602 lines
23 KiB
Python
602 lines
23 KiB
Python
"""
|
||
Memory Storage Service
|
||
|
||
Handles business logic for memory storage operations.
|
||
"""
|
||
|
||
import asyncio
|
||
import json
|
||
import os
|
||
import time
|
||
import uuid
|
||
from datetime import datetime
|
||
from typing import Any, AsyncGenerator, Dict, List, Optional
|
||
|
||
from app.core.logging_config import get_config_logger, get_logger
|
||
from app.core.memory.analytics.hot_memory_tags import get_hot_memory_tags
|
||
from app.core.memory.analytics.recent_activity_stats import get_recent_activity_stats
|
||
from app.models.user_model import User
|
||
from app.repositories.data_config_repository import DataConfigRepository
|
||
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
|
||
from app.schemas.memory_config_schema import ConfigurationError
|
||
from app.schemas.memory_storage_schema import (
|
||
ConfigKey,
|
||
ConfigParamsCreate,
|
||
ConfigParamsDelete,
|
||
ConfigPilotRun,
|
||
ConfigUpdate,
|
||
ConfigUpdateExtracted,
|
||
ConfigUpdateForget,
|
||
)
|
||
from app.services.memory_config_service import MemoryConfigService
|
||
from app.utils.sse_utils import format_sse_message
|
||
from dotenv import load_dotenv
|
||
from sqlalchemy.orm import Session
|
||
|
||
logger = get_logger(__name__)
|
||
config_logger = get_config_logger()
|
||
|
||
# Load environment variables for Neo4j connector
|
||
load_dotenv()
|
||
_neo4j_connector = Neo4jConnector()
|
||
|
||
|
||
class MemoryStorageService:
|
||
"""Service for memory storage operations"""
|
||
|
||
def __init__(self):
|
||
logger.info("MemoryStorageService initialized")
|
||
|
||
async def get_storage_info(self) -> dict:
|
||
"""
|
||
Example wrapper method - retrieves storage information
|
||
|
||
Args:
|
||
|
||
Returns:
|
||
Storage information dictionary
|
||
"""
|
||
logger.info("Getting storage info ")
|
||
|
||
# Empty wrapper - implement your logic here
|
||
result = {
|
||
"status": "active",
|
||
"message": "This is an example wrapper"
|
||
}
|
||
|
||
return result
|
||
|
||
|
||
class DataConfigService: # 数据配置服务类（PostgreSQL）
|
||
"""Service layer for config params CRUD.
|
||
|
||
使用 SQLAlchemy ORM 进行数据库操作。
|
||
"""
|
||
|
||
def __init__(self, db: Session) -> None:
|
||
"""初始化服务
|
||
|
||
Args:
|
||
db: SQLAlchemy 数据库会话
|
||
"""
|
||
self.db = db
|
||
|
||
@staticmethod
|
||
def _convert_timestamps_to_format(data_list: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
||
"""将 created_at 和 updated_at 字段从 datetime 对象转换为 YYYYMMDDHHmmss 格式"""
|
||
|
||
for item in data_list:
|
||
for field in ['created_at', 'updated_at']:
|
||
if field in item and item[field] is not None:
|
||
value = item[field]
|
||
dt = None
|
||
|
||
# 如果是 datetime 对象，直接使用
|
||
if isinstance(value, datetime):
|
||
dt = value
|
||
# 如果是字符串，先解析
|
||
elif isinstance(value, str):
|
||
try:
|
||
dt = datetime.fromisoformat(value.replace('Z', '+00:00'))
|
||
except Exception:
|
||
pass # 保持原值
|
||
|
||
# 转换为 YYYYMMDDHHmmss 格式
|
||
if dt:
|
||
item[field] = dt.strftime('%Y%m%d%H%M%S')
|
||
|
||
return data_list
|
||
|
||
# --- Create ---
|
||
def create(self, params: ConfigParamsCreate) -> Dict[str, Any]: # 创建配置参数（仅名称与描述）
|
||
# 如果workspace_id存在且模型字段未全部指定，则自动获取
|
||
if params.workspace_id and not all([params.llm_id, params.embedding_id, params.rerank_id]):
|
||
configs = self._get_workspace_configs(params.workspace_id)
|
||
if configs is None:
|
||
raise ValueError(f"工作空间不存在: workspace_id={params.workspace_id}")
|
||
|
||
# 只在未指定时填充（允许手动覆盖）
|
||
if not params.llm_id:
|
||
params.llm_id = configs.get('llm')
|
||
if not params.embedding_id:
|
||
params.embedding_id = configs.get('embedding')
|
||
if not params.rerank_id:
|
||
params.rerank_id = configs.get('rerank')
|
||
|
||
config = DataConfigRepository.create(self.db, params)
|
||
self.db.commit()
|
||
return {"affected": 1, "config_id": config.config_id}
|
||
|
||
def _get_workspace_configs(self, workspace_id) -> Optional[Dict[str, Any]]:
|
||
"""获取工作空间模型配置（内部方法，便于测试）"""
|
||
from app.db import SessionLocal
|
||
from app.repositories.workspace_repository import get_workspace_models_configs
|
||
|
||
db_session = SessionLocal()
|
||
try:
|
||
return get_workspace_models_configs(db_session, workspace_id)
|
||
finally:
|
||
db_session.close()
|
||
|
||
# --- Delete ---
|
||
def delete(self, key: ConfigParamsDelete) -> Dict[str, Any]: # 删除配置参数（按配置ID）
|
||
success = DataConfigRepository.delete(self.db, key.config_id)
|
||
if not success:
|
||
raise ValueError("未找到配置")
|
||
return {"affected": 1}
|
||
|
||
# --- Update ---
|
||
def update(self, update: ConfigUpdate) -> Dict[str, Any]: # 部分更新配置参数
|
||
config = DataConfigRepository.update(self.db, update)
|
||
if not config:
|
||
raise ValueError("未找到配置")
|
||
return {"affected": 1}
|
||
|
||
def update_extracted(self, update: ConfigUpdateExtracted) -> Dict[str, Any]: # 更新记忆萃取引擎配置参数
|
||
config = DataConfigRepository.update_extracted(self.db, update)
|
||
if not config:
|
||
raise ValueError("未找到配置")
|
||
return {"affected": 1}
|
||
|
||
# --- Forget config params ---
|
||
def update_forget(self, update: ConfigUpdateForget) -> Dict[str, Any]: # 保存遗忘引擎的配置
|
||
config = DataConfigRepository.update_forget(self.db, update)
|
||
if not config:
|
||
raise ValueError("未找到配置")
|
||
return {"affected": 1}
|
||
|
||
# --- Read ---
|
||
def get_extracted(self, key: ConfigKey) -> Dict[str, Any]: # 获取萃取配置参数
|
||
result = DataConfigRepository.get_extracted_config(self.db, key.config_id)
|
||
if not result:
|
||
raise ValueError("未找到配置")
|
||
return result
|
||
|
||
def get_forget(self, key: ConfigKey) -> Dict[str, Any]: # 获取遗忘配置参数
|
||
result = DataConfigRepository.get_forget_config(self.db, key.config_id)
|
||
if not result:
|
||
raise ValueError("未找到配置")
|
||
return result
|
||
|
||
# --- Read All ---
|
||
def get_all(self, workspace_id = None) -> List[Dict[str, Any]]: # 获取所有配置参数
|
||
configs = DataConfigRepository.get_all(self.db, workspace_id)
|
||
|
||
# 将 ORM 对象转换为字典列表
|
||
data_list = []
|
||
for config in configs:
|
||
config_dict = {
|
||
"config_id": config.config_id,
|
||
"config_name": config.config_name,
|
||
"config_desc": config.config_desc,
|
||
"workspace_id": str(config.workspace_id) if config.workspace_id else None,
|
||
"group_id": config.group_id,
|
||
"user_id": config.user_id,
|
||
"apply_id": config.apply_id,
|
||
"llm_id": config.llm_id,
|
||
"embedding_id": config.embedding_id,
|
||
"rerank_id": config.rerank_id,
|
||
"llm": config.llm,
|
||
"enable_llm_dedup_blockwise": config.enable_llm_dedup_blockwise,
|
||
"enable_llm_disambiguation": config.enable_llm_disambiguation,
|
||
"deep_retrieval": config.deep_retrieval,
|
||
"t_type_strict": config.t_type_strict,
|
||
"t_name_strict": config.t_name_strict,
|
||
"t_overall": config.t_overall,
|
||
"state": config.state,
|
||
"chunker_strategy": config.chunker_strategy,
|
||
"pruning_enabled": config.pruning_enabled,
|
||
"pruning_scene": config.pruning_scene,
|
||
"pruning_threshold": config.pruning_threshold,
|
||
"enable_self_reflexion": config.enable_self_reflexion,
|
||
"iteration_period": config.iteration_period,
|
||
"reflexion_range": config.reflexion_range,
|
||
"baseline": config.baseline,
|
||
"statement_granularity": config.statement_granularity,
|
||
"include_dialogue_context": config.include_dialogue_context,
|
||
"max_context": config.max_context,
|
||
"lambda_time": config.lambda_time,
|
||
"lambda_mem": config.lambda_mem,
|
||
"offset": config.offset,
|
||
"created_at": config.created_at,
|
||
"updated_at": config.updated_at,
|
||
}
|
||
data_list.append(config_dict)
|
||
|
||
# 将 created_at 和 updated_at 转换为 YYYYMMDDHHmmss 格式
|
||
return self._convert_timestamps_to_format(data_list)
|
||
|
||
|
||
async def pilot_run_stream(self, payload: ConfigPilotRun) -> AsyncGenerator[str, None]:
|
||
"""
|
||
流式执行试运行，产生 SSE 格式的进度事件
|
||
|
||
Args:
|
||
payload: 试运行配置和对话文本
|
||
|
||
Yields:
|
||
SSE 格式的字符串，包含以下事件类型：
|
||
- 各种阶段名称: 进度更新 (如 starting, knowledge_extraction_complete 等)
|
||
- result: 最终结果
|
||
- error: 错误信息
|
||
- done: 完成标记
|
||
|
||
Raises:
|
||
ValueError: 当配置无效或参数缺失时
|
||
RuntimeError: 当管线执行失败时
|
||
"""
|
||
project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||
|
||
try:
|
||
# 发出初始进度事件
|
||
yield format_sse_message("starting", {
|
||
"message": "开始试运行...",
|
||
"time": int(time.time() * 1000)
|
||
})
|
||
|
||
# 步骤 1: 配置加载和验证（数据库优先）
|
||
payload_cid = str(getattr(payload, "config_id", "") or "").strip()
|
||
cid: Optional[str] = payload_cid if payload_cid else None
|
||
|
||
if not cid:
|
||
raise ValueError("未提供 payload.config_id，禁止启动试运行")
|
||
|
||
# 验证 dialogue_text 必须提供
|
||
dialogue_text = payload.dialogue_text.strip() if payload.dialogue_text else ""
|
||
logger.info(f"[PILOT_RUN_STREAM] Received dialogue_text length: {len(dialogue_text)}, preview: {dialogue_text[:100]}")
|
||
if not dialogue_text:
|
||
raise ValueError("试运行模式必须提供 dialogue_text 参数")
|
||
|
||
# Load configuration from database only using centralized manager
|
||
try:
|
||
config_service = MemoryConfigService(self.db)
|
||
memory_config = config_service.load_memory_config(
|
||
config_id=int(cid),
|
||
service_name="MemoryStorageService.pilot_run_stream"
|
||
)
|
||
logger.info(f"Configuration loaded successfully: {memory_config.config_name}")
|
||
except ConfigurationError as e:
|
||
raise RuntimeError(f"Configuration loading failed: {e}")
|
||
|
||
# 步骤 2: 创建进度回调函数捕获管线进度
|
||
# 使用队列在回调和生成器之间传递进度事件
|
||
progress_queue: asyncio.Queue = asyncio.Queue()
|
||
|
||
async def progress_callback(stage: str, message: str, data: Optional[Dict[str, Any]] = None) -> None:
|
||
"""
|
||
进度回调函数，将进度事件放入队列
|
||
|
||
Args:
|
||
stage: 阶段标识
|
||
message: 进度消息
|
||
data: 可选的结果数据（用于传递节点执行结果）
|
||
"""
|
||
await progress_queue.put((stage, message, data))
|
||
|
||
# 步骤 3: 在后台任务中执行管线
|
||
async def run_pipeline():
|
||
"""在后台执行管线并捕获异常"""
|
||
try:
|
||
from app.services.pilot_run_service import run_pilot_extraction
|
||
|
||
logger.info(f"[PILOT_RUN_STREAM] Calling run_pilot_extraction with dialogue_text length: {len(dialogue_text)}")
|
||
await run_pilot_extraction(
|
||
memory_config=memory_config,
|
||
dialogue_text=dialogue_text,
|
||
db=self.db,
|
||
progress_callback=progress_callback,
|
||
)
|
||
logger.info("[PILOT_RUN_STREAM] pipeline_main completed")
|
||
|
||
# 标记管线完成
|
||
await progress_queue.put(("__PIPELINE_COMPLETE__", "", None))
|
||
except Exception as e:
|
||
# 将异常放入队列
|
||
await progress_queue.put(("__PIPELINE_ERROR__", str(e), None))
|
||
|
||
# 启动后台任务
|
||
pipeline_task = asyncio.create_task(run_pipeline())
|
||
|
||
# 步骤 4: 从队列中读取进度事件并发出
|
||
while True:
|
||
try:
|
||
# 等待进度事件，设置超时以检测客户端断开
|
||
stage, message, data = await asyncio.wait_for(
|
||
progress_queue.get(),
|
||
timeout=0.5
|
||
)
|
||
|
||
# 检查特殊标记
|
||
if stage == "__PIPELINE_COMPLETE__":
|
||
break
|
||
elif stage == "__PIPELINE_ERROR__":
|
||
raise RuntimeError(message)
|
||
|
||
# 构建进度事件数据
|
||
progress_data = {
|
||
"message": message,
|
||
"time": int(time.time() * 1000)
|
||
}
|
||
|
||
# 如果有结果数据，添加到事件中
|
||
if data:
|
||
progress_data["data"] = data
|
||
|
||
# 发出进度事件，使用 stage 作为事件类型
|
||
yield format_sse_message(stage, progress_data)
|
||
|
||
except TimeoutError:
|
||
# 超时，继续等待（这允许检测客户端断开）
|
||
continue
|
||
|
||
# 等待管线任务完成
|
||
await pipeline_task
|
||
|
||
# 步骤 5: 读取提取结果
|
||
from app.core.config import settings
|
||
result_path = settings.get_memory_output_path("extracted_result.json")
|
||
if not os.path.isfile(result_path):
|
||
raise FileNotFoundError(f"试运行完成，但未找到提取结果文件: {result_path}")
|
||
|
||
with open(result_path, "r", encoding="utf-8") as rf:
|
||
extracted_result = json.load(rf)
|
||
|
||
# 步骤 6: 发出结果事件
|
||
result_data = {
|
||
"config_id": cid,
|
||
"time_log": os.path.join(project_root, "logs", "time.log"),
|
||
"extracted_result": extracted_result,
|
||
}
|
||
yield format_sse_message("result", result_data)
|
||
|
||
# 步骤 7: 发出完成事件
|
||
yield format_sse_message("done", {
|
||
"message": "试运行完成",
|
||
"time": int(time.time() * 1000)
|
||
})
|
||
|
||
except asyncio.CancelledError:
|
||
# 客户端断开连接
|
||
logger.info("[PILOT_RUN_STREAM] Client disconnected during streaming")
|
||
raise
|
||
except Exception as e:
|
||
# 发出错误事件
|
||
logger.error(f"[PILOT_RUN_STREAM] Error during streaming: {e}", exc_info=True)
|
||
yield format_sse_message("error", {
|
||
"code": 5000,
|
||
"message": "试运行失败",
|
||
"error": str(e),
|
||
"time": int(time.time() * 1000)
|
||
})
|
||
|
||
|
||
# -------------------- Neo4j Search & Analytics (fused from data_search_service.py) --------------------
|
||
# Ensure env for connector (e.g., NEO4J_PASSWORD)
|
||
load_dotenv()
|
||
_neo4j_connector = Neo4jConnector()
|
||
|
||
|
||
async def search_dialogue(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_DIALOGUE,
|
||
group_id=end_user_id,
|
||
)
|
||
data = {"search_for": "dialogue", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
async def search_chunk(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_CHUNK,
|
||
group_id=end_user_id,
|
||
)
|
||
data = {"search_for": "chunk", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
async def search_statement(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_STATEMENT,
|
||
group_id=end_user_id,
|
||
)
|
||
data = {"search_for": "statement", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
async def search_entity(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_ENTITY,
|
||
group_id=end_user_id,
|
||
)
|
||
data = {"search_for": "entity", "num": result[0]["num"]}
|
||
return data
|
||
|
||
|
||
async def search_all(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_ALL,
|
||
group_id=end_user_id,
|
||
)
|
||
|
||
# 检查结果是否为空或长度不足
|
||
if not result or len(result) < 4:
|
||
data = {
|
||
"total": 0,
|
||
"counts": {
|
||
"dialogue": 0,
|
||
"chunk": 0,
|
||
"statement": 0,
|
||
"entity": 0,
|
||
},
|
||
}
|
||
return data
|
||
|
||
data = {
|
||
"total": result[-1]["Count"],
|
||
"counts": {
|
||
"dialogue": result[0]["Count"],
|
||
"chunk": result[1]["Count"],
|
||
"statement": result[2]["Count"],
|
||
"entity": result[3]["Count"],
|
||
},
|
||
}
|
||
return data
|
||
|
||
|
||
async def kb_type_distribution(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
"""统一知识库类型分布接口。
|
||
|
||
聚合 dialogue/chunk/statement/entity 四类计数，返回统一的分布结构，便于前端一次性消费。
|
||
"""
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_ALL,
|
||
group_id=end_user_id,
|
||
)
|
||
|
||
# 检查结果是否为空或长度不足
|
||
if not result or len(result) < 4:
|
||
data = {
|
||
"total": 0,
|
||
"distribution": [
|
||
{"type": "dialogue", "count": 0},
|
||
{"type": "chunk", "count": 0},
|
||
{"type": "statement", "count": 0},
|
||
{"type": "entity", "count": 0},
|
||
]
|
||
}
|
||
return data
|
||
|
||
total = result[-1]["Count"]
|
||
distribution = [
|
||
{"type": "dialogue", "count": result[0]["Count"]},
|
||
{"type": "chunk", "count": result[1]["Count"]},
|
||
{"type": "statement", "count": result[2]["Count"]},
|
||
{"type": "entity", "count": result[3]["Count"]},
|
||
]
|
||
|
||
data = {"total": total, "distribution": distribution}
|
||
return data
|
||
|
||
|
||
async def search_detials(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_DETIALS,
|
||
group_id=end_user_id,
|
||
)
|
||
return result
|
||
|
||
|
||
async def search_edges(end_user_id: Optional[str] = None) -> List[Dict[str, Any]]:
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_EDGES,
|
||
group_id=end_user_id,
|
||
)
|
||
return result
|
||
|
||
|
||
async def search_entity_graph(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||
"""搜索所有实体之间的关系网络（group 维度）。"""
|
||
result = await _neo4j_connector.execute_query(
|
||
DataConfigRepository.SEARCH_FOR_ENTITY_GRAPH,
|
||
group_id=end_user_id,
|
||
)
|
||
# 对source_node 和 target_node 的 fact_summary进行截取，只截取前三条的内容（需要提取前三条“来源”）
|
||
for item in result:
|
||
source_fact = item["sourceNode"]["fact_summary"]
|
||
target_fact = item["targetNode"]["fact_summary"]
|
||
# 截取前三条“来源”
|
||
item["sourceNode"]["fact_summary"] = source_fact.split("\n")[:4] if source_fact else []
|
||
item["targetNode"]["fact_summary"] = target_fact.split("\n")[:4] if target_fact else []
|
||
# 与现有返回风格保持一致，携带搜索类型、数量与详情
|
||
data = {
|
||
"search_for": "entity_graph",
|
||
"num": len(result),
|
||
"detials": result,
|
||
}
|
||
return data
|
||
|
||
|
||
async def analytics_hot_memory_tags(
|
||
db: Session,
|
||
current_user: User,
|
||
limit: int = 10
|
||
) -> List[Dict[str, Any]]:
|
||
"""
|
||
获取热门记忆标签，按数量排序并返回前N个
|
||
"""
|
||
workspace_id = current_user.current_workspace_id
|
||
# 获取更多标签供LLM筛选（获取limit*4个标签）
|
||
raw_limit = limit * 4
|
||
from app.services.memory_dashboard_service import get_workspace_end_users
|
||
end_users = get_workspace_end_users(db, workspace_id, current_user)
|
||
|
||
tags = []
|
||
for end_user in end_users:
|
||
tag = await get_hot_memory_tags(str(end_user.id), limit=raw_limit)
|
||
if tag:
|
||
# 将每个用户的标签列表展平到总列表中
|
||
tags.extend(tag)
|
||
|
||
# 按频率降序排序（虽然数据库已经排序，但为了确保正确性再次排序）
|
||
sorted_tags = sorted(tags, key=lambda x: x[1], reverse=True)
|
||
|
||
# 只返回前limit个
|
||
top_tags = sorted_tags[:limit]
|
||
|
||
return [{"name": t, "frequency": f} for t, f in top_tags]
|
||
|
||
|
||
async def analytics_recent_activity_stats() -> Dict[str, Any]:
|
||
stats, _msg = get_recent_activity_stats()
|
||
total = (
|
||
stats.get("chunk_count", 0)
|
||
+ stats.get("statements_count", 0)
|
||
+ stats.get("triplet_entities_count", 0)
|
||
+ stats.get("triplet_relations_count", 0)
|
||
+ stats.get("temporal_count", 0)
|
||
)
|
||
# 精简：仅提供“最新一次活动多久前”
|
||
latest_relative = None
|
||
try:
|
||
info = stats.get("log_path", "")
|
||
idx = info.rfind("最新：")
|
||
if idx != -1:
|
||
latest_path = info[idx + 3 :].strip()
|
||
if latest_path and os.path.exists(latest_path):
|
||
import time
|
||
diff = max(0.0, time.time() - os.path.getmtime(latest_path))
|
||
m = int(diff // 60)
|
||
if m < 1:
|
||
latest_relative = "刚刚"
|
||
elif m < 60:
|
||
latest_relative = f"{m}分钟前"
|
||
else:
|
||
h = int(m // 60)
|
||
latest_relative = f"{h}小时前" if h < 24 else f"{int(h // 24)}天前"
|
||
except Exception:
|
||
pass
|
||
|
||
data = {"total": total, "stats": stats, "latest_relative": latest_relative}
|
||
return data
|
||
|