Merge branch 'master' into prod
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b33acf9da0
@ -83,6 +83,21 @@ Output: {{"facts" : ["Mike Smith helped with bug fix", "Contact: Mike Smith (col
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Input: Mike is coming to the meeting tomorrow.
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Output: {{"facts" : ["Mike Smith is coming to the meeting tomorrow", "Contact: Mike Smith (colleague, also referred as Mike) - DEFAULT when user says 'Mike'"]}}
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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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@ -93,6 +108,11 @@ 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 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 Contact/Relationship Tracking**:
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- ALWAYS use the "Contact: [name] (relationship/context)" format when recording people
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- When you see a short name that matches a known full name, record as "Contact: [Full Name] (relationship, also referred as [Short Name])"
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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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