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3d7e1a4b79
* create wrappers for azure clients * rremove unused crossencoder client * format * chore: update graphiti-core to 0.12.0rc5 and pydantic to 2.11.5 * Update graphiti_core/llm_client/azure_openai_client.py Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com> --------- Co-authored-by: Preston Rasmussen <109292228+prasmussen15@users.noreply.github.com> Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
65 lines
2.3 KiB
Python
65 lines
2.3 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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import logging
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from typing import Any
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from openai import AsyncAzureOpenAI
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from .client import EmbedderClient
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logger = logging.getLogger(__name__)
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class AzureOpenAIEmbedderClient(EmbedderClient):
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"""Wrapper class for AsyncAzureOpenAI that implements the EmbedderClient interface."""
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def __init__(self, azure_client: AsyncAzureOpenAI, model: str = 'text-embedding-3-small'):
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self.azure_client = azure_client
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self.model = model
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async def create(self, input_data: str | list[str] | Any) -> list[float]:
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"""Create embeddings using Azure OpenAI client."""
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try:
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# Handle different input types
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if isinstance(input_data, str):
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text_input = [input_data]
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elif isinstance(input_data, list) and all(isinstance(item, str) for item in input_data):
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text_input = input_data
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else:
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# Convert to string list for other types
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text_input = [str(input_data)]
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response = await self.azure_client.embeddings.create(model=self.model, input=text_input)
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# Return the first embedding as a list of floats
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return response.data[0].embedding
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except Exception as e:
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logger.error(f'Error in Azure OpenAI embedding: {e}')
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raise
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async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
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"""Create batch embeddings using Azure OpenAI client."""
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try:
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response = await self.azure_client.embeddings.create(
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model=self.model, input=input_data_list
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)
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return [embedding.embedding for embedding in response.data]
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except Exception as e:
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logger.error(f'Error in Azure OpenAI batch embedding: {e}')
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raise
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