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.
npxskills add wshobson/agents--skill rag-implementationLoading…
Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.
Purpose: Store and retrieve document embeddings efficiently
Options:
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 |
Approaches:
Purpose: Improve retrieval quality by reordering results
Methods:
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 pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
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