33 lines
1.3 KiB
Python
33 lines
1.3 KiB
Python
from typing import Literal, Optional
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from openai import OpenAI
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from mem0.configs.embeddings.base import BaseEmbedderConfig
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from mem0.embeddings.base import EmbeddingBase
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class LMStudioEmbedding(EmbeddingBase):
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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self.config.model = self.config.model or "nomic-ai/nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
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self.config.embedding_dims = self.config.embedding_dims or 1536
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self.config.api_key = self.config.api_key or "lm-studio"
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self.client = OpenAI(base_url=self.config.lmstudio_base_url, api_key=self.config.api_key)
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def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
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"""
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Get the embedding for the given text using LM Studio.
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Args:
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text (str): The text to embed.
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memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None.
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Returns:
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list: The embedding vector.
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"""
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text = text.replace("\n", " ")
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return (
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self.client.embeddings.create(input=[text], model=self.config.model)
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.data[0]
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.embedding
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) |