LangChain
LangChain Integration | Build Retrieval-Augmented Generation applications with Zilliz Cloud or Milvus Vector Database
Use this integration for FreeLangChain Integration, Build Retrieval-Augmented Generation applications with Zilliz Cloud
LangChain serves as a framework for developing language model-powered applications, offering a range of valuable capabilities:
- Context-Aware Functionality: LangChain empowers applications to be context-aware by seamlessly connecting language models with contextual sources, such as prompt instructions, few-shot examples, or relevant content stored in Vector databases. This integration enhances an application's ability to provide responses grounded in context and reasoning.
- Reasoning Capabilities: With LangChain, applications can rely on language models for advanced reasoning tasks, enabling them to make informed decisions on responding based on provided context and taking appropriate actions.
LangChain's primary value propositions encompass:
- Modular Components: LangChain provides easily accessible abstractions for working with language models, accompanied by a diverse set of implementations for each abstraction. These modular components are user-friendly and can be integrated effortlessly, utilizing the entire LangChain framework or only specific components.
- Off-the-Shelf Chains: LangChain offers pre-configured chains, organized assemblies of components designed to accomplish specific higher-level tasks. These off-the-shelf chains simplify the initiation of projects. For more complex applications, LangChain's components enable straightforward customization of existing chains or creating entirely new ones.
Learn LangChain
- Tutorial | Ultimate Guide to Getting Started with LangChain
- Tutorial | Using LangChain to Self-Query a Vector Database
- Docs | Question Answering over Documents with Zilliz Cloud and LangChain
- Video with Harrison Chase | Memory for LLM applications: Different retrieval techniques for getting the most relevant context
- Video Shorts with Yujian Tang | How to Add Conversational Memory to an LLM Using LangChain
- Video with Lance Martin | Debugging your RAG apps with LangSmith