| name | rag-agent |
| description | Provides a template for building Retrieval-Augmented Generation (RAG) agents. Use for: creating agents that can access and reason over external knowledge bases, semantic search, and contextual retrieval. |
RAG Agent Skill
The Retrieval-Augmented Generation (RAG) Agent template is designed for building agents that can access and reason over external knowledge bases.
Key Features
- Semantic Search: Integration with vector databases (e.g., Pinecone, Milvus, Chroma) for efficient and relevant information retrieval.
- Contextual Retrieval: Dynamic retrieval of relevant documents based on user queries, ensuring the agent has the most pertinent information.
- Source Attribution: Mechanisms to ensure the agent cites its sources for transparency and verifiability.
Usage Instructions
To utilize this RAG agent skill, follow these steps:
- Ingest Data: Load and chunk your documents into manageable pieces.
- Generate Embeddings: Convert text chunks into vector representations suitable for semantic search.
- Query Agent: The agent will automatically retrieve relevant context from the knowledge base before generating a response to a user query.
Example Implementation Sketch
class RAGAgent:
def __init__(self, vector_db, llm):
self.vector_db = vector_db
self.llm = llm
def query(self, question):
context = self.vector_db.search(question)
return self.llm.generate(question, context)
Resources
