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115 lines
4 KiB
Python
115 lines
4 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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from __future__ import annotations
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import os
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from typing import Any, Optional
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from camel.embeddings.base import BaseEmbedding
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from camel.types import EmbeddingModelType, GeminiEmbeddingTaskType
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from camel.utils import api_keys_required
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class GeminiEmbedding(BaseEmbedding[str]):
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r"""Provides text embedding functionalities using Google's Gemini models.
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Args:
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model_type (EmbeddingModelType, optional): The model type to be
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used for text embeddings.
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(default: :obj:`GEMINI_EMBEDDING_EXP`)
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api_key (str, optional): The API key for authenticating with the
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Gemini 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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task_type (GeminiEmbeddingTaskType, optional): The task type for which
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to optimize the embeddings. (default: :obj:`None`)
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Raises:
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RuntimeError: If an unsupported model type is specified.
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"""
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@api_keys_required(
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[
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("api_key", 'GEMINI_API_KEY'),
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]
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)
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def __init__(
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self,
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model_type: EmbeddingModelType = (
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EmbeddingModelType.GEMINI_EMBEDDING_EXP
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),
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api_key: Optional[str] = None,
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dimensions: Optional[int] = None,
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task_type: Optional[GeminiEmbeddingTaskType] = None,
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) -> None:
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from google import genai
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if not model_type.is_gemini:
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raise ValueError("Invalid Gemini embedding model type.")
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self.model_type = model_type
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if dimensions is None:
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self.output_dim = model_type.output_dim
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else:
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assert isinstance(dimensions, int)
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self.output_dim = dimensions
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self._api_key = api_key or os.environ.get("GEMINI_API_KEY")
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self._task_type = task_type
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# Initialize Gemini client
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self._client = genai.Client(api_key=self._api_key)
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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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"""
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from google.genai import types
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# Create embedding config if task_type is specified
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embed_config = None
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if self._task_type:
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embed_config = types.EmbedContentConfig(
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task_type=self._task_type.value
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)
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# Process each text separately since Gemini API
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# expects single content item
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responses = self._client.models.embed_content(
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model=self.model_type.value,
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contents=objs, # type: ignore[arg-type]
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config=embed_config,
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**kwargs,
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)
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return [response.values for response in responses.embeddings] # type: ignore[misc,union-attr]
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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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"""
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return self.output_dim
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