Agentic RAG

Agentic RAG

Agentic RAG goes beyond simple retrieval — the agent decides what to search, when to search again, and how to synthesise results from multiple sources.

from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_community.vectorstores import Chroma
from langchain_core.tools import tool
from langchain_openai import OpenAIEmbeddings

# Build vector store
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
retriever   = vectorstore.as_retriever(search_kwargs={"k":5})

@tool
def search_knowledge_base(query: str) -> str:
    """Search internal knowledge base for relevant information."""
    docs = retriever.invoke(query)
    return "
---
".join(d.page_content for d in docs)

@tool
def search_web(query: str) -> str:
    """Search the web for current information not in knowledge base."""
    return web_search(query)

@tool
def check_date_relevance(topic: str) -> str:
    """Check if information about a topic might be outdated."""
    return llm.invoke(f"Is info about {topic} likely outdated after 2024? Answer yes/no with reason.")

# Agent decides which tool to use and when to re-search
agent = create_tool_calling_agent(llm, [search_knowledge_base, search_web, check_date_relevance], prompt)
executor = AgentExecutor(agent=agent, tools=[search_knowledge_base, search_web, check_date_relevance])