This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.Ĭhains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). These are, in increasing order of complexity: There are six main areas that LangChain is designed to help with. Resources (high-level explanation of core concepts).
How-To examples (demos, integrations, helper functions).Getting started (installation, setting up the environment, simple examples).Please see here for full documentation on: End-to-end Example: Question Answering over Notion Database.❓ Question Answering over specific documents
Common examples of these applications include: This library aims to assist in the development of those types of applications. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. Pip install langsmith & conda install langchain -c conda-forge 🤔 What is this? Production Support: As you move your LangChains into production, we'd love to offer more hands-on support.įill out this form to share more about what you're building, and our team will get in touch. Looking for the JS/TS version? Check out LangChain.js. ⚡ Building applications with LLMs through composability ⚡
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