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14330733e88d-3 | which provides an ecosystem of more than a thousand
ready-made apps called Actors for various scraping, crawling, and extraction use cases.This integration enables you run Actors on the Apify platform and load their results into LangChain to feed your vector
indexes with documents and data from the web, e.g. to generate answers from websites with documentation,
blogs, or knowledge bases.Installation and Setup​Install the Apify API client for Python with pip install apify-clientGet your Apify API token and either set it as
an environment variable (APIFY_API_TOKEN) or pass it to the ApifyWrapper as apify_api_token in the constructor.Wrappers​Utility​You can use the ApifyWrapper to run Actors on the Apify platform.from langchain.utilities import ApifyWrapperFor a more detailed walkthrough of this wrapper, see this notebook.Loader​You can also use our ApifyDatasetLoader to get data from Apify dataset.from langchain.document_loaders import ApifyDatasetLoaderFor a more detailed walkthrough of this loader, see this notebook.PreviousAnyscaleNextArangoDBOverviewInstallation and SetupWrappersUtilityLoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/apify |
d2c4c5e45bf5-0 | Infino | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/infino |
d2c4c5e45bf5-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/infino |
d2c4c5e45bf5-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerInfinoOn this pageInfinoInfino is an open-source observability platform that stores both metrics and application logs together.Key features of infino include:Metrics Tracking: Capture time taken by LLM model to handle request, errors, number of tokens, and costing indication for the particular LLM.Data Tracking: Log and store prompt, request, and response data for each LangChain interaction.Graph Visualization: Generate basic graphs over time, depicting metrics such as request duration, error occurrences, token count, and cost.Installation and Setup​First, you'll need to install the infinopy Python package as follows:pip install infinopyIf you already have an Infino Server running, then you're good to go; but if | https://python.langchain.com/docs/integrations/providers/infino |
d2c4c5e45bf5-3 | you don't, follow the next steps to start it:Make sure you have Docker installedRun the following in your terminal:docker run --rm --detach --name infino-example -p 3000:3000 infinohq/infino:latestUsing Infino​See a usage example of InfinoCallbackHandler.from langchain.callbacks import InfinoCallbackHandlerPreviousIMSDbNextJinaInstallation and SetupUsing InfinoCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/infino |
a4117b586fd4-0 | Pinecone | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/pinecone |
a4117b586fd4-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/pinecone |
a4117b586fd4-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerPineconeOn this pagePineconeThis page covers how to use the Pinecone ecosystem within LangChain. | https://python.langchain.com/docs/integrations/providers/pinecone |
a4117b586fd4-3 | It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.Installation and Setup​Install the Python SDK:pip install pinecone-clientVectorstore​There exists a wrapper around Pinecone indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.from langchain.vectorstores import PineconeFor a more detailed walkthrough of the Pinecone vectorstore, see this notebookPreviousPGVectorNextPipelineAIInstallation and SetupVectorstoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/pinecone |
633b6b645cf6-0 | Psychic | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/psychic |
633b6b645cf6-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/psychic |
633b6b645cf6-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerPsychicOn this pagePsychicPsychic is a platform for integrating with SaaS tools like Notion, Zendesk, | https://python.langchain.com/docs/integrations/providers/psychic |
633b6b645cf6-3 | Confluence, and Google Drive via OAuth and syncing documents from these applications to your SQL or vector
database. You can think of it like Plaid for unstructured data. Installation and Setup​pip install psychicapiPsychic is easy to set up - you import the react library and configure it with your Sidekick API key, which you get
from the Psychic dashboard. When you connect the applications, you
view these connections from the dashboard and retrieve data using the server-side libraries.Create an account in the dashboard.Use the react library to add the Psychic link modal to your frontend react app. You will use this to connect the SaaS apps.Once you have created a connection, you can use the PsychicLoader by following the example notebookAdvantages vs Other Document Loaders​Universal API: Instead of building OAuth flows and learning the APIs for every SaaS app, you integrate Psychic once and leverage our universal API to retrieve data.Data Syncs: Data in your customers' SaaS apps can get stale fast. With Psychic you can configure webhooks to keep your documents up to date on a daily or realtime basis.Simplified OAuth: Psychic handles OAuth end-to-end so that you don't have to spend time creating OAuth clients for each integration, keeping access tokens fresh, and handling OAuth redirect logic.PreviousPromptLayerNextQdrantInstallation and SetupAdvantages vs Other Document LoadersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/psychic |
b2f4e2d3b6e7-0 | TruLens | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/trulens |
b2f4e2d3b6e7-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/trulens |
b2f4e2d3b6e7-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerTruLensOn this pageTruLensThis page covers how to use TruLens to evaluate and track LLM apps built on langchain.What is TruLens?​TruLens is an opensource package that provides instrumentation and evaluation tools for large language model (LLM) based applications.Quick start​Once you've created your LLM chain, you can use TruLens for evaluation and tracking. TruLens has a number of out-of-the-box Feedback Functions, and is also an extensible framework for LLM evaluation.# create a feedback functionfrom trulens_eval.feedback import Feedback, Huggingface, OpenAI# Initialize HuggingFace-based feedback function collection class:hugs = Huggingface()openai = OpenAI()# Define a language match feedback function using HuggingFace.lang_match = Feedback(hugs.language_match).on_input_output()# By default this will check language match on the main app input and main app# output.# Question/answer relevance between overall question and answer.qa_relevance = Feedback(openai.relevance).on_input_output()# By default this will evaluate feedback on main app input and main app output.# Toxicity of inputtoxicity = Feedback(openai.toxicity).on_input()After you've set up Feedback Function(s) for evaluating your LLM, you can wrap your application with TruChain to get detailed tracing, logging and evaluation of your LLM app.# wrap your chain with | https://python.langchain.com/docs/integrations/providers/trulens |
b2f4e2d3b6e7-3 | to get detailed tracing, logging and evaluation of your LLM app.# wrap your chain with TruChaintruchain = TruChain( chain, app_id='Chain1_ChatApplication', feedbacks=[lang_match, qa_relevance, toxicity])# Note: any `feedbacks` specified here will be evaluated and logged whenever the chain is used.truchain("que hora es?")Now you can explore your LLM-based application!Doing so will help you understand how your LLM application is performing at a glance. As you iterate new versions of your LLM application, you can compare their performance across all of the different quality metrics you've set up. You'll also be able to view evaluations at a record level, and explore the chain metadata for each record.tru.run_dashboard() # open a Streamlit app to exploreFor more information on TruLens, visit trulens.orgPreviousTrelloNextTwitterWhat is TruLens?Quick startCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/trulens |
7e2c9f30d130-0 | Anyscale | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/anyscale |
7e2c9f30d130-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/anyscale |
7e2c9f30d130-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerAnyscaleOn this pageAnyscaleThis page covers how to use the Anyscale ecosystem within LangChain. | https://python.langchain.com/docs/integrations/providers/anyscale |
7e2c9f30d130-3 | It is broken into two parts: installation and setup, and then references to specific Anyscale wrappers.Installation and Setup​Get an Anyscale Service URL, route and API key and set them as environment variables (ANYSCALE_SERVICE_URL,ANYSCALE_SERVICE_ROUTE, ANYSCALE_SERVICE_TOKEN). Please see the Anyscale docs for more details.Wrappers​LLM​There exists an Anyscale LLM wrapper, which you can access with from langchain.llms import AnyscalePreviousAnnoyNextApifyInstallation and SetupWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/anyscale |
ecab326bd21e-0 | spaCy | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/spacy |
ecab326bd21e-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/spacy |
ecab326bd21e-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerspaCyOn this pagespaCyspaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython.Installation and Setup​pip install spacyText Splitter​See a usage example.from langchain.llms import SpacyTextSplitterPreviousSlackNextSpreedlyInstallation and SetupText SplitterCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/spacy |
e878b1b96648-0 | Spreedly | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/spreedly |
e878b1b96648-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/spreedly |
e878b1b96648-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerSpreedlyOn this pageSpreedlySpreedly is a service that allows you to securely store credit cards and use them to transact against any number of payment gateways and third party APIs. It does this by simultaneously providing a card tokenization/vault service as well as a gateway and receiver integration service. Payment methods tokenized by Spreedly are stored at Spreedly, allowing you to independently store a card and then pass that card to different end points based on your business requirements.Installation and Setup​See setup instructions.Document Loader​See a usage example.from langchain.document_loaders import SpreedlyLoaderPreviousspaCyNextStarRocksInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/spreedly |
bf4d34623f6d-0 | Google BigQuery | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/google_bigquery |
bf4d34623f6d-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/google_bigquery |
bf4d34623f6d-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerGoogle BigQueryOn this pageGoogle BigQueryGoogle BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. | https://python.langchain.com/docs/integrations/providers/google_bigquery |
bf4d34623f6d-3 | BigQuery is a part of the Google Cloud Platform.Installation and Setup​First, you need to install google-cloud-bigquery python package.pip install google-cloud-bigqueryDocument Loader​See a usage example.from langchain.document_loaders import BigQueryLoaderPreviousGoldenNextGoogle Cloud StorageInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/google_bigquery |
f44419bf9f1f-0 | iFixit | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/ifixit |
f44419bf9f1f-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/ifixit |
f44419bf9f1f-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by provideriFixitOn this pageiFixitiFixit is the largest, open repair community on the web. The site contains nearly 100k | https://python.langchain.com/docs/integrations/providers/ifixit |
f44419bf9f1f-3 | repair manuals, 200k Questions & Answers on 42k devices, and all the data is licensed under CC-BY-NC-SA 3.0.Installation and Setup​There isn't any special setup for it.Document Loader​See a usage example.from langchain.document_loaders import IFixitLoaderPreviousHugging FaceNextIMSDbInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/ifixit |
70ee62961b0a-0 | Banana | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/bananadev |
70ee62961b0a-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/bananadev |
70ee62961b0a-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerBananaOn this pageBananaThis page covers how to use the Banana ecosystem within LangChain. | https://python.langchain.com/docs/integrations/providers/bananadev |
70ee62961b0a-3 | It is broken into two parts: installation and setup, and then references to specific Banana wrappers.Installation and Setup​Install with pip install banana-devGet an Banana api key and set it as an environment variable (BANANA_API_KEY)Define your Banana Template​If you want to use an available language model template you can find one here.
This template uses the Palmyra-Base model by Writer.
You can check out an example Banana repository here.Build the Banana app​Banana Apps must include the "output" key in the return json. | https://python.langchain.com/docs/integrations/providers/bananadev |
70ee62961b0a-4 | There is a rigid response structure.# Return the results as a dictionaryresult = {'output': result}An example inference function would be:def inference(model_inputs:dict) -> dict: global model global tokenizer # Parse out your arguments prompt = model_inputs.get('prompt', None) if prompt == None: return {'message': "No prompt provided"} # Run the model input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda() output = model.generate( input_ids, max_length=100, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1, temperature=0.9, early_stopping=True, no_repeat_ngram_size=3, num_beams=5, length_penalty=1.5, repetition_penalty=1.5, bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]] ) result = tokenizer.decode(output[0], skip_special_tokens=True) # Return the results as a dictionary result = {'output': result} return resultYou can find a full example of a Banana app here.Wrappers​LLM​There exists an Banana LLM wrapper, which you can access withfrom langchain.llms import BananaYou need to provide a model key located in the dashboard:llm = Banana(model_key="YOUR_MODEL_KEY")PreviousAzure OpenAINextBasetenInstallation and SetupDefine your Banana TemplateBuild the Banana appWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/bananadev |
276e703b766b-0 | Elasticsearch | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/elasticsearch |
276e703b766b-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/elasticsearch |
276e703b766b-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerElasticsearchOn this pageElasticsearchElasticsearch is a distributed, RESTful search and analytics engine. | https://python.langchain.com/docs/integrations/providers/elasticsearch |
276e703b766b-3 | It provides a distributed, multi-tenant-capable full-text search engine with an HTTP web interface and schema-free
JSON documents.Installation and Setup​pip install elasticsearchRetriever​In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Spärck Jones, and others.The name of the actual ranking function is BM25. The fuller name, Okapi BM25, includes the name of the first system to use it, which was the Okapi information retrieval system, implemented at London's City University in the 1980s and 1990s. BM25 and its newer variants, e.g. BM25F (a version of BM25 that can take document structure and anchor text into account), represent TF-IDF-like retrieval functions used in document retrieval.See a usage example.from langchain.retrievers import ElasticSearchBM25RetrieverPreviousDuckDBNextEverNoteInstallation and SetupRetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/elasticsearch |
62d6626c85b3-0 | Microsoft OneDrive | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/microsoft_onedrive |
62d6626c85b3-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/microsoft_onedrive |
62d6626c85b3-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerMicrosoft OneDriveOn this pageMicrosoft OneDriveMicrosoft OneDrive (formerly SkyDrive) is a file-hosting service operated by Microsoft.Installation and Setup​First, you need to install a python package.pip install o365Then follow instructions here.Document Loader​See a usage example.from langchain.document_loaders import OneDriveLoaderPreviousMetalNextMicrosoft PowerPointInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/microsoft_onedrive |
f1959003e759-0 | Redis | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/redis |
f1959003e759-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/redis |
f1959003e759-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerRedisOn this pageRedisThis page covers how to use the Redis ecosystem within LangChain. | https://python.langchain.com/docs/integrations/providers/redis |
f1959003e759-3 | It is broken into two parts: installation and setup, and then references to specific Redis wrappers.Installation and Setup​Install the Redis Python SDK with pip install redisWrappers​All wrappers needing a redis url connection string to connect to the database support either a stand alone Redis server
or a High-Availability setup with Replication and Redis Sentinels.Redis Standalone connection url​For standalone Redis server the official redis connection url formats can be used as describe in the python redis modules
"from_url()" method Redis.from_urlExample: redis_url = "redis://:secret-pass@localhost:6379/0"Redis Sentinel connection url​For Redis sentinel setups the connection scheme is "redis+sentinel".
This is an un-offical extensions to the official IANA registered protocol schemes as long as there is no connection url
for Sentinels available.Example: redis_url = "redis+sentinel://:secret-pass@sentinel-host:26379/mymaster/0"The format is redis+sentinel://[[username]:[password]]@[host-or-ip]:[port]/[service-name]/[db-number]
with the default values of "service-name = mymaster" and "db-number = 0" if not set explicit.
The service-name is the redis server monitoring group name as configured within the Sentinel. The current url format limits the connection string to one sentinel host only (no list can be given) and
booth Redis server and sentinel must have the same password set (if used).Redis Cluster connection url​Redis cluster is not supported right now for all methods requiring a "redis_url" parameter.
The only way to use a Redis Cluster is with LangChain classes accepting a preconfigured Redis client like RedisCache | https://python.langchain.com/docs/integrations/providers/redis |
f1959003e759-4 | (example below).Cache​The Cache wrapper allows for Redis to be used as a remote, low-latency, in-memory cache for LLM prompts and responses.Standard Cache​The standard cache is the Redis bread & butter of use case in production for both open source and enterprise users globally.To import this cache:from langchain.cache import RedisCacheTo use this cache with your LLMs:import langchainimport redisredis_client = redis.Redis.from_url(...)langchain.llm_cache = RedisCache(redis_client)Semantic Cache​Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends Redis as both a cache and a vectorstore.To import this cache:from langchain.cache import RedisSemanticCacheTo use this cache with your LLMs:import langchainimport redis# use any embedding provider...from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddingsredis_url = "redis://localhost:6379"langchain.llm_cache = RedisSemanticCache( embedding=FakeEmbeddings(), redis_url=redis_url)VectorStore​The vectorstore wrapper turns Redis into a low-latency vector database for semantic search or LLM content retrieval.To import this vectorstore:from langchain.vectorstores import RedisFor a more detailed walkthrough of the Redis vectorstore wrapper, see this notebook.Retriever​The Redis vector store retriever wrapper generalizes the vectorstore class to perform low-latency document retrieval. To create the retriever, simply call .as_retriever() on the base vectorstore class.Memory​Redis can be used to persist LLM conversations.Vector Store Retriever Memory​For a more detailed walkthrough of the VectorStoreRetrieverMemory wrapper, see this notebook.Chat Message History Memory​For a detailed example of Redis to cache conversation message history, | https://python.langchain.com/docs/integrations/providers/redis |
f1959003e759-5 | Message History Memory​For a detailed example of Redis to cache conversation message history, see this notebook.PreviousRedditNextReplicateInstallation and SetupWrappersRedis Standalone connection urlRedis Sentinel connection urlRedis Cluster connection urlCacheVectorStoreRetrieverMemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/redis |
c41d72baa9dc-0 | WandB Tracing | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/agent_with_wandb_tracing |
c41d72baa9dc-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/agent_with_wandb_tracing |
c41d72baa9dc-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerWandB TracingWandB TracingThere are two recommended ways to trace your LangChains:Setting the LANGCHAIN_WANDB_TRACING environment variable to "true".Using a context manager with tracing_enabled() to trace a particular block of code.Note if the environment variable is set, all code will be traced, regardless of whether or not it's within the context manager.import osos.environ["LANGCHAIN_WANDB_TRACING"] = "true"# wandb documentation to configure wandb using env variables# https://docs.wandb.ai/guides/track/advanced/environment-variables# here we are configuring the wandb project nameos.environ["WANDB_PROJECT"] = "langchain-tracing"from langchain.agents import initialize_agent, load_toolsfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIfrom langchain.callbacks import wandb_tracing_enabled# Agent run with tracing. Ensure that OPENAI_API_KEY is set appropriately to run this example.llm = OpenAI(temperature=0)tools = load_tools(["llm-math"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is 2 raised to .123243 power?") # this should be traced# A url with for the trace sesion like the following should print in your console:# | https://python.langchain.com/docs/integrations/providers/agent_with_wandb_tracing |
c41d72baa9dc-3 | be traced# A url with for the trace sesion like the following should print in your console:# https://wandb.ai/<wandb_entity>/<wandb_project>/runs/<run_id># The url can be used to view the trace session in wandb.# Now, we unset the environment variable and use a context manager.if "LANGCHAIN_WANDB_TRACING" in os.environ: del os.environ["LANGCHAIN_WANDB_TRACING"]# enable tracing using a context managerwith wandb_tracing_enabled(): agent.run("What is 5 raised to .123243 power?") # this should be tracedagent.run("What is 2 raised to .123243 power?") # this should not be traced > Entering new AgentExecutor chain... I need to use a calculator to solve this. Action: Calculator Action Input: 5^.123243 Observation: Answer: 1.2193914912400514 Thought: I now know the final answer. Final Answer: 1.2193914912400514 > Finished chain. > Entering new AgentExecutor chain... I need to use a calculator to solve this. Action: Calculator Action Input: 2^.123243 Observation: Answer: 1.0891804557407723 Thought: I now know the final answer. Final Answer: 1.0891804557407723 > Finished chain. '1.0891804557407723'Here's a view of wandb dashboard for the above tracing session:PreviousGrouped by providerNextAI21 | https://python.langchain.com/docs/integrations/providers/agent_with_wandb_tracing |
c41d72baa9dc-4 | a view of wandb dashboard for the above tracing session:PreviousGrouped by providerNextAI21 LabsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/agent_with_wandb_tracing |
4824b2d688d2-0 | Azure Blob Storage | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/azure_blob_storage |
4824b2d688d2-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/azure_blob_storage |
4824b2d688d2-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerAzure Blob StorageOn this pageAzure Blob StorageAzure Blob Storage is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.Azure Files offers fully managed | https://python.langchain.com/docs/integrations/providers/azure_blob_storage |
4824b2d688d2-3 | file shares in the cloud that are accessible via the industry standard Server Message Block (SMB) protocol,
Network File System (NFS) protocol, and Azure Files REST API. Azure Files are based on the Azure Blob Storage.Azure Blob Storage is designed for:Serving images or documents directly to a browser.Storing files for distributed access.Streaming video and audio.Writing to log files.Storing data for backup and restore, disaster recovery, and archiving.Storing data for analysis by an on-premises or Azure-hosted service.Installation and Setup​pip install azure-storage-blobDocument Loader​See a usage example for the Azure Blob Storage.from langchain.document_loaders import AzureBlobStorageContainerLoaderSee a usage example for the Azure Files.from langchain.document_loaders import AzureBlobStorageFileLoaderPreviousAZLyricsNextAzure Cognitive SearchInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/azure_blob_storage |
caf908e4c7d7-0 | Hazy Research | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/hazy_research |
caf908e4c7d7-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/hazy_research |
caf908e4c7d7-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerHazy ResearchOn this pageHazy ResearchThis page covers how to use the Hazy Research ecosystem within LangChain. | https://python.langchain.com/docs/integrations/providers/hazy_research |
caf908e4c7d7-3 | It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.Installation and Setup​To use the manifest, install it with pip install manifest-mlWrappers​LLM​There exists an LLM wrapper around Hazy Research's manifest library.
manifest is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more.To use this wrapper:from langchain.llms.manifest import ManifestWrapperPreviousHacker NewsNextHeliconeInstallation and SetupWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/hazy_research |
675c3e28c442-0 | CnosDB | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/cnosdb |
675c3e28c442-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/cnosdb |
675c3e28c442-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerCnosDBOn this pageCnosDBCnosDB is an open source distributed time series database with high performance, high compression rate and high ease of use.Installation and Setup​pip install cnos-connectorConnecting to CnosDB​You can connect to CnosDB using the SQLDatabase.from_cnosdb() method.Syntax​def SQLDatabase.from_cnosdb(url: str = "127.0.0.1:8902", user: str = "root", password: str = "", tenant: str = "cnosdb", database: str = "public")Args:url (str): The HTTP connection host name and port number of the CnosDB | https://python.langchain.com/docs/integrations/providers/cnosdb |
675c3e28c442-3 | service, excluding "http://" or "https://", with a default value
of "127.0.0.1:8902".user (str): The username used to connect to the CnosDB service, with a
default value of "root".password (str): The password of the user connecting to the CnosDB service,
with a default value of "".tenant (str): The name of the tenant used to connect to the CnosDB service, | https://python.langchain.com/docs/integrations/providers/cnosdb |
675c3e28c442-4 | with a default value of "cnosdb".database (str): The name of the database in the CnosDB tenant.Examples​# Connecting to CnosDB with SQLDatabase Wrapperfrom langchain import SQLDatabasedb = SQLDatabase.from_cnosdb()# Creating a OpenAI Chat LLM Wrapperfrom langchain.chat_models import ChatOpenAIllm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")SQL Database Chain​This example demonstrates the use of the SQL Chain for answering a question over a CnosDB.from langchain import SQLDatabaseChaindb_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)db_chain.run( "What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?")> Entering new chain...What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?SQLQuery:SELECT AVG(temperature) FROM air WHERE station = 'XiaoMaiDao' AND time >= '2022-10-19' AND time < '2022-10-20'SQLResult: [(68.0,)]Answer:The average temperature of air at station XiaoMaiDao between October 19, 2022 and October 20, 2022 is 68.0.> Finished chain.SQL Database Agent​This example demonstrates the use of the SQL Database Agent for answering questions over a CnosDB.from langchain.agents import create_sql_agentfrom langchain.agents.agent_toolkits import SQLDatabaseToolkittoolkit = SQLDatabaseToolkit(db=db, llm=llm)agent = create_sql_agent(llm=llm, toolkit=toolkit, | https://python.langchain.com/docs/integrations/providers/cnosdb |
675c3e28c442-5 | = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)agent.run( "What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?")> Entering new chain...Action: sql_db_list_tablesAction Input: ""Observation: airThought:The "air" table seems relevant to the question. I should query the schema of the "air" table to see what columns are available.Action: sql_db_schemaAction Input: "air"Observation:CREATE TABLE air ( pressure FLOAT, station STRING, temperature FLOAT, time TIMESTAMP, visibility FLOAT)/*3 rows from air table:pressure station temperature time visibility75.0 XiaoMaiDao 67.0 2022-10-19T03:40:00 54.077.0 XiaoMaiDao 69.0 2022-10-19T04:40:00 56.076.0 XiaoMaiDao 68.0 2022-10-19T05:40:00 55.0*/Thought:The "temperature" column in the "air" table is relevant to the question. I can query the average temperature between the specified dates.Action: sql_db_queryAction Input: "SELECT AVG(temperature) FROM air WHERE station = 'XiaoMaiDao' AND time >= '2022-10-19' AND time <= '2022-10-20'"Observation: [(68.0,)]Thought:The average temperature of air at station XiaoMaiDao between October 19, 2022 and October 20, 2022 is 68.0.Final Answer: | https://python.langchain.com/docs/integrations/providers/cnosdb |
675c3e28c442-6 | 2022 and October 20, 2022 is 68.0.Final Answer: 68.0> Finished chain.PreviousClearMLNextCohereInstallation and SetupConnecting to CnosDBSyntaxExamplesSQL Database ChainSQL Database AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/cnosdb |
08cd26fd43ff-0 | Docugami | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/docugami |
08cd26fd43ff-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/docugami |
08cd26fd43ff-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerDocugamiOn this pageDocugamiDocugami converts business documents into a Document XML Knowledge Graph, generating forests | https://python.langchain.com/docs/integrations/providers/docugami |
08cd26fd43ff-3 | of XML semantic trees representing entire documents. This is a rich representation that includes the semantic and
structural characteristics of various chunks in the document as an XML tree.Installation and Setup​pip install lxmlDocument Loader​See a usage example.from langchain.document_loaders import DocugamiLoaderPreviousDiscordNextDuckDBInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/docugami |
38b352ce9386-0 | EverNote | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/evernote |
38b352ce9386-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/evernote |
38b352ce9386-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerEverNoteOn this pageEverNoteEverNote is intended for archiving and creating notes in which photos, audio and saved web content can be embedded. Notes are stored in virtual "notebooks" and can be tagged, annotated, edited, searched, and exported.Installation and Setup​First, you need to install lxml and html2text python packages.pip install lxmlpip install html2textDocument Loader​See a usage example.from langchain.document_loaders import EverNoteLoaderPreviousElasticsearchNextFacebook ChatInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/evernote |
97c4b8a31ffd-0 | Portkey | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/portkey/ |
97c4b8a31ffd-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeylogging_tracing_portkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search | https://python.langchain.com/docs/integrations/providers/portkey/ |
97c4b8a31ffd-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerPortkeyOn this pagePortkeyLLMOps for Langchain​Portkey brings production readiness to Langchain. With Portkey, you can view detailed metrics & logs for all requests, enable semantic cache to reduce latency & costs, implement automatic retries & fallbacks for failed requests, add custom tags to requests for better tracking and analysis and more.Using Portkey with Langchain​Using Portkey is as simple as just choosing which Portkey features you want, enabling them via headers=Portkey.Config and passing it in your LLM calls.To start, get your Portkey API key by signing up here. (Click the profile icon on the top left, then click on "Copy API Key")For OpenAI, a simple integration with logging feature would look like this:from langchain.llms import OpenAIfrom langchain.utilities import Portkey# Add the Portkey API Key from your accountheaders = Portkey.Config( api_key = "<PORTKEY_API_KEY>")llm = OpenAI(temperature=0.9, headers=headers)llm.predict("What would be a good company name for a company that makes colorful socks?")Your logs will be captured on your Portkey dashboard.A common Portkey X Langchain use case is to trace a chain or an agent and view all the LLM calls originating from that request. Tracing Chains & Agents​from langchain.agents import AgentType, | https://python.langchain.com/docs/integrations/providers/portkey/ |
97c4b8a31ffd-3 | request. Tracing Chains & Agents​from langchain.agents import AgentType, initialize_agent, load_tools from langchain.llms import OpenAIfrom langchain.utilities import Portkey# Add the Portkey API Key from your accountheaders = Portkey.Config( api_key = "<PORTKEY_API_KEY>", trace_id = "fef659")llm = OpenAI(temperature=0, headers=headers) tools = load_tools(["serpapi", "llm-math"], llm=llm) agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) # Let's test it out! agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")You can see the requests' logs along with the trace id on Portkey dashboard:Advanced Features​Logging: Log all your LLM requests automatically by sending them through Portkey. Each request log contains timestamp, model name, total cost, request time, request json, response json, and additional Portkey features.Tracing: Trace id can be passed along with each request and is visibe on the logs on Portkey dashboard. You can also set a distinct trace id for each request. You can append user feedback to a trace id as well.Caching: Respond to previously served customers queries from cache instead of sending them again to OpenAI. Match exact strings OR semantically similar strings. Cache can save costs and reduce latencies by 20x.Retries: Automatically reprocess any unsuccessful API requests upto 5 times. Uses an exponential backoff strategy, which spaces out retry attempts to prevent network overload.Tagging: Track and audit each user interaction in high detail with predefined tags.FeatureConfig KeyValue (Type)Required/OptionalAPI Keyapi_keyAPI Key (string)✅ | https://python.langchain.com/docs/integrations/providers/portkey/ |
97c4b8a31ffd-4 | KeyValue (Type)Required/OptionalAPI Keyapi_keyAPI Key (string)✅ RequiredTracing Requeststrace_idCustom string� OptionalAutomatic Retriesretry_countinteger [1,2,3,4,5]� OptionalEnabling Cachecachesimple OR semantic� OptionalCache Force Refreshcache_force_refreshTrue� OptionalSet Cache Expirycache_ageinteger (in seconds)� OptionalAdd Useruserstring� OptionalAdd Organisationorganisationstring� OptionalAdd Environmentenvironmentstring� OptionalAdd Prompt (version/id/string)promptstring� OptionalEnabling all Portkey Features:​headers = Portkey.Config( # Mandatory api_key="<PORTKEY_API_KEY>", # Cache Options cache="semantic", cache_force_refresh="True", cache_age=1729, # Advanced retry_count=5, trace_id="langchain_agent", # Metadata environment="production", user="john", organisation="acme", | https://python.langchain.com/docs/integrations/providers/portkey/ |
97c4b8a31ffd-5 | organisation="acme", prompt="Frost" )For detailed information on each feature and how to use it, please refer to the Portkey docs. If you have any questions or need further assistance, reach out to us on Twitter..PreviousPipelineAINextlogging_tracing_portkeyLLMOps for LangchainUsing Portkey with LangchainTracing Chains & AgentsAdvanced FeaturesEnabling all Portkey Features:CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/portkey/ |
290f26efe9f5-0 | logging_tracing_portkey | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey |
290f26efe9f5-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeylogging_tracing_portkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search | https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey |
290f26efe9f5-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerPortkeylogging_tracing_portkeyOn this pagelogging_tracing_portkeyLog, Trace, and Monitor Langchain LLM CallsWhen building apps or agents using Langchain, you end up making multiple API calls to fulfill a single user request. However, these requests are not chained when you want to analyse them. With Portkey, all the embeddings, completion, and other requests from a single user request will get logged and traced to a common ID, enabling you to gain full visibility of user interactions.This notebook serves as a step-by-step guide on how to integrate and use Portkey in your Langchain app.First, let's import Portkey, OpenAI, and Agent toolsimport osfrom langchain.agents import AgentType, initialize_agent, load_toolsfrom langchain.llms import OpenAIfrom langchain.utilities import PortkeyPaste your OpenAI API key below. (You can find it here)os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"Get Portkey API Key​Sign up for Portkey hereOn your dashboard, click on the profile icon on the top left, then click on "Copy API Key"Paste it belowPORTKEY_API_KEY = "<PORTKEY_API_KEY>" # Paste your Portkey API Key hereSet Trace ID​Set the trace id for your request belowThe Trace ID can be common for all API calls originating from a single requestTRACE_ID = "portkey_langchain_demo" # Set trace id hereGenerate | https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey |
290f26efe9f5-3 | a single requestTRACE_ID = "portkey_langchain_demo" # Set trace id hereGenerate Portkey Headers​headers = Portkey.Config( api_key=PORTKEY_API_KEY, trace_id=TRACE_ID,)Run your agent as usual. The only change is that we will include the above headers in the request now.llm = OpenAI(temperature=0, headers=headers)tools = load_tools(["serpapi", "llm-math"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)# Let's test it out!agent.run( "What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")How Logging & Tracing Works on Portkey​LoggingSending your request through Portkey ensures that all of the requests are logged by defaultEach request log contains timestamp, model name, total cost, request time, request json, response json, and additional Portkey featuresTracingTrace id is passed along with each request and is visibe on the logs on Portkey dashboardYou can also set a distinct trace id for each request if you wantYou can append user feedback to a trace id as well. More info on this hereAdvanced LLMOps Features - Caching, Tagging, Retries​In addition to logging and tracing, Portkey provides more features that add production capabilities to your existing workflows:CachingRespond to previously served customers queries from cache instead of sending them again to OpenAI. Match exact strings OR semantically similar strings. Cache can save costs and reduce latencies by 20x.RetriesAutomatically reprocess any unsuccessful API requests upto 5 times. Uses an exponential backoff strategy, which spaces out retry attempts to prevent network overload.FeatureConfig KeyValue (Type)� Automatic | https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey |
290f26efe9f5-4 | spaces out retry attempts to prevent network overload.FeatureConfig KeyValue (Type)� Automatic Retriesretry_countinteger [1,2,3,4,5]🧠Enabling Cachecachesimple OR semanticTaggingTrack and audit ach user interaction in high detail with predefined tags.TagConfig KeyValue (Type)User TaguserstringOrganisation TagorganisationstringEnvironment TagenvironmentstringPrompt Tag (version/id/string)promptstringCode Example With All Features​headers = Portkey.Config( # Mandatory api_key="<PORTKEY_API_KEY>", # Cache Options cache="semantic", cache_force_refresh="True", cache_age=1729, # Advanced retry_count=5, trace_id="langchain_agent", # Metadata environment="production", user="john", organisation="acme", prompt="Frost",)llm = OpenAI(temperature=0.9, headers=headers)print(llm("Two roads diverged in the yellow woods"))PreviousPortkeyNextPredibaseGet Portkey API KeySet Trace IDGenerate Portkey HeadersHow Logging & Tracing Works on PortkeyAdvanced LLMOps Features - Caching, Tagging, RetriesCode Example With All FeaturesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/portkey/logging_tracing_portkey |
c289557054db-0 | Qdrant | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/qdrant |
c289557054db-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/qdrant |
c289557054db-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerQdrantOn this pageQdrantThis page covers how to use the Qdrant ecosystem within LangChain. | https://python.langchain.com/docs/integrations/providers/qdrant |
c289557054db-3 | It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.Installation and Setup​Install the Python SDK with pip install qdrant-clientWrappers​VectorStore​There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import QdrantFor a more detailed walkthrough of the Qdrant wrapper, see this notebookPreviousPsychicNextRay ServeInstallation and SetupWrappersVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/providers/qdrant |
6541376b3282-0 | Weights & Biases | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
6541376b3282-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators ✨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
6541376b3282-2 | EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerWeights & BiasesWeights & BiasesThis notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.View Report Note: the WandbCallbackHandler is being deprecated in favour of the WandbTracer . In future please use the WandbTracer as it is more flexible and allows for more granular logging. To know more about the WandbTracer refer to the agent_with_wandb_tracing.html notebook or use the following colab notebook. To know more about Weights & Biases Prompts refer to the following prompts documentation.pip install wandbpip install pandaspip install textstatpip install spacypython -m spacy download en_core_web_smimport osos.environ["WANDB_API_KEY"] = ""# os.environ["OPENAI_API_KEY"] = ""# os.environ["SERPAPI_API_KEY"] = ""from datetime import datetimefrom langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandlerfrom langchain.llms import OpenAICallback Handler that logs to Weights and Biases.Parameters: job_type (str): The type of job. project (str): The project to log to. entity (str): The entity to log to. tags (list): The tags to log. group (str): | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
6541376b3282-3 | tags (list): The tags to log. group (str): The group to log to. name (str): The name of the run. notes (str): The notes to log. visualize (bool): Whether to visualize the run. complexity_metrics (bool): Whether to log complexity metrics. stream_logs (bool): Whether to stream callback actions to W&BDefault values for WandbCallbackHandler(...)visualize: bool = False,complexity_metrics: bool = False,stream_logs: bool = False,NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"""Main function.This function is used to try the callback handler.Scenarios:1. OpenAI LLM2. Chain with multiple SubChains on multiple generations3. Agent with Tools"""session_group = datetime.now().strftime("%m.%d.%Y_%H.%M.%S")wandb_callback = WandbCallbackHandler( job_type="inference", project="langchain_callback_demo", group=f"minimal_{session_group}", name="llm", tags=["test"],)callbacks = [StdOutCallbackHandler(), wandb_callback]llm = OpenAI(temperature=0, callbacks=callbacks)[34m[1mwandb[0m: Currently logged in as: [33mharrison-chase[0m. Use [1m`wandb login --relogin`[0m to force reloginTracking run with wandb version 0.14.0Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914</code>Syncing run <strong><a | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
6541376b3282-4 | run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target="_blank">llm</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">Weights & Biases</a> (<a href='https://wandb.me/run' target="_blank">docs</a>)<br/>View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo</a>View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a>[34m[1mwandb[0m: [33mWARNING[0m The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.# Defaults for WandbCallbackHandler.flush_tracker(...)reset: bool = True,finish: bool = False,The flush_tracker function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By default we reset the session as opposed to concluding the session outright.# SCENARIO 1 - LLMllm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)wandb_callback.flush_tracker(llm, name="simple_sequential")Waiting for W&B process to finish... <strong | https://python.langchain.com/docs/integrations/providers/wandb_tracking |
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