agentic_rag.py
"""
Agentic RAG: Tool-Based Search
================================
The agent gets a search_knowledge_base tool and decides when to query the
knowledge base. This is more flexible than basic RAG - the agent can choose
to search multiple times, refine queries, or skip searching entirely.
This is the default behavior when you set knowledge on an Agent.
Steps:
1. Create a Knowledge base with a vector database
2. Load a document
3. Create an Agent with search_knowledge=True (the default)
4. Ask questions - agent decides when to search
See also: 01_basic_rag.py for automatic context injection.
"""
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
qdrant_url = "http://localhost:6333"
knowledge = Knowledge(
vector_db=Qdrant(
collection="agentic_rag",
url=qdrant_url,
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
# Agentic RAG: the agent gets a search tool and decides when to use it.
# This is the default when knowledge is provided to an Agent.
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
search_knowledge=True,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
async def main():
await knowledge.ainsert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
print("\n" + "=" * 60)
print("Agentic RAG: Agent decides when to search")
print("=" * 60 + "\n")
agent.print_response(
"How do I make chicken and galangal in coconut milk soup",
stream=True,
)
print("\n" + "=" * 60)
print("Multi-part question: agent may search multiple times")
print("=" * 60 + "\n")
agent.print_response(
"I want to make a 3 course Thai meal. Can you recommend a soup, "
"a curry for the main course, and a dessert?",
stream=True,
)
asyncio.run(main())
Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"