rag-implementation

SKILL.md 节选

# RAG Implementation

Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.

## When to Use This Skill

- Building Q&A systems over proprietary documents
- Creating chatbots with current, factual information
- Implementing semantic search with natural language queries
- Reducing hallucinations with grounded responses
- Enabling LLMs to access domain-specific knowledge
- Building documentation assistants
- Creating research tools with source citation

## Core Components

### 1. Vector Databases
**Purpose**: Store and retrieve document embeddings efficiently

**Options:**
- **Pinecone**: Managed, scalable, fast queries
- **Weaviate**: Open-source, hybrid search
- **Milvus**: High performance, on-premise
- **Chroma**: Lightweight, easy to use
- **Qdrant**: Fast, filtered search
- **FAISS**: Meta's library, local deployment

### 2. Embeddings
**Purpose**: Convert text to numerical vectors for similarity search

**Models:**
- **text-embedding-ada-002** (OpenAI): General purpose, 1536 dims
- **all-MiniLM-L6-v2** (Sentence Transformers): Fast, lightweight
- **e5-large-v2**: High quality, multilingual
- **Instructor**: Task-specific instructions
- **bge-large-en-v1.5**: SOTA performance

### 3. Retrieval Strategies
**Approaches:**
- **Dense Retrieval**: Semantic similarity via embeddings
- **Sparse Retrieval**: Keyword matching (BM25, TF-IDF)
- **Hybrid Search**: Combine dense + sparse
- **Multi-Query**: Generate multiple query variations
- **HyDE**: Generate hypothetical documents

### 4. Reranking
**Purpose**: Improve retrieval quality by reordering results

**Methods:**
- **Cross-Encoders**: BERT-based reranking
- **Cohere Rerank**: API-based reranking
- **Maximal Marginal Relevance (MMR)**: Diversity + relevance
- **LLM-based**: Use LLM to score relevance

## Quick Start

```python
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitters import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# 1. Load documents
loader = DirectoryLoader('./docs', glob="**/*.txt")
documents = loader.load()

# 2. Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    length_function=len
)
chunks = text_splitter.split_documents(documents)

# 3. Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

# 4. Create retrieval chain
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 4}),
    return_source_documents=True
)

# 5. Query
result = qa_chain({"query": "What are the main features?"})
print(result['result'])
print(result['source_documents'])
```

## Advanced RAG Patterns

### Pattern 1: Hybrid Search
```python
from langchain.retrievers import BM25Retriever, EnsembleRetriever

# Sparse retriever (BM25)
bm25_retriever = BM25Retriever.from_documents(chunks)
bm25_retriever.k = 5

# Dense retriever (embeddings)
embedding_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Combine with weights
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, embedding_retriever],
    weights=[0.3, 0.7]
)
```

### Pattern 2: Multi-Query Retrieval
```python
from langchain.retrievers.multi_query import MultiQueryRetriever

# Generate multiple query perspectives
retriever = MultiQueryRetriever.from_llm(
    retriever=vectorstore.as_retriever(),
    llm=OpenAI()
)

# Single query → multiple variations → combined results
results = retriever.get_relevant_documents("What is the main topic?")
```

### Pattern 3: Contextual Compression
```python
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor

compressor = LLMChainExtractor.from_llm(llm)

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