Source code for camel.embeddings.gemini_embedding
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
from __future__ import annotations
import os
from typing import Any, Optional
from camel.embeddings.base import BaseEmbedding
from camel.types import EmbeddingModelType, GeminiEmbeddingTaskType
from camel.utils import api_keys_required
[docs]
class GeminiEmbedding(BaseEmbedding[str]):
r"""Provides text embedding functionalities using Google's Gemini models.
Args:
model_type (EmbeddingModelType, optional): The model type to be
used for text embeddings.
(default: :obj:`GEMINI_EMBEDDING_EXP`)
api_key (str, optional): The API key for authenticating with the
Gemini service. (default: :obj:`None`)
dimensions (int, optional): The text embedding output dimensions.
(default: :obj:`None`)
task_type (GeminiEmbeddingTaskType, optional): The task type for which
to optimize the embeddings. (default: :obj:`None`)
Raises:
RuntimeError: If an unsupported model type is specified.
"""
@api_keys_required(
[
("api_key", 'GEMINI_API_KEY'),
]
)
def __init__(
self,
model_type: EmbeddingModelType = (
EmbeddingModelType.GEMINI_EMBEDDING_EXP
),
api_key: Optional[str] = None,
dimensions: Optional[int] = None,
task_type: Optional[GeminiEmbeddingTaskType] = None,
) -> None:
from google import genai
if not model_type.is_gemini:
raise ValueError("Invalid Gemini embedding model type.")
self.model_type = model_type
if dimensions is None:
self.output_dim = model_type.output_dim
else:
assert isinstance(dimensions, int)
self.output_dim = dimensions
self._api_key = api_key or os.environ.get("GEMINI_API_KEY")
self._task_type = task_type
# Initialize Gemini client
self._client = genai.Client(api_key=self._api_key)
[docs]
def embed_list(
self,
objs: list[str],
**kwargs: Any,
) -> list[list[float]]:
r"""Generates embeddings for the given texts.
Args:
objs (list[str]): The texts for which to generate the embeddings.
**kwargs (Any): Extra kwargs passed to the embedding API.
Returns:
list[list[float]]: A list that represents the generated embedding
as a list of floating-point numbers.
"""
from google.genai import types
# Create embedding config if task_type is specified
embed_config = None
if self._task_type:
embed_config = types.EmbedContentConfig(
task_type=self._task_type.value
)
# Process each text separately since Gemini API
# expects single content item
responses = self._client.models.embed_content(
model=self.model_type.value,
contents=objs, # type: ignore[arg-type]
config=embed_config,
**kwargs,
)
return [response.values for response in responses.embeddings] # type: ignore[misc,union-attr]
[docs]
def get_output_dim(self) -> int:
r"""Returns the output dimension of the embeddings.
Returns:
int: The dimensionality of the embedding for the current model.
"""
return self.output_dim