feat: 添加Redis支持和Celery任务处理
- 新增Redis服务模块用于会话状态存储和缓存 - 集成Celery用于后台任务处理 - 更新Docker Compose配置以支持开发环境 - 优化API以支持异步调用和Redis会话存储 - 更新文档以反映新的开发环境配置和使用方法
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penghanyuan
@@ -1,37 +1,54 @@
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"""
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对话 Agent：基于访谈问题清单，动态选择问题，实时生成回应
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支持异步调用和 Redis 会话存储
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"""
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from typing import List, Optional
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import logging
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from typing import List, Optional, Dict, Any
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from langchain.chains import ConversationChain
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from langchain.memory import ConversationBufferMemory
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from langchain.prompts import PromptTemplate
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from langchain_core.messages import HumanMessage, AIMessage
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from services.llm_service import llm_service
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from services.redis_service import redis_service
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from .prompts import ConversationStage, get_conversation_prompt, get_guided_conversation_prompt
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from .state_schema import MemoirStateSchema
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logger = logging.getLogger(__name__)
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class ConversationAgent:
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"""对话 Agent"""
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"""对话 Agent（支持异步和 Redis 存储）"""
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def __init__(self):
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# 使用 LLM 服务获取 LLM 实例
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self.llm = llm_service.get_llm()
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# 对话记忆
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self.memories: dict[str, ConversationBufferMemory] = {}
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def _get_memory(self, conversation_id: str) -> ConversationBufferMemory:
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"""获取或创建对话记忆"""
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if conversation_id not in self.memories:
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self.memories[conversation_id] = ConversationBufferMemory(
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return_messages=True,
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memory_key="history"
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)
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return self.memories[conversation_id]
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async def _get_history_messages(self, conversation_id: str) -> List[Any]:
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"""从 Redis 获取对话历史并转换为 LangChain 消息格式"""
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history = await redis_service.get_conversation_history(conversation_id)
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messages = []
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for msg in history:
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if msg["role"] == "human":
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messages.append(HumanMessage(content=msg["content"]))
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elif msg["role"] == "ai":
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messages.append(AIMessage(content=msg["content"]))
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return messages
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def generate_response(
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async def _save_message(self, conversation_id: str, role: str, content: str):
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"""保存消息到 Redis"""
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await redis_service.add_message(conversation_id, role, content)
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def _format_history_string(self, messages: List[Any]) -> str:
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"""将消息列表格式化为字符串（用于 prompt）"""
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history_parts = []
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for msg in messages:
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if isinstance(msg, HumanMessage):
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history_parts.append(f"Human: {msg.content}")
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elif isinstance(msg, AIMessage):
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history_parts.append(f"Assistant: {msg.content}")
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return "\n\n".join(history_parts)
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async def generate_response(
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self,
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conversation_id: str,
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user_message: str,
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@@ -39,7 +56,7 @@ class ConversationAgent:
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covered_topics: Optional[List[str]] = None
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) -> str:
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"""
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生成 Agent 回应
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异步生成 Agent 回应
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Args:
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conversation_id: 对话 ID
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@@ -60,38 +77,39 @@ class ConversationAgent:
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if not self.llm:
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return "抱歉，LLM 服务未配置。请设置 DEEPSEEK_API_KEY 或 LLM_API_KEY 环境变量。"
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# 获取系统提示词
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system_prompt = get_conversation_prompt(current_stage, covered_topics, user_message)
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# 获取对话记忆
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memory = self._get_memory(conversation_id)
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# 创建对话链
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prompt_template = PromptTemplate(
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input_variables=["history", "input"],
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template=f"{system_prompt}\n\n{{history}}\n\nHuman: {{input}}\n\nAssistant:"
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)
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chain = ConversationChain(
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llm=self.llm,
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prompt=prompt_template,
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memory=memory,
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verbose=False
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)
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# 生成回应
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response = chain.predict(input=user_message)
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return response
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try:
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# 获取系统提示词
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system_prompt = get_conversation_prompt(current_stage, covered_topics, user_message)
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# 从 Redis 获取对话历史
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history_messages = await self._get_history_messages(conversation_id)
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history_string = self._format_history_string(history_messages)
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# 构建完整 prompt
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full_prompt = f"{system_prompt}\n\n{history_string}\n\nHuman: {user_message}\n\nAssistant:"
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# 异步调用 LLM
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response = await self.llm.ainvoke(full_prompt)
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response_text = response.content if hasattr(response, 'content') else str(response)
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# 保存对话到 Redis
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await self._save_message(conversation_id, "human", user_message)
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await self._save_message(conversation_id, "ai", response_text)
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return response_text
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except Exception as e:
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logger.error(f"生成回应失败: {e}")
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return f"抱歉，生成回应时出现错误: {str(e)}"
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def generate_response_with_state(
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async def generate_response_with_state(
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self,
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conversation_id: str,
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user_message: str,
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memoir_state: MemoirStateSchema
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) -> List[str]:
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"""
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基于共享状态生成引导式回复
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基于共享状态异步生成引导式回复
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Args:
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conversation_id: 对话 ID
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@@ -104,39 +122,44 @@ class ConversationAgent:
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if not self.llm:
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return ["抱歉，LLM 服务未配置。请设置 DEEPSEEK_API_KEY 或 LLM_API_KEY 环境变量。"]
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empty_slots = memoir_state.empty_slots_for_current_stage()
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filled_slots = {
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key: value.snippet
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for key, value in memoir_state.slots.get(memoir_state.current_stage, {}).items()
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if value.snippet
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}
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try:
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empty_slots = memoir_state.empty_slots_for_current_stage()
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filled_slots = {
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key: value.snippet
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for key, value in memoir_state.slots.get(memoir_state.current_stage, {}).items()
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if value.snippet
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}
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system_prompt = get_guided_conversation_prompt(
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current_stage=memoir_state.current_stage,
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empty_slots=empty_slots,
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filled_slots=filled_slots,
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user_message=user_message,
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)
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system_prompt = get_guided_conversation_prompt(
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current_stage=memoir_state.current_stage,
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empty_slots=empty_slots,
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filled_slots=filled_slots,
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user_message=user_message,
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)
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memory = self._get_memory(conversation_id)
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prompt_template = PromptTemplate(
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input_variables=["history", "input"],
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template=f"{system_prompt}\n\n{{history}}\n\nHuman: {{input}}\n\nAssistant:"
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)
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chain = ConversationChain(
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llm=self.llm,
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prompt=prompt_template,
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memory=memory,
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verbose=False
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)
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response = chain.predict(input=user_message)
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# 支持多条消息，用 [SPLIT] 分隔
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messages = [msg.strip() for msg in response.split("[SPLIT]") if msg.strip()]
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# 最多返回 3 条
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return messages[:3] if messages else [response]
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# 从 Redis 获取对话历史
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history_messages = await self._get_history_messages(conversation_id)
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history_string = self._format_history_string(history_messages)
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# 构建完整 prompt
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full_prompt = f"{system_prompt}\n\n{history_string}\n\nHuman: {user_message}\n\nAssistant:"
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# 异步调用 LLM
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response = await self.llm.ainvoke(full_prompt)
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response_text = response.content if hasattr(response, 'content') else str(response)
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# 保存对话到 Redis
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await self._save_message(conversation_id, "human", user_message)
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await self._save_message(conversation_id, "ai", response_text)
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# 支持多条消息，用 [SPLIT] 分隔
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messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
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# 最多返回 3 条
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return messages[:3] if messages else [response_text]
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except Exception as e:
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logger.error(f"生成回应失败: {e}")
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return [f"抱歉，生成回应时出现错误: {str(e)}"]
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def detect_stage(self, conversation_id: str, user_message: str) -> ConversationStage:
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"""
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@@ -168,8 +191,7 @@ class ConversationAgent:
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# 默认返回当前阶段或童年阶段
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return ConversationStage.CHILDHOOD
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def clear_memory(self, conversation_id: str):
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"""清除对话记忆"""
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if conversation_id in self.memories:
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del self.memories[conversation_id]
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async def clear_memory(self, conversation_id: str):
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"""清除对话记忆（从 Redis）"""
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await redis_service.clear_conversation_history(conversation_id)
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