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358
orchestrator/debate_orchestrator.py
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358
orchestrator/debate_orchestrator.py
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import asyncio
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import uuid
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from typing import Dict, List, Optional
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from datetime import datetime
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from sqlalchemy.orm import Session
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from models.debate import (
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DebateRequest, DebateSession, DebateRound,
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DebateStance, DebateParticipant, SearchEvidence, SearchResult,
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EvidenceEntry, EvidenceReference
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)
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from providers.provider_factory import ProviderFactory
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from storage.session_manager import SessionManager
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from utils.summarizer import summarize_debate
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from services.search_service import SearchService
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from services.api_key_service import ApiKeyService
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class DebateOrchestrator:
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"""
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Orchestrates the debate between multiple language models
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"""
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def __init__(self, db: Session):
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self.db = db
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self.session_manager = SessionManager()
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self.provider_factory = ProviderFactory()
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async def create_session(self, debate_request: DebateRequest) -> str:
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"""
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Create a new debate session
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"""
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session_id = str(uuid.uuid4())
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session = DebateSession(
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session_id=session_id,
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topic=debate_request.topic,
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participants=debate_request.participants,
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constraints=debate_request.constraints,
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rounds=[],
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status="active",
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created_at=datetime.now()
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)
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await self.session_manager.save_session(self.db, session)
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return session_id
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def _get_search_service(self) -> Optional[SearchService]:
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"""
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Get a SearchService instance if Tavily API key is available.
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"""
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tavily_key = ApiKeyService.get_api_key(self.db, "tavily")
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if tavily_key:
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return SearchService(api_key=tavily_key)
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return None
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async def run_debate(self, session_id: str) -> DebateSession:
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"""
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Run the complete debate process
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"""
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session = await self.session_manager.get_session(self.db, session_id)
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if not session:
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raise ValueError(f"Session {session_id} not found")
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# Initialize providers for each participant
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providers = {}
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for participant in session.participants:
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provider = self.provider_factory.create_provider(
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self.db,
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participant.provider,
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participant.api_key # This can be None, and the provider will fetch from DB
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)
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providers[participant.model_identifier] = provider
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# Initialize search service if web search is enabled
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search_service = None
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web_search_enabled = session.constraints.web_search_enabled
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web_search_mode = session.constraints.web_search_mode
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if web_search_enabled:
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search_service = self._get_search_service()
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if not search_service:
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print("Warning: Web search enabled but no Tavily API key found. Disabling search.")
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web_search_enabled = False
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# Run the debate rounds
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for round_num in range(session.constraints.max_rounds):
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if session.status != "active":
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break
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# Alternate between participants
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current_participant = session.participants[round_num % len(session.participants)]
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provider = providers[current_participant.model_identifier]
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# Perform automatic search if enabled
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search_evidence = None
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if web_search_enabled and web_search_mode in ("auto", "both"):
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search_evidence = self._perform_automatic_search(
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search_service, session, round_num
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)
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# Prepare context for the current turn (with search results if available)
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context = self._prepare_context(session, current_participant.stance, search_evidence)
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# Determine if we should use tool calling for this round
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use_tool_calling = (
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web_search_enabled
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and web_search_mode in ("tool", "both")
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and provider.supports_tools()
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)
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if use_tool_calling:
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response, tool_evidence = await self._handle_tool_calls(
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provider, current_participant.model_identifier,
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context, search_service
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)
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# Merge tool-based evidence with auto evidence
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if tool_evidence:
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if search_evidence:
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search_evidence.results.extend(tool_evidence.results)
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search_evidence.query += f" | {tool_evidence.query}"
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search_evidence.mode = "both"
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else:
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search_evidence = tool_evidence
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else:
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response = await provider.generate_response(
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model=current_participant.model_identifier,
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prompt=context
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)
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# Clean the response to remove any echoed prompt/meta text
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response = self._clean_response(response)
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# Create a new round
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round_data = DebateRound(
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round_number=round_num + 1,
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speaker=current_participant.model_identifier,
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stance=current_participant.stance,
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content=response,
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timestamp=datetime.now(),
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token_count=len(response.split()), # Approximate token count
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search_evidence=search_evidence
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)
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session.rounds.append(round_data)
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# Update evidence library with search results from this round
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if round_data.search_evidence:
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self._update_evidence_library(session, round_data)
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# Update session in storage
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await self.session_manager.update_session(self.db, session)
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# Small delay between rounds to simulate realistic interaction
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await asyncio.sleep(1)
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# Generate summary after all rounds are complete
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summary = await summarize_debate(session)
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session.summary = summary
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session.status = "completed"
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session.completed_at = datetime.now()
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await self.session_manager.update_session(self.db, session)
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return session
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def _perform_automatic_search(
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self, search_service: SearchService, session: DebateSession, round_num: int
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) -> Optional[SearchEvidence]:
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"""
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Perform an automatic web search based on the topic and last opponent argument.
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"""
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last_opponent_arg = None
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if session.rounds:
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last_opponent_arg = session.rounds[-1].content
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query = SearchService.generate_search_query(session.topic, last_opponent_arg)
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results = search_service.search(query, max_results=3)
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if results:
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return SearchEvidence(
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query=query,
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results=results,
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mode="auto"
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)
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return None
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async def _handle_tool_calls(
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self, provider, model: str, context: str, search_service: SearchService
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) -> tuple:
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"""
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Handle tool calling flow: send prompt with tools, execute any tool calls,
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then re-prompt with results. Max 2 tool call iterations.
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Returns (final_response_text, SearchEvidence_or_None).
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"""
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tools = [SearchService.get_tool_definition()]
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all_search_results = []
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all_queries = []
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text, tool_calls = await provider.generate_response_with_tools(
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model=model,
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prompt=context,
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tools=tools,
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max_tokens=500
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)
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# If no tool calls, return the text response directly
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if not tool_calls:
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return text, None
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# Process up to 2 rounds of tool calls
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for iteration in range(2):
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if not tool_calls:
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break
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# Execute each tool call
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tool_results_text = []
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for tc in tool_calls:
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if tc["name"] == "web_search":
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query = tc["arguments"].get("query", "")
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all_queries.append(query)
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results = search_service.search(query, max_results=3)
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all_search_results.extend(results)
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# Format results for the model
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evidence = SearchEvidence(query=query, results=results, mode="tool")
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tool_results_text.append(SearchService.format_results_for_context(evidence))
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# Re-prompt the model with tool results
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augmented_context = context + "\n" + "\n".join(tool_results_text)
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augmented_context += "\n请基于以上搜索结果和辩论历史，给出你的论点。"
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text, tool_calls = await provider.generate_response_with_tools(
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model=model,
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prompt=augmented_context,
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tools=tools,
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max_tokens=500
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)
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# Build combined evidence
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evidence = None
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if all_search_results:
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evidence = SearchEvidence(
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query=" | ".join(all_queries),
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results=all_search_results,
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mode="tool"
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)
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# If we still got no text (model keeps calling tools), fall back
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if not text:
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text = await provider.generate_response(model=model, prompt=context, max_tokens=500)
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return text, evidence
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def _update_evidence_library(self, session: DebateSession, round_data: DebateRound):
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"""
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Merge search results from a round into the session's evidence library, deduplicating by URL.
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"""
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ref = EvidenceReference(
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round_number=round_data.round_number,
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speaker=round_data.speaker,
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stance=round_data.stance
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)
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url_index = {entry.url: i for i, entry in enumerate(session.evidence_library)}
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for result in round_data.search_evidence.results:
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if result.url in url_index:
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entry = session.evidence_library[url_index[result.url]]
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# Avoid duplicate references (same round + speaker)
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if not any(
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r.round_number == ref.round_number and r.speaker == ref.speaker
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for r in entry.references
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):
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entry.references.append(ref)
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else:
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new_entry = EvidenceEntry(
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title=result.title,
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url=result.url,
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snippet=result.snippet,
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score=result.score,
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references=[ref]
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)
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session.evidence_library.append(new_entry)
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url_index[result.url] = len(session.evidence_library) - 1
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def _prepare_context(
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self, session: DebateSession, current_stance: DebateStance,
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search_evidence: Optional[SearchEvidence] = None
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) -> str:
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"""
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Prepare the context/prompt for the current model turn
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"""
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# Determine the stance of the current speaker
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if current_stance == DebateStance.PRO:
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position_desc = "正方（支持方）"
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opposing_desc = "反方（反对方）"
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else:
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position_desc = "反方（反对方）"
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opposing_desc = "正方（支持方）"
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# Build the context with previous rounds
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context_parts = [
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f"辩论主题: {session.topic}",
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f"你的立场: {position_desc}",
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"辩论规则:",
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"- 必须回应对方上一轮的核心论点",
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"- 不得重复自己已提出的观点",
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"- 输出长度限制在合理范围内",
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"\n历史辩论记录:"
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]
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for round_data in session.rounds:
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stance_text = "正方" if round_data.stance == DebateStance.PRO else "反方"
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context_parts.append(f"第{round_data.round_number}轮 - {stance_text}: {round_data.content}")
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# Inject search results if available
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if search_evidence:
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context_parts.append(SearchService.format_results_for_context(search_evidence))
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context_parts.append(f"\n现在轮到你 ({position_desc}) 发言，请基于以上内容进行回应。注意：直接给出你的论点内容，不要重复上述提示词、辩论规则或历史记录。")
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return "\n".join(context_parts)
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def _clean_response(self, response: str) -> str:
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"""
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Clean the model response to remove any echoed prompt/meta text
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"""
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import re
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# Remove common prompt echoes and meta prefixes
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patterns_to_remove = [
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r'^第\d+轮\s*[-:：]\s*(正方|反方)\s*[:：]?\s*', # 第X轮 - 正方/反方:
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r'^(正方|反方)\s*[（(][^)）]*[)）]\s*[:：]?\s*', # 正方（支持方）:
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r'^(正方|反方)\s*[:：]\s*', # 正方: or 反方:
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r'^我的立场\s*[:：]\s*', # 我的立场:
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r'^回应\s*[:：]\s*', # 回应:
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r'^辩论发言\s*[:：]\s*', # 辩论发言:
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]
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cleaned = response.strip()
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for pattern in patterns_to_remove:
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cleaned = re.sub(pattern, '', cleaned, flags=re.MULTILINE)
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return cleaned.strip()
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async def get_session_status(self, session_id: str) -> Optional[DebateSession]:
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"""
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Get the current status of a debate session
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"""
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return await self.session_manager.get_session(self.db, session_id)
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async def terminate_session(self, session_id: str):
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"""
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Terminate a debate session prematurely
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"""
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session = await self.session_manager.get_session(self.db, session_id)
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if session:
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session.status = "terminated"
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await self.session_manager.update_session(self.db, session)
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