harsh-lodha/lc-scraped · Datasets at Hugging Face

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

This page covers how to use llama.cpp within LangChain.

This page covers how to use llama.cpp within LangChain. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

3,801

and toolkitsMemoryCallbacksChat loadersProvidersMoreLlama.cppOn this pageLlama.cppThis page covers how to use llama.cpp within LangChain.

This page covers how to use llama.cpp within LangChain.

This page covers how to use llama.cpp within LangChain. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreLlama.cppOn this pageLlama.cppThis page covers how to use llama.cpp within LangChain.

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It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.Installation and Setup‚ÄãInstall the Python package with pip install llama-cpp-pythonDownload one of the supported models and convert them to the llama.cpp format per the instructionsWrappers‚ÄãLLM‚ÄãThere exists a LlamaCpp LLM wrapper, which you can access with from langchain.llms import LlamaCppFor a more detailed walkthrough of this, see this notebookEmbeddings‚ÄãThere exists a LlamaCpp Embeddings wrapper, which you can access with from langchain.embeddings import LlamaCppEmbeddingsFor a more detailed walkthrough of this, see this notebookPreviousLangChain Decorators ‚ú®NextLog10Installation and SetupWrappersLLMEmbeddingsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

This page covers how to use llama.cpp within LangChain.

This page covers how to use llama.cpp within LangChain. ->: It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.Installation and Setup‚ÄãInstall the Python package with pip install llama-cpp-pythonDownload one of the supported models and convert them to the llama.cpp format per the instructionsWrappers‚ÄãLLM‚ÄãThere exists a LlamaCpp LLM wrapper, which you can access with from langchain.llms import LlamaCppFor a more detailed walkthrough of this, see this notebookEmbeddings‚ÄãThere exists a LlamaCpp Embeddings wrapper, which you can access with from langchain.embeddings import LlamaCppEmbeddingsFor a more detailed walkthrough of this, see this notebookPreviousLangChain Decorators ‚ú®NextLog10Installation and SetupWrappersLLMEmbeddingsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,803

ArangoDB | ü¶úÔ∏èüîó Langchain

ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere.

ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere. ->: ArangoDB | ü¶úÔ∏èüîó Langchain

3,804

Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere.

ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreArangoDBOn this pageArangoDBArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere.Dependencies‚ÄãInstall the ArangoDB Python Driver package withpip install python-arangoGraph QA Chain‚ÄãConnect your ArangoDB Database with a chat model to get insights on your data. See the notebook example here.from arango import ArangoClientfrom langchain.graphs import ArangoGraphfrom langchain.chains import ArangoGraphQAChainPreviousApifyNextArgillaDependenciesGraph QA ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere.

ArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreArangoDBOn this pageArangoDBArangoDB is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud ‚Äì anywhere.Dependencies‚ÄãInstall the ArangoDB Python Driver package withpip install python-arangoGraph QA Chain‚ÄãConnect your ArangoDB Database with a chat model to get insights on your data. See the notebook example here.from arango import ArangoClientfrom langchain.graphs import ArangoGraphfrom langchain.chains import ArangoGraphQAChainPreviousApifyNextArgillaDependenciesGraph QA ChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,806

PubMed | ü¶úÔ∏èüîó Langchain

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine ->: PubMed | ü¶úÔ∏èüîó Langchain

3,807

Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMorePubMedOn this pagePubMedPubMedPubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine ->: and toolkitsMemoryCallbacksChat loadersProvidersMorePubMedOn this pagePubMedPubMedPubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine

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comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.Setup‚ÄãYou need to install a python package.pip install xmltodictRetriever‚ÄãSee a usage example.from langchain.retrievers import PubMedRetrieverDocument Loader‚ÄãSee a usage example.from langchain.document_loaders import PubMedLoaderPreviousPsychicNextQdrantSetupRetrieverDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine

PubMed¬Æ by The National Center for Biotechnology Information, National Library of Medicine ->: comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.Setup‚ÄãYou need to install a python package.pip install xmltodictRetriever‚ÄãSee a usage example.from langchain.retrievers import PubMedRetrieverDocument Loader‚ÄãSee a usage example.from langchain.document_loaders import PubMedLoaderPreviousPsychicNextQdrantSetupRetrieverDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,810

BagelDB | ü¶úÔ∏èüîó Langchain

BagelDB (Open Vector Database for AI), is like GitHub for AI data.

BagelDB (Open Vector Database for AI), is like GitHub for AI data. ->: BagelDB | ü¶úÔ∏èüîó Langchain

3,811

Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

BagelDB (Open Vector Database for AI), is like GitHub for AI data.

BagelDB (Open Vector Database for AI), is like GitHub for AI data. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreBagelDBOn this pageBagelDBBagelDB (Open Vector Database for AI), is like GitHub for AI data.

BagelDB (Open Vector Database for AI), is like GitHub for AI data.

BagelDB (Open Vector Database for AI), is like GitHub for AI data. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreBagelDBOn this pageBagelDBBagelDB (Open Vector Database for AI), is like GitHub for AI data.

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It is a collaborative platform where users can create, share, and manage vector datasets. It can support private projects for independent developers, internal collaborations for enterprises, and public contributions for data DAOs.Installation and Setup‚Äãpip install betabageldbVectorStore‚ÄãSee a usage example.from langchain.vectorstores import BagelPreviousAZLyricsNextBananaInstallation and SetupVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

BagelDB (Open Vector Database for AI), is like GitHub for AI data.

BagelDB (Open Vector Database for AI), is like GitHub for AI data. ->: It is a collaborative platform where users can create, share, and manage vector datasets. It can support private projects for independent developers, internal collaborations for enterprises, and public contributions for data DAOs.Installation and Setup‚Äãpip install betabageldbVectorStore‚ÄãSee a usage example.from langchain.vectorstores import BagelPreviousAZLyricsNextBananaInstallation and SetupVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,814

C Transformers | ü¶úÔ∏èüîó Langchain

This page covers how to use the C Transformers library within LangChain.

This page covers how to use the C Transformers library within LangChain. ->: C Transformers | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

This page covers how to use the C Transformers library within LangChain.

This page covers how to use the C Transformers library within LangChain. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreC TransformersOn this pageC TransformersThis page covers how to use the C Transformers library within LangChain.

This page covers how to use the C Transformers library within LangChain.

This page covers how to use the C Transformers library within LangChain. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreC TransformersOn this pageC TransformersThis page covers how to use the C Transformers library within LangChain.

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It is broken into two parts: installation and setup, and then references to specific C Transformers wrappers.Installation and Setup‚ÄãInstall the Python package with pip install ctransformersDownload a supported GGML model (see Supported Models)Wrappers‚ÄãLLM‚ÄãThere exists a CTransformers LLM wrapper, which you can access with:from langchain.llms import CTransformersIt provides a unified interface for all models:llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')print(llm('AI is going to'))If you are getting illegal instruction error, try using lib='avx' or lib='basic':llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2', lib='avx')It can be used with models hosted on the Hugging Face Hub:llm = CTransformers(model='marella/gpt-2-ggml')If a model repo has multiple model files (.bin files), specify a model file using:llm = CTransformers(model='marella/gpt-2-ggml', model_file='ggml-model.bin')Additional parameters can be passed using the config parameter:config = {'max_new_tokens': 256, 'repetition_penalty': 1.1}llm = CTransformers(model='marella/gpt-2-ggml', config=config)See Documentation for a list of available parameters.For a more detailed walkthrough of this, see this notebook.PreviousConfluenceNextDashVectorInstallation and SetupWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

This page covers how to use the C Transformers library within LangChain.

This page covers how to use the C Transformers library within LangChain. ->: It is broken into two parts: installation and setup, and then references to specific C Transformers wrappers.Installation and Setup‚ÄãInstall the Python package with pip install ctransformersDownload a supported GGML model (see Supported Models)Wrappers‚ÄãLLM‚ÄãThere exists a CTransformers LLM wrapper, which you can access with:from langchain.llms import CTransformersIt provides a unified interface for all models:llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')print(llm('AI is going to'))If you are getting illegal instruction error, try using lib='avx' or lib='basic':llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2', lib='avx')It can be used with models hosted on the Hugging Face Hub:llm = CTransformers(model='marella/gpt-2-ggml')If a model repo has multiple model files (.bin files), specify a model file using:llm = CTransformers(model='marella/gpt-2-ggml', model_file='ggml-model.bin')Additional parameters can be passed using the config parameter:config = {'max_new_tokens': 256, 'repetition_penalty': 1.1}llm = CTransformers(model='marella/gpt-2-ggml', config=config)See Documentation for a list of available parameters.For a more detailed walkthrough of this, see this notebook.PreviousConfluenceNextDashVectorInstallation and SetupWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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Airtable | ü¶úÔ∏èüîó Langchain

Airtable is a cloud collaboration service.

Airtable is a cloud collaboration service. ->: Airtable | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Airtable is a cloud collaboration service.

Airtable is a cloud collaboration service. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreAirtableOn this pageAirtableAirtable is a cloud collaboration service.

Airtable is a cloud collaboration service.

Airtable is a cloud collaboration service. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreAirtableOn this pageAirtableAirtable is a cloud collaboration service.

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Airtable is a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet. The fields in an Airtable table are similar to cells in a spreadsheet, but have types such as 'checkbox', 'phone number', and 'drop-down list', and can reference file attachments like images.Users can create a database, set up column types, add records, link tables to one another, collaborate, sort records and publish views to external websites.Installation and Setup‚Äãpip install pyairtableGet your API key.Get the ID of your base.Get the table ID from the table url.Document Loader‚Äãfrom langchain.document_loaders import AirtableLoaderSee an example.PreviousAirbyteNextAleph AlphaInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Airtable is a cloud collaboration service.

Airtable is a cloud collaboration service. ->: Airtable is a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet. The fields in an Airtable table are similar to cells in a spreadsheet, but have types such as 'checkbox', 'phone number', and 'drop-down list', and can reference file attachments like images.Users can create a database, set up column types, add records, link tables to one another, collaborate, sort records and publish views to external websites.Installation and Setup‚Äãpip install pyairtableGet your API key.Get the ID of your base.Get the table ID from the table url.Document Loader‚Äãfrom langchain.document_loaders import AirtableLoaderSee an example.PreviousAirbyteNextAleph AlphaInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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Log10 | ü¶úÔ∏èüîó Langchain

This page covers how to use the Log10 within LangChain.

This page covers how to use the Log10 within LangChain. ->: Log10 | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

This page covers how to use the Log10 within LangChain.

This page covers how to use the Log10 within LangChain. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreLog10On this pageLog10This page covers how to use the Log10 within LangChain.What is Log10?‚ÄãLog10 is an open-source proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.Quick start‚ÄãCreate your free account at log10.ioAdd your LOG10_TOKEN and LOG10_ORG_ID from the Settings and Organization tabs respectively as environment variables.Also add LOG10_URL=https://log10.io and your usual LLM API key: for e.g. OPENAI_API_KEY or ANTHROPIC_API_KEY to your environmentHow to enable Log10 data management for Langchain‚ÄãIntegration with log10 is a simple one-line log10_callback integration as shown below:from langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagefrom log10.langchain import Log10Callbackfrom log10.llm import Log10Configlog10_callback = Log10Callback(log10_config=Log10Config())messages = [ HumanMessage(content="You are a ping pong machine"), HumanMessage(content="Ping?"),]llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback])Log10 + Langchain + Logs docsMore details + screenshots including instructions for self-hosting logsHow to use tags with Log10‚Äãfrom langchain.llms import OpenAIfrom langchain.chat_models import ChatAnthropicfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagefrom log10.langchain import Log10Callbackfrom log10.llm import Log10Configlog10_callback = Log10Callback(log10_config=Log10Config())messages = [ HumanMessage(content="You are a ping pong machine"), HumanMessage(content="Ping?"),]llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])completion = llm.predict_messages(messages, tags=["foobar"])print(completion)llm = ChatAnthropic(model="claude-2", callbacks=[log10_callback], temperature=0.7, tags=["baz"])llm.predict_messages(messages)print(completion)llm =

This page covers how to use the Log10 within LangChain.

This page covers how to use the Log10 within LangChain. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreLog10On this pageLog10This page covers how to use the Log10 within LangChain.What is Log10?‚ÄãLog10 is an open-source proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.Quick start‚ÄãCreate your free account at log10.ioAdd your LOG10_TOKEN and LOG10_ORG_ID from the Settings and Organization tabs respectively as environment variables.Also add LOG10_URL=https://log10.io and your usual LLM API key: for e.g. OPENAI_API_KEY or ANTHROPIC_API_KEY to your environmentHow to enable Log10 data management for Langchain‚ÄãIntegration with log10 is a simple one-line log10_callback integration as shown below:from langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagefrom log10.langchain import Log10Callbackfrom log10.llm import Log10Configlog10_callback = Log10Callback(log10_config=Log10Config())messages = [ HumanMessage(content="You are a ping pong machine"), HumanMessage(content="Ping?"),]llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback])Log10 + Langchain + Logs docsMore details + screenshots including instructions for self-hosting logsHow to use tags with Log10‚Äãfrom langchain.llms import OpenAIfrom langchain.chat_models import ChatAnthropicfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import HumanMessagefrom log10.langchain import Log10Callbackfrom log10.llm import Log10Configlog10_callback = Log10Callback(log10_config=Log10Config())messages = [ HumanMessage(content="You are a ping pong machine"), HumanMessage(content="Ping?"),]llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])completion = llm.predict_messages(messages, tags=["foobar"])print(completion)llm = ChatAnthropic(model="claude-2", callbacks=[log10_callback], temperature=0.7, tags=["baz"])llm.predict_messages(messages)print(completion)llm =

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= OpenAI(model_name="text-davinci-003", callbacks=[log10_callback], temperature=0.5)completion = llm.predict("You are a ping pong machine.\nPing?\n")print(completion)You can also intermix direct OpenAI calls and Langchain LLM calls:import osfrom log10.load import log10, log10_sessionimport openaifrom langchain.llms import OpenAIlog10(openai)with log10_session(tags=["foo", "bar"]): # Log a direct OpenAI call response = openai.Completion.create( model="text-ada-001", prompt="Where is the Eiffel Tower?", temperature=0, max_tokens=1024, top_p=1, frequency_penalty=0, presence_penalty=0, ) print(response) # Log a call via Langchain llm = OpenAI(model_name="text-ada-001", temperature=0.5) response = llm.predict("You are a ping pong machine.\nPing?\n") print(response)How to debug Langchain calls‚ÄãExample of debuggingMore Langchain examplesPreviousLlama.cppNextMarqoWhat is Log10?Quick startHow to enable Log10 data management for LangchainHow to use tags with Log10How to debug Langchain callsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

This page covers how to use the Log10 within LangChain.

This page covers how to use the Log10 within LangChain. ->: = OpenAI(model_name="text-davinci-003", callbacks=[log10_callback], temperature=0.5)completion = llm.predict("You are a ping pong machine.\nPing?\n")print(completion)You can also intermix direct OpenAI calls and Langchain LLM calls:import osfrom log10.load import log10, log10_sessionimport openaifrom langchain.llms import OpenAIlog10(openai)with log10_session(tags=["foo", "bar"]): # Log a direct OpenAI call response = openai.Completion.create( model="text-ada-001", prompt="Where is the Eiffel Tower?", temperature=0, max_tokens=1024, top_p=1, frequency_penalty=0, presence_penalty=0, ) print(response) # Log a call via Langchain llm = OpenAI(model_name="text-ada-001", temperature=0.5) response = llm.predict("You are a ping pong machine.\nPing?\n") print(response)How to debug Langchain calls‚ÄãExample of debuggingMore Langchain examplesPreviousLlama.cppNextMarqoWhat is Log10?Quick startHow to enable Log10 data management for LangchainHow to use tags with Log10How to debug Langchain callsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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Slack | ü¶úÔ∏èüîó Langchain

Slack is an instant messaging program.

Slack is an instant messaging program. ->: Slack | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Slack is an instant messaging program.

Slack is an instant messaging program. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreSlackOn this pageSlackSlack is an instant messaging program.Installation and Setup‚ÄãThere isn't any special setup for it.Document Loader‚ÄãSee a usage example.from langchain.document_loaders import SlackDirectoryLoaderPreviousscikit-learnNextspaCyInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Slack is an instant messaging program.

Slack is an instant messaging program. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreSlackOn this pageSlackSlack is an instant messaging program.Installation and Setup‚ÄãThere isn't any special setup for it.Document Loader‚ÄãSee a usage example.from langchain.document_loaders import SlackDirectoryLoaderPreviousscikit-learnNextspaCyInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,829

PGVector | ü¶úÔ∏èüîó Langchain

This page covers how to use the Postgres PGVector ecosystem within LangChain

This page covers how to use the Postgres PGVector ecosystem within LangChain ->: PGVector | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

This page covers how to use the Postgres PGVector ecosystem within LangChain

This page covers how to use the Postgres PGVector ecosystem within LangChain ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMorePGVectorOn this pagePGVectorThis page covers how to use the Postgres PGVector ecosystem within LangChain

This page covers how to use the Postgres PGVector ecosystem within LangChain

This page covers how to use the Postgres PGVector ecosystem within LangChain ->: and toolkitsMemoryCallbacksChat loadersProvidersMorePGVectorOn this pagePGVectorThis page covers how to use the Postgres PGVector ecosystem within LangChain

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It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.Installation‚ÄãInstall the Python package with pip install pgvectorSetup‚ÄãThe first step is to create a database with the pgvector extension installed.Follow the steps at PGVector Installation Steps to install the database and the extension. The docker image is the easiest way to get started.Wrappers‚ÄãVectorStore‚ÄãThere exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores.pgvector import PGVectorUsage‚ÄãFor a more detailed walkthrough of the PGVector Wrapper, see this notebookPreviousPostgres EmbeddingNextPineconeInstallationSetupWrappersVectorStoreUsageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

This page covers how to use the Postgres PGVector ecosystem within LangChain

This page covers how to use the Postgres PGVector ecosystem within LangChain ->: It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.Installation‚ÄãInstall the Python package with pip install pgvectorSetup‚ÄãThe first step is to create a database with the pgvector extension installed.Follow the steps at PGVector Installation Steps to install the database and the extension. The docker image is the easiest way to get started.Wrappers‚ÄãVectorStore‚ÄãThere exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores.pgvector import PGVectorUsage‚ÄãFor a more detailed walkthrough of the PGVector Wrapper, see this notebookPreviousPostgres EmbeddingNextPineconeInstallationSetupWrappersVectorStoreUsageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,833

Airbyte | ü¶úÔ∏èüîó Langchain

Airbyte is a data integration platform for ELT pipelines from APIs,

Airbyte is a data integration platform for ELT pipelines from APIs, ->: Airbyte | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Airbyte is a data integration platform for ELT pipelines from APIs,

Airbyte is a data integration platform for ELT pipelines from APIs, ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreAirbyteOn this pageAirbyteAirbyte is a data integration platform for ELT pipelines from APIs,

Airbyte is a data integration platform for ELT pipelines from APIs,

Airbyte is a data integration platform for ELT pipelines from APIs, ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreAirbyteOn this pageAirbyteAirbyte is a data integration platform for ELT pipelines from APIs,

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databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.Installation and Setup‚ÄãThis instruction shows how to load any source from Airbyte into a local JSON file that can be read in as a document.Prerequisites: Have docker desktop installed.Steps:Clone Airbyte from GitHub - git clone https://github.com/airbytehq/airbyte.git.Switch into Airbyte directory - cd airbyte.Start Airbyte - docker compose up.In your browser, just visit http://localhost:8000. You will be asked for a username and password. By default, that's username airbyte and password password.Setup any source you wish.Set destination as Local JSON, with specified destination path - lets say /json_data. Set up a manual sync.Run the connection.To see what files are created, navigate to: file:///tmp/airbyte_local/.Document Loader‚ÄãSee a usage example.from langchain.document_loaders import AirbyteJSONLoaderPreviousAINetworkNextAirtableInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Airbyte is a data integration platform for ELT pipelines from APIs,

Airbyte is a data integration platform for ELT pipelines from APIs, ->: databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.Installation and Setup‚ÄãThis instruction shows how to load any source from Airbyte into a local JSON file that can be read in as a document.Prerequisites: Have docker desktop installed.Steps:Clone Airbyte from GitHub - git clone https://github.com/airbytehq/airbyte.git.Switch into Airbyte directory - cd airbyte.Start Airbyte - docker compose up.In your browser, just visit http://localhost:8000. You will be asked for a username and password. By default, that's username airbyte and password password.Setup any source you wish.Set destination as Local JSON, with specified destination path - lets say /json_data. Set up a manual sync.Run the connection.To see what files are created, navigate to: file:///tmp/airbyte_local/.Document Loader‚ÄãSee a usage example.from langchain.document_loaders import AirbyteJSONLoaderPreviousAINetworkNextAirtableInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,837

Atlas | ü¶úÔ∏èüîó Langchain

Nomic Atlas is a platform for interacting with both

Nomic Atlas is a platform for interacting with both ->: Atlas | ü¶úÔ∏èüîó Langchain

3,838

Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Nomic Atlas is a platform for interacting with both

Nomic Atlas is a platform for interacting with both ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreAtlasOn this pageAtlasNomic Atlas is a platform for interacting with both

Nomic Atlas is a platform for interacting with both

Nomic Atlas is a platform for interacting with both ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreAtlasOn this pageAtlasNomic Atlas is a platform for interacting with both

3,840

small and internet scale unstructured datasets.Installation and Setup‚ÄãInstall the Python package with pip install nomicNomic is also included in langchains poetry extras poetry install -E allVectorStore‚ÄãSee a usage example.from langchain.vectorstores import AtlasDBPreviousArxivNextAwaDBInstallation and SetupVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Nomic Atlas is a platform for interacting with both

Nomic Atlas is a platform for interacting with both ->: small and internet scale unstructured datasets.Installation and Setup‚ÄãInstall the Python package with pip install nomicNomic is also included in langchains poetry extras poetry install -E allVectorStore‚ÄãSee a usage example.from langchain.vectorstores import AtlasDBPreviousArxivNextAwaDBInstallation and SetupVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,841

Gutenberg | ü¶úÔ∏èüîó Langchain

Project Gutenberg is an online library of free eBooks.

Project Gutenberg is an online library of free eBooks. ->: Gutenberg | ü¶úÔ∏èüîó Langchain

3,842

Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Project Gutenberg is an online library of free eBooks.

Project Gutenberg is an online library of free eBooks. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreGutenbergOn this pageGutenbergProject Gutenberg is an online library of free eBooks.Installation and Setup‚ÄãThere isn't any special setup for it.Document Loader‚ÄãSee a usage example.from langchain.document_loaders import GutenbergLoaderPreviousGrobidNextHacker NewsInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Project Gutenberg is an online library of free eBooks.

Project Gutenberg is an online library of free eBooks. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreGutenbergOn this pageGutenbergProject Gutenberg is an online library of free eBooks.Installation and Setup‚ÄãThere isn't any special setup for it.Document Loader‚ÄãSee a usage example.from langchain.document_loaders import GutenbergLoaderPreviousGrobidNextHacker NewsInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,844

Cohere | ü¶úÔ∏èüîó Langchain

Cohere is a Canadian startup that provides natural language processing models

Cohere is a Canadian startup that provides natural language processing models ->: Cohere | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Cohere is a Canadian startup that provides natural language processing models

Cohere is a Canadian startup that provides natural language processing models ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreCohereOn this pageCohereCohere is a Canadian startup that provides natural language processing models

Cohere is a Canadian startup that provides natural language processing models

Cohere is a Canadian startup that provides natural language processing models ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreCohereOn this pageCohereCohere is a Canadian startup that provides natural language processing models

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that help companies improve human-machine interactions.Installation and Setup‚ÄãInstall the Python SDK :pip install cohereGet a Cohere api key and set it as an environment variable (COHERE_API_KEY)LLM‚ÄãThere exists an Cohere LLM wrapper, which you can access with See a usage example.from langchain.llms import CohereText Embedding Model‚ÄãThere exists an Cohere Embedding model, which you can access with from langchain.embeddings import CohereEmbeddingsFor a more detailed walkthrough of this, see this notebookRetriever‚ÄãSee a usage example.from langchain.retrievers.document_compressors import CohereRerankPreviousCnosDBNextCollege ConfidentialInstallation and SetupLLMText Embedding ModelRetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Cohere is a Canadian startup that provides natural language processing models

Cohere is a Canadian startup that provides natural language processing models ->: that help companies improve human-machine interactions.Installation and Setup‚ÄãInstall the Python SDK :pip install cohereGet a Cohere api key and set it as an environment variable (COHERE_API_KEY)LLM‚ÄãThere exists an Cohere LLM wrapper, which you can access with See a usage example.from langchain.llms import CohereText Embedding Model‚ÄãThere exists an Cohere Embedding model, which you can access with from langchain.embeddings import CohereEmbeddingsFor a more detailed walkthrough of this, see this notebookRetriever‚ÄãSee a usage example.from langchain.retrievers.document_compressors import CohereRerankPreviousCnosDBNextCollege ConfidentialInstallation and SetupLLMText Embedding ModelRetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,848

ModelScope | ü¶úÔ∏èüîó Langchain

ModelScope is a big repository of the models and datasets.

ModelScope is a big repository of the models and datasets. ->: ModelScope | ü¶úÔ∏èüîó Langchain

3,849

Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

ModelScope is a big repository of the models and datasets.

ModelScope is a big repository of the models and datasets. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreModelScopeOn this pageModelScopeModelScope is a big repository of the models and datasets.This page covers how to use the modelscope ecosystem within LangChain.

ModelScope is a big repository of the models and datasets.

ModelScope is a big repository of the models and datasets. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreModelScopeOn this pageModelScopeModelScope is a big repository of the models and datasets.This page covers how to use the modelscope ecosystem within LangChain.

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It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.Installation and Setup‚ÄãInstall the modelscope package.pip install modelscopeText Embedding Models‚Äãfrom langchain.embeddings import ModelScopeEmbeddingsFor a more detailed walkthrough of this, see this notebookPreviousModalNextModern TreasuryInstallation and SetupText Embedding ModelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

ModelScope is a big repository of the models and datasets.

ModelScope is a big repository of the models and datasets. ->: It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.Installation and Setup‚ÄãInstall the modelscope package.pip install modelscopeText Embedding Models‚Äãfrom langchain.embeddings import ModelScopeEmbeddingsFor a more detailed walkthrough of this, see this notebookPreviousModalNextModern TreasuryInstallation and SetupText Embedding ModelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

3,852

Alibaba Cloud Opensearch | ü¶úÔ∏èüîó Langchain

Alibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.

Alibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: Alibaba Cloud Opensearch | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Alibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.

Alibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreAlibaba Cloud OpensearchOn this pageAlibaba Cloud OpensearchAlibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.OpenSearch helps you develop high quality, maintenance-free, and high performance intelligent search services to provide your users with high search efficiency and accuracy.OpenSearch provides the vector search feature. In specific scenarios,especially in question retrieval and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results.Purchase an instance and configure it‚ÄãPurchase OpenSearch Vector Search Edition from Alibaba Cloud and configure the instance according to the help documentation.Alibaba Cloud Opensearch Vector Store Wrappers‚Äãsupported functions:add_textsadd_documentsfrom_textsfrom_documentssimilarity_searchasimilarity_searchsimilarity_search_by_vectorasimilarity_search_by_vectorsimilarity_search_with_relevance_scoresdelete_doc_by_textsFor a more detailed walk through of the Alibaba Cloud OpenSearch wrapper, see this notebookIf you encounter any problems during use, please feel free to contact [email protected] , and we will do our best to provide you with assistance and support.PreviousAleph AlphaNextAnalyticDBPurchase an instance and configure itAlibaba Cloud Opensearch Vector Store WrappersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Alibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.

Alibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreAlibaba Cloud OpensearchOn this pageAlibaba Cloud OpensearchAlibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.OpenSearch helps you develop high quality, maintenance-free, and high performance intelligent search services to provide your users with high search efficiency and accuracy.OpenSearch provides the vector search feature. In specific scenarios,especially in question retrieval and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results.Purchase an instance and configure it‚ÄãPurchase OpenSearch Vector Search Edition from Alibaba Cloud and configure the instance according to the help documentation.Alibaba Cloud Opensearch Vector Store Wrappers‚Äãsupported functions:add_textsadd_documentsfrom_textsfrom_documentssimilarity_searchasimilarity_searchsimilarity_search_by_vectorasimilarity_search_by_vectorsimilarity_search_with_relevance_scoresdelete_doc_by_textsFor a more detailed walk through of the Alibaba Cloud OpenSearch wrapper, see this notebookIf you encounter any problems during use, please feel free to contact [email protected] , and we will do our best to provide you with assistance and support.PreviousAleph AlphaNextAnalyticDBPurchase an instance and configure itAlibaba Cloud Opensearch Vector Store WrappersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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MediaWikiDump | ü¶úÔ∏èüîó Langchain

MediaWiki XML Dumps contain the content of a wiki

MediaWiki XML Dumps contain the content of a wiki ->: MediaWikiDump | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

MediaWiki XML Dumps contain the content of a wiki

MediaWiki XML Dumps contain the content of a wiki ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreMediaWikiDumpOn this pageMediaWikiDumpMediaWiki XML Dumps contain the content of a wiki

MediaWiki XML Dumps contain the content of a wiki

MediaWiki XML Dumps contain the content of a wiki ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreMediaWikiDumpOn this pageMediaWikiDumpMediaWiki XML Dumps contain the content of a wiki

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(wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup of the wiki database, the dump does not contain user accounts, images, edit logs, etc.Installation and Setup‚ÄãWe need to install several python packages.The mediawiki-utilities supports XML schema 0.11 in unmerged branches.pip install -qU git+https://github.com/mediawiki-utilities/python-mwtypes@updates_schema_0.11The mediawiki-utilities mwxml has a bug, fix PR pending.pip install -qU git+https://github.com/gdedrouas/python-mwxml@xml_format_0.11pip install -qU mwparserfromhellDocument Loader‚ÄãSee a usage example.from langchain.document_loaders import MWDumpLoaderPreviousMarqoNextMeilisearchInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

MediaWiki XML Dumps contain the content of a wiki

MediaWiki XML Dumps contain the content of a wiki ->: (wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup of the wiki database, the dump does not contain user accounts, images, edit logs, etc.Installation and Setup‚ÄãWe need to install several python packages.The mediawiki-utilities supports XML schema 0.11 in unmerged branches.pip install -qU git+https://github.com/mediawiki-utilities/python-mwtypes@updates_schema_0.11The mediawiki-utilities mwxml has a bug, fix PR pending.pip install -qU git+https://github.com/gdedrouas/python-mwxml@xml_format_0.11pip install -qU mwparserfromhellDocument Loader‚ÄãSee a usage example.from langchain.document_loaders import MWDumpLoaderPreviousMarqoNextMeilisearchInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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SerpAPI | ü¶úÔ∏èüîó Langchain

This page covers how to use the SerpAPI search APIs within LangChain.

This page covers how to use the SerpAPI search APIs within LangChain. ->: SerpAPI | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

This page covers how to use the SerpAPI search APIs within LangChain.

This page covers how to use the SerpAPI search APIs within LangChain. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreSerpAPIOn this pageSerpAPIThis page covers how to use the SerpAPI search APIs within LangChain.

This page covers how to use the SerpAPI search APIs within LangChain.

This page covers how to use the SerpAPI search APIs within LangChain. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreSerpAPIOn this pageSerpAPIThis page covers how to use the SerpAPI search APIs within LangChain.

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It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.Installation and Setup‚ÄãInstall requirements with pip install google-search-resultsGet a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY)Wrappers‚ÄãUtility‚ÄãThere exists a SerpAPI utility which wraps this API. To import this utility:from langchain.utilities import SerpAPIWrapperFor a more detailed walkthrough of this wrapper, see this notebook.Tool‚ÄãYou can also easily load this wrapper as a Tool (to use with an Agent). You can do this with:from langchain.agents import load_toolstools = load_tools(["serpapi"])For more information on this, see this pagePreviousSearxNG Search APINextShale ProtocolInstallation and SetupWrappersUtilityToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

This page covers how to use the SerpAPI search APIs within LangChain.

This page covers how to use the SerpAPI search APIs within LangChain. ->: It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.Installation and Setup‚ÄãInstall requirements with pip install google-search-resultsGet a SerpAPI api key and either set it as an environment variable (SERPAPI_API_KEY)Wrappers‚ÄãUtility‚ÄãThere exists a SerpAPI utility which wraps this API. To import this utility:from langchain.utilities import SerpAPIWrapperFor a more detailed walkthrough of this wrapper, see this notebook.Tool‚ÄãYou can also easily load this wrapper as a Tool (to use with an Agent). You can do this with:from langchain.agents import load_toolstools = load_tools(["serpapi"])For more information on this, see this pagePreviousSearxNG Search APINextShale ProtocolInstallation and SetupWrappersUtilityToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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ClearML | ü¶úÔ∏èüîó Langchain

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: ClearML | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreClearMLOn this pageClearMLClearML is a ML/DL development and production suite, it contains 5 main modules:Experiment Manager - Automagical experiment tracking, environments and resultsMLOps - Orchestration, Automation & Pipelines solution for ML/DL jobs (K8s / Cloud / bare-metal)Data-Management - Fully differentiable data management & version control solution on top of object-storage (S3 / GS / Azure / NAS)Model-Serving - cloud-ready Scalable model serving solution!

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreClearMLOn this pageClearMLClearML is a ML/DL development and production suite, it contains 5 main modules:Experiment Manager - Automagical experiment tracking, environments and resultsMLOps - Orchestration, Automation & Pipelines solution for ML/DL jobs (K8s / Cloud / bare-metal)Data-Management - Fully differentiable data management & version control solution on top of object-storage (S3 / GS / Azure / NAS)Model-Serving - cloud-ready Scalable model serving solution!

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Deploy new model endpoints in under 5 minutes Includes optimized GPU serving support backed by Nvidia-Triton

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: Deploy new model endpoints in under 5 minutes Includes optimized GPU serving support backed by Nvidia-Triton

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with out-of-the-box Model MonitoringFire Reports - Create and share rich MarkDown documents supporting embeddable online contentIn order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. We use the ClearML Experiment Manager that neatly tracks and organizes all your experiment runs.Installation and Setup‚Äãpip install clearmlpip install pandaspip install textstatpip install spacypython -m spacy download en_core_web_smGetting API Credentials‚ÄãWe'll be using quite some APIs in this notebook, here is a list and where to get them:ClearML: https://app.clear.ml/settings/workspace-configurationOpenAI: https://platform.openai.com/account/api-keysSerpAPI (google search): https://serpapi.com/dashboardimport osos.environ["CLEARML_API_ACCESS_KEY"] = ""os.environ["CLEARML_API_SECRET_KEY"] = ""os.environ["OPENAI_API_KEY"] = ""os.environ["SERPAPI_API_KEY"] = ""Callbacks‚Äãfrom langchain.callbacks import ClearMLCallbackHandlerfrom datetime import datetimefrom langchain.callbacks import StdOutCallbackHandlerfrom langchain.llms import OpenAI# Setup and use the ClearML Callbackclearml_callback = ClearMLCallbackHandler( task_type="inference", project_name="langchain_callback_demo", task_name="llm", tags=["test"], # Change the following parameters based on the amount of detail you want tracked visualize=True, complexity_metrics=True, stream_logs=True,)callbacks = [StdOutCallbackHandler(), clearml_callback]# Get the OpenAI model ready to gollm = OpenAI(temperature=0, callbacks=callbacks) The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.Scenario 1: Just an LLM‚ÄãFirst, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML# SCENARIO 1 - LLMllm_result = llm.generate(["Tell me a joke", "Tell me a poem"] *

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: with out-of-the-box Model MonitoringFire Reports - Create and share rich MarkDown documents supporting embeddable online contentIn order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. We use the ClearML Experiment Manager that neatly tracks and organizes all your experiment runs.Installation and Setup‚Äãpip install clearmlpip install pandaspip install textstatpip install spacypython -m spacy download en_core_web_smGetting API Credentials‚ÄãWe'll be using quite some APIs in this notebook, here is a list and where to get them:ClearML: https://app.clear.ml/settings/workspace-configurationOpenAI: https://platform.openai.com/account/api-keysSerpAPI (google search): https://serpapi.com/dashboardimport osos.environ["CLEARML_API_ACCESS_KEY"] = ""os.environ["CLEARML_API_SECRET_KEY"] = ""os.environ["OPENAI_API_KEY"] = ""os.environ["SERPAPI_API_KEY"] = ""Callbacks‚Äãfrom langchain.callbacks import ClearMLCallbackHandlerfrom datetime import datetimefrom langchain.callbacks import StdOutCallbackHandlerfrom langchain.llms import OpenAI# Setup and use the ClearML Callbackclearml_callback = ClearMLCallbackHandler( task_type="inference", project_name="langchain_callback_demo", task_name="llm", tags=["test"], # Change the following parameters based on the amount of detail you want tracked visualize=True, complexity_metrics=True, stream_logs=True,)callbacks = [StdOutCallbackHandler(), clearml_callback]# Get the OpenAI model ready to gollm = OpenAI(temperature=0, callbacks=callbacks) The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.Scenario 1: Just an LLM‚ÄãFirst, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML# SCENARIO 1 - LLMllm_result = llm.generate(["Tell me a joke", "Tell me a poem"] *

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me a joke", "Tell me a poem"] * 3)# After every generation run, use flush to make sure all the metrics# prompts and other output are properly saved separatelyclearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential") {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138,

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: me a joke", "Tell me a poem"] * 3)# After every generation run, use flush to make sure all the metrics# prompts and other output are properly saved separatelyclearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential") {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138,

3,869

24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name':

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name':

3,870

'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2,

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2,

3,871

'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17} {'action_records': action name step starts ends errors text_ctr chain_starts \ 0 on_llm_start OpenAI 1 1 0 0 0 0 1 on_llm_start OpenAI 1 1 0 0 0 0

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17} {'action_records': action name step starts ends errors text_ctr chain_starts \ 0 on_llm_start OpenAI 1 1 0 0 0 0 1 on_llm_start OpenAI 1 1 0 0 0 0

3,872

1 0 0 0 0 2 on_llm_start OpenAI 1 1 0 0 0 0 3 on_llm_start OpenAI 1 1 0 0 0 0 4 on_llm_start OpenAI 1 1 0 0 0 0 5 on_llm_start OpenAI 1 1 0 0 0 0 6 on_llm_end NaN 2 1 1 0 0 0 7 on_llm_end NaN 2 1 1 0 0 0 8 on_llm_end NaN 2 1 1 0 0 0 9 on_llm_end NaN 2 1 1 0 0 0 10 on_llm_end NaN 2 1 1 0 0 0 11 on_llm_end NaN 2 1 1 0 0 0 12 on_llm_start OpenAI 3 2 1 0 0 0 13 on_llm_start OpenAI 3 2 1 0 0 0 14 on_llm_start OpenAI 3 2 1 0 0 0 15 on_llm_start OpenAI 3 2 1 0 0 0 16 on_llm_start OpenAI 3 2 1 0 0 0 17 on_llm_start OpenAI 3 2 1 0 0 0 18 on_llm_end NaN 4 2 2 0 0 0 19 on_llm_end NaN 4 2 2 0 0 0 20 on_llm_end NaN 4 2 2 0 0 0 21 on_llm_end NaN 4 2 2 0 0 0 22 on_llm_end NaN 4 2 2 0 0 0 23 on_llm_end NaN 4 2 2 0 0 0 chain_ends llm_starts ... difficult_words linsear_write_formula \ 0 0 1 ...

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 1 0 0 0 0 2 on_llm_start OpenAI 1 1 0 0 0 0 3 on_llm_start OpenAI 1 1 0 0 0 0 4 on_llm_start OpenAI 1 1 0 0 0 0 5 on_llm_start OpenAI 1 1 0 0 0 0 6 on_llm_end NaN 2 1 1 0 0 0 7 on_llm_end NaN 2 1 1 0 0 0 8 on_llm_end NaN 2 1 1 0 0 0 9 on_llm_end NaN 2 1 1 0 0 0 10 on_llm_end NaN 2 1 1 0 0 0 11 on_llm_end NaN 2 1 1 0 0 0 12 on_llm_start OpenAI 3 2 1 0 0 0 13 on_llm_start OpenAI 3 2 1 0 0 0 14 on_llm_start OpenAI 3 2 1 0 0 0 15 on_llm_start OpenAI 3 2 1 0 0 0 16 on_llm_start OpenAI 3 2 1 0 0 0 17 on_llm_start OpenAI 3 2 1 0 0 0 18 on_llm_end NaN 4 2 2 0 0 0 19 on_llm_end NaN 4 2 2 0 0 0 20 on_llm_end NaN 4 2 2 0 0 0 21 on_llm_end NaN 4 2 2 0 0 0 22 on_llm_end NaN 4 2 2 0 0 0 23 on_llm_end NaN 4 2 2 0 0 0 chain_ends llm_starts ... difficult_words linsear_write_formula \ 0 0 1 ...

3,873

\ 0 0 1 ... NaN NaN 1 0 1 ... NaN NaN 2 0 1 ... NaN NaN 3 0 1 ... NaN NaN 4 0 1 ... NaN NaN 5 0 1 ... NaN NaN 6 0 1 ... 0.0 5.5 7 0 1 ... 2.0 6.5 8 0 1 ... 0.0 5.5 9 0 1 ... 2.0 6.5 10 0 1 ... 0.0 5.5 11 0 1 ... 2.0 6.5 12 0 2 ... NaN NaN 13 0 2 ... NaN NaN 14 0 2 ... NaN NaN 15 0 2 ... NaN NaN 16 0 2 ... NaN NaN 17 0 2 ... NaN NaN 18 0 2 ... 0.0 5.5 19 0 2 ... 2.0 6.5 20 0 2 ... 0.0 5.5 21 0 2 ... 2.0 6.5 22 0 2 ... 0.0 5.5 23 0 2 ... 2.0 6.5 gunning_fog text_standard fernandez_huerta szigriszt_pazos \ 0 NaN NaN

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: \ 0 0 1 ... NaN NaN 1 0 1 ... NaN NaN 2 0 1 ... NaN NaN 3 0 1 ... NaN NaN 4 0 1 ... NaN NaN 5 0 1 ... NaN NaN 6 0 1 ... 0.0 5.5 7 0 1 ... 2.0 6.5 8 0 1 ... 0.0 5.5 9 0 1 ... 2.0 6.5 10 0 1 ... 0.0 5.5 11 0 1 ... 2.0 6.5 12 0 2 ... NaN NaN 13 0 2 ... NaN NaN 14 0 2 ... NaN NaN 15 0 2 ... NaN NaN 16 0 2 ... NaN NaN 17 0 2 ... NaN NaN 18 0 2 ... 0.0 5.5 19 0 2 ... 2.0 6.5 20 0 2 ... 0.0 5.5 21 0 2 ... 2.0 6.5 22 0 2 ... 0.0 5.5 23 0 2 ... 2.0 6.5 gunning_fog text_standard fernandez_huerta szigriszt_pazos \ 0 NaN NaN

3,874

\ 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN 6 5.20 5th and 6th grade 133.58 131.54 7 8.28 6th and 7th grade 115.58 112.37 8 5.20 5th and 6th grade 133.58 131.54 9 8.28 6th and 7th grade 115.58 112.37 10 5.20 5th and 6th grade 133.58 131.54 11 8.28 6th and 7th grade 115.58 112.37 12 NaN NaN NaN NaN 13 NaN NaN NaN NaN 14 NaN NaN NaN NaN 15 NaN NaN NaN NaN 16 NaN NaN NaN NaN 17 NaN NaN NaN NaN 18 5.20 5th and 6th grade 133.58 131.54 19 8.28 6th and 7th grade 115.58 112.37 20 5.20 5th and 6th grade 133.58 131.54 21 8.28 6th and 7th grade 115.58 112.37 22 5.20 5th and 6th grade 133.58 131.54 23 8.28 6th and 7th grade 115.58 112.37 gutierrez_polini crawford gulpease_index osman 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: \ 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN 6 5.20 5th and 6th grade 133.58 131.54 7 8.28 6th and 7th grade 115.58 112.37 8 5.20 5th and 6th grade 133.58 131.54 9 8.28 6th and 7th grade 115.58 112.37 10 5.20 5th and 6th grade 133.58 131.54 11 8.28 6th and 7th grade 115.58 112.37 12 NaN NaN NaN NaN 13 NaN NaN NaN NaN 14 NaN NaN NaN NaN 15 NaN NaN NaN NaN 16 NaN NaN NaN NaN 17 NaN NaN NaN NaN 18 5.20 5th and 6th grade 133.58 131.54 19 8.28 6th and 7th grade 115.58 112.37 20 5.20 5th and 6th grade 133.58 131.54 21 8.28 6th and 7th grade 115.58 112.37 22 5.20 5th and 6th grade 133.58 131.54 23 8.28 6th and 7th grade 115.58 112.37 gutierrez_polini crawford gulpease_index osman 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2

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NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN 6 62.30 -0.2 79.8 116.91 7 54.83 1.4 72.1 100.17 8 62.30 -0.2 79.8 116.91 9 54.83 1.4 72.1 100.17 10 62.30 -0.2 79.8 116.91 11 54.83 1.4 72.1 100.17 12 NaN NaN NaN NaN 13 NaN NaN NaN NaN 14 NaN NaN NaN NaN 15 NaN NaN NaN NaN 16 NaN NaN NaN NaN 17 NaN NaN NaN NaN 18 62.30 -0.2 79.8 116.91 19 54.83 1.4 72.1 100.17 20 62.30 -0.2 79.8 116.91 21 54.83 1.4 72.1 100.17 22 62.30 -0.2 79.8 116.91 23 54.83 1.4 72.1 100.17 [24 rows x 39 columns], 'session_analysis': prompt_step prompts name output_step \ 0 1 Tell me a joke OpenAI 2 1 1 Tell me a poem OpenAI 2 2 1 Tell me a joke OpenAI 2 3 1 Tell me a poem OpenAI 2 4 1 Tell me a joke OpenAI 2 5 1 Tell me a poem OpenAI 2 6 3 Tell me a joke OpenAI 4 7 3 Tell me a poem OpenAI 4 8 3 Tell me a joke OpenAI 4 9

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN NaN NaN NaN 5 NaN NaN NaN NaN 6 62.30 -0.2 79.8 116.91 7 54.83 1.4 72.1 100.17 8 62.30 -0.2 79.8 116.91 9 54.83 1.4 72.1 100.17 10 62.30 -0.2 79.8 116.91 11 54.83 1.4 72.1 100.17 12 NaN NaN NaN NaN 13 NaN NaN NaN NaN 14 NaN NaN NaN NaN 15 NaN NaN NaN NaN 16 NaN NaN NaN NaN 17 NaN NaN NaN NaN 18 62.30 -0.2 79.8 116.91 19 54.83 1.4 72.1 100.17 20 62.30 -0.2 79.8 116.91 21 54.83 1.4 72.1 100.17 22 62.30 -0.2 79.8 116.91 23 54.83 1.4 72.1 100.17 [24 rows x 39 columns], 'session_analysis': prompt_step prompts name output_step \ 0 1 Tell me a joke OpenAI 2 1 1 Tell me a poem OpenAI 2 2 1 Tell me a joke OpenAI 2 3 1 Tell me a poem OpenAI 2 4 1 Tell me a joke OpenAI 2 5 1 Tell me a poem OpenAI 2 6 3 Tell me a joke OpenAI 4 7 3 Tell me a poem OpenAI 4 8 3 Tell me a joke OpenAI 4 9

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3 Tell me a joke OpenAI 4 9 3 Tell me a poem OpenAI 4 10 3 Tell me a joke OpenAI 4 11 3 Tell me a poem OpenAI 4 output \ 0 \n\nQ: What did the fish say when it hit the w... 1 \n\nRoses are red,\nViolets are blue,\nSugar i... 2 \n\nQ: What did the fish say when it hit the w... 3 \n\nRoses are red,\nViolets are blue,\nSugar i... 4 \n\nQ: What did the fish say when it hit the w... 5 \n\nRoses are red,\nViolets are blue,\nSugar i... 6 \n\nQ: What did the fish say when it hit the w... 7 \n\nRoses are red,\nViolets are blue,\nSugar i... 8 \n\nQ: What did the fish say when it hit the w... 9 \n\nRoses are red,\nViolets are blue,\nSugar i... 10 \n\nQ: What did the fish say when it hit the w... 11 \n\nRoses are red,\nViolets are blue,\nSugar i... token_usage_total_tokens token_usage_prompt_tokens \ 0 162 24 1 162 24 2 162 24 3 162 24 4 162 24 5 162 24 6 162 24 7 162 24 8 162 24 9 162 24 10 162 24 11 162 24 token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \ 0 138 109.04 1.3 1

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 3 Tell me a joke OpenAI 4 9 3 Tell me a poem OpenAI 4 10 3 Tell me a joke OpenAI 4 11 3 Tell me a poem OpenAI 4 output \ 0 \n\nQ: What did the fish say when it hit the w... 1 \n\nRoses are red,\nViolets are blue,\nSugar i... 2 \n\nQ: What did the fish say when it hit the w... 3 \n\nRoses are red,\nViolets are blue,\nSugar i... 4 \n\nQ: What did the fish say when it hit the w... 5 \n\nRoses are red,\nViolets are blue,\nSugar i... 6 \n\nQ: What did the fish say when it hit the w... 7 \n\nRoses are red,\nViolets are blue,\nSugar i... 8 \n\nQ: What did the fish say when it hit the w... 9 \n\nRoses are red,\nViolets are blue,\nSugar i... 10 \n\nQ: What did the fish say when it hit the w... 11 \n\nRoses are red,\nViolets are blue,\nSugar i... token_usage_total_tokens token_usage_prompt_tokens \ 0 162 24 1 162 24 2 162 24 3 162 24 4 162 24 5 162 24 6 162 24 7 162 24 8 162 24 9 162 24 10 162 24 11 162 24 token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \ 0 138 109.04 1.3 1

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109.04 1.3 1 138 83.66 4.8 2 138 109.04 1.3 3 138 83.66 4.8 4 138 109.04 1.3 5 138 83.66 4.8 6 138 109.04 1.3 7 138 83.66 4.8 8 138 109.04 1.3 9 138 83.66 4.8 10 138 109.04 1.3 11 138 83.66 4.8 ... difficult_words linsear_write_formula gunning_fog \ 0 ... 0 5.5 5.20 1 ... 2 6.5 8.28 2 ... 0 5.5 5.20 3 ... 2 6.5 8.28 4 ... 0 5.5 5.20 5 ... 2 6.5 8.28 6 ... 0 5.5 5.20 7 ... 2 6.5 8.28 8 ... 0 5.5 5.20 9 ... 2 6.5 8.28 10 ... 0 5.5 5.20 11 ... 2 6.5 8.28 text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \ 0 5th and 6th grade 133.58 131.54 62.30 1 6th

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 109.04 1.3 1 138 83.66 4.8 2 138 109.04 1.3 3 138 83.66 4.8 4 138 109.04 1.3 5 138 83.66 4.8 6 138 109.04 1.3 7 138 83.66 4.8 8 138 109.04 1.3 9 138 83.66 4.8 10 138 109.04 1.3 11 138 83.66 4.8 ... difficult_words linsear_write_formula gunning_fog \ 0 ... 0 5.5 5.20 1 ... 2 6.5 8.28 2 ... 0 5.5 5.20 3 ... 2 6.5 8.28 4 ... 0 5.5 5.20 5 ... 2 6.5 8.28 6 ... 0 5.5 5.20 7 ... 2 6.5 8.28 8 ... 0 5.5 5.20 9 ... 2 6.5 8.28 10 ... 0 5.5 5.20 11 ... 2 6.5 8.28 text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \ 0 5th and 6th grade 133.58 131.54 62.30 1 6th

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131.54 62.30 1 6th and 7th grade 115.58 112.37 54.83 2 5th and 6th grade 133.58 131.54 62.30 3 6th and 7th grade 115.58 112.37 54.83 4 5th and 6th grade 133.58 131.54 62.30 5 6th and 7th grade 115.58 112.37 54.83 6 5th and 6th grade 133.58 131.54 62.30 7 6th and 7th grade 115.58 112.37 54.83 8 5th and 6th grade 133.58 131.54 62.30 9 6th and 7th grade 115.58 112.37 54.83 10 5th and 6th grade 133.58 131.54 62.30 11 6th and 7th grade 115.58 112.37 54.83 crawford gulpease_index osman 0 -0.2 79.8 116.91 1 1.4 72.1 100.17 2 -0.2 79.8 116.91 3 1.4 72.1 100.17 4 -0.2 79.8 116.91 5 1.4 72.1 100.17 6 -0.2 79.8 116.91 7 1.4 72.1 100.17 8 -0.2 79.8 116.91 9 1.4 72.1 100.17 10 -0.2 79.8 116.91 11 1.4 72.1 100.17 [12 rows x 24 columns]} 2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequentialAt this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 131.54 62.30 1 6th and 7th grade 115.58 112.37 54.83 2 5th and 6th grade 133.58 131.54 62.30 3 6th and 7th grade 115.58 112.37 54.83 4 5th and 6th grade 133.58 131.54 62.30 5 6th and 7th grade 115.58 112.37 54.83 6 5th and 6th grade 133.58 131.54 62.30 7 6th and 7th grade 115.58 112.37 54.83 8 5th and 6th grade 133.58 131.54 62.30 9 6th and 7th grade 115.58 112.37 54.83 10 5th and 6th grade 133.58 131.54 62.30 11 6th and 7th grade 115.58 112.37 54.83 crawford gulpease_index osman 0 -0.2 79.8 116.91 1 1.4 72.1 100.17 2 -0.2 79.8 116.91 3 1.4 72.1 100.17 4 -0.2 79.8 116.91 5 1.4 72.1 100.17 6 -0.2 79.8 116.91 7 1.4 72.1 100.17 8 -0.2 79.8 116.91 9 1.4 72.1 100.17 10 -0.2 79.8 116.91 11 1.4 72.1 100.17 [12 rows x 24 columns]} 2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequentialAt this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and

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as an artifact, there are also console logs and under the plots section, you'll find tables that represent the flow of the chain.Finally, if you enabled visualizations, these are stored as HTML files under debug samples.Scenario 2: Creating an agent with tools‚ÄãTo show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.You can now also see the use of the finish=True keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation.from langchain.agents import initialize_agent, load_toolsfrom langchain.agents import AgentType# SCENARIO 2 - Agent with Toolstools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callbacks=callbacks,)agent.run("Who is the wife of the person who sang summer of 69?")clearml_callback.flush_tracker( langchain_asset=agent, name="Agent with Tools", finish=True) > Entering new AgentExecutor chain... {'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events.

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: as an artifact, there are also console logs and under the plots section, you'll find tables that represent the flow of the chain.Finally, if you enabled visualizations, these are stored as HTML files under debug samples.Scenario 2: Creating an agent with tools‚ÄãTo show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.You can now also see the use of the finish=True keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation.from langchain.agents import initialize_agent, load_toolsfrom langchain.agents import AgentType# SCENARIO 2 - Agent with Toolstools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callbacks=callbacks,)agent.run("Who is the wife of the person who sang summer of 69?")clearml_callback.flush_tracker( langchain_asset=agent, name="Agent with Tools", finish=True) > Entering new AgentExecutor chain... {'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events.

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need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought:'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16} I need to find out who sang summer of 69 and then find out who their wife is. Action: Search Action Input: "Who sang summer of 69"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought:'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16} I need to find out who sang summer of 69 and then find out who their wife is. Action: Search Action Input: "Who sang summer of 69"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who

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out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0} {'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0} Observation: Bryan Adams - Summer Of 69 (Official Music Video). Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0} {'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0} Observation: Bryan Adams - Summer Of 69 (Official Music Video). Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N

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Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought:'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2} I need to find out who Bryan Adams is married to. Action: Search Action Input: "Who is Bryan Adams married to"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0} {'action': 'on_tool_start',

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought:'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2} I need to find out who Bryan Adams is married to. Action: Search Action Input: "Who is Bryan Adams married to"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0} {'action': 'on_tool_start',

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'agent_ends': 0} {'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0} Observation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ... Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 'agent_ends': 0} {'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0} Observation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ... Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0} {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input

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Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought: I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\nThought:'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14} I now know the final answer. Final Answer: Bryan Adams has never been married. {'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3,

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought: I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\nThought:'} {'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14} I now know the final answer. Final Answer: Bryan Adams has never been married. {'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3,

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'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1} > Finished chain. {'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1} {'action_records': action name step starts ends errors text_ctr \ 0 on_llm_start OpenAI 1 1 0 0 0 1 on_llm_start OpenAI 1 1 0 0 0 2 on_llm_start OpenAI 1 1 0 0 0 3 on_llm_start OpenAI 1 1 0 0 0 4 on_llm_start OpenAI 1 1 0 0 0 .. ... ... ... ... ... ... ... 66 on_tool_end NaN 11 7 4 0 0 67 on_llm_start OpenAI 12 8 4 0 0 68 on_llm_end NaN 13 8 5 0 0 69 on_agent_finish NaN 14 8 6 0 0 70 on_chain_end NaN 15 8 7 0 0 chain_starts chain_ends llm_starts ... gulpease_index osman input \ 0 0 0 1 ... NaN NaN NaN 1 0 0 1 ... NaN NaN NaN 2 0 0 1 ... NaN NaN NaN 3 0 0 1 ... NaN NaN NaN 4 0 0 1 ... NaN NaN NaN .. ... ... ... ... ... ... ... 66 1 0 2 ... NaN NaN NaN 67 1 0

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1} > Finished chain. {'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1} {'action_records': action name step starts ends errors text_ctr \ 0 on_llm_start OpenAI 1 1 0 0 0 1 on_llm_start OpenAI 1 1 0 0 0 2 on_llm_start OpenAI 1 1 0 0 0 3 on_llm_start OpenAI 1 1 0 0 0 4 on_llm_start OpenAI 1 1 0 0 0 .. ... ... ... ... ... ... ... 66 on_tool_end NaN 11 7 4 0 0 67 on_llm_start OpenAI 12 8 4 0 0 68 on_llm_end NaN 13 8 5 0 0 69 on_agent_finish NaN 14 8 6 0 0 70 on_chain_end NaN 15 8 7 0 0 chain_starts chain_ends llm_starts ... gulpease_index osman input \ 0 0 0 1 ... NaN NaN NaN 1 0 0 1 ... NaN NaN NaN 2 0 0 1 ... NaN NaN NaN 3 0 0 1 ... NaN NaN NaN 4 0 0 1 ... NaN NaN NaN .. ... ... ... ... ... ... ... 66 1 0 2 ... NaN NaN NaN 67 1 0

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NaN NaN 67 1 0 3 ... NaN NaN NaN 68 1 0 3 ... 85.4 83.14 NaN 69 1 0 3 ... NaN NaN NaN 70 1 1 3 ... NaN NaN NaN tool tool_input log \ 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 NaN NaN NaN 4 NaN NaN NaN .. ... ... ... 66 NaN NaN NaN 67 NaN NaN NaN 68 NaN NaN NaN 69 NaN NaN I now know the final answer.\nFinal Answer: B... 70 NaN NaN NaN input_str description output \ 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 NaN NaN NaN 4 NaN NaN NaN .. ... ... ... 66 NaN NaN Bryan Adams has never married. In the 1990s, h... 67 NaN NaN

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: NaN NaN 67 1 0 3 ... NaN NaN NaN 68 1 0 3 ... 85.4 83.14 NaN 69 1 0 3 ... NaN NaN NaN 70 1 1 3 ... NaN NaN NaN tool tool_input log \ 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 NaN NaN NaN 4 NaN NaN NaN .. ... ... ... 66 NaN NaN NaN 67 NaN NaN NaN 68 NaN NaN NaN 69 NaN NaN I now know the final answer.\nFinal Answer: B... 70 NaN NaN NaN input_str description output \ 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 NaN NaN NaN 4 NaN NaN NaN .. ... ... ... 66 NaN NaN Bryan Adams has never married. In the 1990s, h... 67 NaN NaN

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h... 67 NaN NaN NaN 68 NaN NaN NaN 69 NaN NaN Bryan Adams has never been married. 70 NaN NaN NaN outputs 0 NaN 1 NaN 2 NaN 3 NaN 4 NaN .. ... 66 NaN 67 NaN 68 NaN 69 NaN 70 Bryan Adams has never been married. [71 rows x 47 columns], 'session_analysis': prompt_step prompts name \ 0 2 Answer the following questions as best you can... OpenAI 1 7 Answer the following questions as best you can... OpenAI 2 12 Answer the following questions as best you can... OpenAI output_step output \ 0 3 I need to find out who sang summer of 69 and ... 1 8 I need to find out who Bryan Adams is married... 2 13 I now know the final answer.\nFinal Answer: B... token_usage_total_tokens token_usage_prompt_tokens \ 0 223 189 1 270 242 2 332 314 token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \ 0 34 91.61 3.8 1 28

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: h... 67 NaN NaN NaN 68 NaN NaN NaN 69 NaN NaN Bryan Adams has never been married. 70 NaN NaN NaN outputs 0 NaN 1 NaN 2 NaN 3 NaN 4 NaN .. ... 66 NaN 67 NaN 68 NaN 69 NaN 70 Bryan Adams has never been married. [71 rows x 47 columns], 'session_analysis': prompt_step prompts name \ 0 2 Answer the following questions as best you can... OpenAI 1 7 Answer the following questions as best you can... OpenAI 2 12 Answer the following questions as best you can... OpenAI output_step output \ 0 3 I need to find out who sang summer of 69 and ... 1 8 I need to find out who Bryan Adams is married... 2 13 I now know the final answer.\nFinal Answer: B... token_usage_total_tokens token_usage_prompt_tokens \ 0 223 189 1 270 242 2 332 314 token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \ 0 34 91.61 3.8 1 28

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3.8 1 28 94.66 2.7 2 18 81.29 3.7 ... difficult_words linsear_write_formula gunning_fog \ 0 ... 2 5.75 5.4 1 ... 2 4.25 4.2 2 ... 1 2.50 2.8 text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \ 0 3rd and 4th grade 121.07 119.50 54.91 1 4th and 5th grade 124.13 119.20 52.26 2 3rd and 4th grade 115.70 110.84 49.79 crawford gulpease_index osman 0 0.9 72.7 92.16 1 0.7 74.7 84.20 2 0.7 85.4 83.14 [3 rows x 24 columns]} Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updatedTips and Next Steps‚ÄãMake sure you always use a unique name argument for the clearml_callback.flush_tracker function. If not, the model parameters used for a run will override the previous run!If you close the ClearML Callback using clearml_callback.flush_tracker(..., finish=True) the Callback cannot be used anymore. Make a new one if you want to keep logging.Check out the rest of the open-source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!PreviousClarifaiNextClickHouseInstallation and SetupGetting API CredentialsCallbacksScenario 1: Just an LLMScenario 2: Creating an agent with toolsTips and Next StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

ClearML is a ML/DL development and production suite, it contains 5 main modules:

ClearML is a ML/DL development and production suite, it contains 5 main modules: ->: 3.8 1 28 94.66 2.7 2 18 81.29 3.7 ... difficult_words linsear_write_formula gunning_fog \ 0 ... 2 5.75 5.4 1 ... 2 4.25 4.2 2 ... 1 2.50 2.8 text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \ 0 3rd and 4th grade 121.07 119.50 54.91 1 4th and 5th grade 124.13 119.20 52.26 2 3rd and 4th grade 115.70 110.84 49.79 crawford gulpease_index osman 0 0.9 72.7 92.16 1 0.7 74.7 84.20 2 0.7 85.4 83.14 [3 rows x 24 columns]} Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updatedTips and Next Steps‚ÄãMake sure you always use a unique name argument for the clearml_callback.flush_tracker function. If not, the model parameters used for a run will override the previous run!If you close the ClearML Callback using clearml_callback.flush_tracker(..., finish=True) the Callback cannot be used anymore. Make a new one if you want to keep logging.Check out the rest of the open-source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!PreviousClarifaiNextClickHouseInstallation and SetupGetting API CredentialsCallbacksScenario 1: Just an LLMScenario 2: Creating an agent with toolsTips and Next StepsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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Javelin AI Gateway | ü¶úÔ∏èüîó Langchain

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications. ->: Javelin AI Gateway | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreJavelin AI GatewayOn this pageJavelin AI GatewayThe Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreJavelin AI GatewayOn this pageJavelin AI GatewayThe Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications.

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It is designed to streamline the usage and access of various large language model (LLM) providers, such as OpenAI, Cohere, Anthropic and custom large language models within an organization by incorporating robust access security for all interactions with LLMs. Javelin offers a high-level interface that simplifies the interaction with LLMs by providing a unified endpoint to handle specific LLM related requests. See the Javelin AI Gateway documentation for more details.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications. ->: It is designed to streamline the usage and access of various large language model (LLM) providers, such as OpenAI, Cohere, Anthropic and custom large language models within an organization by incorporating robust access security for all interactions with LLMs. Javelin offers a high-level interface that simplifies the interaction with LLMs by providing a unified endpoint to handle specific LLM related requests. See the Javelin AI Gateway documentation for more details.

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Javelin Python SDK is an easy to use client library meant to be embedded into AI ApplicationsInstallation and Setup‚ÄãInstall javelin_sdk to interact with Javelin AI Gateway:pip install 'javelin_sdk'Set the Javelin's API key as an environment variable:export JAVELIN_API_KEY=...Completions Example‚Äãfrom langchain.chains import LLMChainfrom langchain.llms import JavelinAIGatewayfrom langchain.prompts import PromptTemplateroute_completions = "eng_dept03"gateway = JavelinAIGateway( gateway_uri="http://localhost:8000", route=route_completions, model_name="text-davinci-003",)llmchain = LLMChain(llm=gateway, prompt=prompt)result = llmchain.run("podcast player")print(result)Embeddings Example‚Äãfrom langchain.embeddings import JavelinAIGatewayEmbeddingsfrom langchain.embeddings.openai import OpenAIEmbeddingsembeddings = JavelinAIGatewayEmbeddings( gateway_uri="http://localhost:8000", route="embeddings",)print(embeddings.embed_query("hello"))print(embeddings.embed_documents(["hello"]))Chat Example‚Äãfrom langchain.chat_models import ChatJavelinAIGatewayfrom langchain.schema import HumanMessage, SystemMessagemessages = [ SystemMessage( content="You are a helpful assistant that translates English to French." ), HumanMessage( content="Artificial Intelligence has the power to transform humanity and make the world a better place" ),]chat = ChatJavelinAIGateway( gateway_uri="http://localhost:8000", route="mychatbot_route", model_name="gpt-3.5-turbo" params={ "temperature": 0.1 })print(chat(messages))PreviousInfinoNextJinaInstallation and SetupCompletions ExampleEmbeddings ExampleChat ExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications.

The Javelin AI Gateway service is a high-performance, enterprise grade API Gateway for AI applications. ->: Javelin Python SDK is an easy to use client library meant to be embedded into AI ApplicationsInstallation and Setup‚ÄãInstall javelin_sdk to interact with Javelin AI Gateway:pip install 'javelin_sdk'Set the Javelin's API key as an environment variable:export JAVELIN_API_KEY=...Completions Example‚Äãfrom langchain.chains import LLMChainfrom langchain.llms import JavelinAIGatewayfrom langchain.prompts import PromptTemplateroute_completions = "eng_dept03"gateway = JavelinAIGateway( gateway_uri="http://localhost:8000", route=route_completions, model_name="text-davinci-003",)llmchain = LLMChain(llm=gateway, prompt=prompt)result = llmchain.run("podcast player")print(result)Embeddings Example‚Äãfrom langchain.embeddings import JavelinAIGatewayEmbeddingsfrom langchain.embeddings.openai import OpenAIEmbeddingsembeddings = JavelinAIGatewayEmbeddings( gateway_uri="http://localhost:8000", route="embeddings",)print(embeddings.embed_query("hello"))print(embeddings.embed_documents(["hello"]))Chat Example‚Äãfrom langchain.chat_models import ChatJavelinAIGatewayfrom langchain.schema import HumanMessage, SystemMessagemessages = [ SystemMessage( content="You are a helpful assistant that translates English to French." ), HumanMessage( content="Artificial Intelligence has the power to transform humanity and make the world a better place" ),]chat = ChatJavelinAIGateway( gateway_uri="http://localhost:8000", route="mychatbot_route", model_name="gpt-3.5-turbo" params={ "temperature": 0.1 })print(chat(messages))PreviousInfinoNextJinaInstallation and SetupCompletions ExampleEmbeddings ExampleChat ExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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Upstash Redis | ü¶úÔ∏èüîó Langchain

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale. ->: Upstash Redis | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

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and toolkitsMemoryCallbacksChat loadersProvidersMoreUpstash RedisOn this pageUpstash RedisUpstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.This page covers how to use Upstash Redis with LangChain.Installation and Setup‚ÄãUpstash Redis Python SDK can be installed with pip install upstash-redisA globally distributed, low-latency and highly available database can be created at the Upstash ConsoleIntegrations‚ÄãAll of Upstash-LangChain integrations are based on upstash-redis Python SDK being utilized as wrappers for LangChain.

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale. ->: and toolkitsMemoryCallbacksChat loadersProvidersMoreUpstash RedisOn this pageUpstash RedisUpstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.This page covers how to use Upstash Redis with LangChain.Installation and Setup‚ÄãUpstash Redis Python SDK can be installed with pip install upstash-redisA globally distributed, low-latency and highly available database can be created at the Upstash ConsoleIntegrations‚ÄãAll of Upstash-LangChain integrations are based on upstash-redis Python SDK being utilized as wrappers for LangChain.

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This SDK utilizes Upstash Redis DB by giving UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN parameters from the console. One significant advantage of this is that, this SDK uses a REST API. This means, you can run this in serverless platforms, edge or any platform that does not support TCP connections.Cache‚ÄãUpstash Redis can be used as a cache for LLM prompts and responses.To import this cache:from langchain.cache import UpstashRedisCacheTo use with your LLMs:import langchainfrom upstash_redis import RedisURL = "<UPSTASH_REDIS_REST_URL>"TOKEN = "<UPSTASH_REDIS_REST_TOKEN>"langchain.llm_cache = UpstashRedisCache(redis_=Redis(url=URL, token=TOKEN))Memory‚ÄãUpstash Redis can be used to persist LLM conversations.Chat Message History Memory‚ÄãAn example of Upstash Redis for caching conversation message history can be seen in this notebook.PreviousUnstructuredNextUSearchInstallation and SetupIntegrationsCacheMemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.

Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale. ->: This SDK utilizes Upstash Redis DB by giving UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN parameters from the console. One significant advantage of this is that, this SDK uses a REST API. This means, you can run this in serverless platforms, edge or any platform that does not support TCP connections.Cache‚ÄãUpstash Redis can be used as a cache for LLM prompts and responses.To import this cache:from langchain.cache import UpstashRedisCacheTo use with your LLMs:import langchainfrom upstash_redis import RedisURL = "<UPSTASH_REDIS_REST_URL>"TOKEN = "<UPSTASH_REDIS_REST_TOKEN>"langchain.llm_cache = UpstashRedisCache(redis_=Redis(url=URL, token=TOKEN))Memory‚ÄãUpstash Redis can be used to persist LLM conversations.Chat Message History Memory‚ÄãAn example of Upstash Redis for caching conversation message history can be seen in this notebook.PreviousUnstructuredNextUSearchInstallation and SetupIntegrationsCacheMemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.

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Supabase (Postgres) | ü¶úÔ∏èüîó Langchain

Supabase is an open-source Firebase alternative.

Supabase is an open-source Firebase alternative. ->: Supabase (Postgres) | ü¶úÔ∏èüîó Langchain

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Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat

Supabase is an open-source Firebase alternative.

Supabase is an open-source Firebase alternative. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreActiveloop Deep LakeAI21 LabsAimAINetworkAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasAwaDBAWS DynamoDBAZLyricsBagelDBBananaBasetenBeamBeautiful SoupBiliBiliNIBittensorBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLClickHouseCnosDBCohereCollege ConfidentialCometConfident AIConfluenceC TransformersDashVectorDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeepSparseDiffbotDingoDiscordDocArrayDoctranDocugamiDuckDBElasticsearchEpsillaEverNoteFacebook ChatFacebook FaissFigmaFireworksFlyteForefrontAIGitGitBookGoldenGoogle Document AIGoogle SerperGooseAIGPT4AllGradientGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHTML to textHugging FaceiFixitIMSDbInfinoJavelin AI GatewayJinaKonkoLanceDBLangChain Decorators ‚ú®Llama.cppLog10MarqoMediaWikiDumpMeilisearchMetalMilvusMinimaxMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMongoDB AtlasMotherduckMot√∂rheadMyScaleNeo4jNLPCloudNotion DBNucliaObsidianOpenLLMOpenSearchOpenWeatherMapPetalsPostgres EmbeddingPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerprovidersPsychicPubMedQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4ScaNNSearchApiSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeSupabase (Postgres)NebulaTairTelegramTencentVectorDBTensorFlow DatasetsTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredUpstash RedisUSearchVearchVectaraVespaWandB TracingWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterXataXorbits Inference (Xinference)YandexYeager.aiYouTubeZepZillizComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat