85 lines
3.1 KiB
Python
85 lines
3.1 KiB
Python
# services/llm/openai_service.py
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"""
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OpenAI service implementation
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"""
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from openai import OpenAI
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from typing import Dict, List
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import json
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from .base import LLMService
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from config.api_keys import APIKeyManager
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from utils.logger import setup_logger
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class OpenAIService(LLMService):
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def __init__(
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self,
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model: str = "gpt-4o-mini",
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temperature: float = 0.3,
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max_tokens: int = 16000,
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):
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api_key = APIKeyManager.get_openai_key()
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if not api_key:
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raise ValueError(
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"OpenAI API key not found. Please set the OPENAI_API_KEY environment variable."
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)
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self.client = OpenAI(api_key=api_key)
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self.model = model
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self.temperature = temperature
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self.max_tokens = max_tokens
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self.logger = setup_logger("openai")
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def generate_text(self, prompt: str) -> str:
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self.logger.info(f"--- PROMPT ---\n{prompt}")
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": "user", "content": prompt}],
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temperature=self.temperature,
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max_tokens=self.max_tokens,
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)
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response_content = response.choices[0].message.content
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self.logger.info(f"--- RESPONSE ---\n{response_content}")
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return response_content
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except Exception as e:
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self.logger.error(f"Error in OpenAI API call: {e}")
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print(f"Error in OpenAI API call: {e}")
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return None
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def get_similarity_scores(self, texts_pairs: Dict[str, List[str]]) -> List[float]:
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system_prompt = (
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"Evaluate the semantic similarity between the following table of pairs of texts in json format on a scale from 0 to 1. "
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"Return the similarity scores for every row in JSON format as a list of numbers, without any additional text or formatting."
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)
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request_payload = json.dumps(texts_pairs)
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": request_payload},
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],
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temperature=self.temperature,
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max_tokens=self.max_tokens,
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)
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response_content = response.choices[0].message.content
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cleaned_response = response_content.strip().strip("'```json").strip("```")
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try:
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scores = json.loads(cleaned_response)
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if isinstance(scores, dict) and "similarity_scores" in scores:
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return scores["similarity_scores"]
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elif isinstance(scores, list):
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return scores
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else:
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raise ValueError("Unexpected response format")
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except json.JSONDecodeError:
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raise ValueError("Could not decode response as JSON")
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except Exception as e:
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print(f"Error in OpenAI similarity calculation: {e}")
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return None
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