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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>
67 lines
2.1 KiB
Python
67 lines
2.1 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 openai import AsyncAzureOpenAI, AsyncOpenAI
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from openai.types import EmbeddingModel
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from .client import EmbedderClient, EmbedderConfig
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DEFAULT_EMBEDDING_MODEL = 'text-embedding-3-small'
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class OpenAIEmbedderConfig(EmbedderConfig):
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embedding_model: EmbeddingModel | str = DEFAULT_EMBEDDING_MODEL
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api_key: str | None = None
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base_url: str | None = None
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class OpenAIEmbedder(EmbedderClient):
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"""
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OpenAI Embedder Client
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This client supports both AsyncOpenAI and AsyncAzureOpenAI clients.
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"""
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def __init__(
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self,
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config: OpenAIEmbedderConfig | None = None,
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client: AsyncOpenAI | AsyncAzureOpenAI | None = None,
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):
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if config is None:
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config = OpenAIEmbedderConfig()
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self.config = config
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if client is not None:
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self.client = client
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else:
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self.client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
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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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result = await self.client.embeddings.create(
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input=input_data, model=self.config.embedding_model
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)
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return result.data[0].embedding[: self.config.embedding_dim]
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async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
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result = await self.client.embeddings.create(
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input=input_data_list, model=self.config.embedding_model
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)
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return [embedding.embedding[: self.config.embedding_dim] for embedding in result.data]
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