156 lines
5.3 KiB
Python
156 lines
5.3 KiB
Python
import logging
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import threading
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import time
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from typing import Literal, Dict, Any
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import config
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from dto.requests import GameStartRequestDTO, GameDecisionRequestDTO
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from services.extractor import extract_profile, extract_passport, extract_description, extract_account
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from services.julius_baer_api_client import JuliusBaerApiClient
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from utils.storage.game_files_manager import store_game_round_data
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from langchain_openai.chat_models import ChatOpenAI
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from validation.llm_validate import AdvisorDecision
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import PydanticOutputParser
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log = logging.getLogger(__name__)
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class Advisor:
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def __init__(self):
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self.client = JuliusBaerApiClient()
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self._thread = None
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def start(self):
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self.play()
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def play_on_separate_thread(self):
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if self._thread and self._thread.is_alive():
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log.warning('Game loop already running.')
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return self._thread
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self._thread = threading.Thread(target=self.play, daemon=True)
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self._thread.start()
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return self._thread
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def play(self):
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log.info('playing')
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payload = GameStartRequestDTO(player_name=config.API_TEAM)
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start_response = self.client.start_game(payload)
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log.info('game started, session id: %s', start_response.session_id)
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client_id = start_response.client_id
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decision = self.make_decision(start_response.client_data)
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decision_counter = 0
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is_game_running = True
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while is_game_running:
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payload = GameDecisionRequestDTO(
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decision=decision.answer,
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session_id=start_response.session_id,
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client_id=client_id,
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)
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log.info('client id: %s', client_id)
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decision_response = self.client.send_decision(payload)
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log.info(f'decision: {decision}, response status: {decision_response.status}, score: {decision_response.score}')
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status = decision_response.status
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is_game_running = status not in ['gameover', 'complete']
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client_id = decision_response.client_id
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decision = self.make_decision(decision_response.client_data)
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# Handle first response from game initialization logic
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if decision_counter == 0:
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# Store start response
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store_game_round_data(decision.answer, start_response, decision_counter, str(start_response.session_id) ,status)
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else:
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# store ongoing decision response
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store_game_round_data(decision.answer, decision_response, decision_counter, str(start_response.session_id), status)
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decision_counter += 1
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time.sleep(1)
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def make_decision(self, client_data: Dict[str, Any]) -> AdvisorDecision:
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# 1. Extraction des données
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profile = extract_profile(client_data)
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passport = extract_passport(client_data)
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description = extract_description(client_data)
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account = extract_account(client_data)
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# 2. Création du parser
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parser = PydanticOutputParser(pydantic_object=AdvisorDecision)
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format_instructions = parser.get_format_instructions()
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# 3. Prompt enrichi
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prompt = ChatPromptTemplate.from_template(
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"""You are a compliance analyst in a private bank.
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You are given structured data extracted from four different documents of a new client application.
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Your task is to accept or reject the client's application for private banking.
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Only reject the application if there is an inconsistency in the data provided.
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Inconsistencies include:
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- Incorrect names used accros all docs, what is stated in one of the doc should be true on other documents.
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- Typos in any fields
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- Incorrect validity date passport or documents
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- Wrong address, city, street name, zip code and country. Some don't exist
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- Multiple country of domiciles
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- Implausible or suspicious details
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- Amounts in profile and the description file
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- Incorrect nationality regarding the passport data
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Use the extracted profile, description, and account details to cross-check the information in the passport and other documents.
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Take into consideration that occupation history might be in the past not match the actual situation.
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Pay attention to the currency when dealing with amounts.
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Be highly critical. Reject if there's any doubt or if anything feels wrong.
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DO NOT HALLUCINATE at any point !
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Return only JSON matching this format:
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{format_instructions}
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---
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**Document: Passport**
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{passport}
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---
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**Document: Profile**
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{profile}
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---
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**Document: Description**
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{description}
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---
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**Document: Account**
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{account}"""
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)
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# 4. Chaîne LLM
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chain = prompt | ChatOpenAI(model="gpt-4o-mini") | parser
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# 5. Invocation
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result: AdvisorDecision = chain.invoke({
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"passport": passport,
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"profile": profile,
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"description": description,
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"account": account,
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"format_instructions": format_instructions,
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})
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# 6. Logs et retour
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if result.answer == "Reject":
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log.warning(f"Client rejected. Reason: {result.reason}")
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else:
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log.info("Client accepted.")
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return result
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