0b94e0e603
* add batch embeddings * bulk edge and node embeddings * update embeddings during add_episode * Update graphiti_core/embedder/client.py Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com> * mypy --------- Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
78 lines
2.6 KiB
Python
78 lines
2.6 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 google import genai # type: ignore
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from google.genai import types # type: ignore
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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__(self, config: GeminiEmbedderConfig | None = None):
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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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# Configure the Gemini API
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self.client = genai.Client(
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api_key=config.api_key,
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)
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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],
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config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
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)
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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,
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config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
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)
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return [embedding.values for embedding in result.embeddings]
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