37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import os
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from typing import Literal, Optional
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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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try:
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from langchain.embeddings.base import Embeddings
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except ImportError:
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raise ImportError("langchain is not installed. Please install it using `pip install langchain`")
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class LangchainEmbedding(EmbeddingBase):
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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if self.config.model is None:
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raise ValueError("`model` parameter is required")
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if not isinstance(self.config.model, Embeddings):
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raise ValueError("`model` must be an instance of Embeddings")
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self.langchain_model = self.config.model
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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 Langchain.
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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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return self.langchain_model.embed_query(text)
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