Method load_embedding_config

AskAgent/python/core/config.py:280–316

Load embedding model configuration.

(self, path: str = "config_embedding.yaml")

                )

    def load_embedding_config(self, path: str = "config_embedding.yaml"):
        """Load embedding model configuration."""
        # Build the full path to the config file using the config directory
        full_path = os.path.join(self.config_directory, path)

        try:
            with open(full_path) as f:
                data = yaml.safe_load(f)
        except FileNotFoundError:
            # If config file doesn't exist, use defaults
            print(f"Warning: {path} not found. Using default embedding configuration.")
            data = {
                "preferred_provider": "openai",
                "providers": {}
            }

        self.preferred_embedding_provider: str = data["preferred_provider"]
        self.embedding_providers: dict[str, EmbeddingProviderConfig] = {}

        for name, cfg in data.get("providers", {}).items():
            # Extract configuration values from the YAML
            api_key = self._get_config_value(cfg.get("api_key_env"))
            api_endpoint = self._get_config_value(cfg.get("api_endpoint_env"))
            api_version = self._get_config_value(cfg.get("api_version_env"))
            model = self._get_config_value(cfg.get("model"))
            config = self._get_config_value(cfg.get("config"))
            auth_method = self._get_config_value(cfg.get("auth_method"), "api_key")

            # Create the embedding provider config
            self.embedding_providers[name] = EmbeddingProviderConfig(
                api_key=api_key,
                endpoint=api_endpoint,
                api_version=api_version,
                model=model,
                config=config,
                auth_method=auth_method
            )

    def load_retrieval_config(self, path: str = "config_retrieval.yaml"):
        # Build the full path to the config file using the config directory