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Merge branch 'feat/improve-agent-prompt'

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This commit is contained in:
penghanyuan
2026-03-01 10:12:23 +01:00
parent a69d5c625c
commit c1e2fb31a0
11 changed files with 644 additions and 65 deletions
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110
api/agents/conversation_agent.py
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@@ -1,7 +1,9 @@
"""
对话 Agent：基于访谈问题清单，动态选择问题，实时生成回应
支持异步调用和 Redis 会话存储
支持用户基础资料收集和时代背景融入
"""
import json
import logging
from typing import List, Optional, Dict, Any
@@ -11,6 +13,13 @@ from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from services.llm_service import llm_service
from services.redis_service import redis_service
from .prompts import ConversationStage, get_conversation_prompt, get_guided_conversation_prompt
from .prompts.profile_prompts import (
get_profile_greeting_prompt,
get_profile_extraction_prompt,
get_profile_followup_prompt,
format_user_profile_context,
get_missing_profile_fields,
)
from .state_schema import MemoirStateSchema
logger = logging.getLogger(__name__)
@@ -102,6 +111,87 @@ class ConversationAgent:
logger.error(f"生成回应失败: {e}")
return f"抱歉，生成回应时出现错误: {str(e)}"
async def generate_profile_greeting(
self,
conversation_id: str,
missing_fields: List[str],
nickname: str = "",
) -> List[str]:
"""生成资料收集的开场白（首次对话时使用）"""
if not self.llm:
return ["你好！在开始之前，能告诉我你是哪一年出生的吗？"]
try:
prompt = get_profile_greeting_prompt(missing_fields, nickname)
history_messages = await self._get_history_messages(conversation_id)
history_string = self._format_history_string(history_messages)
full_prompt = f"{prompt}\n\n{history_string}" if history_string else prompt
response = await self.llm.ainvoke(full_prompt)
response_text = response.content if hasattr(response, 'content') else str(response)
await self._save_message(conversation_id, "ai", response_text)
messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
return messages[:3] if messages else [response_text]
except Exception as e:
logger.error(f"生成资料收集开场白失败: {e}")
return ["你好！在我们开始聊人生故事之前，能先简单介绍一下你自己吗？比如你是哪一年出生的？"]
async def extract_profile_from_message(self, user_message: str, missing_fields: List[str]) -> Dict[str, Any]:
"""从用户消息中提取基础资料信息"""
if not self.llm or not missing_fields:
return {}
try:
prompt = get_profile_extraction_prompt(user_message, missing_fields)
response = await self.llm.ainvoke(prompt)
content = response.content.strip()
parsed = json.loads(content)
result = {}
if "birth_year" in parsed and isinstance(parsed["birth_year"], int):
result["birth_year"] = parsed["birth_year"]
if "birth_place" in parsed and parsed["birth_place"]:
result["birth_place"] = str(parsed["birth_place"])
if "grew_up_place" in parsed and parsed["grew_up_place"]:
result["grew_up_place"] = str(parsed["grew_up_place"])
if "occupation" in parsed and parsed["occupation"]:
result["occupation"] = str(parsed["occupation"])
return result
except (json.JSONDecodeError, Exception) as e:
logger.error(f"提取资料信息失败: {e}")
return {}
async def generate_profile_followup(
self,
conversation_id: str,
user_message: str,
missing_fields: List[str],
filled_fields: Dict[str, str],
nickname: str = "",
) -> List[str]:
"""在资料收集过程中生成跟进回复"""
if not self.llm:
return ["谢谢！还能告诉我更多吗？"]
try:
prompt = get_profile_followup_prompt(missing_fields, filled_fields, user_message, nickname)
history_messages = await self._get_history_messages(conversation_id)
history_string = self._format_history_string(history_messages)
full_prompt = f"{prompt}\n\n{history_string}\n\nHuman: {user_message}\n\nAssistant:"
response = await self.llm.ainvoke(full_prompt)
response_text = response.content if hasattr(response, 'content') else str(response)
await self._save_message(conversation_id, "human", user_message)
await self._save_message(conversation_id, "ai", response_text)
messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
return messages[:3] if messages else [response_text]
except Exception as e:
logger.error(f"生成资料跟进回复失败: {e}")
return ["谢谢分享！能再告诉我一些吗？"]
def _detect_user_stage(self, user_message: str) -> str:
"""
通过关键词检测用户当前正在谈论的人生阶段。
@@ -126,7 +216,8 @@ class ConversationAgent:
self,
conversation_id: str,
user_message: str,
memoir_state: MemoirStateSchema
memoir_state: MemoirStateSchema,
user_profile_context: str = "",
) -> List[str]:
"""
基于共享状态异步生成引导式回复
@@ -135,6 +226,7 @@ class ConversationAgent:
conversation_id: 对话 ID
user_message: 用户消息
memoir_state: 共享状态
user_profile_context: 用户基础资料上下文
Returns:
Agent 回应文本列表（支持多条消息）
@@ -150,18 +242,11 @@ class ConversationAgent:
if value.snippet
}
# 检测用户当前正在谈论的阶段
detected_user_stage = self._detect_user_stage(user_message)
# 从 Redis 获取对话历史，用于计算对话轮数
history_messages = await self._get_history_messages(conversation_id)
conversation_turn = len(history_messages) // 2 # 每轮包括一个用户消息和一个AI回复
# 计算同一话题的轮数（简单估算：基于已填充槽位的变化）
# 如果槽位数量没有增加，说明还在同一话题深入
conversation_turn = len(history_messages) // 2
same_topic_turns = self._estimate_same_topic_turns(history_messages, filled_slots)
# 获取所有阶段的覆盖情况
all_stages_coverage = memoir_state.all_stages_coverage()
system_prompt = get_guided_conversation_prompt(
@@ -173,24 +258,19 @@ class ConversationAgent:
same_topic_turns=same_topic_turns,
all_stages_coverage=all_stages_coverage,
detected_user_stage=detected_user_stage,
user_profile_context=user_profile_context,
)
history_string = self._format_history_string(history_messages)
# 构建完整 prompt
full_prompt = f"{system_prompt}\n\n{history_string}\n\nHuman: {user_message}\n\nAssistant:"
# 异步调用 LLM
response = await self.llm.ainvoke(full_prompt)
response_text = response.content if hasattr(response, 'content') else str(response)
# 保存对话到 Redis
await self._save_message(conversation_id, "human", user_message)
await self._save_message(conversation_id, "ai", response_text)
# 支持多条消息，用 [SPLIT] 分隔
messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
# 最多返回 3 条
return messages[:3] if messages else [response_text]
except Exception as e: