RAG Implementation Patterns Guide - From Basics to Advanced Techniques

RAG Published: 2025年11月22日 Updated: 2026年01月04日
RAG Vector Database LangChain LlamaIndex Retrieval

What is RAG?

RAG (Retrieval-Augmented Generation) enhances LLM capabilities by retrieving relevant information from external knowledge bases. It solves LLM limitations like hallucinations and knowledge cutoff.

Basic RAG Architecture

User Query → Embedding → Vector Search → Retrieve Documents → LLM + Context → Answer

Implementation Steps

  1. Document Processing

    • Load documents (PDF, HTML, etc.)
    • Chunk with appropriate size (500-1000 tokens)
    • Generate embeddings

  2. Vector Storage

    • Store embeddings in vector database
    • Add metadata for filtering

  3. Retrieval

    • Embed user query
    • Similarity search (k-NN)
    • Return top-k documents

  4. Generation

    • Combine query + retrieved context
    • Generate answer with LLM

Advanced RAG Patterns

Combines BM25 (keyword) and vector (semantic) search:

# LangChain example
from langchain.retrievers import BM25Retriever, EnsembleRetriever

bm25_retriever = BM25Retriever.from_documents(docs)
vector_retriever = vectorstore.as_retriever()

ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, vector_retriever],
    weights=[0.5, 0.5]
)

2. Re-ranking

Use cross-encoder to re-rank retrieved documents:

from sentence_transformers import CrossEncoder

reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
scores = reranker.predict([(query, doc) for doc in retrieved_docs])

3. Query Expansion

Expand queries to improve retrieval:

# Generate multiple query variations
expanded_queries = [
    query,
    llm.invoke(f"Rephrase: {query}"),
    llm.invoke(f"Simplify: {query}")
]

Best Practices

AspectRecommendation
Chunk Size500-1000 tokens with 10-20% overlap
Embedding Modeltext-embedding-3-large or E5
Top-k5-10 documents
Temperature0.1-0.3 for factual tasks

🛠 Key Tools

ToolPurposeLink
LangChainRAG FrameworkDetails
LlamaIndexData FrameworkDetails
PineconeVector DBDetails

FAQ

Q1: What is the basic RAG architecture?

Document ingestion → Embedding → Vector storage → Similarity search → Context-augmented generation

Q2: How to improve RAG accuracy?

Use hybrid search, re-ranking, query expansion, and metadata filtering

Q3: Which vector database should I use?

Pinecone for managed, Qdrant for cost-effective, Milvus for large scale

Summary

RAG is essential for production LLM applications. Start with basic implementation, then add advanced techniques like hybrid search and re-ranking for better performance.

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