95 lines
4.4 KiB
Python
95 lines
4.4 KiB
Python
from abc import ABC
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from typing import Dict, Optional, Union
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import httpx
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from mem0.configs.base import AzureConfig
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class BaseEmbedderConfig(ABC):
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"""
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Config for Embeddings.
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"""
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def __init__(
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self,
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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embedding_dims: Optional[int] = None,
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# Ollama specific
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ollama_base_url: Optional[str] = None,
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# Openai specific
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openai_base_url: Optional[str] = None,
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# Huggingface specific
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model_kwargs: Optional[dict] = None,
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huggingface_base_url: Optional[str] = None,
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# AzureOpenAI specific
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azure_kwargs: Optional[AzureConfig] = {},
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http_client_proxies: Optional[Union[Dict, str]] = None,
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# VertexAI specific
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vertex_credentials_json: Optional[str] = None,
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memory_add_embedding_type: Optional[str] = None,
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memory_update_embedding_type: Optional[str] = None,
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memory_search_embedding_type: Optional[str] = None,
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# LM Studio specific
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lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
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):
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"""
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Initializes a configuration class instance for the Embeddings.
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:param model: Embedding model to use, defaults to None
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:type model: Optional[str], optional
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:param api_key: API key to be use, defaults to None
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:type api_key: Optional[str], optional
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:param embedding_dims: The number of dimensions in the embedding, defaults to None
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:type embedding_dims: Optional[int], optional
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:param ollama_base_url: Base URL for the Ollama API, defaults to None
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:type ollama_base_url: Optional[str], optional
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:param model_kwargs: key-value arguments for the huggingface embedding model, defaults a dict inside init
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:type model_kwargs: Optional[Dict[str, Any]], defaults a dict inside init
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:param huggingface_base_url: Huggingface base URL to be use, defaults to None
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:type huggingface_base_url: Optional[str], optional
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:param openai_base_url: Openai base URL to be use, defaults to "https://api.openai.com/v1"
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:type openai_base_url: Optional[str], optional
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:param azure_kwargs: key-value arguments for the AzureOpenAI embedding model, defaults a dict inside init
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:type azure_kwargs: Optional[Dict[str, Any]], defaults a dict inside init
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:param http_client_proxies: The proxy server settings used to create self.http_client, defaults to None
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:type http_client_proxies: Optional[Dict | str], optional
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:param vertex_credentials_json: The path to the Vertex AI credentials JSON file, defaults to None
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:type vertex_credentials_json: Optional[str], optional
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:param memory_add_embedding_type: The type of embedding to use for the add memory action, defaults to None
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:type memory_add_embedding_type: Optional[str], optional
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:param memory_update_embedding_type: The type of embedding to use for the update memory action, defaults to None
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:type memory_update_embedding_type: Optional[str], optional
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:param memory_search_embedding_type: The type of embedding to use for the search memory action, defaults to None
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:type memory_search_embedding_type: Optional[str], optional
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:param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
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:type lmstudio_base_url: Optional[str], optional
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"""
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self.model = model
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self.api_key = api_key
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self.openai_base_url = openai_base_url
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self.embedding_dims = embedding_dims
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# AzureOpenAI specific
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self.http_client = httpx.Client(proxies=http_client_proxies) if http_client_proxies else None
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# Ollama specific
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self.ollama_base_url = ollama_base_url
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# Huggingface specific
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self.model_kwargs = model_kwargs or {}
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self.huggingface_base_url = huggingface_base_url
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# AzureOpenAI specific
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self.azure_kwargs = AzureConfig(**azure_kwargs) or {}
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# VertexAI specific
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self.vertex_credentials_json = vertex_credentials_json
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self.memory_add_embedding_type = memory_add_embedding_type
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self.memory_update_embedding_type = memory_update_embedding_type
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self.memory_search_embedding_type = memory_search_embedding_type
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# LM Studio specific
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self.lmstudio_base_url = lmstudio_base_url
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