| name | rag-systems |
| description | Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization |
| sasmp_version | 1.3.0 |
| bonded_agent | 03-rag-systems |
| bond_type | PRIMARY_BOND |
| version | 2.0.0 |
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
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 generationagent-memory - Memory retrievalai-agent-basics - Agentic RAG
References