Code
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate
# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filters_sample_data(
num_files=5, file_extension=SampleDataFileExtension.PDF
)
# Step 1: Initialize knowledge with documents and metadata
# ------------------------------------------------------------------------------
# When initializing the knowledge, we can attach metadata that will be used for filtering
# This metadata can include user IDs, document types, dates, or any other attributes
vector_db = Weaviate(
collection="recipes",
vector_index=VectorIndex.HNSW,
distance=Distance.COSINE,
local=False, # Set to False if using Weaviate Cloud and True if using local instance
)
knowledge = Knowledge(
name="Weaviate Knowledge Base",
description="A knowledge base for Weaviate",
vector_db=vector_db,
)
knowledge.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,
},
},
]
)
# Step 2: Query the knowledge base with different filter combinations
# ------------------------------------------------------------------------------
agent = Agent(
knowledge=knowledge,
search_knowledge=True,
)
agent.print_response(
"Tell me about Jordan Mitchell's experience and skills",
knowledge_filters={"user_id": "jordan_mitchell"},
markdown=True,
)
Usage
1
Install libraries
pip install -U agno weaviate-client openai
2
Set environment variables
export OPENAI_API_KEY=xxx
3
Setup Weaviate
# 1. Create account at https://console.weaviate.cloud/
# 2. Create a cluster and copy the "REST endpoint" and "Admin" API Key
# 3. Set environment variables:
export WCD_URL="your-cluster-url"
export WCD_API_KEY="your-api-key"
# 4. Set local=False in the code
# 1. Install Docker from https://docs.docker.com/get-docker/
# 2. Run Weaviate locally:
docker run -d \
-p 8080:8080 \
-p 50051:50051 \
--name weaviate \
cr.weaviate.io/semitechnologies/weaviate:1.28.4
# 3. Set local=True in the code
4
Run the example
python cookbook/knowledge/filters/vector_dbs/filtering_weaviate.py
python cookbook/knowledge/filters/vector_dbs/filtering_weaviate.py