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 library that provides several modules, including prompts, LLMs, document loaders, utils, chains, indexes, agents, memory, and chat. Each module comes with examples, how-to guides, reference docs, and conceptual guides. LangChain is designed to help with prompt management, prompt optimization, LLMs, chains, data-augmented generation, agents, memory, and evaluation.
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 modules
- 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.
Conclusion
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.
