knowledge_filters.py
"""
Knowledge Filters
=============================
Filter knowledge base searches using static filters or agentic filters.
Static filters are set at agent creation time and apply to every search.
Agentic filters let the agent dynamically choose filter values at runtime.
"""
from agno.agent import Agent
from agno.filters import EQ
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(
vector_db=PgVector(
table_name="recipes_filters_demo",
db_url=db_url,
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
# ---------------------------------------------------------------------------
# Create Agent With Static Filters
# ---------------------------------------------------------------------------
# Static filters: only retrieve documents matching these criteria
agent_static = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
search_knowledge=True,
# Use FilterExpr objects for type-safe filtering
knowledge_filters=[EQ("cuisine", "thai")],
markdown=True,
)
# ---------------------------------------------------------------------------
# Create Agent With Agentic Filters
# ---------------------------------------------------------------------------
# Agentic filters: the agent decides filter values dynamically
agent_agentic = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
search_knowledge=True,
# Let the agent choose filter values based on the user's query
enable_agentic_knowledge_filters=True,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
knowledge.insert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")
print("--- Static filters (cuisine=thai) ---")
agent_static.print_response(
"What soup recipes do you have?",
stream=True,
)
print("\n--- Agentic filters ---")
agent_agentic.print_response(
"Find me a Thai dessert recipe.",
stream=True,
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai pgvector psycopg-binary pypdf sqlalchemy
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18
5
Run the example
Save the code above as
knowledge_filters.py, then run:python knowledge_filters.py