cognee/cognee/config.py

"""Configuration for cognee - cognitive architecture framework."""
import logging
import os
import configparser
import uuid
from typing import Optional, Dict, Any
from dataclasses import dataclass, field
from pathlib import Path
from dotenv import load_dotenv
from cognee.root_dir import get_absolute_path
from cognee.shared.data_models import ChunkStrategy, DefaultGraphModel
logging.basicConfig(level=logging.DEBUG)
def load_dontenv():
    base_dir = Path(__file__).resolve().parent.parent
    # Load the .env file from the base directory
    dotenv_path = base_dir / ".env"
    load_dotenv(dotenv_path=dotenv_path, override = True)

try:
    load_dontenv()
except:
    pass

@dataclass
class Config:
    """ Configuration for cognee - cognitive architecture framework. """
    cognee_dir: str = field(
        default_factory=lambda: os.getenv("COG_ARCH_DIR", "cognee")
    )
    config_path: str = field(
        default_factory=lambda: os.path.join(
            os.getenv("COG_ARCH_DIR", "cognee"), "config"
        )
    )


    system_root_directory = get_absolute_path(".cognee_system")
    logging.info("system_root_directory: %s", system_root_directory)
    data_root_directory = os.getenv("DATA_PATH", get_absolute_path(".data"))

    vectordb: str = os.getenv("VECTORDB", "weaviate")

    qdrant_path: str = os.getenv("QDRANT_PATH", None)
    qdrant_url: str = os.getenv("QDRANT_URL", None)
    qdrant_api_key: str = os.getenv("QDRANT_API_KEY", None)



    graph_filename = os.getenv("GRAPH_NAME", "cognee_graph.pkl")

    # Model parameters
    llm_provider: str = os.getenv("LLM_PROVIDER", "openai") #openai, or custom or ollama
    llm_model: str = os.getenv("LLM_MODEL", "gpt-4")
    llm_api_key: str = os.getenv("LLM_API_KEY", os.getenv("OPENAI_API_KEY"))
    llm_endpoint: str = os.getenv("LLM_ENDPOINT", None)

    # custom_model: str = os.getenv("CUSTOM_LLM_MODEL", "llama3-70b-8192") #"mistralai/Mixtral-8x7B-Instruct-v0.1"
    # custom_endpoint: str = os.getenv("CUSTOM_ENDPOINT", "https://api.endpoints.anyscale.com/v1") #"https://api.endpoints.anyscale.com/v1" # pass claude endpoint
    # custom_key: Optional[str] = os.getenv("CUSTOM_LLM_API_KEY")
    # ollama_endpoint: str = os.getenv("CUSTOM_OLLAMA_ENDPOINT", "http://localhost:11434/v1") #"http://localhost:11434/v1"
    # ollama_key: Optional[str] = "ollama"
    # ollama_model: str = os.getenv("CUSTOM_OLLAMA_MODEL", "mistral:instruct") #"mistral:instruct"
    # openai_model: str = os.getenv("OPENAI_MODEL", "gpt-4o" ) #"gpt-4o"
    # model_endpoint: str = "openai"
    # llm_api_key: Optional[str] = os.getenv("OPENAI_API_KEY")
    openai_temperature: float = float(os.getenv("OPENAI_TEMPERATURE", 0.0))
    # openai_embedding_model = "text-embedding-3-large"
    # openai_embedding_dimensions = 3072
    # litellm_embedding_model = "text-embedding-3-large"
    # litellm_embedding_dimensions = 3072

    graphistry_username = os.getenv("GRAPHISTRY_USERNAME")
    graphistry_password = os.getenv("GRAPHISTRY_PASSWORD")

    # Embedding parameters
    embedding_model: str = "BAAI/bge-large-en-v1.5"
    embedding_dimensions: int = 1024
    connect_documents: bool = False

    # Database parameters
    graph_database_provider: str = os.getenv("GRAPH_DB_PROVIDER", "NETWORKX")
    graph_topology:str = DefaultGraphModel
    cognitive_layers_limit: int = 2

    from cognee.shared.data_models import MonitoringTool

    # Monitoring tool
    monitoring_tool: str = os.getenv("MONITORING_TOOL", MonitoringTool.LANGFUSE)

    weaviate_url: str = os.getenv("WEAVIATE_URL")
    weaviate_api_key: str = os.getenv("WEAVIATE_API_KEY")

    # Model parameters and configuration for interlayer scoring
    intra_layer_score_treshold: float = 0.98


    # Client ID
    anon_clientid: Optional[str] = field(default_factory=lambda: uuid.uuid4().hex)

    #Chunking parameters
    # chunk_size: int = 1500
    # chunk_overlap: int = 0
    # chunk_strategy: str = ChunkStrategy.PARAGRAPH

    def load(self):
        """Loads the configuration from a file or environment variables."""
        try:
            load_dontenv()
        except:
            pass
        config = configparser.ConfigParser()
        config.read(self.config_path)

        # Override with environment variables if they exist
        for attr in self.__annotations__:
            env_value = os.getenv(attr.upper())
            if env_value is not None:
                setattr(self, attr, env_value)

        # Load from config file
        if config.sections():
            for section in config.sections():
                for key, value in config.items(section):
                    if hasattr(self, key):
                        setattr(self, key, value)

    def save(self):
        """Saves the current configuration to a file."""
        config = configparser.ConfigParser()

        # Save the current settings to the config file
        for attr, value in self.__dict__.items():
            section, option = attr.split("_", 1)
            if not config.has_section(section):
                config.add_section(section)
            config.set(section, option, str(value))

        with open(self.config_path, "w") as configfile:
            config.write(configfile)

    def to_dict(self) -> Dict[str, Any]:
        """Returns a dictionary representation of the configuration."""
        return {attr: getattr(self, attr) for attr in self.__annotations__}

    @classmethod
    def from_dict(cls, config_dict: Dict[str, Any]) -> "Config":
        """Creates a Config instance from a dictionary."""
        config = cls()
        for attr, value in config_dict.items():
            if hasattr(config, attr):
                setattr(config, attr, value)
        return config