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96 | https://python.langchain.com/docs/use_cases/question_answering/how_to/flare | Question AnsweringHow toRetrieve as you generate with FLAREOn this pageRetrieve as you generate with FLAREThis notebook is an implementation of Forward-Looking Active REtrieval augmented generation (FLARE).Please see the original repo here.The basic idea is:Start answering a questionIf you start generating tokens the m... |
was created in 2009 by an unknown person using the alias Satoshi Nakamoto. LangChain was created in late October 2022 by Harrison Chase. Bitcoin is a decentralized cryptocurrency | while LangChain is a framework built around LLMs. 'PreviousPerform context-aware text splittingNextImprove document indexing with HyDEImportsRetrieverFLARE Chain" | null |
97 | https://python.langchain.com/docs/use_cases/question_answering/how_to/hyde | Question AnsweringHow toImprove document indexing with HyDEOn this pageImprove document indexing with HyDEThis notebook goes over how to use Hypothetical Document Embeddings (HyDE), as described in this paper. At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical answer, and then ... |
98 | https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa | Question AnsweringHow toUse local LLMsOn this pageUse local LLMsThe popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.LangChain has integrations with many open source LLMs that can be run locally.See here for setup instructions for these LLMs. For example, ... |
99 | https://python.langchain.com/docs/use_cases/question_answering/how_to/multi_retrieval_qa_router | Question AnsweringHow toDynamically select from multiple retrieversDynamically select from multiple retrieversThis notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. Specifically we show how to use the MultiRetrievalQAChain to create a que... |
100 | https://python.langchain.com/docs/use_cases/question_answering/how_to/multiple_retrieval | Question AnsweringHow toMultiple Retrieval SourcesOn this pageMultiple Retrieval SourcesOften times you may want to do retrieval over multiple sources. These can be different vectorstores (where one contains information about topic X and the other contains info about topic Y). They could also be completely different da... |
101 | https://python.langchain.com/docs/use_cases/question_answering/how_to/qa_citations | Question AnsweringHow toCite sourcesCite sourcesThis notebook shows how to use OpenAI functions ability to extract citations from text.from langchain.chains import create_citation_fuzzy_match_chainfrom langchain.chat_models import ChatOpenAI /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/sit... |
102 | https://python.langchain.com/docs/use_cases/question_answering/how_to/question_answering | Question AnsweringHow toQA over in-memory documentsOn this pageQA over in-memory documentsHere we walk through how to use LangChain for question answering over a list of documents. Under the hood we'll be using our Document chains.Prepare DataFirst we prepare the data. For this example we do similarity search over a v... |
103 | https://python.langchain.com/docs/use_cases/question_answering/how_to/vector_db_text_generation | Question AnsweringHow toRetrieve from vector stores directlyOn this pageRetrieve from vector stores directlyThis notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a large body of custom text, for example, generating ... |
104 | https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa | Question AnsweringIntegration-specificStructure answers with OpenAI functionsOn this pageStructure answers with OpenAI functionsOpenAI functions allows for structuring of response output. This is often useful in question answering when you want to not only get the final answer but also supporting evidence, citations, e... |
105 | https://python.langchain.com/docs/use_cases/question_answering/integrations/semantic-search-over-chat | Question AnsweringIntegration-specificQA using Activeloop's DeepLakeOn this pageQA using Activeloop's DeepLakeIn this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to semantically search and ask questions over a group chat.View a working demo here1. Install required packagespython3 -m pip ... |
106 | https://python.langchain.com/docs/use_cases/qa_structured/sql | QA over structured dataSQLOn this pageSQLUse caseEnterprise data is often stored in SQL databases.LLMs make it possible to interact with SQL databases using natural langugae.LangChain offers SQL Chains and Agents to build and run SQL queries based on natural language prompts. These are compatible with any SQL dialect ... |
107 | https://python.langchain.com/docs/use_cases/qa_structured/integrations/databricks | QA over structured dataIntegration-specificDatabricksOn this pageDatabricksThis notebook covers how to connect to the Databricks runtimes and Databricks SQL using the SQLDatabase wrapper of LangChain.
It is broken into 3 parts: installation and setup, connecting to Databricks, and examples.Installation and Setuppip in... |
108 | https://python.langchain.com/docs/use_cases/qa_structured/integrations/elasticsearch | QA over structured dataIntegration-specificElasticsearchElasticsearchWe can use LLMs to interact with Elasticsearch analytics databases in natural language.This chain builds search queries via the Elasticsearch DSL API (filters and aggregations).The Elasticsearch client must have permissions for index listing, mapping ... |
109 | https://python.langchain.com/docs/use_cases/qa_structured/integrations/myscale_vector_sql | QA over structured dataIntegration-specificVector SQL Retriever with MyScaleOn this pageVector SQL Retriever with MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain. MyScale can make a use of various data types and functions for filters. It will boost up y... |
110 | https://python.langchain.com/docs/use_cases/qa_structured/integrations/sqlite | QA over structured dataIntegration-specificSQL Database ChainSQL Database ChainThis example demonstrates the use of the SQLDatabaseChain for answering questions over a SQL database.Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The SQLDatabaseChain can therefore be used with any SQL dialect supp... |
111 | https://python.langchain.com/docs/use_cases/apis | Interacting with APIsOn this pageInteracting with APIsUse caseSuppose you want an LLM to interact with external APIs.This can be very useful for retrieving context for the LLM to utilize.And, more generally, it allows us to interact with APIs using natural language! OverviewThere are two primary ways to interface LLM... |
112 | https://python.langchain.com/docs/use_cases/chatbots | ChatbotsOn this pageChatbotsUse caseChatbots are one of the central LLM use-cases. The core features of chatbots are that they can have long-running conversations and have access to information that users want to know about.Aside from basic prompting and LLMs, memory and retrieval are the core components of a chatbot.... |
113 | https://python.langchain.com/docs/use_cases/code_understanding | Code understandingOn this pageCode understandingUse caseSource code analysis is one of the most popular LLM applications (e.g., GitHub Co-Pilot, Code Interpreter, Codium, and Codeium) for use-cases such as:Q&A over the code base to understand how it worksUsing LLMs for suggesting refactors or improvementsUsing LLMs fo... |
114 | https://python.langchain.com/docs/use_cases/extraction | ExtractionOn this pageExtractionUse caseGetting structured output from raw LLM generations is hard.For example, suppose you need the model output formatted with a specific schema for:Extracting a structured row to insert into a database Extracting API parametersExtracting different parts of a user query (e.g., for sem... |
115 | https://python.langchain.com/docs/use_cases/summarization | SummarizationOn this pageSummarizationUse caseSuppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. LLMs are a great tool for this given their proficiency in understanding and synthesizing text.In this walkthrough we'll go over how to perform document... |
116 | https://python.langchain.com/docs/use_cases/tagging | TaggingOn this pageTaggingUse caseTagging means labeling a document with classes such as:sentimentlanguagestyle (formal, informal etc.)covered topicspolitical tendencyOverviewTagging has a few components:function: Like extraction, tagging uses functions to specify how the model should tag a documentschema: defines ho... |
117 | https://python.langchain.com/docs/use_cases/web_scraping | Web scrapingOn this pageWeb scrapingUse caseWeb research is one of the killer LLM applications:Users have highlighted it as one of his top desired AI tools. OSS repos like gpt-researcher are growing in popularity. OverviewGathering content from the web has a few components:Search: Query to url (e.g., using GoogleSear... |
118 | https://python.langchain.com/docs/use_cases/more/agents/ | MoreAgentsOn this pageAgentsUse caseLLM-based agents are powerful general problem solvers.The primary LLM agent components include at least 3 things:Planning: The ability to break down tasks into smaller sub-goalsMemory: The ability to retain and recall informationTools: The ability to get information from external so... |
119 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/ | MoreAgentsAgent simulationsOn this pageAgent simulationsAgent simulations involve interacting one or more agents with each other.
Agent simulations generally involve two main components:Long Term MemorySimulation EnvironmentSpecific implementations of agent simulations (or parts of agent simulations) include:Simulation... |
120 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/camel_role_playing | MoreAgentsAgent simulationsCAMEL Role-Playing Autonomous Cooperative AgentsOn this pageCAMEL Role-Playing Autonomous Cooperative AgentsThis is a langchain implementation of paper: "CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society".Overview:The rapid advancement of conversational ... |
121 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/characters | MoreAgentsAgent simulationsGenerative Agents in LangChainOn this pageGenerative Agents in LangChainThis notebook implements a generative agent based on the paper Generative Agents: Interactive Simulacra of Human Behavior by Park, et. al.In it, we leverage a time-weighted Memory object backed by a LangChain Retriever.# ... |
122 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/gymnasium | MoreAgentsAgent simulationsSimulated Environment: GymnasiumOn this pageSimulated Environment: GymnasiumFor many applications of LLM agents, the environment is real (internet, database, REPL, etc). However, we can also define agents to interact in simulated environments like text-based games. This is an example of how t... |
123 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multi_player_dnd | MoreAgentsAgent simulationsMulti-Player Dungeons & DragonsOn this pageMulti-Player Dungeons & DragonsThis notebook shows how the DialogueAgent and DialogueSimulator class make it easy to extend the Two-Player Dungeons & Dragons example to multiple players.The main difference between simulating two players and multiple ... |
124 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_authoritarian | MoreAgentsAgent simulationsMulti-agent authoritarian speaker selectionOn this pageMulti-agent authoritarian speaker selectionThis notebook showcases how to implement a multi-agent simulation where a privileged agent decides who to speak.
This follows the polar opposite selection scheme as multi-agent decentralized spea... |
125 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_bidding | MoreAgentsAgent simulationsMulti-agent decentralized speaker selectionOn this pageMulti-agent decentralized speaker selectionThis notebook showcases how to implement a multi-agent simulation without a fixed schedule for who speaks when. Instead the agents decide for themselves who speaks. We can implement this by havin... |
126 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/petting_zoo | MoreAgentsAgent simulationsMulti-Agent Simulated Environment: Petting ZooOn this pageMulti-Agent Simulated Environment: Petting ZooIn this example, we show how to define multi-agent simulations with simulated environments. Like ours single-agent example with Gymnasium, we create an agent-environment loop with an extern... |
127 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/two_agent_debate_tools | MoreAgentsAgent simulationsAgent Debates with ToolsOn this pageAgent Debates with ToolsThis example shows how to simulate multi-agent dialogues where agents have access to tools.Import LangChain related modulesfrom typing import List, Dict, Callablefrom langchain.chains import ConversationChainfrom langchain.chat_mode... |
128 | https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/two_player_dnd | MoreAgentsAgent simulationsTwo-Player Dungeons & DragonsOn this pageTwo-Player Dungeons & DragonsIn this notebook, we show how we can use concepts from CAMEL to simulate a role-playing game with a protagonist and a dungeon master. To simulate this game, we create an DialogueSimulator class that coordinates the dialogue... |
129 | https://python.langchain.com/docs/use_cases/more/agents/agents/ | MoreAgentsAgentsOn this pageAgentsAgents can be used for a variety of tasks.
Agents combine the decision making ability of a language model with tools in order to create a system
that can execute and implement solutions on your behalf. Before reading any more, it is highly
recommended that you read the documentation in... |
130 | https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/ | MoreAgentsAutonomous (long-running) agentsOn this pageAutonomous (long-running) agentsAutonomous Agents are agents that designed to be more long running.
You give them one or multiple long term goals, and they independently execute towards those goals.
The applications combine tool usage and long term memory.At the mom... |
131 | https://python.langchain.com/docs/use_cases/more/code_writing/ | MoreCode writingCode writingdangerAll program-writing chains should be treated as VERY experimental and should not be used in any environment where sensitive/important data is stored, as there is arbitrary code execution involved in using these.Much like humans, LLMs are great at writing out programs, but not always gr... |
132 | https://python.langchain.com/docs/use_cases/more/code_writing/cpal | MoreCode writingCausal program-aided language (CPAL) chainOn this pageCausal program-aided language (CPAL) chainThe CPAL chain builds on the recent PAL to stop LLM hallucination. The problem with the PAL approach is that it hallucinates on a math problem with a nested chain of dependence. The innovation here is that th... |
133 | https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash | MoreCode writingBash chainOn this pageBash chainThis notebook showcases using LLMs and a bash process to perform simple filesystem commands.from langchain_experimental.llm_bash.base import LLMBashChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0)text = "Please write a bash script that prints 'Hello World... |
134 | https://python.langchain.com/docs/use_cases/more/code_writing/llm_math | MoreCode writingMath chainMath chainThis notebook showcases using LLMs and Python REPLs to do complex word math problems.from langchain.llms import OpenAIfrom langchain.chains import LLMMathChainllm = OpenAI(temperature=0)llm_math = LLMMathChain.from_llm(llm, verbose=True)llm_math.run("What is 13 raised to the .3432 po... |
135 | https://python.langchain.com/docs/use_cases/more/code_writing/llm_symbolic_math | MoreCode writingLLM Symbolic MathOn this pageLLM Symbolic MathThis notebook showcases using LLMs and Python to Solve Algebraic Equations. Under the hood is makes use of SymPy.from langchain.llms import OpenAIfrom langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChainllm = OpenAI(temperature=0)llm_sym... |
136 | https://python.langchain.com/docs/use_cases/more/code_writing/pal | MoreCode writingProgram-aided language model (PAL) chainOn this pageProgram-aided language model (PAL) chainImplements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.from langchain_experimental.pal_chain import PALChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0, max_tokens=51... |
137 | https://python.langchain.com/docs/use_cases/more/data_generation | MoreSynthetic Data generationOn this pageSynthetic Data generationUse caseSynthetic data is artificially generated data, rather than data collected from real-world events. It's used to simulate real data without compromising privacy or encountering real-world limitations. Benefits of Synthetic Data:Privacy and Securit... |
138 | https://python.langchain.com/docs/use_cases/more/graph/ | MoreAnalyzing graph dataAnalyzing graph dataGraph databases give us a powerful way to represent and query real-world relationships. There are a number of chains that make it easy to use LLMs to interact with various graph DBs.📄️ Diffbot Graph TransformerOpen In Collab📄️ ArangoDB QA chainOpen In Collab📄️ Neo4j DB QA ... |
139 | https://python.langchain.com/docs/use_cases/more/graph/diffbot_graphtransformer | MoreAnalyzing graph dataDiffbot Graph TransformerOn this pageDiffbot Graph TransformerUse caseText data often contain rich relationships and insights that can be useful for various analytics, recommendation engines, or knowledge management applications.Diffbot's NLP API allows for the extraction of entities, relations... |
140 | https://python.langchain.com/docs/use_cases/more/graph/graph_arangodb_qa | MoreAnalyzing graph dataArangoDB QA chainOn this pageArangoDB QA chainThis notebook shows how to use LLMs to provide a natural language interface to an ArangoDB database.You can get a local ArangoDB instance running via the ArangoDB Docker image: docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodbAn alt... |
141 | https://python.langchain.com/docs/use_cases/more/graph/graph_cypher_qa | MoreAnalyzing graph dataNeo4j DB QA chainOn this pageNeo4j DB QA chainThis notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.You will need to have a running Neo4j instance. One option is to create a free Neo4j database instance in the... |
142 | https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa | MoreAnalyzing graph dataFalkorDBQAChainOn this pageFalkorDBQAChainThis notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.FalkorDB is a low latency property graph database management system. You can simply run its docker locally:docker run -p 6379:6379 -it --rm falkordb/falkordb... |
143 | https://python.langchain.com/docs/use_cases/more/graph/graph_hugegraph_qa | MoreAnalyzing graph dataHugeGraph QA ChainOn this pageHugeGraph QA ChainThis notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.You will need to have a running HugeGraph instance.
You can run a local docker container by running the executing the following script:docker run \ ... |
144 | https://python.langchain.com/docs/use_cases/more/graph/graph_kuzu_qa | MoreAnalyzing graph dataKuzuQAChainOn this pageKuzuQAChainThis notebook shows how to use LLMs to provide a natural language interface to Kùzu database.Kùzu is an in-process property graph database management system. You can simply install it with pip:pip install kuzuOnce installed, you can simply import it and start cr... |
145 | https://python.langchain.com/docs/use_cases/more/graph/graph_memgraph_qa | MoreAnalyzing graph dataMemgraph QA chainOn this pageMemgraph QA chainThis notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.To follow along with this tutorial, ensure you have a running Memgraph instan... |
146 | https://python.langchain.com/docs/use_cases/more/graph/graph_nebula_qa | MoreAnalyzing graph dataNebulaGraphQAChainOn this pageNebulaGraphQAChainThis notebook shows how to use LLMs to provide a natural language interface to NebulaGraph database.You will need to have a running NebulaGraph cluster, for which you can run a containerized cluster by running the following script:curl -fsSL nebula... |
147 | https://python.langchain.com/docs/use_cases/more/graph/graph_qa | MoreAnalyzing graph dataGraph QAOn this pageGraph QAThis notebook goes over how to do question answering over a graph data structure.Create the graphIn this section, we construct an example graph. At the moment, this works best for small pieces of text.from langchain.indexes import GraphIndexCreatorfrom langchain.llms... |
148 | https://python.langchain.com/docs/use_cases/more/graph/graph_sparql_qa | MoreAnalyzing graph dataGraphSparqlQAChainOn this pageGraphSparqlQAChainGraph databases are an excellent choice for applications based on network-like models. To standardize the syntax and semantics of such graphs, the W3C recommends Semantic Web Technologies, cp. Semantic Web. SPARQL serves as a query language analogo... |
149 | https://python.langchain.com/docs/use_cases/more/graph/neptune_cypher_qa | MoreAnalyzing graph dataNeptune Open Cypher QA ChainNeptune Open Cypher QA ChainThis QA chain queries Neptune graph database using openCypher and returns human readable responsefrom langchain.graphs import NeptuneGraphhost = "<neptune-host>"port = 8182use_https = Truegraph = NeptuneGraph(host=host, port=port, use_https... |
150 | https://python.langchain.com/docs/use_cases/more/graph/tot | MoreAnalyzing graph dataTree of Thought (ToT) exampleOn this pageTree of Thought (ToT) exampleThe Tree of Thought (ToT) is a chain that allows you to query a Large Language Model (LLM) using the Tree of Thought technique. This is based on the paper "Large Language Model Guided Tree-of-Thought"from langchain.llms import... |
151 | https://python.langchain.com/docs/use_cases/more/learned_prompt_optimization | MoreLearned Prompt Variable Injection via RLOn this pageLearned Prompt Variable Injection via RLLLM prompts can be enhanced by injecting specific terms into template sentences. Selecting the right terms is crucial for obtaining high-quality responses. This notebook introduces automated prompt engineering through term i... |
152 | https://python.langchain.com/docs/use_cases/more/self_check/ | MoreSelf-checkingSelf-checkingOne of the main issues with using LLMs is that they can often hallucinate and make false claims. One of the surprisingly effective ways to remediate this is to use the LLM itself to check its own answers.📄️ Self-checking chainThis notebook showcases how to use LLMCheckerChain.📄️ Summariz... |
153 | https://python.langchain.com/docs/use_cases/more/self_check/llm_checker | MoreSelf-checkingSelf-checking chainSelf-checking chainThis notebook showcases how to use LLMCheckerChain.from langchain.chains import LLMCheckerChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0.7)text = "What type of mammal lays the biggest eggs?"checker_chain = LLMCheckerChain.from_llm(llm, verbose=Tru... |
154 | https://python.langchain.com/docs/use_cases/more/self_check/llm_summarization_checker | MoreSelf-checkingSummarization checker chainSummarization checker chainThis notebook shows some examples of LLMSummarizationCheckerChain in use with different types of texts. It has a few distinct differences from the LLMCheckerChain, in that it doesn't have any assumptions to the format of the input text (or summary)... |
155 | https://python.langchain.com/docs/use_cases/more/self_check/smart_llm | MoreSelf-checkingHow to use a SmartLLMChainOn this pageHow to use a SmartLLMChainA SmartLLMChain is a form of self-critique chain that can help you if have particularly complex questions to answer. Instead of doing a single LLM pass, it instead performs these 3 steps:Ideation: Pass the user prompt n times through the L... |
156 | https://python.langchain.com/docs/integrations/providers | ProvidersProviders📄️ AnthropicAll functionality related to Anthropic models.📄️ AWSAll functionality related to Amazon AWS platform📄️ GoogleAll functionality related to Google Cloud Platform📄️ MicrosoftAll functionality related to Microsoft📄️ OpenAIAll functionality related to OpenAI🗃️ More182 itemsNextAnthropic |
157 | https://python.langchain.com/docs/integrations/platforms/anthropic | ProvidersAnthropicOn this pageAnthropicAll functionality related to Anthropic models.Anthropic is an AI safety and research company, and is the creator of Claude.
This page covers all integrations between Anthropic models and LangChain.Prompting OverviewClaude is chat-based model, meaning it is trained on conversation... |
158 | https://python.langchain.com/docs/integrations/platforms/aws | ProvidersAWSOn this pageAWSAll functionality related to Amazon AWS platformLLMsBedrockSee a usage example.from langchain.llms.bedrock import BedrockAmazon API GatewayAmazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. AP... |
159 | https://python.langchain.com/docs/integrations/platforms/google | ProvidersGoogleOn this pageGoogleAll functionality related to Google Cloud PlatformLLMsVertex AIAccess PaLM LLMs like text-bison and code-bison via Google Cloud.from langchain.llms import VertexAIModel GardenAccess PaLM and hundreds of OSS models via Vertex AI Model Garden.from langchain.llms import VertexAIModelGar... |
160 | https://python.langchain.com/docs/integrations/platforms/microsoft | ProvidersMicrosoftOn this pageMicrosoftAll functionality related to MicrosoftLLMAzure 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 capa... |
161 | https://python.langchain.com/docs/integrations/platforms/openai | ProvidersOpenAIOn this pageOpenAIAll functionality related to OpenAIOpenAI is American artificial intelligence (AI) research laboratory
consisting of the non-profit OpenAI Incorporated
and its for-profit subsidiary corporation OpenAI Limited Partnership.
OpenAI conducts AI research with the declared intention of promot... |
162 | https://python.langchain.com/docs/integrations/providers/activeloop_deeplake | ProvidersMoreActiveloop Deep LakeOn this pageActiveloop Deep 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 aut... |
163 | https://python.langchain.com/docs/integrations/providers/ai21 | ProvidersMoreAI21 LabsOn this pageAI21 LabsThis page covers how to use the AI21 ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific AI21 wrappers.Installation and SetupGet an AI21 api key and set it as an environment variable (AI21_API_KEY)WrappersLLMThere... |
164 | https://python.langchain.com/docs/integrations/providers/aim_tracking | ProvidersMoreAimAimAim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents. With Aim, you can easily debug and examine an individual execution:Additionally, you have the option to compare multiple executions side by side:Aim is f... |
165 | https://python.langchain.com/docs/integrations/providers/ainetwork | ProvidersMoreAINetworkOn this pageAINetworkAI Network is a layer 1 blockchain designed to accommodate
large-scale AI models, utilizing a decentralized GPU network powered by the
$AIN token, enriching AI-driven NFTs (AINFTs).Installation and SetupYou need to install ain-py python package.pip install ain-pyYou need to s... |
166 | https://python.langchain.com/docs/integrations/providers/airbyte | ProvidersMoreAirbyteOn this pageAirbyteAirbyte is a data integration platform for ELT pipelines from APIs,
databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.Installation and SetupThis instruction shows how to load any source from Airbyte into a local... |
167 | https://python.langchain.com/docs/integrations/providers/airtable | ProvidersMoreAirtableOn this pageAirtableAirtable is a cloud collaboration service.
Airtable is a spreadsheet-database hybrid, with the features of a database but applied to a spreadsheet.
The fields in an Airtable table are similar to cells in a spreadsheet, but have types such as 'checkbox',
'phone number', and 'drop... |
168 | https://python.langchain.com/docs/integrations/providers/aleph_alpha | ProvidersMoreAleph 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 multimoda... |
169 | https://python.langchain.com/docs/integrations/providers/alibabacloud_opensearch | ProvidersMoreAlibaba Cloud OpensearchOn this pageAlibaba Cloud OpensearchAlibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alib... |
170 | https://python.langchain.com/docs/integrations/providers/analyticdb | ProvidersMoreAnalyticDBOn this pageAnalyticDBThis page covers how to use the AnalyticDB ecosystem within LangChain.VectorStoreThere exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore,
whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import... |
171 | https://python.langchain.com/docs/integrations/providers/annoy | ProvidersMoreAnnoyOn this pageAnnoyAnnoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same da... |
172 | https://python.langchain.com/docs/integrations/providers/anyscale | ProvidersMoreAnyscaleOn this pageAnyscaleThis page covers how to use the Anyscale ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Anyscale wrappers.Installation and SetupGet an Anyscale Service URL, route and API key and set them as environment variables... |
173 | https://python.langchain.com/docs/integrations/providers/apify | ProvidersMoreApifyOn this pageApifyThis page covers how to use Apify within LangChain.OverviewApify is a cloud platform for web scraping and data extraction,
which provides an ecosystem of more than a thousand
ready-made apps called Actors for various scraping, crawling, and extraction use cases.This integration enabl... |
174 | https://python.langchain.com/docs/integrations/providers/arangodb | ProvidersMoreArangoDBOn 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.DependenciesInstall the ArangoDB Python Driver pack... |
175 | https://python.langchain.com/docs/integrations/providers/argilla | ProvidersMoreArgillaOn this pageArgillaArgilla is an open-source data curation platform for LLMs.
Using Argilla, everyone can build robust language models through faster data curation
using both human and machine feedback. We provide support for each step in the MLOps cycle,
from data labelling to model monitoring.Inst... |
176 | https://python.langchain.com/docs/integrations/providers/arthur_tracking | ProvidersMoreArthurArthurArthur is a model monitoring and observability platform.The following guide shows how to run a registered chat LLM with the Arthur callback handler to automatically log model inferences to Arthur.If you do not have a model currently onboarded to Arthur, visit our onboarding guide for generative... |
177 | https://python.langchain.com/docs/integrations/providers/arxiv | ProvidersMoreArxivOn this pageArxivarXiv is an open-access archive for 2 million scholarly articles in the fields of physics,
mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and
systems science, and economics.Installation and SetupFirst, you need to install... |
178 | https://python.langchain.com/docs/integrations/providers/atlas | ProvidersMoreAtlasOn this pageAtlasNomic Atlas is a platform for interacting with both
small and internet scale unstructured datasets.Installation and SetupInstall the Python package with pip install nomicNomic is also included in langchains poetry extras poetry install -E allVectorStoreSee a usage example.from langc... |
179 | https://python.langchain.com/docs/integrations/providers/awadb | ProvidersMoreAwaDBOn this pageAwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.Installation and Setuppip install awadbVector Storefrom langchain.vectorstores import AwaDBSee a usage example.Text Embedding Modelfrom langchain.embeddings import AwaEmbeddingsS... |
180 | https://python.langchain.com/docs/integrations/providers/aws_dynamodb | ProvidersAWSOn this pageAWSAll functionality related to Amazon AWS platformLLMsBedrockSee a usage example.from langchain.llms.bedrock import BedrockAmazon API GatewayAmazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale. AP... |
181 | https://python.langchain.com/docs/integrations/providers/azlyrics | ProvidersMoreAZLyricsOn this pageAZLyricsAZLyrics is a large, legal, every day growing collection of lyrics.Installation and SetupThere isn't any special setup for it.Document LoaderSee a usage example.from langchain.document_loaders import AZLyricsLoaderPreviousAWS DynamoDBNextBagelDBInstallation and SetupDocument L... |
182 | https://python.langchain.com/docs/integrations/providers/bageldb | ProvidersMoreBagelDBOn this pageBagelDBBagelDB (Open Vector Database for AI), is like GitHub for AI data.
It is a collaborative platform where users can create,
share, and manage vector datasets. It can support private projects for independent developers,
internal collaborations for enterprises, and public contribution... |
183 | https://python.langchain.com/docs/integrations/providers/bananadev | ProvidersMoreBananaOn this pageBananaBanana provided serverless GPU inference for AI models, including a CI/CD build pipeline and a simple Python framework (Potassium) to server your models.This page covers how to use the Banana ecosystem within LangChain.It is broken into two parts: installation and setup, and then re... |
184 | https://python.langchain.com/docs/integrations/providers/baseten | ProvidersMoreBasetenOn this pageBasetenLearn how to use LangChain with models deployed on Baseten.Installation and setupCreate a Baseten account and API key.Install the Baseten Python client with pip install basetenUse your API key to authenticate with baseten loginInvoking a modelBaseten integrates with LangChain th... |
185 | https://python.langchain.com/docs/integrations/providers/beam | ProvidersMoreBeamOn this pageBeamThis page covers how to use Beam within LangChain.
It is broken into two parts: installation and setup, and then references to specific Beam wrappers.Installation and SetupCreate an accountInstall the Beam CLI with curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam... |
186 | https://python.langchain.com/docs/integrations/providers/beautiful_soup | ProvidersMoreBeautiful SoupOn this pageBeautiful SoupBeautiful Soup is a Python package for parsing
HTML and XML documents (including having malformed markup, i.e. non-closed tags, so named after tag soup).
It creates a parse tree for parsed pages that can be used to extract data from HTML,[3] which
is useful for web s... |
187 | https://python.langchain.com/docs/integrations/providers/bilibili | ProvidersMoreBiliBiliOn this pageBiliBiliBilibili is one of the most beloved long-form video sites in China.Installation and Setuppip install bilibili-api-pythonDocument LoaderSee a usage example.from langchain.document_loaders import BiliBiliLoaderPreviousBeautiful SoupNextNIBittensorInstallation and SetupDocument L... |
188 | https://python.langchain.com/docs/integrations/providers/bittensor | ProvidersMoreNIBittensorOn this pageNIBittensorThis page covers how to use the BittensorLLM inference runtime within LangChain.
It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.Installation and SetupInstall the Python package with pip install langchainWrappersLLMThere ex... |
189 | https://python.langchain.com/docs/integrations/providers/blackboard | ProvidersMoreBlackboardOn this pageBlackboardBlackboard Learn (previously the Blackboard Learning Management System)
is a web-based virtual learning environment and learning management system developed by Blackboard Inc.
The software features course management, customizable open architecture, and scalable design that a... |
190 | https://python.langchain.com/docs/integrations/providers/brave_search | ProvidersMoreBrave SearchOn this pageBrave SearchBrave Search is a search engine developed by Brave Software.Brave Search uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92%
of search results without relying on any third-parties, with the remainder being retrieved
server-s... |
191 | https://python.langchain.com/docs/integrations/providers/cassandra | ProvidersMoreCassandraOn this pageCassandraApache Cassandra® is a free and open-source, distributed, wide-column
store, NoSQL database management system designed to handle large amounts of data across many commodity servers,
providing high availability with no single point of failure. Cassandra offers support for clust... |
192 | https://python.langchain.com/docs/integrations/providers/cerebriumai | ProvidersMoreCerebriumAIOn this pageCerebriumAIThis page covers how to use the CerebriumAI ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.Installation and SetupInstall with pip install cerebriumGet an CerebriumAI api key and set it ... |
193 | https://python.langchain.com/docs/integrations/providers/chaindesk | ProvidersMoreChaindeskOn this pageChaindeskChaindesk is an open source document retrieval platform that helps to connect your personal data with Large Language Models.Installation and SetupWe need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url.
We need the API Key.R... |
194 | https://python.langchain.com/docs/integrations/providers/chroma | ProvidersMoreChromaOn this pageChromaChroma is a database for building AI applications with embeddings.Installation and Setuppip install chromadbVectorStoreThere exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore,
whether for semantic search or example selection.from langchain.ve... |
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