Cognee Community Weaviate Vector Adapter

This is a community-maintained adapter that enables Cognee to work with Weaviate as a vector database.

Installation

If published, the package can be simply installed via pip:

pip install cognee-community-vector-adapter-weaviate

In case it is not published yet, you can use poetry to locally build the adapter package:

pip install poetry
poetry install # run this command in the directory containing the pyproject.toml file

Connection Setup

The provided code creates an async client connected to a remote instance of Weaviate. If you want to connect to a local instance, like running a docker container locally and connecting to it, you need to change a few lines of code. In the weaviate_adapter.py file inside the .../weaviate/cognee_community_vector_adapter_weaviate directory, replace the following lines in the constructor:

self.client = weaviate.use_async_with_weaviate_cloud(
    cluster_url=url,
    auth_credentials=weaviate.auth.AuthApiKey(api_key),
    additional_config=wvc.init.AdditionalConfig(timeout=wvc.init.Timeout(init=30)),
)

with the following:

self.client = weaviate.use_async_with_local(
    host="localhost",
    port=8080,
    grpc_port=50051
)

You can use the docker command provided by Weaviate (https://docs.weaviate.io/deploy/installation-guides/docker-installation) to run Weaviate with default settings. The command looks something like this, specifying the ports for connection:

docker run -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate:1.32.4

Usage

import asyncio
import os
from cognee import config, prune, add, cognify, search, SearchType

# Import the register module to enable Weaviate support
import cognee_community_vector_adapter_weaviate.register


async def main():
    # Configure databases
    config.set_relational_db_config(
        {
            "db_provider": "sqlite",
        }
    )
    config.set_vector_db_config(
        {
            "vector_db_provider": "weaviate",
            "vector_db_url": os.getenv("VECTOR_DB_URL"),  # or your Weaviate URL
            "vector_db_key": os.getenv("VECTOR_DB_KEY"),  # or your API key
        }
    )
    config.set_graph_db_config(
        {
            "graph_database_provider": "networkx",
        }
    )

    # Optional: Clean previous data
    await prune.prune_data()
    await prune.prune_system()

    # Add and process your content
    text = "Your text content here"
    await add(text)
    await cognify()

    # Search
    search_results = await search(
        query_type=SearchType.GRAPH_COMPLETION, query_text="Your search query"
    )

    for result in search_results:
        print(result)


if __name__ == "__main__":
    asyncio.run(main())

Configuration

The Weaviate adapter requires the following configuration parameters:

• vector_db_url: Your Weaviate cluster endpoint URL
• vector_db_key: Your Weaviate API key
• vector_db_provider: Set to "weaviate"

Environment Variables

Set the following environment variables or pass them directly in the config:

export VECTOR_DB_URL="https://your-weaviate-instance.weaviate.network"
export VECTOR_DB_KEY="your-api-key"

Alternative: You can also use the .env.template file from the main cognee repository. Copy it to your project directory, rename it to .env, and fill in your Weaviate configuration values.

Requirements

• Python >= 3.11, <= 3.13
• weaviate-client >= 4.9.6, < 5.0.0
• cognee == 1.4.2

Features

• Full vector search capabilities
• Batch operations support
• Async/await support
• Retry logic for better reliability
• Collection management
• Data point indexing and retrieval