Documentation Index
Fetch the complete documentation index at: https://docs.agno.com/llms.txt
Use this file to discover all available pages before exploring further.
Follow steps mentioned in Weaviate setup guide to setup Weaviate.
Setup
Install weaviate packages
uv pip install weaviate-client
docker run -d \
-p 8080:8080 \
-p 50051:50051 \
--name weaviate \
cr.weaviate.io/semitechnologies/weaviate:1.28.4
./cookbook/scripts/run_weaviate.sh
Example
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
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 = Knowledge(
vector_db=vector_db,
)
# Create and use the agent
agent = Agent(
knowledge=knowledge_base,
search_knowledge=True,
)
if __name__ == "__main__":
knowledge_base.insert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
agent.print_response("How to make Thai curry?", markdown=True)
Async Support ⚡
Weaviate also supports asynchronous operations, enabling concurrency and leading to better performance.
import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
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 = Knowledge(
vector_db=vector_db,
)
agent = Agent(
knowledge=knowledge_base,
search_knowledge=True,
)
if __name__ == "__main__":
# Load knowledge base asynchronously
asyncio.run(knowledge_base.ainsert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
)
# Create and use the agent asynchronously
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 |
