SelfMemory

import os import httpx from selfmemory.configs.base import AzureConfig class BaseEmbedderConfig: """ Config for Embeddings. """ def __init__( self, model: str | None = None, api_key: str | None = None, embedding_dims: int | None = None, # Ollama specific ollama_base_url: str | None = None, # Openai specific openai_base_url: str | None = None, # Huggingface specific model_kwargs: dict | None = None, huggingface_base_url: str | None = None, # AzureOpenAI specific azure_kwargs: AzureConfig | None = None, http_client_proxies: dict | str | None = None, # VertexAI specific vertex_credentials_json: str | None = None, memory_add_embedding_type: str | None = None, memory_update_embedding_type: str | None = None, memory_search_embedding_type: str | None = None, # Gemini specific output_dimensionality: str | None = None, # LM Studio specific lmstudio_base_url: str | None = "http://localhost:1234/v1", # AWS Bedrock specific aws_access_key_id: str | None = None, aws_secret_access_key: str | None = None, aws_region: str | None = None, ): """ Initializes a configuration class instance for the Embeddings. :param model: Embedding model to use, defaults to None :type model: Optional[str], optional :param api_key: API key to be use, defaults to None :type api_key: Optional[str], optional :param embedding_dims: The number of dimensions in the embedding, defaults to None :type embedding_dims: Optional[int], optional :param ollama_base_url: Base URL for the Ollama API, defaults to None :type ollama_base_url: Optional[str], optional :param model_kwargs: key-value arguments for the huggingface embedding model, defaults a dict inside init :type model_kwargs: Optional[Dict[str, Any]], defaults a dict inside init :param huggingface_base_url: Huggingface base URL to be use, defaults to None :type huggingface_base_url: Optional[str], optional :param openai_base_url: Openai base URL to be use, defaults to "https://api.openai.com/v1" :type openai_base_url: Optional[str], optional :param azure_kwargs: key-value arguments for the AzureOpenAI embedding model, defaults a dict inside init :type azure_kwargs: Optional[Dict[str, Any]], defaults a dict inside init :param http_client_proxies: The proxy server settings used to create self.http_client, defaults to None :type http_client_proxies: Optional[Dict | str], optional :param vertex_credentials_json: The path to the Vertex AI credentials JSON file, defaults to None :type vertex_credentials_json: Optional[str], optional :param memory_add_embedding_type: The type of embedding to use for the add memory action, defaults to None :type memory_add_embedding_type: Optional[str], optional :param memory_update_embedding_type: The type of embedding to use for the update memory action, defaults to None :type memory_update_embedding_type: Optional[str], optional :param memory_search_embedding_type: The type of embedding to use for the search memory action, defaults to None :type memory_search_embedding_type: Optional[str], optional :param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1" :type lmstudio_base_url: Optional[str], optional """ self.model = model self.api_key = api_key self.openai_base_url = openai_base_url self.embedding_dims = embedding_dims # AzureOpenAI specific self.http_client = ( httpx.Client(proxies=http_client_proxies) if http_client_proxies else None ) # Ollama specific self.ollama_base_url = ollama_base_url # Huggingface specific self.model_kwargs = model_kwargs or {} self.huggingface_base_url = huggingface_base_url # AzureOpenAI specific self.azure_kwargs = ( AzureConfig(**azure_kwargs) if azure_kwargs else AzureConfig() ) # VertexAI specific self.vertex_credentials_json = vertex_credentials_json self.memory_add_embedding_type = memory_add_embedding_type self.memory_update_embedding_type = memory_update_embedding_type self.memory_search_embedding_type = memory_search_embedding_type # Gemini specific self.output_dimensionality = output_dimensionality # LM Studio specific self.lmstudio_base_url = lmstudio_base_url # AWS Bedrock specific self.aws_access_key_id = aws_access_key_id self.aws_secret_access_key = aws_secret_access_key self.aws_region = aws_region or os.environ.get("AWS_REGION") or "us-west-2"