Configuration

🚀 Configure Vector and Graph Stores

You can configure the vector and graph stores using the environment variables in your .env file or programmatically. We use Pydantic Settings

We have a global configuration object (cognee.config) and individual configurations on pipeline and data store levels

Check available configuration options:

from cognee.infrastructure.databases.vector import get_vectordb_config
from cognee.infrastructure.databases.graph.config import get_graph_config
from cognee.infrastructure.databases.relational import get_relational_config
from cognee.infrastructure.llm.config import get_llm_config
print(get_vectordb_config().to_dict())
print(get_graph_config().to_dict())
print(get_relational_config().to_dict())
print(get_llm_config().to_dict())
 

Setting the environment variables in your .env file, and Pydantic will pick them up:

GRAPH_DATABASE_PROVIDER = 'lancedb'
 

Otherwise, you can set the configuration yourself:

cognee.config.set_llm_provider('ollama')

🚀 Getting Started with Local Models

You’ll need to run the local model on your machine or use one of the providers hosting the model. !!! note “We had some success with mixtral, but 7b models did not work well. We recommend using mixtral for now.”

Ollama

Set up Ollama by following instructions on Ollama website

Set the environment variable in your .env to use the model

LLM_PROVIDER = 'ollama'
 

Otherwise, you can set the configuration for the model:

cognee.config.set_llm_provider('ollama')
 

You can also set the HOST and model name:

cognee.config.set_llm_endpoint("http://localhost:11434/v1")
cognee.config.set_llm_model("mistral:instruct")

Anyscale

LLM_PROVIDER = 'custom'
 

Otherwise, you can set the configuration for the model:

cognee.config.set_llm_provider('custom')
 

You can also set the HOST and model name:

LLM_MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"
LLM_ENDPOINT = "https://api.endpoints.anyscale.com/v1"
LLM_API_KEY = "your_api_key"