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', document_model_name='text-embedding-ada-002', query_model_name='text-embedding-ada-002', embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "\n",
@@ -417,11 +223,33 @@
    "from langchain.vectorstores import DeepLake\n",
    "\n",
    "db = DeepLake.from_documents(\n",
-    "    texts, embeddings, dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\"\n",
+    "    texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\"\n",
    ")\n",
    "db"
   ]
  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from langchain.vectorstores import DeepLake\n",
+    "\n",
+    "# db = DeepLake.from_documents(\n",
+    "#     texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
+    "# )\n",
+    "# db"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
@@ -433,66 +261,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "-"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/user_name/langchain-code\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hub://user_name/langchain-code loaded successfully.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Deep Lake Dataset in hub://user_name/langchain-code already exists, loading from the storage\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset(path='hub://user_name/langchain-code', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
-      "\n",
-      "  tensor     htype      shape       dtype  compression\n",
-      "  -------   -------    -------     -------  ------- \n",
-      " embedding  generic  (3477, 1536)  float32   None   \n",
-      "    ids      text     (3477, 1)      str     None   \n",
-      " metadata    json     (3477, 1)      str     None   \n",
-      "   text      text     (3477, 1)      str     None   \n"
-     ]
-    }
-   ],
+   "outputs": [],
   "source": [
    "db = DeepLake(\n",
-    "    dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\",\n",
+    "    dataset_path=f\"hub://{<org_id>}/langchain-code\",\n",
    "    read_only=True,\n",
    "    embedding_function=embeddings,\n",
    ")"
@@ -500,7 +276,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
@@ -523,7 +299,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
@@ -545,7 +321,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
   "metadata": {
    "tags": []
   },
@@ -658,7 +434,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.6"
  }
 },
 "nbformat": 4,
--- a/docs/extras/use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb
+++ b/docs/extras/use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb
@@ -5,8 +5,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Deep Lake\n",
-    "In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
+    "# Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake\n",
+    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to analyze the code base of the twitter algorithm. "
   ]
  },
  {
@@ -15,7 +15,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "!python3 -m pip install --upgrade langchain deeplake openai tiktoken"
+    "!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
   ]
  },
  {
@@ -41,7 +41,8 @@
    "from langchain.vectorstores import DeepLake\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")"
+    "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
+    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
   ]
  },
  {
@@ -149,6 +150,29 @@
    "db.add_documents(texts)"
   ]
  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# username = \"davitbun\"  # replace with your username from app.activeloop.ai\n",
+    "# db = DeepLake(\n",
+    "#     dataset_path=f\"hub://{username}/twitter-algorithm\",\n",
+    "#     embedding_function=embeddings,\n",
+    "#     runtime={\"tensor_db\": True}\n",
+    "# )\n",
+    "# db.add_documents(texts)"
+   ]
+  },
  {
   "attachments": {},
   "cell_type": "markdown",
@@ -176,6 +200,7 @@
    "    dataset_path=\"hub://davitbun/twitter-algorithm\",\n",
    "    read_only=True,\n",
    "    embedding_function=embeddings,\n",
+    "    \n",
    ")"
   ]
  },
--- a/docs/extras/use_cases/question_answering/semantic-search-over-chat.ipynb
+++ b/docs/extras/use_cases/question_answering/semantic-search-over-chat.ipynb
@@ -1,16 +1,18 @@
 {
 "cells": [
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Question answering over a group chat messages\n",
-    "In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to semantically search and ask questions over a group chat.\n",
+    "# Question answering over a group chat messages using Activeloop's DeepLake\n",
+    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to semantically search and ask questions over a group chat.\n",
    "\n",
    "View a working demo [here](https://twitter.com/thisissukh_/status/1647223328363679745)"
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -23,10 +25,11 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "!python3 -m pip install --upgrade langchain deeplake openai tiktoken"
+    "!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -34,6 +37,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
@@ -58,16 +62,18 @@
    "from langchain.llms import OpenAI\n",
    "\n",
    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")\n",
+    "activeloop_token =  getpass.getpass(\"Activeloop Token:\")\n",
+    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token\n",
    "os.environ[\"ACTIVELOOP_ORG\"] = getpass.getpass(\"Activeloop Org:\")\n",
    "\n",
-    "org = os.environ[\"ACTIVELOOP_ORG\"]\n",
+    "org_id = os.environ[\"ACTIVELOOP_ORG\"]\n",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
-    "dataset_path = \"hub://\" + org + \"/data\""
+    "dataset_path = \"hub://\" + org_id + \"/data\""
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -77,6 +83,7 @@
   ]
  },
  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
@@ -117,6 +124,38 @@
   ]
  },
  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# with open(\"messages.txt\") as f:\n",
+    "#     state_of_the_union = f.read()\n",
+    "# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "# pages = text_splitter.split_text(state_of_the_union)\n",
+    "\n",
+    "# text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
+    "# texts = text_splitter.create_documents(pages)\n",
+    "\n",
+    "# print(texts)\n",
+    "\n",
+    "# dataset_path = \"hub://\" + org + \"/data\"\n",
+    "# embeddings = OpenAIEmbeddings()\n",
+    "# db = DeepLake.from_documents(\n",
+    "#     texts, embeddings, dataset_path=dataset_path, overwrite=True, runtime=\"tensor_db\"\n",
+    "# )"
+   ]
+  },
+  {
+   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
--- a/docs/snippets/modules/chains/additional/multi_retrieval_qa_router.mdx
+++ b/docs/snippets/modules/chains/additional/multi_retrieval_qa_router.mdx
@@ -35,7 +35,7 @@ retriever_infos = [
    },
    {
        "name": "pg essay", 
-        "description": "Good for answer quesitons about Paul Graham's essay on his career", 
+        "description": "Good for answering questions about Paul Graham's essay on his career", 
        "retriever": pg_retriever
    },
    {
--- a/docs/snippets/modules/data_connection/retrievers/get_started.mdx
+++ b/docs/snippets/modules/data_connection/retrievers/get_started.mdx
@@ -66,7 +66,7 @@ from langchain.chains import RetrievalQA
 from langchain.llms import OpenAI
 ```

-Next in the generic setup, let's specify the document loader we want to use. You can download the `state_of_the_union.txt` file [here](https://github.com/hwchase17/langchain/blob/master/docs/modules/state_of_the_union.txt)
+Next in the generic setup, let's specify the document loader we want to use. You can download the `state_of_the_union.txt` file [here](https://github.com/hwchase17/langchain/blob/master/docs/extras/modules/state_of_the_union.txt)


 ```python
--- 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/csv/base.py
+++ b/langchain/agents/agent_toolkits/csv/base.py
@@ -3,7 +3,7 @@ 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/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 (
--- 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

--- a/langchain/agents/agent_toolkits/powerbi/base.py
+++ b/langchain/agents/agent_toolkits/powerbi/base.py
@@ -9,9 +9,9 @@ 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


--- a/langchain/agents/agent_toolkits/powerbi/toolkit.py
+++ b/langchain/agents/agent_toolkits/powerbi/toolkit.py
@@ -4,7 +4,6 @@ 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
@@ -14,6 +13,7 @@ from langchain.prompts.chat import (
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
 )
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import BaseTool
 from langchain.tools.powerbi.prompt import (
    QUESTION_TO_QUERY_BASE,
--- 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/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
--- 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,
--- 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,
--- 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
@@ -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
--- 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
@@ -1,105 +1,6 @@
+"""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):
-    """Base class for all language models."""
-
-    @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/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/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/tracers/evaluation.py
+++ b/langchain/callbacks/tracers/evaluation.py
@@ -4,7 +4,7 @@ from concurrent.futures import Future, ThreadPoolExecutor, wait
 from typing import Any, Optional, Sequence, Set, Union
 from uuid import UUID

-from langchainplus_sdk import LangChainPlusClient, RunEvaluator
+from langsmith import Client, RunEvaluator

 from langchain.callbacks.manager import tracing_v2_enabled
 from langchain.callbacks.tracers.base import BaseTracer
@@ -23,8 +23,8 @@ class EvaluatorCallbackHandler(BaseTracer):
    max_workers : int, optional
        The maximum number of worker threads to use for running the evaluators.
        If not specified, it will default to the number of evaluators.
-    client : LangChainPlusClient, optional
-        The LangChainPlusClient instance to use for evaluating the runs.
+    client : LangSmith Client, optional
+        The LangSmith client instance to use for evaluating the runs.
        If not specified, a new instance will be created.
    example_id : Union[UUID, str], optional
        The example ID to be associated with the runs.
@@ -35,8 +35,8 @@ class EvaluatorCallbackHandler(BaseTracer):
    ----------
    example_id : Union[UUID, None]
        The example ID associated with the runs.
-    client : LangChainPlusClient
-        The LangChainPlusClient instance used for evaluating the runs.
+    client : Client
+        The LangSmith client instance used for evaluating the runs.
    evaluators : Sequence[RunEvaluator]
        The sequence of run evaluators to be executed.
    executor : ThreadPoolExecutor
@@ -56,7 +56,7 @@ class EvaluatorCallbackHandler(BaseTracer):
        self,
        evaluators: Sequence[RunEvaluator],
        max_workers: Optional[int] = None,
-        client: Optional[LangChainPlusClient] = None,
+        client: Optional[Client] = None,
        example_id: Optional[Union[UUID, str]] = None,
        skip_unfinished: bool = True,
        project_name: Optional[str] = None,
@@ -66,7 +66,7 @@ class EvaluatorCallbackHandler(BaseTracer):
        self.example_id = (
            UUID(example_id) if isinstance(example_id, str) else example_id
        )
-        self.client = client or LangChainPlusClient()
+        self.client = client or Client()
        self.evaluators = evaluators
        self.executor = ThreadPoolExecutor(
            max_workers=max(max_workers or len(evaluators), 1)
--- a/langchain/callbacks/tracers/langchain.py
+++ b/langchain/callbacks/tracers/langchain.py
@@ -8,7 +8,7 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional, Set, Union
 from uuid import UUID

-from langchainplus_sdk import LangChainPlusClient
+from langsmith import Client

 from langchain.callbacks.tracers.base import BaseTracer
 from langchain.callbacks.tracers.schemas import Run, RunTypeEnum, TracerSession
@@ -44,7 +44,7 @@ class LangChainTracer(BaseTracer):
        self,
        example_id: Optional[Union[UUID, str]] = None,
        project_name: Optional[str] = None,
-        client: Optional[LangChainPlusClient] = None,
+        client: Optional[Client] = None,
        tags: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> None:
@@ -59,7 +59,7 @@ class LangChainTracer(BaseTracer):
        )
        # set max_workers to 1 to process tasks in order
        self.executor = ThreadPoolExecutor(max_workers=1)
-        self.client = client or LangChainPlusClient()
+        self.client = client or Client()
        self._futures: Set[Future] = set()
        self.tags = tags or []
        global _TRACERS
@@ -109,8 +109,6 @@ class LangChainTracer(BaseTracer):

    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", {})
@@ -136,12 +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))
        )
@@ -160,6 +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))
        )
@@ -178,6 +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))
        )
@@ -196,6 +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/langchain/callbacks/tracers/schemas.py
+++ b/langchain/callbacks/tracers/schemas.py
@@ -5,8 +5,8 @@ import datetime
 from typing import Any, Dict, List, Optional
 from uuid import UUID

-from langchainplus_sdk.schemas import RunBase as BaseRunV2
-from langchainplus_sdk.schemas import RunTypeEnum
+from langsmith.schemas import RunBase as BaseRunV2
+from langsmith.schemas import RunTypeEnum
 from pydantic import BaseModel, Field, root_validator

 from langchain.schema import LLMResult
--- a/langchain/chains/api/base.py
+++ b/langchain/chains/api/base.py
@@ -5,7 +5,6 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
@@ -15,6 +14,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.requests import TextRequestsWrapper
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class APIChain(Chain):
--- a/langchain/chains/api/openapi/chain.py
+++ b/langchain/chains/api/openapi/chain.py
@@ -7,13 +7,13 @@ from typing import Any, Dict, List, NamedTuple, Optional, cast
 from pydantic import BaseModel, Field
 from requests import Response

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
 from langchain.chains.api.openapi.requests_chain import APIRequesterChain
 from langchain.chains.api.openapi.response_chain import APIResponderChain
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.requests import Requests
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.openapi.utils.api_models import APIOperation


--- a/langchain/chains/api/openapi/requests_chain.py
+++ b/langchain/chains/api/openapi/requests_chain.py
@@ -4,11 +4,11 @@ import json
 import re
 from typing import Any

-from langchain.base_language import BaseLanguageModel
 from langchain.chains.api.openapi.prompts import REQUEST_TEMPLATE
 from langchain.chains.llm import LLMChain
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import BaseOutputParser
+from langchain.schema.language_model import BaseLanguageModel


 class APIRequesterOutputParser(BaseOutputParser):
--- a/langchain/chains/api/openapi/response_chain.py
+++ b/langchain/chains/api/openapi/response_chain.py
@@ -4,11 +4,11 @@ import json
 import re
 from typing import Any

-from langchain.base_language import BaseLanguageModel
 from langchain.chains.api.openapi.prompts import RESPONSE_TEMPLATE
 from langchain.chains.llm import LLMChain
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import BaseOutputParser
+from langchain.schema.language_model import BaseLanguageModel


 class APIResponderOutputParser(BaseOutputParser):
--- a/langchain/chains/constitutional_ai/base.py
+++ b/langchain/chains/constitutional_ai/base.py
@@ -1,7 +1,6 @@
 """Chain for applying constitutional principles to the outputs of another chain."""
 from typing import Any, Dict, List, Optional

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
@@ -9,6 +8,7 @@ from langchain.chains.constitutional_ai.principles import PRINCIPLES
 from langchain.chains.constitutional_ai.prompts import CRITIQUE_PROMPT, REVISION_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class ConstitutionalChain(Chain):
--- a/langchain/chains/conversational_retrieval/base.py
+++ b/langchain/chains/conversational_retrieval/base.py
@@ -9,7 +9,6 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union

 from pydantic import Extra, Field, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
@@ -22,6 +21,7 @@ from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_
 from langchain.chains.llm import LLMChain
 from langchain.chains.question_answering import load_qa_chain
 from langchain.schema import BasePromptTemplate, BaseRetriever, Document
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage
 from langchain.vectorstores.base import VectorStore

@@ -72,7 +72,7 @@ class BaseConversationalRetrievalChain(Chain):
    """Return the retrieved source documents as part of the final result."""
    return_generated_question: bool = False
    """Return the generated question as part of the final result."""
-    get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
+    get_chat_history: Optional[Callable[[List[CHAT_TURN_TYPE]], str]] = None
    """An optional function to get a string of the chat history.
    If None is provided, will use a default."""

--- a/langchain/chains/flare/base.py
+++ b/langchain/chains/flare/base.py
@@ -7,7 +7,6 @@ from typing import Any, Dict, List, Optional, Sequence, Tuple
 import numpy as np
 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
    CallbackManagerForChainRun,
 )
@@ -20,6 +19,7 @@ from langchain.chains.flare.prompts import (
 from langchain.chains.llm import LLMChain
 from langchain.llms import OpenAI
 from langchain.schema import BasePromptTemplate, BaseRetriever, Generation
+from langchain.schema.language_model import BaseLanguageModel


 class _ResponseChain(LLMChain):
--- a/langchain/chains/graph_qa/base.py
+++ b/langchain/chains/graph_qa/base.py
@@ -5,13 +5,13 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import ENTITY_EXTRACTION_PROMPT, GRAPH_QA_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.networkx_graph import NetworkxEntityGraph, get_entities
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class GraphQAChain(Chain):
@@ -75,9 +75,10 @@ class GraphQAChain(Chain):
        )
        entities = get_entities(entity_string)
        context = ""
+        all_triplets = []
        for entity in entities:
-            triplets = self.graph.get_entity_knowledge(entity)
-            context += "\n".join(triplets)
+            all_triplets.extend(self.graph.get_entity_knowledge(entity))
+        context = "\n".join(all_triplets)
        _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose)
        _run_manager.on_text(context, color="green", end="\n", verbose=self.verbose)
        result = self.qa_chain(
--- a/langchain/chains/graph_qa/cypher.py
+++ b/langchain/chains/graph_qa/cypher.py
@@ -6,13 +6,13 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.neo4j_graph import Neo4jGraph
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel

 INTERMEDIATE_STEPS_KEY = "intermediate_steps"

--- a/langchain/chains/graph_qa/hugegraph.py
+++ b/langchain/chains/graph_qa/hugegraph.py
@@ -5,7 +5,6 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import (
@@ -15,6 +14,7 @@ from langchain.chains.graph_qa.prompts import (
 from langchain.chains.llm import LLMChain
 from langchain.graphs.hugegraph import HugeGraph
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class HugeGraphQAChain(Chain):
--- a/langchain/chains/graph_qa/kuzu.py
+++ b/langchain/chains/graph_qa/kuzu.py
@@ -5,13 +5,13 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import CYPHER_QA_PROMPT, KUZU_GENERATION_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.kuzu_graph import KuzuGraph
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class KuzuQAChain(Chain):
--- a/langchain/chains/graph_qa/nebulagraph.py
+++ b/langchain/chains/graph_qa/nebulagraph.py
@@ -5,13 +5,13 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import CYPHER_QA_PROMPT, NGQL_GENERATION_PROMPT
 from langchain.chains.llm import LLMChain
 from langchain.graphs.nebula_graph import NebulaGraph
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class NebulaGraphQAChain(Chain):
--- a/langchain/chains/graph_qa/sparql.py
+++ b/langchain/chains/graph_qa/sparql.py
@@ -7,7 +7,6 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.graph_qa.prompts import (
@@ -19,6 +18,7 @@ from langchain.chains.graph_qa.prompts import (
 from langchain.chains.llm import LLMChain
 from langchain.graphs.rdf_graph import RdfGraph
 from langchain.prompts.base import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class GraphSparqlQAChain(Chain):
--- a/langchain/chains/hyde/base.py
+++ b/langchain/chains/hyde/base.py
@@ -9,12 +9,12 @@ from typing import Any, Dict, List, Optional
 import numpy as np
 from pydantic import Extra

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.hyde.prompts import PROMPT_MAP
 from langchain.chains.llm import LLMChain
 from langchain.embeddings.base import Embeddings
+from langchain.schema.language_model import BaseLanguageModel


 class HypotheticalDocumentEmbedder(Chain, Embeddings):
--- a/langchain/chains/llm.py
+++ b/langchain/chains/llm.py
@@ -6,7 +6,6 @@ from typing import Any, Dict, List, Optional, Sequence, Tuple, Union

 from pydantic import Extra, Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
    AsyncCallbackManager,
    AsyncCallbackManagerForChainRun,
@@ -25,6 +24,7 @@ from langchain.schema import (
    NoOpOutputParser,
    PromptValue,
 )
+from langchain.schema.language_model import BaseLanguageModel


 class LLMChain(Chain):
--- a/langchain/chains/llm_bash/base.py
+++ b/langchain/chains/llm_bash/base.py
@@ -7,12 +7,12 @@ from typing import Any, Dict, List, Optional

 from pydantic import Extra, Field, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.prompt import PROMPT
 from langchain.schema import BasePromptTemplate, OutputParserException
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities.bash import BashProcess

 logger = logging.getLogger(__name__)
--- a/langchain/chains/llm_checker/base.py
+++ b/langchain/chains/llm_checker/base.py
@@ -6,7 +6,6 @@ from typing import Any, Dict, List, Optional

 from pydantic import Extra, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
@@ -18,6 +17,7 @@ from langchain.chains.llm_checker.prompt import (
 )
 from langchain.chains.sequential import SequentialChain
 from langchain.prompts import PromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 def _load_question_to_checked_assertions_chain(
--- a/langchain/chains/llm_math/base.py
+++ b/langchain/chains/llm_math/base.py
@@ -9,7 +9,6 @@ from typing import Any, Dict, List, Optional
 import numexpr
 from pydantic import Extra, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
@@ -18,6 +17,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_math.prompt import PROMPT
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class LLMMathChain(Chain):
--- a/langchain/chains/llm_summarization_checker/base.py
+++ b/langchain/chains/llm_summarization_checker/base.py
@@ -8,12 +8,12 @@ from typing import Any, Dict, List, Optional

 from pydantic import Extra, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.sequential import SequentialChain
 from langchain.prompts.prompt import PromptTemplate
+from langchain.schema.language_model import BaseLanguageModel

 PROMPTS_DIR = Path(__file__).parent / "prompts"

--- a/langchain/chains/mapreduce.py
+++ b/langchain/chains/mapreduce.py
@@ -9,7 +9,6 @@ from typing import Any, Dict, List, Mapping, Optional

 from pydantic import Extra

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
 from langchain.chains import ReduceDocumentsChain
 from langchain.chains.base import Chain
@@ -19,6 +18,7 @@ from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
 from langchain.docstore.document import Document
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.text_splitter import TextSplitter


--- a/langchain/chains/natbot/base.py
+++ b/langchain/chains/natbot/base.py
@@ -6,12 +6,12 @@ from typing import Any, Dict, List, Optional

 from pydantic import Extra, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.natbot.prompt import PROMPT
 from langchain.llms.openai import OpenAI
+from langchain.schema.language_model import BaseLanguageModel


 class NatBotChain(Chain):
--- a/langchain/chains/openai_functions/citation_fuzzy_match.py
+++ b/langchain/chains/openai_functions/citation_fuzzy_match.py
@@ -2,13 +2,13 @@ from typing import Iterator, List

 from pydantic import BaseModel, Field

-from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import get_llm_kwargs
 from langchain.output_parsers.openai_functions import (
    PydanticOutputFunctionsParser,
 )
 from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import HumanMessage, SystemMessage


--- a/langchain/chains/openai_functions/extraction.py
+++ b/langchain/chains/openai_functions/extraction.py
@@ -2,7 +2,6 @@ from typing import Any, List

 from pydantic import BaseModel

-from langchain.base_language import BaseLanguageModel
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import (
@@ -15,6 +14,7 @@ from langchain.output_parsers.openai_functions import (
    PydanticAttrOutputFunctionsParser,
 )
 from langchain.prompts import ChatPromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 def _get_extraction_function(entity_schema: dict) -> dict:
--- a/langchain/chains/openai_functions/openapi.py
+++ b/langchain/chains/openai_functions/openapi.py
@@ -8,7 +8,6 @@ from openapi_schema_pydantic import Parameter
 from requests import Response

 from langchain import LLMChain
-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.sequential import SequentialChain
@@ -17,6 +16,7 @@ from langchain.input import get_colored_text
 from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools import APIOperation
 from langchain.utilities.openapi import OpenAPISpec

--- a/langchain/chains/openai_functions/qa_with_structure.py
+++ b/langchain/chains/openai_functions/qa_with_structure.py
@@ -2,7 +2,6 @@ from typing import Any, List, Optional, Type, Union

 from pydantic import BaseModel, Field

-from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import get_llm_kwargs
 from langchain.output_parsers.openai_functions import (
@@ -12,6 +11,7 @@ from langchain.output_parsers.openai_functions import (
 from langchain.prompts import PromptTemplate
 from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
 from langchain.schema import BaseLLMOutputParser
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import HumanMessage, SystemMessage


--- a/langchain/chains/openai_functions/tagging.py
+++ b/langchain/chains/openai_functions/tagging.py
@@ -1,6 +1,5 @@
 from typing import Any

-from langchain.base_language import BaseLanguageModel
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.openai_functions.utils import _convert_schema, get_llm_kwargs
@@ -9,6 +8,7 @@ from langchain.output_parsers.openai_functions import (
    PydanticOutputFunctionsParser,
 )
 from langchain.prompts import ChatPromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 def _get_tagging_function(schema: dict) -> dict:
--- a/langchain/chains/pal/base.py
+++ b/langchain/chains/pal/base.py
@@ -9,13 +9,13 @@ from typing import Any, Dict, List, Optional

 from pydantic import Extra, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
 from langchain.chains.pal.math_prompt import MATH_PROMPT
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.utilities import PythonREPL


--- a/langchain/chains/prompt_selector.py
+++ b/langchain/chains/prompt_selector.py
@@ -3,10 +3,10 @@ from typing import Callable, List, Tuple

 from pydantic import BaseModel, Field

-from langchain.base_language import BaseLanguageModel
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms.base import BaseLLM
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class BasePromptSelector(BaseModel, ABC):
--- a/langchain/chains/qa_generation/base.py
+++ b/langchain/chains/qa_generation/base.py
@@ -5,12 +5,12 @@ from typing import Any, Dict, List, Optional

 from pydantic import Field

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter


--- a/langchain/chains/qa_with_sources/base.py
+++ b/langchain/chains/qa_with_sources/base.py
@@ -9,7 +9,6 @@ from typing import Any, Dict, List, Optional

 from pydantic import Extra, root_validator

-from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
@@ -28,6 +27,7 @@ from langchain.chains.qa_with_sources.map_reduce_prompt import (
 )
 from langchain.docstore.document import Document
 from langchain.schema import BasePromptTemplate
+from langchain.schema.language_model import BaseLanguageModel


 class BaseQAWithSourcesChain(Chain, ABC):
--- a/langchain/chains/qa_with_sources/loading.py
+++ b/langchain/chains/qa_with_sources/loading.py
@@ -3,11 +3,10 @@ from __future__ import annotations

 from typing import Any, Mapping, Optional, Protocol

-from langchain.base_language import BaseLanguageModel
-from langchain.chains import ReduceDocumentsChain
 from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
 from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
 from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
+from langchain.chains.combine_documents.reduce import ReduceDocumentsChain
 from langchain.chains.combine_documents.refine import RefineDocumentsChain
 from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.llm import LLMChain
@@ -19,6 +18,7 @@ from langchain.chains.qa_with_sources import (
 from langchain.chains.question_answering.map_rerank_prompt import (
    PROMPT as MAP_RERANK_PROMPT,
 )
+from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.prompt_template import BasePromptTemplate


--- a/langchain/chains/query_constructor/base.py
+++ b/langchain/chains/query_constructor/base.py
@@ -5,7 +5,6 @@ import json
 from typing import Any, Callable, List, Optional, Sequence

 from langchain import FewShotPromptTemplate, LLMChain
-from langchain.base_language import BaseLanguageModel
 from langchain.chains.query_constructor.ir import (
    Comparator,
    Operator,
@@ -24,6 +23,7 @@ from langchain.chains.query_constructor.prompt import (
 from langchain.chains.query_constructor.schema import AttributeInfo
 from langchain.output_parsers.json import parse_and_check_json_markdown
 from langchain.schema import BaseOutputParser, BasePromptTemplate, OutputParserException
+from langchain.schema.language_model import BaseLanguageModel


 class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]):
--- a/langchain/chains/query_constructor/parser.py
+++ b/langchain/chains/query_constructor/parser.py
@@ -145,6 +145,11 @@ def get_parser(
    Returns:
        Lark parser for the query language.
    """
+    # QueryTransformer is None when Lark cannot be imported.
+    if QueryTransformer is None:
+        raise ImportError(
+            "Cannot import lark, please install it with 'pip install lark'."
+        )
    transformer = QueryTransformer(
        allowed_comparators=allowed_comparators, allowed_operators=allowed_operators
    )
--- a/Show More
+++ b/Show More