basic_rag.py
"""
Basic RAG: Context Injection
=============================
The simplest way to give an agent access to documents. Content is automatically
retrieved and injected into the system prompt before the agent responds.
This pattern works well for simple Q&A over documents. The agent doesn't need
to decide whether to search - it always gets relevant context.
Steps:
1. Create a Knowledge base with a vector database
2. Load a document
3. Create an Agent with add_knowledge_to_context=True
4. Ask questions - context is injected automatically
See also: 02_agentic_rag.py for agent-driven search decisions.
"""
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="basic_rag",
url=qdrant_url,
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
# Traditional RAG: context is fetched and injected into the prompt automatically.
# The agent doesn't get a search tool - it just sees the relevant context.
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
add_knowledge_to_context=True,
search_knowledge=False,
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("Basic RAG: Context injected into prompt automatically")
print("=" * 60 + "\n")
agent.print_response(
"How do I make chicken and galangal in coconut milk soup",
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"