113 lines
4 KiB
Python
113 lines
4 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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"""
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from collections.abc import Iterable
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from google import genai
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from google.genai import types
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else:
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try:
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from google import genai
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from google.genai import types
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except ImportError:
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raise ImportError(
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'google-genai is required for GeminiEmbedder. '
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'Install it with: pip install graphiti-core[google-genai]'
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) from None
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from pydantic import Field
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from .client import EmbedderClient, EmbedderConfig
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DEFAULT_EMBEDDING_MODEL = 'embedding-001'
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class GeminiEmbedderConfig(EmbedderConfig):
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embedding_model: str = Field(default=DEFAULT_EMBEDDING_MODEL)
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api_key: str | None = None
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class GeminiEmbedder(EmbedderClient):
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"""
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Google Gemini Embedder Client
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"""
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def __init__(
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self,
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config: GeminiEmbedderConfig | None = None,
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client: 'genai.Client | None' = None,
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):
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"""
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Initialize the GeminiEmbedder with the provided configuration and client.
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Args:
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config (GeminiEmbedderConfig | None): The configuration for the GeminiEmbedder, including API key, model, base URL, temperature, and max tokens.
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client (genai.Client | None): An optional async client instance to use. If not provided, a new genai.Client is created.
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"""
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if config is None:
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config = GeminiEmbedderConfig()
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self.config = config
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if client is None:
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self.client = genai.Client(api_key=config.api_key)
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else:
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self.client = client
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async def create(
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self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
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) -> list[float]:
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"""
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Create embeddings for the given input data using Google's Gemini embedding model.
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Args:
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input_data: The input data to create embeddings for. Can be a string, list of strings,
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or an iterable of integers or iterables of integers.
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Returns:
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A list of floats representing the embedding vector.
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"""
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# Generate embeddings
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result = await self.client.aio.models.embed_content(
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model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
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contents=[input_data], # type: ignore[arg-type] # mypy fails on broad union type
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config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
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)
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if not result.embeddings or len(result.embeddings) == 0 or not result.embeddings[0].values:
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raise ValueError('No embeddings returned from Gemini API in create()')
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return result.embeddings[0].values
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async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
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# Generate embeddings
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result = await self.client.aio.models.embed_content(
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model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
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contents=input_data_list, # type: ignore[arg-type] # mypy fails on broad union type
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config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
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)
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if not result.embeddings or len(result.embeddings) == 0:
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raise Exception('No embeddings returned')
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embeddings = []
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for embedding in result.embeddings:
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if not embedding.values:
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raise ValueError('Empty embedding values returned')
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embeddings.append(embedding.values)
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return embeddings
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