[add] Added an interface for refreshing RAG storage image data
This commit is contained in:
lanceyq
@@ -422,26 +422,18 @@ async def get_chunk_summary_tag(
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current_user: User = Depends(get_current_user),
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):
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"""
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获取chunk总结、提取的标签和人物形象
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读取RAG摘要、标签和人物形象（纯读库，不触发生成）。
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返回格式：
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{
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"summary": "chunk内容的总结",
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"tags": [
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{"tag": "标签1", "frequency": 5},
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{"tag": "标签2", "frequency": 3},
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...
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],
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"personas": [
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"产品设计师",
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"旅行爱好者",
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"摄影发烧友",
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...
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]
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"summary": "用户摘要",
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"tags": [{"tag": "标签1", "frequency": 5}, ...],
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"personas": ["产品设计师", ...],
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"generated": true/false // false表示尚未生产，请调用 /generate_rag_profile
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}
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"""
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api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk摘要、标签和人物形象")
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api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG摘要/标签/人物形象")
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data = await memory_dashboard_service.get_chunk_summary_and_tags(
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end_user_id=end_user_id,
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limit=limit,
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@@ -449,9 +441,8 @@ async def get_chunk_summary_tag(
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db=db,
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current_user=current_user
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)
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api_logger.info(f"成功获取chunk摘要、{len(data.get('tags', []))} 个标签和 {len(data.get('personas', []))} 个人物形象")
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return success(data=data, msg="chunk摘要、标签和人物形象获取成功")
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return success(data=data, msg="获取成功")
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@router.get("/chunk_insight", response_model=ApiResponse)
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@@ -462,24 +453,53 @@ async def get_chunk_insight(
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current_user: User = Depends(get_current_user),
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):
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"""
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获取chunk的洞察内容
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读取RAG洞察报告（纯读库，不触发生成）。
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返回格式：
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{
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"insight": "对chunk内容的深度洞察分析"
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"insight": "总体概述",
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"behavior_pattern": "行为模式",
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"key_findings": "关键发现",
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"growth_trajectory": "成长轨迹",
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"generated": true/false // false表示尚未生产，请调用 /generate_rag_profile
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}
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"""
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api_logger.info(f"用户 {current_user.username} 请求获取宿主 {end_user_id} 的chunk洞察")
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api_logger.info(f"用户 {current_user.username} 读取宿主 {end_user_id} 的RAG洞察")
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data = await memory_dashboard_service.get_chunk_insight(
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end_user_id=end_user_id,
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limit=limit,
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db=db,
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current_user=current_user
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)
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api_logger.info("成功获取chunk洞察")
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return success(data=data, msg="chunk洞察获取成功")
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return success(data=data, msg="获取成功")
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@router.post("/generate_rag_profile", response_model=ApiResponse)
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async def generate_rag_profile(
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end_user_id: str = Query(..., description="宿主ID"),
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limit: int = Query(15, description="参与生成的chunk数量上限"),
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max_tags: int = Query(10, description="最大标签数量"),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user),
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):
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"""
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生产接口：为RAG存储模式的宿主全量重新生成完整画像并持久化到end_user表。
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每次请求都会重新生成，覆盖已有数据。
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"""
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api_logger.info(f"用户 {current_user.username} 触发RAG画像生产: end_user_id={end_user_id}")
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data = await memory_dashboard_service.generate_rag_profile(
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end_user_id=end_user_id,
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limit=limit,
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max_tags=max_tags,
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db=db,
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current_user=current_user,
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)
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api_logger.info(f"RAG画像生产完成: {data}")
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return success(data=data, msg="RAG画像生产完成")
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@router.get("/dashboard_data", response_model=ApiResponse)
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@@ -646,67 +646,26 @@ async def get_chunk_summary_and_tags(
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current_user: User
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) -> dict:
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"""
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获取chunk的总结、标签和人物形象
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优先返回end_user表中的缓存，若无缓存则实时生成并写库
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纯读库：从end_user表返回RAG摘要、标签和人物形象缓存。
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无数据时返回空结构，不触发LLM生成。
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"""
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import json
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from app.repositories.end_user_repository import EndUserRepository
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business_logger.info(f"获取chunk摘要、标签和人物形象: end_user_id={end_user_id}, limit={limit}, 操作者: {current_user.username}")
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business_logger.info(f"读取chunk摘要/标签/人物形象缓存: end_user_id={end_user_id}")
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try:
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(uuid.UUID(end_user_id))
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(uuid.UUID(end_user_id))
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# 读缓存：user_summary / rag_tags / rag_personas 均有值时直接返回
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if (
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end_user
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and end_user.user_summary
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and end_user.rag_tags
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and end_user.rag_personas
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):
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business_logger.info(f"命中缓存，直接返回end_user {end_user_id} 的摘要/标签/人物形象")
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return {
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"summary": end_user.user_summary,
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"tags": json.loads(end_user.rag_tags),
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"personas": json.loads(end_user.rag_personas),
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}
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if not end_user:
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return {"summary": "", "tags": [], "personas": [], "generated": False}
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# 无缓存：实时生成
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rag_content = get_rag_content(end_user_id, limit, db, current_user)
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chunks = rag_content.get("contents", [])
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if not chunks:
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business_logger.warning(f"未找到chunk内容: end_user_id={end_user_id}")
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return {"summary": "暂无内容", "tags": [], "personas": []}
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from app.core.rag_utils import generate_chunk_summary, extract_chunk_tags, extract_chunk_persona
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import asyncio
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summary, tags_with_freq, personas = await asyncio.gather(
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generate_chunk_summary(chunks, max_chunks=limit, end_user_id=end_user_id),
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extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit, end_user_id=end_user_id),
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extract_chunk_persona(chunks, max_personas=5, max_chunks=limit, end_user_id=end_user_id),
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)
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tags = [{"tag": tag, "frequency": freq} for tag, freq in tags_with_freq]
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# 写库缓存
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if end_user:
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repo.update_rag_summary_tags(
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end_user_id=end_user.id,
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user_summary=summary,
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rag_tags=json.dumps(tags, ensure_ascii=False),
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rag_personas=json.dumps(personas, ensure_ascii=False),
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)
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result = {"summary": summary, "tags": tags, "personas": personas}
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business_logger.info(f"成功获取chunk摘要、{len(tags)} 个标签和 {len(personas)} 个人物形象")
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return result
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except Exception as e:
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business_logger.error(f"获取chunk摘要、标签和人物形象失败: end_user_id={end_user_id} - {str(e)}")
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raise
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return {
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"summary": end_user.user_summary or "",
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"tags": json.loads(end_user.rag_tags) if end_user.rag_tags else [],
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"personas": json.loads(end_user.rag_personas) if end_user.rag_personas else [],
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"generated": bool(end_user.user_summary),
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}
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async def get_chunk_insight(
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@@ -716,55 +675,98 @@ async def get_chunk_insight(
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current_user: User
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) -> dict:
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"""
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获取chunk的洞察分析
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优先返回end_user表中的缓存，若无缓存则实时生成并写库
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纯读库：从end_user表返回RAG洞察缓存。
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无数据时返回空结构，不触发LLM生成。
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"""
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from app.repositories.end_user_repository import EndUserRepository
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business_logger.info(f"获取chunk洞察: end_user_id={end_user_id}, limit={limit}, 操作者: {current_user.username}")
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business_logger.info(f"读取chunk洞察缓存: end_user_id={end_user_id}")
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try:
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(uuid.UUID(end_user_id))
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(uuid.UUID(end_user_id))
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# 读缓存
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if end_user and end_user.memory_insight:
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business_logger.info(f"命中缓存，直接返回end_user {end_user_id} 的洞察")
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return {"insight": end_user.memory_insight}
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if not end_user:
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return {"insight": "", "behavior_pattern": "", "key_findings": "", "growth_trajectory": "", "generated": False}
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# 无缓存：实时生成
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rag_content = get_rag_content(end_user_id, limit, db, current_user)
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chunks = rag_content.get("contents", [])
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return {
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"insight": end_user.memory_insight or "",
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"behavior_pattern": end_user.behavior_pattern or "",
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"key_findings": end_user.key_findings or "",
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"growth_trajectory": end_user.growth_trajectory or "",
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"generated": bool(end_user.memory_insight),
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}
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if not chunks:
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business_logger.warning(f"未找到chunk内容: end_user_id={end_user_id}")
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return {"insight": "暂无足够数据生成洞察报告"}
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from app.core.rag_utils import generate_chunk_insight_sections
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async def generate_rag_profile(
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end_user_id: str,
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limit: int,
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max_tags: int,
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db: Session,
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current_user: User,
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) -> dict:
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"""
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生产接口：为RAG存储模式的end_user全量重新生成并持久化完整画像数据。
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每次调用都会重新生成，覆盖已有数据。
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sections = await generate_chunk_insight_sections(chunks, max_chunks=limit, end_user_id=end_user_id)
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insight = sections.get("memory_insight") or sections.get("_raw", "")
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生成内容：
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- user_summary / rag_tags / rag_personas
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- memory_insight / behavior_pattern / key_findings / growth_trajectory
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"""
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import json
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import asyncio
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from app.repositories.end_user_repository import EndUserRepository
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from app.core.rag_utils import (
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generate_chunk_summary,
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extract_chunk_tags,
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extract_chunk_persona,
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generate_chunk_insight_sections,
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)
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# 写库缓存（四维度全部入库）
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if end_user:
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from app.repositories.end_user_repository import EndUserRepository as _Repo
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_repo = _Repo(db)
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_repo.update_memory_insight(
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end_user_id=end_user.id,
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memory_insight=insight,
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behavior_pattern=sections.get("behavior_pattern", ""),
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key_findings=sections.get("key_findings", ""),
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growth_trajectory=sections.get("growth_trajectory", ""),
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)
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# 同时标记 storage_type 为 rag
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db.query(end_user.__class__).filter(
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end_user.__class__.id == end_user.id
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).update({"storage_type": "rag"}, synchronize_session=False)
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db.commit()
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business_logger.info(f"开始生产RAG画像: end_user_id={end_user_id}, 操作者: {current_user.username}")
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business_logger.info("成功获取chunk洞察")
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return {"insight": insight}
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repo = EndUserRepository(db)
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end_user = repo.get_by_id(uuid.UUID(end_user_id))
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except Exception as e:
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business_logger.error(f"获取chunk洞察失败: end_user_id={end_user_id} - {str(e)}")
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raise
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if not end_user:
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raise ValueError(f"end_user {end_user_id} 不存在")
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rag_content = get_rag_content(end_user_id, limit, db, current_user)
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chunks = rag_content.get("contents", [])
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if not chunks:
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business_logger.warning(f"未找到chunk内容，无法生产RAG画像: end_user_id={end_user_id}")
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raise ValueError("暂无chunk内容，无法生成画像")
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summary, tags_with_freq, personas, insight_sections = await asyncio.gather(
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generate_chunk_summary(chunks, max_chunks=limit, end_user_id=end_user_id),
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extract_chunk_tags(chunks, max_tags=max_tags, max_chunks=limit, end_user_id=end_user_id),
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extract_chunk_persona(chunks, max_personas=5, max_chunks=limit, end_user_id=end_user_id),
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generate_chunk_insight_sections(chunks, max_chunks=limit, end_user_id=end_user_id),
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)
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tags = [{"tag": tag, "frequency": freq} for tag, freq in tags_with_freq]
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repo.update_rag_summary_tags(
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end_user_id=end_user.id,
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user_summary=summary,
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rag_tags=json.dumps(tags, ensure_ascii=False),
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rag_personas=json.dumps(personas, ensure_ascii=False),
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)
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repo.update_memory_insight(
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end_user_id=end_user.id,
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memory_insight=insight_sections.get("memory_insight", ""),
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behavior_pattern=insight_sections.get("behavior_pattern", ""),
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key_findings=insight_sections.get("key_findings", ""),
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growth_trajectory=insight_sections.get("growth_trajectory", ""),
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)
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business_logger.info(f"RAG画像生产完成: end_user_id={end_user_id}, tags={len(tags)}, personas={len(personas)}")
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return {
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"end_user_id": end_user_id,
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"summary_length": len(summary),
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"tags_count": len(tags),
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"personas_count": len(personas),
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"insight_generated": bool(insight_sections.get("memory_insight")),
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}
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Block a user