RAG实战：用LangChain构建企业知识库问答系统
RAG in Practice: Building Enterprise Knowledge Base Q&A with LangChain
| iDev Tech | 2026-08-07T14:00:00
手把手教你使用LangChain + OpenAI + ChromaDB构建生产级RAG问答系统。
Step-by-step guide to building a production-grade RAG Q&A system using LangChain + OpenAI + ChromaDB.
RAG架构RAG（Retrieval-Augmented Generation）的核心思路：用户提问 → 从知识库中检索相关文档片段 → 将检索结果作为上下文注入LLM Prompt → LLM基于上下文生成回答。这种方式解决了LLM的知识截止日期和幻觉问题。文档处理流水线文档加载（PDF/Word/网页）→ 文本分块（RecursiveCharacterTextSplitter，chunk_size=500，overlap=50）→ Embedding生成（OpenAI text-embedding-3-small）→ 存入向量数据库（ChromaDB/Pinecone）。分块策略直接影响检索质量，建议按语义段落分块而非固定字符数。检索优化基础检索用余弦相似度，但生产环境需要更多优化：混合检索（关键词+向量）、重排序（Reranker模型对检索结果二次排序）、Parent Document Retriever（检索小块但返回大块上下文）、以及多查询检索（LLM改写用户问题为多个角度的查询）。
RAG ArchitectureRAG (Retrieval-Augmented Generation) core flow: user asks a question → retrieve relevant document chunks from knowledge base → inject retrieved context into LLM prompt → LLM generates contextual answer. This approach addresses LLM knowledge cutoff dates and hallucination issues.Document Processing PipelineDocument loading (PDF/Word/web pages) → Text chunking (RecursiveCharacterTextSplitter, chunk_size=500, overlap=50) → Embedding generation (OpenAI text-embedding-3-small) → Store in vector database (ChromaDB/Pinecone). Chunking strategy directly impacts retrieval quality — recommend semantic paragraph chunking over fixed character counts.Retrieval OptimizationBasic retrieval uses cosine similarity, but production needs more: hybrid retrieval (keyword + vector), reranking (Reranker models for second-pass sorting), Parent Document Retriever (retrieve small chunks but return larger context), and multi-query retrieval (LLM rewrites user questions into multiple query perspectives).