- What is LangChain?
- How to use LangChain
- LangChain modules
- LangChain use cases
- Connect OpenAI To +5,000 Tools (LangChain + Zapier)
- Structured Output From OpenAI (Clean Dirty Data)
LangChain is a library that supports developers in building applications that combine Large Language Models (LLMs) with other sources of computation or knowledge. With LangChain, developers can create chatbots, generate similar examples, query tabular data, summarize long documents, answer questions over specific documents, and evaluate generative models.
What is LangChain?
LangChain is a Python package that provides a framework for building blockchain applications. The Quickstart guide provides a guide for familiarizing oneself with the framework, and the documentation for LangChain.js provides documentation for the JS/TS version of the framework.
How to use LangChain
Developers can use LangChain to build applications that combine LLMs with other sources of computation or knowledge. LangChain provides a standard interface for chains, agents, and memory, along with examples of end-to-end chains/agents. Developers can experiment with different prompts, models, and chains using the ModelLaboratory provided by LangChain.
LangChain provides a standard interface for language models, an interface for application-specific data, a construct for sequences of calls, and let chains choose which tools to use given high-level directives. LangChain also provides persistence of application state between runs of a chain, logging and streaming of intermediate steps of any chain.
Prompts: Provides prompt templates that use LLMs to decide what actions should be taken.
LLMs: Provides a framework for chatbots, Generative Question-Answering (GQA), summarization, and more.
Document loaders: Provides methods of combining LLMs with text data.
Utils: Provides a collection of utilities for working with LLMs.
Chains: Allows developers to chain together different components to create more advanced use cases.
Indexes: Provides an interface for querying large datasets.
Agents: Provides a standard interface for agents and a selection of agents to choose from.
Memory: Provides a standard interface for memory and a collection of memory implementations.
Chat: Provides an interface for chatbots.
LangChain use cases
Chatbots: LangChain can be used to create chatbots that use LLMs to interact with other tools and do more grounded question-answering or take actions.
Data augmented generation: LangChain allows developers to generate similar examples to a given input, experiment with different prompts, models, and chains.
Tabular data querying: LangChain can be used to query data that is stored in a tabular format.
Summarization: Developers can use LangChain to summarize longer documents into shorter, more condensed chunks of information.
Question answering: Developers can answer questions over specific documents, only utilizing the information in those documents to construct an answer.
Evaluation: LangChain provides prompts/chains for assisting in evaluating generative models.
With LangChain, developers can create chatbots, generate similar examples, query tabular data, summarize long documents, answer questions over specific documents, and evaluate generative models. LangChain provides a standard interface for chains, agents, and memory and examples of end-to-end chains/agents.
Connect OpenAI To +5,000 Tools (LangChain + Zapier)
Structured Output From OpenAI (Clean Dirty Data)
Python repository: https://github.com/hwchase17/langchain
Python documentation (English): https://langchain.readthedocs.io/en/latest/
TypeScript repository: https://github.com/hwchase17/langchainjs
TypeScript documentation: https://js.langchain.com/docs/