import os from typing import Literal, Optional from together import Together from mem0.configs.embeddings.base import BaseEmbedderConfig from mem0.embeddings.base import EmbeddingBase class TogetherEmbedding(EmbeddingBase): def __init__(self, config: Optional[BaseEmbedderConfig] = None): super().__init__(config) self.config.model = self.config.model or "intfloat/multilingual-e5-large-instruct" api_key = self.config.api_key or os.getenv("TOGETHER_API_KEY") self.config.embedding_dims = self.config.embedding_dims or 1024 self.client = Together(api_key=api_key) def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None): """ Get the embedding for the given text using OpenAI. Args: text (str): The text to embed. memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None. Returns: list: The embedding vector. """ return self.client.embeddings.create(model=self.config.model, input=text).data[0].embedding def embed_batch(self, texts, memory_action="add"): if not texts: return [] response = self.client.embeddings.create(model=self.config.model, input=texts) sorted_data = sorted(response.data, key=lambda x: x.index) embeddings = [item.embedding for item in sorted_data] if len(embeddings) != len(texts): raise ValueError( f"Together embed_batch() returned {len(embeddings)} embeddings for {len(texts)} texts" f" using model '{self.config.model}'" ) return embeddings