사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/wshobson/agents --skill rag-implementation시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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, serverless
- Weaviate: Open-source, hybrid search, GraphQL
- Milvus: High performance, on-premise
- Chroma: Lightweight, easy to use, local development
- Qdrant: Fast, filtered search, Rust-based
- pgvector: PostgreSQL extension, SQL integration
2. Embeddings
Purpose: Convert text to numerical vectors for similarity search
Models (2026):
| Model | Dimensions | Best For |
|---|---|---|
| voyage-3-large | 1024 | Claude apps (Anthropic recommended) |
| voyage-code-3 | 1024 | Code search |
| text-embedding-3-large | 3072 | OpenAI apps, high accuracy |
| text-embedding-3-small | 1536 | OpenAI apps, cost-effective |
| bge-large-en-v1.5 | 1024 | Open source, local deployment |
| multilingual-e5-large | 1024 | Multi-language support |
3. Retrieval Strategies
Approaches:
- Dense Retrieval: Semantic similarity via embeddings
- Sparse Retrieval: Keyword matching (BM25, TF-IDF)
- Hybrid Search: Combine dense + sparse with weighted fusion
- Multi-Query: Generate multiple query variations
- HyDE: Generate hypothetical documents for better retrieval
4. Reranking
Purpose: Improve retrieval quality by reordering results
Methods:
- Cross-Encoders: BERT-based reranking (ms-marco-MiniLM)
- Cohere Rerank: API-based reranking
- Maximal Marginal Relevance (MMR): Diversity + relevance
- LLM-based: Use LLM to score relevance
Quick Start with LangGraph
python
from langgraph.graph import StateGraph, START, END
from langchain_anthropic import ChatAnthropic
from langchain_voyageai import VoyageAIEmbeddings
from langchain_pinecone import PineconeVectorStore
from langchain_core.documents import Document
from langchain_core.prompts import ChatPromptTemplate
from langchain_text_splitters import RecursiveCharacterTextSplitter
from typing import TypedDict, Annotated
class RAGState(TypedDict):
question: str
context: list[Document]
answer: str
# Initialize components
llm = ChatAnthropic(model="claude-sonnet-5")
embeddings = VoyageAIEmbeddings(model="voyage-3-large")
vectorstore = PineconeVectorStore(index_name="docs", embedding=embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
# RAG prompt
rag_prompt = ChatPromptTemplate.from_template(
"""Answer based on the context below. If you cannot answer, say so.
Context:
{context}
Question: {question}
Answer:"""
)
async def retrieve(state: RAGState) -> RAGState:
"""Retrieve relevant documents."""
docs = await retriever.ainvoke(state["question"])
return {"context": docs}
async def generate(state: RAGState) -> RAGState:
"""Generate answer from context."""
context_text = "\n\n".join(doc.page_content for doc in state["context"])
messages = rag_prompt.format_messages(
context=context_text,
question=state["question"]
)
response = await llm.ainvoke(messages)
return {"answer": response.content}
# Build RAG graph
builder = StateGraph(RAGState)
builder.add_node("retrieve", retrieve)
builder.add_node("generate", generate)
builder.add_edge(START, "retrieve")
builder.add_edge("retrieve", "generate")
builder.add_edge("generate", END)
rag_chain = builder.compile()
# Use
result = await rag_chain.ainvoke({"question": "What are the main features?"})
print(result["answer"])Detailed patterns and worked examples
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
