RAG Systems
Build Retrieval-Augmented Generation systems for grounded responses.
When to Use This Skill
Invoke this skill when:
- •Building Q&A over custom documents
- •Implementing semantic search
- •Setting up vector databases
- •Optimizing retrieval quality
Parameter Schema
| Parameter | Type | Required | Description | Default |
|---|---|---|---|---|
task | string | Yes | RAG goal | - |
vector_db | enum | No | pinecone, weaviate, chroma, pgvector | chroma |
embedding_model | string | No | Embedding model | text-embedding-3-small |
chunk_size | int | No | Chunk size in chars | 1000 |
Quick Start
python
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
# 1. Split documents
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)
# 2. Create vector store
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_documents(chunks, embeddings)
# 3. Retrieve
docs = vectorstore.similarity_search("query", k=5)
Chunking Strategy
| Content Type | Size | Overlap | Rationale |
|---|---|---|---|
| Technical docs | 500-800 | 100 | Preserve code |
| Legal docs | 1000-1500 | 200 | Keep clauses |
| Q&A/FAQ | 200-400 | 50 | Atomic answers |
Embedding Costs
| Model | Cost/1M tokens |
|---|---|
| text-embedding-3-small | $0.02 |
| text-embedding-3-large | $0.13 |
| Cohere embed-v3 | $0.10 |
Troubleshooting
| Issue | Solution |
|---|---|
| Irrelevant results | Improve chunking, add reranking |
| Missing context | Increase k, use parent retriever |
| Hallucinations | Add "only use context" prompt |
| Slow retrieval | Add caching, reduce k |
Best Practices
- •Always include source attribution
- •Use hybrid search (dense + BM25)
- •Implement reranking for quality
- •Evaluate with RAGAS metrics
Related Skills
- •
llm-integration- LLM for generation - •
agent-memory- Memory retrieval - •
ai-agent-basics- Agentic RAG