Weaviate

Code

cookbook/08_knowledge/vector_db/weaviate_db/weaviate_db.py

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Weaviate
from agno.vectordb.weaviate.index import Distance, VectorIndex

vector_db = Weaviate(
    collection="vectors",
    search_type=SearchType.vector,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=False,  # Set to True if using Weaviate locally
)

# Create Knowledge Instance with Weaviate
knowledge = Knowledge(
    name="Basic SDK Knowledge Base",
    description="Agno 2.0 Knowledge Implementation with Weaviate",
    vector_db=vector_db,
)

knowledge.insert(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
    skip_if_exists=True,
)

# Create and use the agent
agent = Agent(knowledge=knowledge)
agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)

# Delete operations
vector_db.delete_by_name("Recipes")
# or
vector_db.delete_by_metadata({"doc_type": "recipe_book"})

Usage

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   
2. Install dependencies

   uv pip install -U weaviate-client pypdf openai agno
   
3. Setup Weaviate

   Weaviate Cloud

   # 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
   
4. Set environment variables

   export OPENAI_API_KEY=xxx
   
5. Run Agent

   python cookbook/08_knowledge/vector_db/weaviate_db/weaviate_db.py