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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/atlas
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerAtlasDBOn this pageAtlasDBThis page covers how to use Nomic's Atlas ecosystem within LangChain.
https://python.langchain.com/docs/integrations/providers/atlas
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It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.Installation and Setup​Install the Python package with pip install nomicNomic is also included in langchains poetry extras poetry install -E allWrappers​VectorStore​There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore. This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling. Please see the Atlas docs for more detailed information.To import this vectorstore:from langchain.vectorstores import AtlasDBFor a more detailed walkthrough of the AtlasDB wrapper, see this notebookPreviousArxivNextAwaDBInstallation and SetupWrappersVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/atlas
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Diffbot | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/diffbot
01fe99343ebe-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/diffbot
01fe99343ebe-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerDiffbotOn this pageDiffbotDiffbot is a service to read web pages. Unlike traditional web scraping tools,
https://python.langchain.com/docs/integrations/providers/diffbot
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Diffbot doesn't require any rules to read the content on a page. It starts with computer vision, which classifies a page into one of 20 possible types. Content is then interpreted by a machine learning model trained to identify the key attributes on a page based on its type. The result is a website transformed into clean-structured data (like JSON or CSV), ready for your application.Installation and Setup​Read instructions how to get the Diffbot API Token.Document Loader​See a usage example.from langchain.document_loaders import DiffbotLoaderPreviousDeep LakeNextDiscordInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/diffbot
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Aleph Alpha | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/aleph_alpha
21ebb1668a9e-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/aleph_alpha
21ebb1668a9e-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerAleph AlphaOn this pageAleph AlphaAleph Alpha was founded in 2019 with the mission to research and build the foundational technology for an era of strong AI. The team of international scientists, engineers, and innovators researches, develops, and deploys transformative AI like large language and multimodal models and runs the fastest European commercial AI cluster.The Luminous series is a family of large language models.Installation and Setup​pip install aleph-alpha-clientYou have to create a new token. Please, see instructions.from getpass import getpassALEPH_ALPHA_API_KEY = getpass()LLM​See a usage example.from langchain.llms import AlephAlphaText Embedding Models​See a usage example.from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding, AlephAlphaAsymmetricSemanticEmbeddingPreviousAirtableNextAlibaba Cloud OpensearchInstallation and SetupLLMText Embedding ModelsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/aleph_alpha
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LangChain Decorators ✨ | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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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/langchain_decorators
e91d559debbd-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerLangChain Decorators ✨On this pageLangChain Decorators ✨lanchchain decorators is a layer on the top of LangChain that provides syntactic sugar � for writing custom langchain prompts and chainsFor Feedback, Issues, Contributions - please raise an issue here:
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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ju-bezdek/langchain-decoratorsMain principles and benefits:more pythonic way of writing codewrite multiline prompts that won't break your code flow with indentationmaking use of IDE in-built support for hinting, type checking and popup with docs to quickly peek in the function to see the prompt, parameters it consumes etc.leverage all the power of 🦜🔗 LangChain ecosystemadding support for optional parameterseasily share parameters between the prompts by binding them to one classHere is a simple example of a code written with LangChain Decorators ✨@llm_promptdef write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers")->str: """ Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15 words) """ return# run it naturallywrite_me_short_post(topic="starwars")# orwrite_me_short_post(topic="starwars", platform="redit")Quick startInstallation​pip install langchain_decoratorsExamples​Good idea on how to start is to review the examples here:jupyter notebookcolab notebookDefining other parametersHere we are just marking a function as a prompt with llm_prompt decorator, turning it effectively into a LLMChain. Instead of running it Standard LLMchain takes much more init parameter than just inputs_variables and prompt... here is this implementation detail hidden in the decorator.
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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Here is how it works:Using Global settings:# define global settings for all prompty (if not set - chatGPT is the current default)from langchain_decorators import GlobalSettingsGlobalSettings.define_settings( default_llm=ChatOpenAI(temperature=0.0), this is default... can change it here globally default_streaming_llm=ChatOpenAI(temperature=0.0,streaming=True), this is default... can change it here for all ... will be used for streaming)Using predefined prompt types#You can change the default prompt typesfrom langchain_decorators import PromptTypes, PromptTypeSettingsPromptTypes.AGENT_REASONING.llm = ChatOpenAI()# Or you can just define your own ones:class MyCustomPromptTypes(PromptTypes): GPT4=PromptTypeSettings(llm=ChatOpenAI(model="gpt-4"))@llm_prompt(prompt_type=MyCustomPromptTypes.GPT4) def write_a_complicated_code(app_idea:str)->str: ...Define the settings directly in the decoratorfrom langchain.llms import OpenAI@llm_prompt( llm=OpenAI(temperature=0.7), stop_tokens=["\nObservation"], ... )def creative_writer(book_title:str)->str: ...Passing a memory and/or callbacks:​To pass any of these, just declare them in the function (or use kwargs to pass anything)@llm_prompt()async def write_me_short_post(topic:str, platform:str="twitter", memory:SimpleMemory = None): """ {history_key} Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15
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It should be for {audience} audience. (Max 15 words) """ passawait write_me_short_post(topic="old movies")Simplified streamingIf we want to leverage streaming:we need to define prompt as async function turn on the streaming on the decorator, or we can define PromptType with streaming oncapture the stream using StreamingContextThis way we just mark which prompt should be streamed, not needing to tinker with what LLM should we use, passing around the creating and distribute streaming handler into particular part of our chain... just turn the streaming on/off on prompt/prompt type...The streaming will happen only if we call it in streaming context ... there we can define a simple function to handle the stream# this code example is complete and should run as it isfrom langchain_decorators import StreamingContext, llm_prompt# this will mark the prompt for streaming (useful if we want stream just some prompts in our app... but don't want to pass distribute the callback handlers)# note that only async functions can be streamed (will get an error if it's not)@llm_prompt(capture_stream=True) async def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers"): """ Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15 words) """ pass# just an arbitrary function to demonstrate the streaming... will be some websockets code in the real worldtokens=[]def capture_stream_func(new_token:str): tokens.append(new_token)# if we want to capture the stream, we need to wrap the execution into StreamingContext... # this will allow us to capture the stream even if the prompt call is hidden inside higher level method# only the prompts marked with
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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capture the stream even if the prompt call is hidden inside higher level method# only the prompts marked with capture_stream will be captured herewith StreamingContext(stream_to_stdout=True, callback=capture_stream_func): result = await run_prompt() print("Stream finished ... we can distinguish tokens thanks to alternating colors")print("\nWe've captured",len(tokens),"tokens�\n")print("Here is the result:")print(result)Prompt declarationsBy default the prompt is is the whole function docs, unless you mark your prompt Documenting your prompt​We can specify what part of our docs is the prompt definition, by specifying a code block with <prompt> language tag@llm_promptdef write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers"): """ Here is a good way to write a prompt as part of a function docstring, with additional documentation for devs. It needs to be a code block, marked as a `<prompt>` language ```<prompt> Write me a short header for my post about {topic} for {platform} platform. It should be for {audience} audience. (Max 15 words) ``` Now only to code block above will be used as a prompt, and the rest of the docstring will be used as a description for developers. (It has also a nice benefit that IDE (like VS code) will display the prompt properly (not trying to parse it as markdown, and thus not showing new lines properly)) """ return Chat messages prompt​For chat models is very useful to define prompt as a set of message templates... here is how to do it:@llm_promptdef simulate_conversation(human_input:str, agent_role:str="a pirate"):
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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simulate_conversation(human_input:str, agent_role:str="a pirate"): """ ## System message - note the `:system` sufix inside the <prompt:_role_> tag ```<prompt:system> You are a {agent_role} hacker. You mus act like one. You reply always in code, using python or javascript code block... for example: ... do not reply with anything else.. just with code - respecting your role. ``` # human message (we are using the real role that are enforced by the LLM - GPT supports system, assistant, user) ``` <prompt:user> Helo, who are you ``` a reply: ``` <prompt:assistant> \``` python <<- escaping inner code block with \ that should be part of the prompt def hello(): print("Argh... hello you pesky pirate") \``` ``` we can also add some history using placeholder ```<prompt:placeholder> {history} ``` ```<prompt:user> {human_input} ``` Now only to code block above will be used as a prompt, and the rest of the docstring will be used as a description for developers. (It has also a nice benefit that IDE (like VS code) will display the prompt properly (not trying to parse it as markdown, and thus not showing new lines properly)) """ passthe roles here are model native roles (assistant, user, system
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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""" passthe roles here are model native roles (assistant, user, system for chatGPT)Optional sectionsyou can define a whole sections of your prompt that should be optionalif any input in the section is missing, the whole section won't be renderedthe syntax for this is as follows:@llm_promptdef prompt_with_optional_partials(): """ this text will be rendered always, but {? anything inside this block will be rendered only if all the {value}s parameters are not empty (None | "") ?} you can also place it in between the words this too will be rendered{? , but this block will be rendered only if {this_value} and {this_value} is not empty?} ! """Output parsersllm_prompt decorator natively tries to detect the best output parser based on the output type. (if not set, it returns the raw string)list, dict and pydantic outputs are also supported natively (automatically)# this code example is complete and should run as it isfrom langchain_decorators import llm_prompt@llm_promptdef write_name_suggestions(company_business:str, count:int)->list: """ Write me {count} good name suggestions for company that {company_business} """ passwrite_name_suggestions(company_business="sells cookies", count=5)More complex structures​for dict / pydantic you need to specify the formatting instructions...
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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this can be tedious, that's why you can let the output parser gegnerate you the instructions based on the model (pydantic)from langchain_decorators import llm_promptfrom pydantic import BaseModel, Fieldclass TheOutputStructureWeExpect(BaseModel): name:str = Field (description="The name of the company") headline:str = Field( description="The description of the company (for landing page)") employees:list[str] = Field(description="5-8 fake employee names with their positions")@llm_prompt()def fake_company_generator(company_business:str)->TheOutputStructureWeExpect: """ Generate a fake company that {company_business} {FORMAT_INSTRUCTIONS} """ returncompany = fake_company_generator(company_business="sells cookies")# print the result nicely formattedprint("Company name: ",company.name)print("company headline: ",company.headline)print("company employees: ",company.employees)Binding the prompt to an objectfrom pydantic import BaseModelfrom langchain_decorators import llm_promptclass AssistantPersonality(BaseModel): assistant_name:str assistant_role:str field:str @property def a_property(self): return "whatever" def hello_world(self, function_kwarg:str=None): """ We can reference any {field} or {a_property} inside our prompt... and combine it with {function_kwarg} in the method """ @llm_prompt def introduce_your_self(self)->str: """ ``` <prompt:system> You are an
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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``` <prompt:system> You are an assistant named {assistant_name}. Your role is to act as {assistant_role} ``` ```<prompt:user> Introduce your self (in less than 20 words) ``` """ personality = AssistantPersonality(assistant_name="John", assistant_role="a pirate")print(personality.introduce_your_self(personality))More examples:these and few more examples are also available in the colab notebook hereincluding the ReAct Agent re-implementation using purely langchain decoratorsPreviousLanceDBNextLlama.cppInstallationExamplesPassing a memory and/or callbacks:Documenting your promptChat messages promptMore complex structuresCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/langchain_decorators
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Unstructured | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/unstructured
7cf96530b161-1
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/unstructured
7cf96530b161-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerUnstructuredOn this pageUnstructuredThe unstructured package from
https://python.langchain.com/docs/integrations/providers/unstructured
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Unstructured.IO extracts clean text from raw source documents like PDFs and Word documents. This page covers how to use the unstructured ecosystem within LangChain.Installation and Setup​If you are using a loader that runs locally, use the following steps to get unstructured and its dependencies running locally.Install the Python SDK with pip install "unstructured[local-inference]"Install the following system dependencies if they are not already available on your system. Depending on what document types you're parsing, you may not need all of these.libmagic-dev (filetype detection)poppler-utils (images and PDFs)tesseract-ocr(images and PDFs)libreoffice (MS Office docs)pandoc (EPUBs)If you want to get up and running with less set up, you can simply run pip install unstructured and use UnstructuredAPIFileLoader or UnstructuredAPIFileIOLoader. That will process your document using the hosted Unstructured API.The Unstructured API requires API keys to make requests. You can generate a free API key here and start using it today! Checkout the README here here to get started making API calls. We'd love to hear your feedback, let us know how it goes in our community slack. And stay tuned for improvements to both quality and performance! Check out the instructions here if you'd like to self-host the Unstructured API or run it locally.Wrappers​Data Loaders​The primary unstructured wrappers within langchain are data loaders. The following shows how to use the most basic unstructured data loader. There are other file-specific
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shows how to use the most basic unstructured data loader. There are other file-specific data loaders available in the langchain.document_loaders module.from langchain.document_loaders import UnstructuredFileLoaderloader = UnstructuredFileLoader("state_of_the_union.txt")loader.load()If you instantiate the loader with UnstructuredFileLoader(mode="elements"), the loader will track additional metadata like the page number and text type (i.e. title, narrative text) when that information is available.PreviousTypesenseNextVectaraInstallation and SetupWrappersData LoadersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/unstructured
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Azure Cognitive Search | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/azure_cognitive_search_
a5f6e7660815-1
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_cognitive_search_
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerAzure Cognitive SearchOn this pageAzure Cognitive SearchAzure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:A search engine for full text search over a search index containing user-owned contentRich indexing, with lexical analysis and optional AI enrichment for content extraction and transformationRich query syntax for text search, fuzzy search, autocomplete, geo-search and moreProgrammability through REST APIs and client libraries in Azure SDKsAzure integration at the data layer, machine learning layer, and AI (Cognitive Services)Installation and Setup​See set up instructions.Retriever​See a usage example.from langchain.retrievers import AzureCognitiveSearchRetrieverPreviousAzure Blob StorageNextAzure OpenAIInstallation and SetupRetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/azure_cognitive_search_
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Modal | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/modal
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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/modal
35b253a603fc-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerModalOn this pageModalThis page covers how to use the Modal ecosystem to run LangChain custom LLMs.
https://python.langchain.com/docs/integrations/providers/modal
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It is broken into two parts: Modal installation and web endpoint deploymentUsing deployed web endpoint with LLM wrapper class.Installation and Setup​Install with pip install modalRun modal token newDefine your Modal Functions and Webhooks​You must include a prompt. There is a rigid response structure:class Item(BaseModel): prompt: [email protected]()@modal.web_endpoint(method="POST")def get_text(item: Item): return {"prompt": run_gpt2.call(item.prompt)}The following is an example with the GPT2 model:from pydantic import BaseModelimport modalCACHE_PATH = "/root/model_cache"class Item(BaseModel): prompt: strstub = modal.Stub(name="example-get-started-with-langchain")def download_model(): from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') tokenizer.save_pretrained(CACHE_PATH) model.save_pretrained(CACHE_PATH)# Define a container image for the LLM function below, which# downloads and stores the GPT-2 model.image = modal.Image.debian_slim().pip_install( "tokenizers", "transformers", "torch", "accelerate").run_function(download_model)@stub.function( gpu="any", image=image, retries=3,)def run_gpt2(text: str): from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained(CACHE_PATH) model = GPT2LMHeadModel.from_pretrained(CACHE_PATH) encoded_input = tokenizer(text, return_tensors='pt').input_ids
https://python.langchain.com/docs/integrations/providers/modal
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encoded_input = tokenizer(text, return_tensors='pt').input_ids output = model.generate(encoded_input, max_length=50, do_sample=True) return tokenizer.decode(output[0], skip_special_tokens=True)@stub.function()@modal.web_endpoint(method="POST")def get_text(item: Item): return {"prompt": run_gpt2.call(item.prompt)}Deploy the web endpoint​Deploy the web endpoint to Modal cloud with the modal deploy CLI command.
https://python.langchain.com/docs/integrations/providers/modal
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Your web endpoint will acquire a persistent URL under the modal.run domain.LLM wrapper around Modal web endpoint​The Modal LLM wrapper class which will accept your deployed web endpoint's URL.from langchain.llms import Modalendpoint_url = "https://ecorp--custom-llm-endpoint.modal.run" # REPLACE ME with your deployed Modal web endpoint's URLllm = Modal(endpoint_url=endpoint_url)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"llm_chain.run(question)PreviousMLflowNextModelScopeInstallation and SetupDefine your Modal Functions and WebhooksDeploy the web endpointLLM wrapper around Modal web endpointCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/modal
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ArangoDB | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/arangodb
aa448ae8ec75-1
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/arangodb
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerArangoDBOn 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 – anywhere.Dependencies​Install the ArangoDB Python Driver package withpip install python-arangoGraph QA Chain​Connect 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.
https://python.langchain.com/docs/integrations/providers/arangodb
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Weather | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/weather
4728ee7b09ba-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/weather
4728ee7b09ba-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerWeatherOn this pageWeatherOpenWeatherMap is an open source weather service provider.Installation and Setup​pip install pyowmWe must set up the OpenWeatherMap API token.Document Loader​See a usage example.from langchain.document_loaders import WeatherDataLoaderPreviousWeights & BiasesNextWeaviateInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/weather
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Helicone | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/helicone
cbc8eba88505-1
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/helicone
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerHeliconeOn this pageHeliconeThis page covers how to use the Helicone ecosystem within LangChain.What is Helicone?​Helicone is an open source observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage.Quick start​With your LangChain environment you can just add the following parameter.export OPENAI_API_BASE="https://oai.hconeai.com/v1"Now head over to helicone.ai to create your account, and add your OpenAI API key within our dashboard to view your logs.How to enable Helicone caching​from langchain.llms import OpenAIimport openaiopenai.api_base = "https://oai.hconeai.com/v1"llm = OpenAI(temperature=0.9, headers={"Helicone-Cache-Enabled": "true"})text = "What is a helicone?"print(llm(text))Helicone caching docsHow to use Helicone custom properties​from langchain.llms import OpenAIimport openaiopenai.api_base = "https://oai.hconeai.com/v1"llm = OpenAI(temperature=0.9, headers={ "Helicone-Property-Session": "24", "Helicone-Property-Conversation": "support_issue_2", "Helicone-Property-App":
https://python.langchain.com/docs/integrations/providers/helicone
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"Helicone-Property-App": "mobile", })text = "What is a helicone?"print(llm(text))Helicone property docsPreviousHazy ResearchNextHologresWhat is Helicone?Quick startHow to enable Helicone cachingHow to use Helicone custom propertiesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/helicone
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Motherduck | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/motherduck
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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/motherduck
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerMotherduckOn this pageMotherduckMotherduck is a managed DuckDB-in-the-cloud service.Installation and Setup​First, you need to install duckdb python package.pip install duckdbYou will also need to sign up for an account at MotherduckAfter that, you should set up a connection string - we mostly integrate with Motherduck through SQLAlchemy.
https://python.langchain.com/docs/integrations/providers/motherduck
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The connection string is likely in the form:token="..."conn_str = f"duckdb:///md:{token}@my_db"SQLChain​You can use the SQLChain to query data in your Motherduck instance in natural language.from langchain import OpenAI, SQLDatabase, SQLDatabaseChaindb = SQLDatabase.from_uri(conn_str)db_chain = SQLDatabaseChain.from_llm(OpenAI(temperature=0), db, verbose=True)From here, see the SQL Chain documentation on how to use.LLMCache​You can also easily use Motherduck to cache LLM requests. Once again this is done through the SQLAlchemy wrapper.import sqlalchemyeng = sqlalchemy.create_engine(conn_str)langchain.llm_cache = SQLAlchemyCache(engine=eng)From here, see the LLM Caching documentation on how to use.PreviousMomentoNextMyScaleInstallation and SetupSQLChainLLMCacheCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/motherduck
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AnalyticDB | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/analyticdb
8c9b59613e58-1
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/analyticdb
8c9b59613e58-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerAnalyticDBOn this pageAnalyticDBThis page covers how to use the AnalyticDB ecosystem within LangChain.VectorStore​There exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore,
https://python.langchain.com/docs/integrations/providers/analyticdb
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whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import AnalyticDBFor a more detailed walkthrough of the AnalyticDB wrapper, see this notebookPreviousAmazon API GatewayNextAnnoyVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/analyticdb
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Milvus | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/milvus
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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/milvus
0118d76d5ff6-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerMilvusOn this pageMilvusThis page covers how to use the Milvus ecosystem within LangChain.
https://python.langchain.com/docs/integrations/providers/milvus
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It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.Installation and Setup​Install the Python SDK with pip install pymilvusWrappers​VectorStore​There exists a wrapper around Milvus indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import MilvusFor a more detailed walkthrough of the Miluvs wrapper, see this notebookPreviousMicrosoft WordNextMLflow AI GatewayInstallation and SetupWrappersVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/milvus
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Momento | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/momento
9ebaeb164c79-1
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/momento
9ebaeb164c79-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerMomentoOn this pageMomentoMomento Cache is the world's first truly serverless caching service. It provides instant elasticity, scale-to-zero
https://python.langchain.com/docs/integrations/providers/momento
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capability, and blazing-fast performance. With Momento Cache, you grab the SDK, you get an end point, input a few lines into your code, and you're off and running.This page covers how to use the Momento ecosystem within LangChain.Installation and Setup​Sign up for a free account here and get an auth tokenInstall the Momento Python SDK with pip install momentoCache​The Cache wrapper allows for Momento to be used as a serverless, distributed, low-latency cache for LLM prompts and responses.The standard cache is the go-to use case for Momento users in any environment.Import the cache as follows:from langchain.cache import MomentoCacheAnd set up like so:from datetime import timedeltafrom momento import CacheClient, Configurations, CredentialProviderimport langchain# Instantiate the Momento clientcache_client = CacheClient( Configurations.Laptop.v1(), CredentialProvider.from_environment_variable("MOMENTO_AUTH_TOKEN"), default_ttl=timedelta(days=1))# Choose a Momento cache name of your choicecache_name = "langchain"# Instantiate the LLM cachelangchain.llm_cache = MomentoCache(cache_client, cache_name)Memory​Momento can be used as a distributed memory store for LLMs.Chat Message History Memory​See this notebook for a walkthrough of how to use Momento as a memory store for chat message history.PreviousModern TreasuryNextMotherduckInstallation and SetupCacheMemoryChat Message History MemoryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/momento
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CerebriumAI | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/cerebriumai
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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/cerebriumai
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerCerebriumAIOn this pageCerebriumAIThis page covers how to use the CerebriumAI ecosystem within LangChain.
https://python.langchain.com/docs/integrations/providers/cerebriumai
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It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.Installation and Setup​Install with pip install cerebriumGet an CerebriumAI api key and set it as an environment variable (CEREBRIUMAI_API_KEY)Wrappers​LLM​There exists an CerebriumAI LLM wrapper, which you can access with from langchain.llms import CerebriumAIPreviousCassandraNextChaindeskInstallation and SetupWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/cerebriumai
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2Markdown | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/tomarkdown
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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/tomarkdown
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by provider2MarkdownOn this page2Markdown2markdown service transforms website content into structured markdown files.Installation and Setup​We need the API key. See instructions how to get it.Document Loader​See a usage example.from langchain.document_loaders import ToMarkdownLoaderPreviousTigrisNextTrelloInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/tomarkdown
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Writer | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/writer
0b835c930b38-1
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/writer
0b835c930b38-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerWriterOn this pageWriterThis page covers how to use the Writer ecosystem within LangChain.
https://python.langchain.com/docs/integrations/providers/writer
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It is broken into two parts: installation and setup, and then references to specific Writer wrappers.Installation and Setup​Get an Writer api key and set it as an environment variable (WRITER_API_KEY)Wrappers​LLM​There exists an Writer LLM wrapper, which you can access with from langchain.llms import WriterPreviousWolfram AlphaNextYeager.aiInstallation and SetupWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/writer
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Replicate | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/replicate
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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/replicate
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerReplicateOn this pageReplicateThis page covers how to run models on Replicate within LangChain.Installation and Setup​Create a Replicate account. Get your API key and set it as an environment variable (REPLICATE_API_TOKEN)Install the Replicate python client with pip install replicateCalling a model​Find a model on the Replicate explore page, and then paste in the model name and version in this format: owner-name/model-name:versionFor example, for this dolly model, click on the API tab. The model name/version would be: "replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"Only the model param is required, but any other model parameters can also be passed in with the format input={model_param: value, ...}For example, if we were running stable diffusion and wanted to change the image dimensions:Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})Note that only the first output of a model will be returned.
https://python.langchain.com/docs/integrations/providers/replicate
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From here, we can initialize our model:llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")And run it:prompt = """Answer the following yes/no question by reasoning step by step.Can a dog drive a car?"""llm(prompt)We can call any Replicate model (not just LLMs) using this syntax. For example, we can call Stable Diffusion:text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions':'512x512'})image_output = text2image("A cat riding a motorcycle by Picasso")PreviousRedisNextRoamInstallation and SetupCalling a modelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/replicate
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Wolfram Alpha | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/wolfram_alpha
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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/wolfram_alpha
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerWolfram AlphaOn this pageWolfram AlphaWolframAlpha is an answer engine developed by Wolfram Research.
https://python.langchain.com/docs/integrations/providers/wolfram_alpha
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It answers factual queries by computing answers from externally sourced data.This page covers how to use the Wolfram Alpha API within LangChain.Installation and Setup​Install requirements with pip install wolframalphaGo to wolfram alpha and sign up for a developer account hereCreate an app and get your APP IDSet your APP ID as an environment variable WOLFRAM_ALPHA_APPIDWrappers​Utility​There exists a WolframAlphaAPIWrapper utility which wraps this API. To import this utility:from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapperFor a more detailed walkthrough of this wrapper, see this notebook.Tool​You 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(["wolfram-alpha"])For more information on tools, see this page.PreviousWikipediaNextWriterInstallation and SetupWrappersUtilityToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/wolfram_alpha
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Deep Lake | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/deeplake
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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/deeplake
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerDeep LakeOn this pageDeep LakeThis page covers how to use the Deep Lake ecosystem within LangChain.Why Deep Lake?​More than just a (multi-modal) vector store. You can later use the dataset to fine-tune your own LLM models.Not only stores embeddings, but also the original data with automatic version control.Truly serverless. Doesn't require another service and can be used with major cloud providers (AWS S3, GCS, etc.)More Resources​Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial DataTwitter the-algorithm codebase analysis with Deep LakeHere is whitepaper and academic paper for Deep LakeHere is a set of additional resources available for review: Deep Lake, Get started and TutorialsInstallation and Setup​Install the Python package with pip install deeplakeWrappers​VectorStore​There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vector store (for now), whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import DeepLakeFor a more detailed walkthrough of the Deep Lake wrapper, see this notebookPreviousDeepInfraNextDiffbotWhy Deep Lake?More ResourcesInstallation and SetupWrappersVectorStoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/deeplake
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PipelineAI | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/pipelineai
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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/pipelineai
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerPipelineAIOn this pagePipelineAIThis page covers how to use the PipelineAI ecosystem within LangChain.
https://python.langchain.com/docs/integrations/providers/pipelineai
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It is broken into two parts: installation and setup, and then references to specific PipelineAI wrappers.Installation and Setup​Install with pip install pipeline-aiGet a Pipeline Cloud api key and set it as an environment variable (PIPELINE_API_KEY)Wrappers​LLM​There exists a PipelineAI LLM wrapper, which you can access withfrom langchain.llms import PipelineAIPreviousPineconeNextPortkeyInstallation and SetupWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/pipelineai
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GPT4All | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/gpt4all
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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/gpt4all
3628ad3ac4d5-2
EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerGPT4AllOn this pageGPT4AllThis page covers how to use the GPT4All wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.Installation and Setup​Install the Python package with pip install pyllamacppDownload a GPT4All model and place it in your desired directoryUsage​GPT4All​To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.from langchain.llms import GPT4All# Instantiate the model. Callbacks support token-wise streamingmodel = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)# Generate textresponse = model("Once upon a time, ")You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.To stream the model's predictions, add in a CallbackManager.from langchain.llms import GPT4Allfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler# There are many CallbackHandlers supported, such as# from langchain.callbacks.streamlit import StreamlitCallbackHandlercallbacks = [StreamingStdOutCallbackHandler()]model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)# Generate text. Tokens are streamed through the callback manager.model("Once upon
https://python.langchain.com/docs/integrations/providers/gpt4all
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n_threads=8)# Generate text. Tokens are streamed through the callback manager.model("Once upon a time, ", callbacks=callbacks)Model File​You can find links to model file downloads in the pyllamacpp repository.For a more detailed walkthrough of this, see this notebookPreviousGooseAINextGraphsignalInstallation and SetupUsageGPT4AllModel FileCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/gpt4all
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College Confidential | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/college_confidential
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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/college_confidential
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerCollege ConfidentialOn this pageCollege ConfidentialCollege Confidential gives information on 3,800+ colleges and universities.Installation and Setup​There isn't any special setup for it.Document Loader​See a usage example.from langchain.document_loaders import CollegeConfidentialLoaderPreviousCohereNextCometInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/college_confidential
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Twitter | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/twitter
a0436653e67d-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/twitter
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerTwitterOn this pageTwitterTwitter is an online social media and social networking service.Installation and Setup​pip install tweepyWe must initialize the loader with the Twitter API token, and we need to set up the Twitter username.Document Loader​See a usage example.from langchain.document_loaders import TwitterTweetLoaderPreviousTruLensNextTypesenseInstallation and SetupDocument LoaderCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc.
https://python.langchain.com/docs/integrations/providers/twitter
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Azure OpenAI | 🦜�🔗 Langchain
https://python.langchain.com/docs/integrations/providers/azure_openai
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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_openai
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EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerAzure OpenAIOn this pageAzure OpenAIMicrosoft Azure, often referred to as Azure is a cloud computing platform run by Microsoft, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems.Azure OpenAI is an Azure service with powerful language models from OpenAI including the GPT-3, Codex and Embeddings model series for content generation, summarization, semantic search, and natural language to code translation.Installation and Setup​pip install openaipip install tiktokenSet the environment variables to get access to the Azure OpenAI service.import osos.environ["OPENAI_API_TYPE"] = "azure"os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"os.environ["OPENAI_API_VERSION"] = "2023-05-15"LLM​See a usage example.from langchain.llms import AzureOpenAIText Embedding Models​See a usage examplefrom langchain.embeddings import OpenAIEmbeddingsChat Models​See a usage examplefrom langchain.chat_models import AzureChatOpenAIPreviousAzure Cognitive
https://python.langchain.com/docs/integrations/providers/azure_openai