Traditional Rag

"""
Traditional Rag
=============================

1. Run: `./cookbook/run_pgvector.sh` to start a postgres container with pgvector.
"""

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.pgvector import PgVector, SearchType

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    # Use PgVector as the vector database and store embeddings in the `ai.recipes` table
    vector_db=PgVector(
        table_name="recipes",
        db_url=db_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    # Enable RAG by adding context from the `knowledge` to the user prompt.
    add_knowledge_to_context=True,
    # Set as False because Agents default to `search_knowledge=True`
    search_knowledge=False,
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    knowledge.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")
    agent.print_response(
        "How do I make chicken and galangal in coconut milk soup", stream=True
    )

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/02_agents/07_knowledge

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

# Optiona: Run PgVector (needs docker)
./cookbook/scripts/run_pgvector.sh

python traditional_rag.py