Merge branch 'feature/moshui20260409-guideline-assistant-message-error' into staging
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朱潮
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feb8557c23
@ -6,7 +6,7 @@ from utils.fastapi_utils import (extract_block_from_system_prompt, format_messag
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from langchain.chat_models import BaseChatModel
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from langgraph.runtime import Runtime
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from langchain_core.messages import SystemMessage
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from langchain_core.messages import SystemMessage, HumanMessage
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from typing import Any, Callable
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.outputs import LLMResult
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@ -124,10 +124,11 @@ Action: Provide concise, friendly, and personified natural responses.
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response.additional_kwargs["message_tag"] = "THINK"
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response.content = f"<think>{response.content}</think>"
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# 将响应添加到原始消息列表
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state['messages'] = state['messages'] + [response]
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# 将响应添加到原始消息列表，并追加 HumanMessage 确保消息以 user 结尾
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# 某些模型不支持 assistant message prefill，要求最后一条消息必须是 user
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state['messages'] = state['messages'] + [response, HumanMessage(content=self._get_follow_up_prompt())]
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return state
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async def abefore_agent(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | None:
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if not self.guidelines:
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return None
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@ -148,10 +149,23 @@ Action: Provide concise, friendly, and personified natural responses.
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response.additional_kwargs["message_tag"] = "THINK"
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response.content = f"<think>{response.content}</think>"
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# 将响应添加到原始消息列表
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state['messages'] = state['messages'] + [response]
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# 将响应添加到原始消息列表，并追加 HumanMessage 确保消息以 user 结尾
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# 某些模型不支持 assistant message prefill，要求最后一条消息必须是 user
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state['messages'] = state['messages'] + [response, HumanMessage(content=self._get_follow_up_prompt())]
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return state
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def _get_follow_up_prompt(self) -> str:
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"""根据语言返回引导主 agent 回复的提示"""
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prompts = {
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"ja": "以上の分析に基づいて、ユーザーに返信してください。",
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"jp": "以上の分析に基づいて、ユーザーに返信してください。",
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"zh": "请根据以上分析，回复用户。",
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"zh-TW": "請根據以上分析，回覆用戶。",
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"ko": "위 분석을 바탕으로 사용자에게 답변해 주세요.",
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"en": "Based on the above analysis, please respond to the user.",
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}
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return prompts.get(self.language, prompts["en"])
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def wrap_model_call(
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self,
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request: ModelRequest,
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@ -148,6 +148,21 @@ Output: {{"facts" : []}}
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Input: DR1の照明状態を教えて
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Output: {{"facts" : []}}
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Input: 私は林檎好きです
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Output: {{"facts" : ["林檎が好き"]}}
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Input: コーヒー飲みたい、毎朝
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Output: {{"facts" : ["毎朝コーヒーを飲みたい"]}}
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Input: 昨日映画見た、すごくよかった
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Output: {{"facts" : ["昨日映画を見た", "映画がすごくよかった"]}}
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Input: 我喜欢吃苹果
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Output: {{"facts" : ["喜欢吃苹果"]}}
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Input: 나는 사과를 좋아해
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Output: {{"facts" : ["사과를 좋아함"]}}
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Return the facts and preferences in a json format as shown above.
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Remember the following:
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@ -159,12 +174,15 @@ Remember the following:
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- If you do not find anything relevant in the below conversation, you can return an empty list corresponding to the "facts" key.
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- Create the facts based on the user and assistant messages only. Do not pick anything from the system messages.
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- Make sure to return the response in the format mentioned in the examples. The response should be in json with a key as "facts" and corresponding value will be a list of strings.
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- **CRITICAL - Do NOT memorize actions or operations**: Do not extract facts about queries the user asked you to perform, devices the user asked you to operate, or any one-time transient actions. Only memorize information ABOUT the user (preferences, relationships, personal details, plans), not actions the user asked the assistant to DO. Ask yourself: "Is this a fact about WHO the user IS, or what the user asked me to DO?" Only remember the former.
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- **CRITICAL for Semantic Completeness**:
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- Each extracted fact MUST preserve the complete semantic meaning. Never truncate or drop key parts of the meaning.
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- For colloquial or grammatically informal expressions (common in spoken Japanese, Chinese, Korean, etc.), understand the full intended meaning and record it in a clear, semantically complete form.
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- In Japanese, spoken language often omits particles (e.g., が, を, に). When extracting facts, include the necessary particles to make the meaning unambiguous. For example: "私は林檎好きです" should be understood as "林檎が好き" (likes apples), not literally "私は林檎好き".
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- When the user expresses a preference or opinion in casual speech, record the core preference/opinion clearly. Remove the subject pronoun (私は/I) since facts are about the user by default, but keep all other semantic components intact.
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- **CRITICAL for People/Relationship Tracking**:
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- Write people-related facts in plain, natural language. Do NOT use structured formats like "Contact:", "referred as", or "DEFAULT when user says".
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- Good examples: "Michael Johnson is a colleague, also called Mike", "田中さんは友達", "滨田太郎は「滨田」とも呼ばれている"
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112
routes/memory.py
112
routes/memory.py
@ -1,13 +1,13 @@
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"""
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Memory 管理 API 路由
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提供记忆查看和删除功能
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提供记忆查看、添加和删除功能
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"""
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import logging
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from typing import Optional, List, Dict, Any
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from typing import Literal, Optional, List, Dict, Any
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from fastapi import APIRouter, HTTPException, Header, Query
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel
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from pydantic import BaseModel, Field
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logger = logging.getLogger('app')
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@ -33,6 +33,26 @@ class DeleteAllResponse(BaseModel):
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deleted_count: int
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class ConversationMessage(BaseModel):
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"""对话消息"""
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role: Literal["user", "assistant"]
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content: str = Field(..., min_length=1)
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class AddMemoryRequest(BaseModel):
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"""添加记忆的请求体"""
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bot_id: str = Field(..., min_length=1)
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user_id: str = Field(..., min_length=1)
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messages: List[ConversationMessage] = Field(..., max_length=200)
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class AddMemoryResponse(BaseModel):
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"""添加记忆的响应"""
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success: bool
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pairs_processed: int
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pairs_failed: int = 0
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async def get_user_identifier_from_request(
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authorization: Optional[str],
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user_id: Optional[str] = None
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@ -63,6 +83,92 @@ async def get_user_identifier_from_request(
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)
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@router.post("/memory", response_model=AddMemoryResponse)
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async def add_memory_from_conversation(data: AddMemoryRequest):
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"""
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从对话消息中提取并保存记忆
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将用户和助手的对话配对，通过 Mem0 提取关键事实并存储。
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用于 realtime 语音对话等不经过 Agent 中间件的场景。
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此端点供内部服务调用（如 felo-mygpt），不暴露给外部用户。
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"""
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try:
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from agent.mem0_manager import get_mem0_manager
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from utils.settings import MEM0_ENABLED
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if not MEM0_ENABLED:
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raise HTTPException(
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status_code=503,
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detail="Memory feature is not enabled"
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)
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if not data.messages:
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return AddMemoryResponse(success=True, pairs_processed=0)
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manager = get_mem0_manager()
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# 将消息配对为 user-assistant 对，然后调用 add_memory
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pairs_processed = 0
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pairs_failed = 0
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i = 0
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while i < len(data.messages):
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msg = data.messages[i]
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if msg.role == 'user':
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# 收集连续的 user 消息
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user_contents = [msg.content]
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j = i + 1
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while j < len(data.messages) and data.messages[j].role == 'user':
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user_contents.append(data.messages[j].content)
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j += 1
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user_text = '\n'.join(user_contents)
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# 检查是否有对应的 assistant 回复
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assistant_text = ""
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if j < len(data.messages) and data.messages[j].role == 'assistant':
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assistant_text = data.messages[j].content or ""
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j += 1
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if user_text and assistant_text:
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conversation_text = f"User: {user_text}\nAssistant: {assistant_text}"
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try:
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await manager.add_memory(
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text=conversation_text,
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user_id=data.user_id,
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agent_id=data.bot_id,
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metadata={"type": "realtime_conversation"},
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)
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pairs_processed += 1
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except Exception as pair_error:
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pairs_failed += 1
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logger.error(
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f"Failed to add memory for pair: {pair_error}"
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)
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i = j
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else:
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i += 1
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logger.info(
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f"Added {pairs_processed} memory pairs (failed={pairs_failed}) "
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f"for user={data.user_id}, bot={data.bot_id}"
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)
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return AddMemoryResponse(
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success=pairs_failed == 0,
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pairs_processed=pairs_processed,
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pairs_failed=pairs_failed,
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"Failed to add memory from conversation: {e}")
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raise HTTPException(
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status_code=500,
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detail="Failed to add memory from conversation"
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)
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@router.get("/memory", response_model=MemoryListResponse)
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async def get_memories(
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bot_id: str = Query(..., description="Bot ID (对应 agent_id)"),
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