mirror of
https://github.com/getzep/graphiti.git
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c5e52153c4
* feat: Update project name and description The project name and description in the `pyproject.toml` file have been updated to reflect the changes made to the project. * chore: Update pyproject.toml to include core package The `pyproject.toml` file has been updated to include the `core` package in the list of packages. This change ensures that the `core` package is included when building the project. * fix imports * fix importats
59 lines
2.0 KiB
Python
59 lines
2.0 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 json
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import logging
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import typing
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from openai import AsyncOpenAI
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from openai.types.chat import ChatCompletionMessageParam
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from ..prompts.models import Message
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from .client import LLMClient
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from .config import LLMConfig
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logger = logging.getLogger(__name__)
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class OpenAIClient(LLMClient):
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def __init__(self, config: LLMConfig):
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self.client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
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self.model = config.model
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def get_embedder(self) -> typing.Any:
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return self.client.embeddings
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async def generate_response(self, messages: list[Message]) -> dict[str, typing.Any]:
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openai_messages: list[ChatCompletionMessageParam] = []
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for m in messages:
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if m.role == 'user':
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openai_messages.append({'role': 'user', 'content': m.content})
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elif m.role == 'system':
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openai_messages.append({'role': 'system', 'content': m.content})
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try:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=openai_messages,
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temperature=0,
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max_tokens=3000,
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response_format={'type': 'json_object'},
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)
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result = response.choices[0].message.content or ''
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return json.loads(result)
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except Exception as e:
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logger.error(f'Error in generating LLM response: {e}')
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raise
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