Agent-Skills.md

Agent Skills: RAG Systems

Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization

UncategorizedID: pluginagentmarketplace/custom-plugin-ai-agents/rag-systems

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Skill Metadata

Name
rag-systems
Description
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization

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 generation
  • agent-memory - Memory retrieval
  • ai-agent-basics - Agentic RAG

References

RAG Systems Skill | Agent Skills