Rag Implementation

---
name: rag-implementation
description: 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.
---

# 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.

Run this skill on a real model without leaving the page. Every run is saved to your history for this skill.

How to use

1. 1Save the content below as SKILL.md in your agent's skills directory (e.g. .claude/skills/<name>/SKILL.md).
2. 2Or paste it directly into the conversation as context before asking the agent to do the task.
3. 3Adjust any project-specific paths or conventions mentioned in the skill to match your setup.