rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill rag-implementation- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads rag-implementation automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
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.
FAQ
What does the rag-implementation skill do?
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
How do I install rag-implementation?
Run npx skills add https://github.com/wshobson/agents --skill rag-implementation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/rag-implementation/ (personal) or .claude/skills/rag-implementation/ (project).
Which AI agents can use rag-implementation?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is rag-implementation safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains rag-implementation?
It is published by wshobson in wshobson/agents. It has 14,030 recorded installs.
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