Team with Knowledge Filters

This example demonstrates how to use knowledge filters with teams to restrict knowledge searches to specific documents or metadata criteria, enabling personalized and contextual responses based on predefined filter conditions.

Code

cookbook/examples/teams/knowledge/02_team_with_knowledge_filters.py

"""
This example demonstrates how to use knowledge filters with teams.

Knowledge filters allow you to restrict knowledge searches to specific documents
or metadata criteria, enabling personalized and contextual responses.
"""

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.utils.media import (
    SampleDataFileExtension,
    download_knowledge_filters_sample_data,
)
from agno.vectordb.lancedb import LanceDb

# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filters_sample_data(
    num_files=5, file_extension=SampleDataFileExtension.PDF
)

# Initialize LanceDB vector database
# By default, it stores data in /tmp/lancedb
vector_db = LanceDb(
    table_name="recipes",
    uri="tmp/lancedb",  # You can change this path to store data elsewhere
)

# Create knowledge base
knowledge_base = Knowledge(
    vector_db=vector_db,
)

# Add documents with metadata for filtering
knowledge_base.add_contents(
    [
        {
            "path": downloaded_cv_paths[0],
            "metadata": {
                "user_id": "jordan_mitchell",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[1],
            "metadata": {
                "user_id": "taylor_brooks",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[2],
            "metadata": {
                "user_id": "morgan_lee",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[3],
            "metadata": {
                "user_id": "casey_jordan",
                "document_type": "cv",
                "year": 2025,
            },
        },
        {
            "path": downloaded_cv_paths[4],
            "metadata": {
                "user_id": "alex_rivera",
                "document_type": "cv",
                "year": 2025,
            },
        },
    ],
    reader=PDFReader(chunk=True),
)

# Create knowledge search agent
web_agent = Agent(
    name="Knowledge Search Agent",
    role="Handle knowledge search",
    knowledge=knowledge_base,
    model=OpenAIChat(id="gpt-5-mini"),
)

# Create team with knowledge filters
team_with_knowledge = Team(
    name="Team with Knowledge",
    members=[
        web_agent
    ],  # If you omit the member, the leader will search the knowledge base itself.
    model=OpenAIChat(id="gpt-5-mini"),
    knowledge=knowledge_base,
    show_members_responses=True,
    markdown=True,
    knowledge_filters={
        "user_id": "jordan_mitchell"
    },  # Filter to specific user's documents
)

# Test knowledge filtering
team_with_knowledge.print_response(
    "Tell me about Jordan Mitchell's work and experience"
)

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install required libraries

   pip install agno openai lancedb
   
3. Set environment variables

   export OPENAI_API_KEY=****
   
4. Run the agent

   python cookbook/examples/teams/knowledge/02_team_with_knowledge_filters.py