mirror of
https://github.com/getzep/graphiti.git
synced 2025-06-27 02:00:02 +00:00
90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
"""
|
|
Copyright 2024, Zep Software, Inc.
|
|
|
|
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.
|
|
"""
|
|
|
|
from collections.abc import Iterable
|
|
|
|
from google import genai # type: ignore
|
|
from google.genai import types # type: ignore
|
|
from pydantic import Field
|
|
|
|
from .client import EmbedderClient, EmbedderConfig
|
|
|
|
DEFAULT_EMBEDDING_MODEL = 'embedding-001'
|
|
|
|
|
|
class GeminiEmbedderConfig(EmbedderConfig):
|
|
embedding_model: str = Field(default=DEFAULT_EMBEDDING_MODEL)
|
|
api_key: str | None = None
|
|
|
|
|
|
class GeminiEmbedder(EmbedderClient):
|
|
"""
|
|
Google Gemini Embedder Client
|
|
"""
|
|
|
|
def __init__(self, config: GeminiEmbedderConfig | None = None):
|
|
if config is None:
|
|
config = GeminiEmbedderConfig()
|
|
self.config = config
|
|
|
|
# Configure the Gemini API
|
|
self.client = genai.Client(
|
|
api_key=config.api_key,
|
|
)
|
|
|
|
async def create(
|
|
self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
|
|
) -> list[float]:
|
|
"""
|
|
Create embeddings for the given input data using Google's Gemini embedding model.
|
|
|
|
Args:
|
|
input_data: The input data to create embeddings for. Can be a string, list of strings,
|
|
or an iterable of integers or iterables of integers.
|
|
|
|
Returns:
|
|
A list of floats representing the embedding vector.
|
|
"""
|
|
# Generate embeddings
|
|
result = await self.client.aio.models.embed_content(
|
|
model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
|
|
contents=[input_data], # type: ignore[arg-type] # mypy fails on broad union type
|
|
config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
|
|
)
|
|
|
|
if not result.embeddings or len(result.embeddings) == 0 or not result.embeddings[0].values:
|
|
raise ValueError('No embeddings returned from Gemini API in create()')
|
|
|
|
return result.embeddings[0].values
|
|
|
|
async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
|
|
# Generate embeddings
|
|
result = await self.client.aio.models.embed_content(
|
|
model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
|
|
contents=input_data_list, # type: ignore[arg-type] # mypy fails on broad union type
|
|
config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
|
|
)
|
|
|
|
if not result.embeddings or len(result.embeddings) == 0:
|
|
raise Exception('No embeddings returned')
|
|
|
|
embeddings = []
|
|
for embedding in result.embeddings:
|
|
if not embedding.values:
|
|
raise ValueError('Empty embedding values returned')
|
|
embeddings.append(embedding.values)
|
|
return embeddings
|