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my-project-public — gitlawb

#1	import os
#2	from abc import ABC
#3	from typing import Dict, Optional, Union
#4
#5	import httpx
#6
#7	from mem0.configs.base import AzureConfig
#8
#9
#10	class BaseEmbedderConfig(ABC):
#11	"""
#12	Config for Embeddings.
#13	"""
#14
#15	def __init__(
#16	self,
#17	model: Optional[str] = None,
#18	api_key: Optional[str] = None,
#19	embedding_dims: Optional[int] = None,
#20	# Ollama specific
#21	ollama_base_url: Optional[str] = None,
#22	# Openai specific
#23	openai_base_url: Optional[str] = None,
#24	# Huggingface specific
#25	model_kwargs: Optional[dict] = None,
#26	huggingface_base_url: Optional[str] = None,
#27	# AzureOpenAI specific
#28	azure_kwargs: Optional[AzureConfig] = {},
#29	http_client_proxies: Optional[Union[Dict, str]] = None,
#30	# VertexAI specific
#31	vertex_credentials_json: Optional[str] = None,
#32	memory_add_embedding_type: Optional[str] = None,
#33	memory_update_embedding_type: Optional[str] = None,
#34	memory_search_embedding_type: Optional[str] = None,
#35	# Gemini specific
#36	output_dimensionality: Optional[str] = None,
#37	# LM Studio specific
#38	lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
#39	# AWS Bedrock specific
#40	aws_access_key_id: Optional[str] = None,
#41	aws_secret_access_key: Optional[str] = None,
#42	aws_region: Optional[str] = None,
#43	):
#44	"""
#45	Initializes a configuration class instance for the Embeddings.
#46
#47	:param model: Embedding model to use, defaults to None
#48	:type model: Optional[str], optional
#49	:param api_key: API key to be use, defaults to None
#50	:type api_key: Optional[str], optional
#51	:param embedding_dims: The number of dimensions in the embedding, defaults to None
#52	:type embedding_dims: Optional[int], optional
#53	:param ollama_base_url: Base URL for the Ollama API, defaults to None
#54	:type ollama_base_url: Optional[str], optional
#55	:param model_kwargs: key-value arguments for the huggingface embedding model, defaults a dict inside init
#56	:type model_kwargs: Optional[Dict[str, Any]], defaults a dict inside init
#57	:param huggingface_base_url: Huggingface base URL to be use, defaults to None
#58	:type huggingface_base_url: Optional[str], optional
#59	:param openai_base_url: Openai base URL to be use, defaults to "https://api.openai.com/v1"
#60	:type openai_base_url: Optional[str], optional
#61	:param azure_kwargs: key-value arguments for the AzureOpenAI embedding model, defaults a dict inside init
#62	:type azure_kwargs: Optional[Dict[str, Any]], defaults a dict inside init
#63	:param http_client_proxies: The proxy server settings used to create self.http_client, defaults to None
#64	:type http_client_proxies: Optional[Dict \| str], optional
#65	:param vertex_credentials_json: The path to the Vertex AI credentials JSON file, defaults to None
#66	:type vertex_credentials_json: Optional[str], optional
#67	:param memory_add_embedding_type: The type of embedding to use for the add memory action, defaults to None
#68	:type memory_add_embedding_type: Optional[str], optional
#69	:param memory_update_embedding_type: The type of embedding to use for the update memory action, defaults to None
#70	:type memory_update_embedding_type: Optional[str], optional
#71	:param memory_search_embedding_type: The type of embedding to use for the search memory action, defaults to None
#72	:type memory_search_embedding_type: Optional[str], optional
#73	:param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
#74	:type lmstudio_base_url: Optional[str], optional
#75	"""
#76
#77	self.model = model
#78	self.api_key = api_key
#79	self.openai_base_url = openai_base_url
#80	self.embedding_dims = embedding_dims
#81
#82	# AzureOpenAI specific
#83	self.http_client = httpx.Client(proxies=http_client_proxies) if http_client_proxies else None
#84
#85	# Ollama specific
#86	self.ollama_base_url = ollama_base_url
#87
#88	# Huggingface specific
#89	self.model_kwargs = model_kwargs or {}
#90	self.huggingface_base_url = huggingface_base_url
#91	# AzureOpenAI specific
#92	self.azure_kwargs = AzureConfig(**azure_kwargs) or {}
#93
#94	# VertexAI specific
#95	self.vertex_credentials_json = vertex_credentials_json
#96	self.memory_add_embedding_type = memory_add_embedding_type
#97	self.memory_update_embedding_type = memory_update_embedding_type
#98	self.memory_search_embedding_type = memory_search_embedding_type
#99
#100	# Gemini specific
#101	self.output_dimensionality = output_dimensionality
#102
#103	# LM Studio specific
#104	self.lmstudio_base_url = lmstudio_base_url
#105
#106	# AWS Bedrock specific
#107	self.aws_access_key_id = aws_access_key_id
#108	self.aws_secret_access_key = aws_secret_access_key
#109	self.aws_region = aws_region or os.environ.get("AWS_REGION") or "us-west-2"
#110
#111

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