feat: implement council v3 round-table mode
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@ -5,27 +5,37 @@ import config
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class ResearchAgent:
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"""研究模式专用 Agent"""
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def __init__(self, role: str, llm_client: LLMClient):
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def __init__(self, role: str, llm_client: LLMClient, name: str = None):
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self.role = role
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self.llm_client = llm_client
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self.role_config = config.RESEARCH_MODEL_ROLES.get(role, {})
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self.name = self.role_config.get("name", role.capitalize())
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self.name = name if name else self.role_config.get("name", role.capitalize())
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@property
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def model_name(self) -> str:
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return self.llm_client.model
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def _get_system_prompt(self, context: str = "") -> str:
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if self.role == "expert_a":
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if self.role == "council_member":
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return f"""You are {self.name}, a member of the Multi-Model Decision Council.
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Your goal is to participate in a round-table discussion to solve the user's problem.
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Be conversational, insightful, and constructive.
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Build upon others' ideas or respectfully disagree with valid reasoning.
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Context: {context}"""
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elif self.role == "expert_a":
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return f"""You are Expert A, a Senior Analyst.
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Your goal is to provide a deep, foundational analysis of the user's topic.
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Structure your thinking clearly. Propose a solid initial framework or solution.
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You are participating in a round-table discussion.
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Your goal is to analyze the topic and propose solutions.
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Be conversational, direct, and responsive to other experts.
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Do not write a full final report; focus on the current discussion turn.
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Context: {context}"""
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elif self.role == "expert_b":
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return f"""You are Expert B, a Critical Reviewer.
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Your goal is to find flaws, risks, and missed opportunities in Expert A's analysis.
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Be constructive but rigorous. Don't just agree; add value by challenging assumptions.
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You are participating in a round-table discussion.
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Your goal is to critique Expert A's points and offer alternative perspectives.
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Be conversational and constructive. Challenge assumptions directly.
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Context: {context}"""
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elif self.role == "expert_c":
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99
app.py
99
app.py
@ -131,20 +131,8 @@ with st.sidebar:
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st.divider()
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if mode == "Deep Research":
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st.subheader("🧪 研究模型配置")
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# 3 个角色的模型配置
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roles_config = {}
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for role_key, role_info in config.RESEARCH_MODEL_ROLES.items():
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roles_config[role_key] = st.selectbox(
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f"{role_info['name']} ({role_info['description']})",
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options=list(AVAILABLE_MODELS.keys()),
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index=list(AVAILABLE_MODELS.keys()).index(role_info['default_model']) if role_info['default_model'] in AVAILABLE_MODELS else 0,
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key=f"model_{role_key}"
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)
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else: # Debate Workshop
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if mode == "Debate Workshop": # Debate Workshop Settings
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# 模型选择
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model = st.selectbox(
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"🤖 选择通用模型",
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@ -205,11 +193,39 @@ with st.sidebar:
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# ==================== 主界面逻辑 ====================
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if mode == "Deep Research":
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st.title("🧪 Multi-Model Council V2")
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st.markdown("*多模型智囊团：分析 (Expert A) -> 批判 (Expert B) -> 改进 (Expert A) -> 决策 (Expert C)*")
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st.title("🧪 Multi-Model Council V4")
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st.markdown("*多模型智囊团：自定义 N 个专家进行多轮对话讨论，最后由最后一位专家决策*")
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col1, col2 = st.columns([3, 1])
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with col1:
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research_topic = st.text_area("研究/决策主题", placeholder="请输入你想深入研究或决策的主题...", height=100)
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with col2:
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max_rounds = st.number_input("讨论轮数", min_value=1, max_value=5, value=2, help="专家们进行对话的轮数")
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# Expert Configuration
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st.subheader("👥 专家配置")
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num_experts = st.number_input("专家数量", min_value=2, max_value=5, value=3)
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experts_config = []
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cols = st.columns(num_experts)
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for i in range(num_experts):
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with cols[i]:
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default_model_key = list(AVAILABLE_MODELS.keys())[i % len(AVAILABLE_MODELS)]
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st.markdown(f"**Expert {i+1}**")
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# Default names
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default_name = f"Expert {i+1}"
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if i == num_experts - 1:
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default_name = f"Expert {i+1} (Synthesizer)"
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expert_name = st.text_input(f"名称 #{i+1}", value=default_name, key=f"expert_name_{i}")
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expert_model = st.selectbox(f"模型 #{i+1}", options=list(AVAILABLE_MODELS.keys()), index=list(AVAILABLE_MODELS.keys()).index(default_model_key), key=f"expert_model_{i}")
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experts_config.append({
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"name": expert_name,
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"model": expert_model
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})
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# Input
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research_topic = st.text_area("研究/决策主题", placeholder="请输入你想深入研究或决策的主题...", height=100)
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research_context = st.text_area("补充背景 (可选)", placeholder="任何额外的背景信息...", height=80)
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start_research_btn = st.button("🚀 开始多模型协作", type="primary", disabled=not research_topic)
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@ -219,49 +235,48 @@ if mode == "Deep Research":
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st.session_state.research_output = ""
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st.session_state.research_steps_output = []
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manager = ResearchManager(api_key=api_key)
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manager = ResearchManager(
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api_key=api_key,
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base_url=base_url,
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provider=provider_id
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)
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config_obj = ResearchConfig(
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topic=research_topic,
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context=research_context,
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expert_a_model=roles_config['expert_a'],
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expert_b_model=roles_config['expert_b'],
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expert_c_model=roles_config['expert_c']
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experts=experts_config
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)
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manager.create_agents(config_obj)
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st.divider()
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st.subheader("🧠 智囊团思考中...")
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st.subheader("🗣️ 智囊团讨论中...")
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# Collaborative Execution
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current_step_name = ""
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current_step_content = ""
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step_placeholder = st.empty()
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status_container = st.status("正在初始化...", expanded=True)
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chat_container = st.container()
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try:
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for event in manager.collaborate(research_topic, research_context):
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for event in manager.collaborate(research_topic, research_context, max_rounds=max_rounds):
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if event["type"] == "step_start":
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current_step_name = event["step"]
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current_agent = event["agent"]
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current_model = event["model"]
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status_container.update(label=f"🔄 {current_step_name} [{current_agent}] ({current_model})", state="running")
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step_placeholder = st.empty()
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current_step_content = ""
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# Create a chat message block
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with chat_container:
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st.markdown(f"#### {current_step_name}")
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st.caption(f"🤖 {current_agent} ({current_model})")
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message_placeholder = st.empty()
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current_content = ""
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elif event["type"] == "content":
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current_step_content += event["content"]
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step_placeholder.markdown(f"**Thinking...**\n\n{current_step_content}")
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current_content += event["content"]
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message_placeholder.markdown(current_content)
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elif event["type"] == "step_end":
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# Save step result
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# Save step result for history
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st.session_state.research_steps_output.append({
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"step": current_step_name,
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"output": event["output"]
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})
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status_container.write(f"### {current_step_name}\n{event['output']}")
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status_container.update(label=f"✅ {current_step_name} 完成", state="running")
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status_container.update(label="✅ 所有步骤完成", state="complete", expanded=False)
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st.divider() # Separator between turns
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# The last step output is the final plan
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if st.session_state.research_steps_output:
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@ -423,9 +438,9 @@ elif mode == "Debate Workshop":
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try:
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# 初始化默认客户端
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llm_client = LLMClient(
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provider="aihubmix",
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provider=provider_id,
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api_key=api_key,
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base_url="https://aihubmix.com/v1",
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base_url=base_url,
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model=model
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)
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@ -434,9 +449,9 @@ elif mode == "Debate Workshop":
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for ag_id, ag_model in agent_model_map.items():
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if ag_model != model: # Only create new client if different from default
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agent_clients[ag_id] = LLMClient(
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provider="aihubmix",
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provider=provider_id,
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api_key=api_key,
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base_url="https://aihubmix.com/v1",
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base_url=base_url,
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model=ag_model
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)
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58
config.py
58
config.py
@ -10,8 +10,39 @@ load_dotenv()
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ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
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AIHUBMIX_API_KEY = os.getenv("AIHUBMIX_API_KEY", "sk-yd8Tik0nFW5emKYcBdFc433b7c8b4dC182848f76819bBe73")
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DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY", "")
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SILICONFLOW_API_KEY = os.getenv("SILICONFLOW_API_KEY", "")
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# AIHubMix 配置
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# LLM Providers Configuration
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LLM_PROVIDERS = {
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"AIHubMix": {
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"base_url": "https://aihubmix.com/v1",
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"api_key_var": "AIHUBMIX_API_KEY",
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"default_model": "gpt-4o"
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},
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"DeepSeek": {
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"base_url": "https://api.deepseek.com",
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"api_key_var": "DEEPSEEK_API_KEY",
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"default_model": "deepseek-chat"
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},
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"SiliconFlow": {
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"base_url": "https://api.siliconflow.cn/v1",
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"api_key_var": "SILICONFLOW_API_KEY",
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"default_model": "deepseek-ai/DeepSeek-V3" # SiliconFlow often uses full path
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},
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"OpenAI": {
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"base_url": "https://api.openai.com/v1",
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"api_key_var": "OPENAI_API_KEY",
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"default_model": "gpt-4o"
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},
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"Custom": {
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"base_url": "http://localhost:8000/v1",
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"api_key_var": "CUSTOM_API_KEY",
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"default_model": "local-model"
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}
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}
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# AIHubMix 配置 (Legacy, keeping for backward compatibility if needed, but main logic will use LLM_PROVIDERS)
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AIHUBMIX_BASE_URL = "https://aihubmix.com/v1"
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# 模型配置
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@ -20,17 +51,42 @@ LLM_PROVIDER = "aihubmix" # 默认使用 AIHubMix
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# 支持的模型列表
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AVAILABLE_MODELS = {
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# OpenAI
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"gpt-4o": "GPT-4o (OpenAI)",
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"gpt-4o-mini": "GPT-4o Mini (OpenAI)",
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"gpt-4-turbo": "GPT-4 Turbo (OpenAI)",
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# Anthropic
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"claude-3-5-sonnet-20241022": "Claude 3.5 Sonnet (Anthropic)",
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"claude-3-opus-20240229": "Claude 3 Opus (Anthropic)",
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"claude-3-haiku-20240307": "Claude 3 Haiku (Anthropic)",
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# Google
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"gemini-1.5-pro": "Gemini 1.5 Pro (Google)",
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"gemini-1.5-flash": "Gemini 1.5 Flash (Google)",
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"gemini-2.0-flash-exp": "Gemini 2.0 Flash Exp (Google)",
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# DeepSeek
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"deepseek-chat": "DeepSeek V3 (DeepSeek)",
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"deepseek-reasoner": "DeepSeek R1 (DeepSeek)",
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"deepseek-coder": "DeepSeek Coder (DeepSeek)",
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"deepseek-v2.5": "DeepSeek V2.5 (DeepSeek)",
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"deepseek-coder-v2": "DeepSeek Coder V2 (DeepSeek)",
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# Meta
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"llama-3.3-70b-instruct": "Llama 3.3 70B (Meta)",
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"llama-3.1-405b-instruct": "Llama 3.1 405B (Meta)",
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# Alibaba
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"qwen-2.5-72b-instruct": "Qwen 2.5 72B (Alibaba)",
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"qwen-plus": "Qwen Plus (Alibaba)",
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"qwen-turbo": "Qwen Turbo (Alibaba)",
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# Mistral
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"mistral-large-latest": "Mistral Large (Mistral)",
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# Perplexity
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"llama-3.1-sonar-huge-128k-online": "Sonar Huge Online (Perplexity)",
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}
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# 辩论配置
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@ -8,9 +8,8 @@ import config
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class ResearchConfig:
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topic: str
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context: str = ""
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expert_a_model: str = "gpt-4o"
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expert_b_model: str = "gemini-1.5-pro"
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expert_c_model: str = "claude-3-5-sonnet-20241022"
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# Dynamic list of experts: [{"name": "Expert 1", "model": "gpt-4o", "role": "analyst"}, ...]
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experts: List[Dict[str, str]] = None
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class ResearchManager:
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"""Manages the Multi-Model Council workflow"""
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@ -19,62 +18,88 @@ class ResearchManager:
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self.api_key = api_key
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self.base_url = base_url
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self.provider = provider
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self.agents = {}
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self.agents = []
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def _get_client(self, model: str) -> LLMClient:
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return LLMClient(
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provider=self.provider,
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provider=self.provider, # Configured to respect provider or default to aihubmix logic inside client
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api_key=self.api_key,
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base_url=self.base_url,
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model=model
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)
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def create_agents(self, config: ResearchConfig):
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"""Initialize agents with specific models"""
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self.agents["expert_a"] = ResearchAgent("expert_a", self._get_client(config.expert_a_model))
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self.agents["expert_b"] = ResearchAgent("expert_b", self._get_client(config.expert_b_model))
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self.agents["expert_c"] = ResearchAgent("expert_c", self._get_client(config.expert_c_model))
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"""Initialize agents with specific models from config"""
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self.agents = []
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if config.experts:
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for idx, expert_conf in enumerate(config.experts):
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# Assign role based on position or config
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# First agents are discussion members, last one is Synthesizer usually,
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# but for equality we treat them all as members until the end.
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# We'll assign a generic "member" role or specific if provided.
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def collaborate(self, topic: str, context: str) -> Generator[Dict[str, str], None, None]:
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role_type = "council_member"
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# If it's the last one, maybe give them synthesizer duty?
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# For now, all are members, and we explicitly pick one for synthesis.
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agent = ResearchAgent(
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role=role_type,
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llm_client=self._get_client(expert_conf["model"]),
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name=expert_conf.get("name", f"Expert {idx+1}")
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)
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self.agents.append(agent)
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def collaborate(self, topic: str, context: str, max_rounds: int = 3) -> Generator[Dict[str, str], None, None]:
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"""
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Execute the collaborative research process:
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1. Expert A: Propose Analysis
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2. Expert B: Critique
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3. Expert C: Synthesis & Final Plan
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Execute the collaborative research process with multi-round discussion:
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1. Conversation Loop (All Experts Round Robin)
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2. Final Synthesis (Last Expert)
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"""
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# Step 1: Expert A Analysis
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findings_a = ""
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yield {"type": "step_start", "step": "Expert A Analysis", "agent": self.agents["expert_a"].name, "model": self.agents["expert_a"].model_name}
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prompt_a = f"Please provide a comprehensive analysis and initial proposal for the topic: '{topic}'.\nContext: {context}"
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for chunk in self.agents["expert_a"].generate(prompt_a, context):
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findings_a += chunk
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yield {"type": "content", "content": chunk}
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yield {"type": "step_end", "output": findings_a}
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conversation_history = []
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discussion_context = f"Topic: '{topic}'\nBackground Context: {context}\n\n"
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# Step 2: Expert B Critique
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findings_b = ""
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yield {"type": "step_start", "step": "Expert B Critique", "agent": self.agents["expert_b"].name, "model": self.agents["expert_b"].model_name}
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prompt_b = f"Review Expert A's proposal on '{topic}'. Critique it, find gaps, and suggest improvements.\nExpert A's Proposal:\n{findings_a}"
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for chunk in self.agents["expert_b"].generate(prompt_b, context):
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findings_b += chunk
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yield {"type": "content", "content": chunk}
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yield {"type": "step_end", "output": findings_b}
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# Round-Robin Discussion
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for round_num in range(1, max_rounds + 1):
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for agent in self.agents:
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yield {"type": "step_start", "step": f"Round {round_num}: {agent.name}", "agent": agent.name, "model": agent.model_name}
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# Step 3: Expert A Refinement (Innovative Optimization)
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findings_a_refined = ""
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yield {"type": "step_start", "step": "Expert A Refinement", "agent": self.agents["expert_a"].name, "model": self.agents["expert_a"].model_name}
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prompt_a_refine = f"Expert B has critiqued your initial proposal.\nCritique: {findings_b}\n\nPlease refine your proposal to address these points. Strengthen your arguments and fix the gaps."
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for chunk in self.agents["expert_a"].generate(prompt_a_refine, context):
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findings_a_refined += chunk
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yield {"type": "content", "content": chunk}
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yield {"type": "step_end", "output": findings_a_refined}
|
||||
# Construct prompt
|
||||
if round_num == 1 and not conversation_history:
|
||||
prompt = f"You are {agent.name}. You are starting the discussion on '{topic}'. Provide your initial analysis and key points. Be conversational but substantive."
|
||||
else:
|
||||
prompt = f"You are {agent.name}. Review the discussion so far. Respond to previous points. Defend your views or refine them. Keep the discussion moving towards a solution.\n\nDiscussion History:\n{_format_history(conversation_history)}"
|
||||
|
||||
# Step 4: Expert C Synthesis & Visualization
|
||||
findings_c = ""
|
||||
yield {"type": "step_start", "step": "Expert C Synthesis & Visualization", "agent": self.agents["expert_c"].name, "model": self.agents["expert_c"].model_name}
|
||||
prompt_c = f"Synthesize a final comprehensive plan for '{topic}' based on the refined proposal.\nRefined Proposal:\n{findings_a_refined}\nCritique Reference:\n{findings_b}\n\nIMPORTANT: Include a Mermaid.js diagram (sequenceDiagram, gantt, or flowchart) to visualize the roadmap or process at the end of your report."
|
||||
for chunk in self.agents["expert_c"].generate(prompt_c, context):
|
||||
findings_c += chunk
|
||||
response = ""
|
||||
for chunk in agent.generate(prompt, context=discussion_context):
|
||||
response += chunk
|
||||
yield {"type": "content", "content": chunk}
|
||||
|
||||
conversation_history.append({"agent": agent.name, "content": response})
|
||||
yield {"type": "step_end", "output": response}
|
||||
|
||||
# Final Synthesis by the LAST agent (or a specific designated one)
|
||||
synthesizer = self.agents[-1]
|
||||
yield {"type": "step_start", "step": f"Final Synthesis ({synthesizer.name})", "agent": synthesizer.name, "model": synthesizer.model_name}
|
||||
|
||||
prompt_syn = f"""Synthesize the entire discussion into a final comprehensive plan for '{topic}'.
|
||||
|
||||
Discussion History:
|
||||
{_format_history(conversation_history)}
|
||||
|
||||
IMPORTANT:
|
||||
1. Reconcile the different viewpoints from all experts.
|
||||
2. Provide a concrete action plan.
|
||||
3. You MUST include a Mermaid.js diagram (using ```mermaid code block) to visualize the roadmap or process."""
|
||||
|
||||
findings_syn = ""
|
||||
for chunk in synthesizer.generate(prompt_syn, context=discussion_context):
|
||||
findings_syn += chunk
|
||||
yield {"type": "content", "content": chunk}
|
||||
yield {"type": "step_end", "output": findings_c}
|
||||
yield {"type": "step_end", "output": findings_syn}
|
||||
|
||||
def _format_history(history: List[Dict[str, str]]) -> str:
|
||||
formatted = ""
|
||||
for turn in history:
|
||||
formatted += f"[{turn['agent']}]: {turn['content']}\n\n"
|
||||
return formatted
|
||||
|
||||
Binary file not shown.
@ -36,24 +36,25 @@ class LLMClient:
|
||||
self.client = OpenAI(api_key=api_key)
|
||||
self.model = model or "gpt-4o"
|
||||
|
||||
elif self.provider == "aihubmix":
|
||||
# AIHubMix 兼容 OpenAI API 格式
|
||||
elif self.provider in ["aihubmix", "deepseek", "siliconflow", "custom"]:
|
||||
# OpenAI 兼容接口 Providers
|
||||
from openai import OpenAI
|
||||
|
||||
default_urls = {
|
||||
"aihubmix": "https://aihubmix.com/v1",
|
||||
"deepseek": "https://api.deepseek.com",
|
||||
"siliconflow": "https://api.siliconflow.cn/v1",
|
||||
"custom": "http://localhost:8000/v1"
|
||||
}
|
||||
|
||||
final_base_url = base_url or default_urls.get(self.provider)
|
||||
|
||||
self.client = OpenAI(
|
||||
api_key=api_key,
|
||||
base_url=base_url or "https://aihubmix.com/v1"
|
||||
base_url=final_base_url
|
||||
)
|
||||
self.model = model or "gpt-4o"
|
||||
|
||||
elif self.provider == "custom":
|
||||
# 自定义 OpenAI 兼容接口（vLLM、Ollama、TGI 等）
|
||||
from openai import OpenAI
|
||||
self.client = OpenAI(
|
||||
api_key=api_key or "not-needed",
|
||||
base_url=base_url or "http://localhost:8000/v1"
|
||||
)
|
||||
self.model = model or "local-model"
|
||||
|
||||
else:
|
||||
raise ValueError(f"不支持的 provider: {self.provider}")
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user