67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
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import os
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import json
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from openai import OpenAI
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from pydantic import BaseModel, Field
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from typing import List, Dict, Optional
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from src.prompts.sops import *
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from src.prompts.chatbot import *
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from src.models.sop_response_schemas import *
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from src.models.bot_response_schema import *
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from dotenv import load_dotenv
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load_dotenv()
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#SopGeneratorDocument
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class Chatbot:
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def __init__(self):
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self.api_key = os.getenv("OPENAI_API_KEY")
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self.client = OpenAI(api_key=self.api_key)
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self.model = "gpt-4o-mini"
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def _extract_text_from_docs(self, docs):
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"""Extract text content from document objects."""
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return [doc.page_content for doc in docs]
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# Existing methods...
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def validate_worker(self, question, docs) -> VisionMissionResponse:
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"""
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This method is responsible for validating the worker's response ("yes" or "no") using the provided document(s).
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The system generates a prompt, extracts document content, and validates the response using the GPT-4 model.
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:param question: The yes/no question asked to the worker.
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:param docs: A list of document(s) uploaded by the worker.
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:return: VisionMissionResponse containing the validated result or None if an error occurs.
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"""
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try:
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docs_text = self._extract_text_from_docs(docs)
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prompt = validate_worker_prompt()
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response = self.client.beta.chat.completions.parse(
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model=self.model,
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messages=[
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{
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"role": "system",
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"content": f'''{prompt} The document is uploaded next.'''
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},
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{
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"role": "user",
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"content": f"Question :{question}",
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},
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{
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"role": "user",
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"content": [{"type": "text", "text": text} for text in docs_text],
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}
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],
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response_format=ValidateWorker,
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max_tokens=4096,
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temperature=0.1
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)
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# Parse the response from the LLM
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extracted_text = json.loads(response.choices[0].message.content)
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return extracted_text
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except Exception as e:
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print(f"An error occurred: {e}")
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return None
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