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rag-implementation skill

by wshobson·wshobson/agents·40k stars·MIT

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 the rag-implementation skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p ~/.claude/skills
cp -r /tmp/agents/plugins/llm-application-dev/skills/rag-implementation ~/.claude/skills/rag-implementation
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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):

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

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

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