Source code for neo4j_graphrag.llm.openai_llm

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#  You may obtain a copy of the License at
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#      https://www.apache.org/licenses/LICENSE-2.0
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from __future__ import annotations

from typing import Any, Optional

from ..exceptions import LLMGenerationError
from .base import LLMInterface
from .types import LLMResponse

try:
    import openai
except ImportError:
    openai = None  # type: ignore


[docs] class OpenAILLM(LLMInterface): def __init__( self, model_name: str, model_params: Optional[dict[str, Any]] = None, **kwargs: Any, ): """ Args: model_name (str): model_params (str): Parameters like temperature and such that will be passed to the model kwargs: All other parameters will be passed to the openai.OpenAI init. """ if openai is None: raise ImportError( "Could not import openai python client. " "Please install it with `pip install openai`." ) super().__init__(model_name, model_params) self.client = openai.OpenAI(**kwargs) self.async_client = openai.AsyncOpenAI(**kwargs)
[docs] def get_messages( self, input: str, ) -> list[dict[str, str]]: return [ {"role": "system", "content": input}, ]
[docs] def invoke(self, input: str) -> LLMResponse: """Sends a text input to the OpenAI chat completion model and returns the response's content. Args: input (str): Text sent to the LLM Returns: LLMResponse: The response from OpenAI. Raises: LLMGenerationError: If anything goes wrong. """ try: response = self.client.chat.completions.create( messages=self.get_messages(input), # type: ignore model=self.model_name, **self.model_params, ) content = response.choices[0].message.content or "" return LLMResponse(content=content) except openai.OpenAIError as e: raise LLMGenerationError(e)
[docs] async def ainvoke(self, input: str) -> LLMResponse: """Asynchronously sends a text input to the OpenAI chat completion model and returns the response's content. Args: input (str): Text sent to the LLM Returns: LLMResponse: The response from OpenAI. Raises: LLMGenerationError: If anything goes wrong. """ try: response = await self.async_client.chat.completions.create( messages=self.get_messages(input), # type: ignore model=self.model_name, **self.model_params, ) content = response.choices[0].message.content or "" return LLMResponse(content=content) except openai.OpenAIError as e: raise LLMGenerationError(e)