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136 lines
4.7 KiB
Python
136 lines
4.7 KiB
Python
# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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import os
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from typing import Any, Optional
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from openai import OpenAI
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from camel.embeddings.base import BaseEmbedding
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from camel.logger import get_logger
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from camel.utils import api_keys_required
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logger = get_logger(__name__)
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class TogetherEmbedding(BaseEmbedding[str]):
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r"""Provides text embedding functionalities using Together AI's models.
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Args:
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model_type (str, optional): The model name to be used for text
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embeddings.
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(default: :obj:`togethercomputer/m2-bert-80M-8k-retrieval`)
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api_key (str, optional): The API key for authenticating with the
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Together service. (default: :obj:`None`)
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dimensions (int, optional): The text embedding output dimensions.
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(default: :obj:`None`)
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Raises:
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ValueError: If the model name format is invalid or if an empty input
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list is provided.
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RuntimeError: If the API request fails.
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"""
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@api_keys_required([("api_key", 'TOGETHER_API_KEY')])
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def __init__(
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self,
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model_type: str = "togethercomputer/m2-bert-80M-8k-retrieval",
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api_key: Optional[str] = None,
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dimensions: Optional[int] = None,
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) -> None:
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if not isinstance(model_type, str) or not model_type.strip():
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raise ValueError("Model name must be a non-empty string")
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if dimensions is not None and dimensions <= 0:
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raise ValueError("Dimensions must be a positive integer")
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self.model_type = model_type
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self._api_key = api_key or os.environ.get("TOGETHER_API_KEY")
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self.output_dim = dimensions
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# Initialize OpenAI client with Together AI configuration
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self.client = OpenAI(
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timeout=180,
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max_retries=3,
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api_key=self._api_key,
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base_url="https://api.together.xyz/v1",
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)
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def embed_list(
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self,
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objs: list[str],
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**kwargs: Any,
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) -> list[list[float]]:
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r"""Generates embeddings for the given texts.
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Args:
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objs (list[str]): The texts for which to generate the embeddings.
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**kwargs (Any): Extra kwargs passed to the embedding API.
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Returns:
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list[list[float]]: A list that represents the generated embedding
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as a list of floating-point numbers.
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Raises:
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ValueError: If the input list is empty.
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RuntimeError: If the API request fails.
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"""
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if not objs:
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raise ValueError("Input list cannot be empty")
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try:
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response = self.client.embeddings.create(
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input=objs,
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model=self.model_type,
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**kwargs,
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)
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# Set output dimension if not already set
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if self.output_dim is None and response.data:
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self.output_dim = len(response.data[0].embedding)
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logger.debug(
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f"Set output dimension to {self.output_dim} for model "
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f"{self.model_type}"
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)
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return [data.embedding for data in response.data]
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except Exception as e:
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raise RuntimeError(
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f"Failed to get embeddings from Together AI: {e}"
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)
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def get_output_dim(self) -> int:
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r"""Returns the output dimension of the embeddings.
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Returns:
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int: The dimensionality of the embedding for the current model.
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Raises:
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ValueError: If the embedding dimension cannot be determined.
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"""
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if self.output_dim is None:
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logger.debug(
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"Output dimension not set, "
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"making test embedding to determine it"
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)
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# Make a test embedding to determine the dimension
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self.embed_list(["test"])
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if self.output_dim is None:
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raise ValueError(
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"Failed to determine embedding dimension for model: "
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f"{self.model_type}"
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)
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return self.output_dim
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