Weaviate Agent Knowledge

Follow steps mentioned in Weaviate setup guide to setup Weaviate.

Setup

Install weaviate packages

pip install weaviate-client

Run weaviate

docker run -d \
-p 8080:8080 \
-p 50051:50051 \
--name weaviate \
cr.weaviate.io/semitechnologies/weaviate:1.28.4 

or

./cookbook/scripts/run_weaviate.sh

Example

agent_with_knowledge.py

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

vector_db = Weaviate(
    collection="recipes",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=True,  # Set to False if using Weaviate Cloud and True if using local instance
)
# Create knowledge base
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)
knowledge_base.load(recreate=False)  # Comment out after first run

# Create and use the agent
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
    show_tool_calls=True,
)
agent.print_response("How to make Thai curry?", markdown=True)

Async Support ⚡

Weaviate also supports asynchronous operations, enabling concurrency and leading to better performance.

async_weaviate_db.py

import asyncio

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

vector_db = Weaviate(
    collection="recipes_async",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=True,  # Set to False if using Weaviate Cloud and True if using local instance
)

# Create knowledge base
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
    show_tool_calls=True,
)

if __name__ == "__main__":
    # Comment out after first run
    asyncio.run(knowledge_base.aload(recreate=False))

    # Create and use the agent
    asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))

Weaviate’s async capabilities leverage WeaviateAsyncClient to provide non-blocking vector operations. This is particularly valuable for applications requiring high concurrency and throughput.

Weaviate Params

Parameter	Type	Description	Default
wcd_url	Optional[str]	Weaviate Cloud URL (or use WCD_URL env var)	None
wcd_api_key	Optional[str]	Weaviate Cloud API key (or use WCD_API_KEY env var)	None
client	Optional[weaviate.WeaviateClient]	Pre-configured Weaviate client	None
local	bool	Whether to use a local Weaviate instance	False
collection	str	Name of the Weaviate collection	"default"
vector_index	VectorIndex	Type of vector index (HNSW, FLAT, DYNAMIC)	VectorIndex.HNSW
distance	Distance	Distance metric (COSINE, DOT, etc.)	Distance.COSINE
embedder	Optional[Embedder]	Embedder to use for generating embeddings	OpenAIEmbedder()
search_type	SearchType	Search type (vector, keyword, hybrid)	SearchType.vector
reranker	Optional[Reranker]	Reranker to refine search results	None
hybrid_search_alpha	float	Weighting factor for hybrid search	0.5

Developer Resources

• View Cookbook (Sync)
• View Cookbook (Async)