283c64a358
- Remove deprecated main.py entry point from memory module - Reorganize imports across controllers and services for consistency - Update emotion controller to pass db session instead of config_id to services - Enhance memory agent controller with db session parameter for status_type and user_profile endpoints - Refactor memory agent service to accept db parameter in classify_message_type method - Improve configuration handling in celery_app by removing automatic database reload - Update all memory-related services to use centralized config management - Standardize import ordering and remove unused imports across 50+ files - Add pilot_run_service for new pilot execution workflow - Refactor extraction engine, reflection engine, and search services for better modularity - Update LLM utilities and embedder configuration for improved flexibility - Enhance type classifier and verification tools with better error handling - Improve memory evaluation modules (LOCOMO, LongMemEval, MemSciQA) with consistent patterns
666 lines
25 KiB
Python
666 lines
25 KiB
Python
import asyncio
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import json
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import os
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import time
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import uuid
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from datetime import datetime, timezone
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from math import ceil
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from typing import Any, Dict, List, Optional
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import redis
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import requests
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# Import a unified Celery instance
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from app.celery_app import celery_app
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from app.core.config import settings
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from app.core.rag.graphrag.utils import get_llm_cache, set_llm_cache
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from app.core.rag.llm.chat_model import Base
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from app.core.rag.llm.cv_model import QWenCV
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from app.core.rag.models.chunk import DocumentChunk
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from app.core.rag.prompts.generator import question_proposal
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from app.core.rag.vdb.elasticsearch.elasticsearch_vector import (
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ElasticSearchVectorFactory,
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)
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from app.db import get_db
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from app.models.document_model import Document
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from app.models.knowledge_model import Knowledge
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from app.services.memory_agent_service import MemoryAgentService
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@celery_app.task(name="tasks.process_item")
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def process_item(item: dict):
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"""
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A simulated long-running task that processes an item.
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In a real-world scenario, this could be anything:
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- Sending an email
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- Generating a report
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- Performing a complex calculation
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- Calling a third-party API
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"""
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print(f"Processing item: {item['name']}")
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# Simulate work for 5 seconds
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time.sleep(5)
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result = f"Item '{item['name']}' processed successfully at a price of ${item['price']}."
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print(result)
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return result
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@celery_app.task(name="app.core.rag.tasks.parse_document")
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def parse_document(file_path: str, document_id: uuid.UUID):
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"""
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Document parsing, vectorization, and storage
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"""
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db = next(get_db()) # Manually call the generator
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db_document = None
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db_knowledge = None
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progress_msg = f"{datetime.now().strftime('%H:%M:%S')} Task has been received.\n"
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try:
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db_document = db.query(Document).filter(Document.id == document_id).first()
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db_knowledge = db.query(Knowledge).filter(Knowledge.id == db_document.kb_id).first()
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# 1. Document parsing & segmentation
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Start to parse.\n"
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start_time = time.time()
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db_document.progress = 0.0
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db_document.progress_msg = progress_msg
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db_document.process_begin_at = datetime.now(tz=timezone.utc)
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db_document.process_duration = 0.0
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db_document.run = 1
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db.commit()
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db.refresh(db_document)
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def progress_callback(prog=None, msg=None):
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nonlocal progress_msg # Declare the use of an external progress_msg variable
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} parse progress: {prog} msg: {msg}.\n"
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# Prepare to configure chat_mdl、vision_model information
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chat_model = Base(
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key=db_knowledge.llm.api_keys[0].api_key,
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model_name=db_knowledge.llm.api_keys[0].model_name,
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base_url=db_knowledge.llm.api_keys[0].api_base
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)
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vision_model = QWenCV(
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key=db_knowledge.image2text.api_keys[0].api_key,
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model_name=db_knowledge.image2text.api_keys[0].model_name,
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lang="Chinese",
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base_url=db_knowledge.image2text.api_keys[0].api_base
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)
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from app.core.rag.app.naive import chunk
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res = chunk(filename=file_path,
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from_page=0,
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to_page=100000,
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callback=progress_callback,
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vision_model=vision_model,
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parser_config=db_document.parser_config,
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is_root=False)
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Finish parsing.\n"
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db_document.progress = 0.8
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db_document.progress_msg = progress_msg
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db.commit()
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db.refresh(db_document)
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# 2. Document vectorization and storage
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total_chunks = len(res)
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Generate {total_chunks} chunks.\n"
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batch_size = 100
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total_batches = ceil(total_chunks / batch_size)
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progress_per_batch = 0.2 / total_batches # Progress of each batch
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vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
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# 2.1 Delete document vector index
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vector_service.delete_by_metadata_field(key="document_id", value=str(document_id))
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# 2.2 Vectorize and import batch documents
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for batch_start in range(0, total_chunks, batch_size):
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batch_end = min(batch_start + batch_size, total_chunks) # prevent out-of-bounds
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batch = res[batch_start: batch_end] # Retrieve the current batch
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chunks = []
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# Process the current batch
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for idx_in_batch, item in enumerate(batch):
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global_idx = batch_start + idx_in_batch # Calculate global index
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metadata = {
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"doc_id": uuid.uuid4().hex,
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"file_id": str(db_document.file_id),
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"file_name": db_document.file_name,
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"file_created_at": int(db_document.created_at.timestamp() * 1000),
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"document_id": str(db_document.id),
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"knowledge_id": str(db_document.kb_id),
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"sort_id": global_idx,
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"status": 1,
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}
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if db_document.parser_config.get("auto_questions", 0):
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topn = db_document.parser_config["auto_questions"]
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cached = get_llm_cache(chat_model.model_name, item["content_with_weight"], "question", {"topn": topn})
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if not cached:
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cached = question_proposal(chat_model, item["content_with_weight"], topn)
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set_llm_cache(chat_model.model_name, item["content_with_weight"], cached, "question", {"topn": topn})
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chunks.append(DocumentChunk(page_content=f"question: {cached} answer: {item['content_with_weight']}", metadata=metadata))
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else:
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chunks.append(DocumentChunk(page_content=item["content_with_weight"], metadata=metadata))
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# Bulk segmented vector import
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vector_service.add_chunks(chunks)
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# Update progress
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db_document.progress += progress_per_batch
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Embedding progress ({db_document.progress}).\n"
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db_document.progress_msg = progress_msg
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db_document.process_duration = time.time() - start_time
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db_document.run = 0
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db.commit()
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db.refresh(db_document)
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# Vectorization and data entry completed
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Indexing done.\n"
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db_document.chunk_num = total_chunks
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db_document.progress = 1.0
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db_document.process_duration = time.time() - start_time
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Task done ({db_document.process_duration}s).\n"
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db_document.progress_msg = progress_msg
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db_document.run = 0
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db.commit()
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result = f"parse document '{db_document.file_name}' processed successfully."
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return result
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except Exception as e:
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if 'db_document' in locals():
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db_document.progress_msg += f"Failed to vectorize and import the parsed document:{str(e)}\n"
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db_document.run = 0
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db.commit()
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result = f"parse document '{db_document.file_name}' failed."
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return result
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finally:
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db.close()
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@celery_app.task(name="app.core.memory.agent.read_message", bind=True)
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def read_message_task(self, group_id: str, message: str, history: List[Dict[str, Any]], search_switch: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]:
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"""Celery task to process a read message via MemoryAgentService.
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Args:
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group_id: Group ID for the memory agent (also used as end_user_id)
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message: User message to process
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history: Conversation history
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search_switch: Search switch parameter
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config_id: Optional configuration ID
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Returns:
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Dict containing the result and metadata
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Raises:
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Exception on failure
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"""
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start_time = time.time()
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# Resolve config_id if None
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actual_config_id = config_id
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if actual_config_id is None:
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try:
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from app.services.memory_agent_service import get_end_user_connected_config
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db = next(get_db())
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try:
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connected_config = get_end_user_connected_config(group_id, db)
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actual_config_id = connected_config.get("memory_config_id")
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finally:
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db.close()
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except Exception as e:
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# Log but continue - will fail later with proper error
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pass
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async def _run() -> str:
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db = next(get_db())
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try:
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service = MemoryAgentService()
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return await service.read_memory(group_id, message, history, search_switch, actual_config_id, db, storage_type, user_rag_memory_id)
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finally:
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db.close()
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try:
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# 使用 nest_asyncio 来避免事件循环冲突
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try:
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import nest_asyncio
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nest_asyncio.apply()
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except ImportError:
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pass
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# 尝试获取现有事件循环，如果不存在则创建新的
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try:
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loop = asyncio.get_event_loop()
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if loop.is_closed():
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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result = loop.run_until_complete(_run())
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elapsed_time = time.time() - start_time
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return {
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"status": "SUCCESS",
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"result": result,
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"group_id": group_id,
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"config_id": config_id,
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"elapsed_time": elapsed_time,
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"task_id": self.request.id
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}
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except BaseException as e:
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elapsed_time = time.time() - start_time
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# Handle ExceptionGroup from TaskGroup
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if hasattr(e, 'exceptions'):
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error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions]
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detailed_error = "; ".join(error_messages)
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else:
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detailed_error = str(e)
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return {
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"status": "FAILURE",
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"error": detailed_error,
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"group_id": group_id,
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"config_id": config_id,
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"elapsed_time": elapsed_time,
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"task_id": self.request.id
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}
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@celery_app.task(name="app.core.memory.agent.write_message", bind=True)
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def write_message_task(self, group_id: str, message: str, config_id: str,storage_type:str,user_rag_memory_id:str) -> Dict[str, Any]:
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"""Celery task to process a write message via MemoryAgentService.
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Args:
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group_id: Group ID for the memory agent (also used as end_user_id)
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message: Message to write
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config_id: Optional configuration ID
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Returns:
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Dict containing the result and metadata
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Raises:
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Exception on failure
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"""
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start_time = time.time()
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# Resolve config_id if None
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actual_config_id = config_id
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if actual_config_id is None:
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try:
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from app.services.memory_agent_service import get_end_user_connected_config
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db = next(get_db())
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try:
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connected_config = get_end_user_connected_config(group_id, db)
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actual_config_id = connected_config.get("memory_config_id")
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finally:
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db.close()
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except Exception as e:
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# Log but continue - will fail later with proper error
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pass
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async def _run() -> str:
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db = next(get_db())
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try:
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service = MemoryAgentService()
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return await service.write_memory(group_id, message, actual_config_id, db, storage_type, user_rag_memory_id)
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finally:
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db.close()
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try:
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# 使用 nest_asyncio 来避免事件循环冲突
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try:
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import nest_asyncio
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nest_asyncio.apply()
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except ImportError:
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pass
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# 尝试获取现有事件循环，如果不存在则创建新的
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try:
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loop = asyncio.get_event_loop()
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if loop.is_closed():
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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except RuntimeError:
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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result = loop.run_until_complete(_run())
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elapsed_time = time.time() - start_time
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return {
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"status": "SUCCESS",
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"result": result,
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"group_id": group_id,
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"config_id": config_id,
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"elapsed_time": elapsed_time,
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"task_id": self.request.id
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}
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except BaseException as e:
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elapsed_time = time.time() - start_time
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# Handle ExceptionGroup from TaskGroup
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if hasattr(e, 'exceptions'):
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error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions]
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detailed_error = "; ".join(error_messages)
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else:
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detailed_error = str(e)
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return {
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"status": "FAILURE",
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"error": detailed_error,
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"group_id": group_id,
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"config_id": config_id,
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"elapsed_time": elapsed_time,
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"task_id": self.request.id
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}
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def reflection_engine() -> None:
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"""Empty function placeholder for timed background reflection.
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Intentionally left blank; replace with real reflection logic later.
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"""
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import asyncio
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from app.core.memory.utils.self_reflexion_utils.self_reflexion import self_reflexion
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host_id = uuid.UUID("2f6ff1eb-50c7-4765-8e89-e4566be19122")
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asyncio.run(self_reflexion(host_id))
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@celery_app.task(name="app.core.memory.agent.reflection.timer")
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def reflection_timer_task() -> None:
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"""Periodic Celery task that invokes reflection_engine.
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Raises an exception on failure.
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"""
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reflection_engine()
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@celery_app.task(name="app.core.memory.agent.health.check_read_service")
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def check_read_service_task() -> Dict[str, str]:
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"""Call read_service and write latest status to Redis.
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Returns status data dict that gets written to Redis.
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"""
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client = redis.Redis(
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host=settings.REDIS_HOST,
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port=settings.REDIS_PORT,
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db=settings.REDIS_DB,
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password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None
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)
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try:
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api_url = f"http://{settings.SERVER_IP}:8000/api/memory/read_service"
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payload = {
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"user_id": "健康检查",
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"apply_id": "健康检查",
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"group_id": "健康检查",
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"message": "你好",
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"history": [],
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"search_switch": "2",
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}
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resp = requests.post(api_url, json=payload, timeout=15)
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ok = resp.status_code == 200
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status = "Success" if ok else "Fail"
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msg = "接口请求成功" if ok else f"接口请求失败: {resp.status_code}"
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error = "" if ok else resp.text
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code = 0 if ok else 500
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except Exception as e:
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status = "Fail"
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msg = "接口请求失败"
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error = str(e)
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code = 500
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data = {
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"status": status,
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"msg": msg,
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"error": error,
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"code": str(code),
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"time": str(int(time.time())),
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}
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client.hset("memsci:health:read_service", mapping=data)
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client.expire("memsci:health:read_service", int(settings.HEALTH_CHECK_SECONDS))
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return data
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@celery_app.task(name="app.controllers.memory_storage_controller.search_all")
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def write_total_memory_task(workspace_id: str) -> Dict[str, Any]:
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"""定时任务：查询工作空间下所有宿主的记忆总量并写入数据库
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Args:
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workspace_id: 工作空间ID
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Returns:
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包含任务执行结果的字典
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"""
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start_time = time.time()
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async def _run() -> Dict[str, Any]:
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from app.models.app_model import App
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from app.models.end_user_model import EndUser
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from app.repositories.memory_increment_repository import write_memory_increment
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from app.services.memory_storage_service import search_all
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db = next(get_db())
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try:
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workspace_uuid = uuid.UUID(workspace_id)
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# 1. 查询当前workspace下的所有app
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apps = db.query(App).filter(App.workspace_id == workspace_uuid).all()
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if not apps:
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# 如果没有app，总量为0
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memory_increment = write_memory_increment(
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db=db,
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workspace_id=workspace_uuid,
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total_num=0
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)
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return {
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"status": "SUCCESS",
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"workspace_id": workspace_id,
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"total_num": 0,
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"end_user_count": 0,
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"memory_increment_id": str(memory_increment.id),
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"created_at": memory_increment.created_at.isoformat(),
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}
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# 2. 查询所有app下的end_user_id（去重）
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app_ids = [app.id for app in apps]
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end_users = db.query(EndUser.id).filter(
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EndUser.app_id.in_(app_ids)
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).distinct().all()
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|
||
# 3. 遍历所有end_user，查询每个宿主的记忆总量并累加
|
||
total_num = 0
|
||
end_user_details = []
|
||
|
||
for (end_user_id,) in end_users:
|
||
try:
|
||
# 调用 search_all 接口查询该宿主的总量
|
||
result = await search_all(str(end_user_id))
|
||
user_total = result.get("total", 0)
|
||
total_num += user_total
|
||
end_user_details.append({
|
||
"end_user_id": str(end_user_id),
|
||
"total": user_total
|
||
})
|
||
except Exception as e:
|
||
# 记录单个用户查询失败，但继续处理其他用户
|
||
end_user_details.append({
|
||
"end_user_id": str(end_user_id),
|
||
"total": 0,
|
||
"error": str(e)
|
||
})
|
||
|
||
# 4. 写入数据库
|
||
memory_increment = write_memory_increment(
|
||
db=db,
|
||
workspace_id=workspace_uuid,
|
||
total_num=total_num
|
||
)
|
||
|
||
return {
|
||
"status": "SUCCESS",
|
||
"workspace_id": workspace_id,
|
||
"total_num": total_num,
|
||
"end_user_count": len(end_users),
|
||
"end_user_details": end_user_details,
|
||
"memory_increment_id": str(memory_increment.id),
|
||
"created_at": memory_increment.created_at.isoformat(),
|
||
}
|
||
finally:
|
||
db.close()
|
||
|
||
try:
|
||
result = asyncio.run(_run())
|
||
elapsed_time = time.time() - start_time
|
||
result["elapsed_time"] = elapsed_time
|
||
return result
|
||
except Exception as e:
|
||
elapsed_time = time.time() - start_time
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": str(e),
|
||
"workspace_id": workspace_id,
|
||
"elapsed_time": elapsed_time,
|
||
}
|
||
|
||
|
||
@celery_app.task(name="app.tasks.workspace_reflection_task", bind=True)
|
||
def workspace_reflection_task(self) -> Dict[str, Any]:
|
||
"""定时任务：每30秒运行工作空间反思功能
|
||
|
||
Returns:
|
||
包含任务执行结果的字典
|
||
"""
|
||
start_time = time.time()
|
||
|
||
async def _run() -> Dict[str, Any]:
|
||
from app.core.logging_config import get_api_logger
|
||
from app.models.workspace_model import Workspace
|
||
from app.services.memory_reflection_service import (
|
||
MemoryReflectionService,
|
||
WorkspaceAppService,
|
||
)
|
||
|
||
api_logger = get_api_logger()
|
||
db = next(get_db())
|
||
|
||
try:
|
||
# 获取所有工作空间
|
||
workspaces = db.query(Workspace).all()
|
||
|
||
if not workspaces:
|
||
return {
|
||
"status": "SUCCESS",
|
||
"message": "没有找到工作空间",
|
||
"workspace_count": 0,
|
||
"reflection_results": []
|
||
}
|
||
|
||
all_reflection_results = []
|
||
|
||
# 遍历每个工作空间
|
||
for workspace in workspaces:
|
||
workspace_id = workspace.id
|
||
api_logger.info(f"开始处理工作空间反思，workspace_id: {workspace_id}")
|
||
|
||
try:
|
||
reflection_service = MemoryReflectionService(db)
|
||
|
||
# 使用服务类处理复杂查询逻辑
|
||
service = WorkspaceAppService(db)
|
||
result = service.get_workspace_apps_detailed(str(workspace_id))
|
||
|
||
workspace_reflection_results = []
|
||
|
||
for data in result['apps_detailed_info']:
|
||
if data['data_configs'] == []:
|
||
continue
|
||
|
||
releases = data['releases']
|
||
data_configs = data['data_configs']
|
||
end_users = data['end_users']
|
||
|
||
for base, config, user in zip(releases, data_configs, end_users):
|
||
if int(base['config']) == int(config['config_id']) and base['app_id'] == user['app_id']:
|
||
# 调用反思服务
|
||
api_logger.info(f"为用户 {user['id']} 启动反思，config_id: {config['config_id']}")
|
||
|
||
reflection_result = await reflection_service.start_reflection_from_data(
|
||
config_data=config,
|
||
end_user_id=user['id']
|
||
)
|
||
|
||
workspace_reflection_results.append({
|
||
"app_id": base['app_id'],
|
||
"config_id": config['config_id'],
|
||
"end_user_id": user['id'],
|
||
"reflection_result": reflection_result
|
||
})
|
||
|
||
all_reflection_results.append({
|
||
"workspace_id": str(workspace_id),
|
||
"reflection_count": len(workspace_reflection_results),
|
||
"reflection_results": workspace_reflection_results
|
||
})
|
||
|
||
api_logger.info(
|
||
f"工作空间 {workspace_id} 反思处理完成，处理了 {len(workspace_reflection_results)} 个任务")
|
||
|
||
except Exception as e:
|
||
api_logger.error(f"处理工作空间 {workspace_id} 反思失败: {str(e)}")
|
||
all_reflection_results.append({
|
||
"workspace_id": str(workspace_id),
|
||
"error": str(e),
|
||
"reflection_count": 0,
|
||
"reflection_results": []
|
||
})
|
||
|
||
total_reflections = sum(r.get("reflection_count", 0) for r in all_reflection_results)
|
||
|
||
return {
|
||
"status": "SUCCESS",
|
||
"message": f"成功处理 {len(workspaces)} 个工作空间，总共 {total_reflections} 个反思任务",
|
||
"workspace_count": len(workspaces),
|
||
"total_reflections": total_reflections,
|
||
"workspace_results": all_reflection_results
|
||
}
|
||
|
||
except Exception as e:
|
||
api_logger.error(f"工作空间反思任务执行失败: {str(e)}")
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": str(e),
|
||
"workspace_count": 0,
|
||
"reflection_results": []
|
||
}
|
||
finally:
|
||
db.close()
|
||
|
||
try:
|
||
# 使用 nest_asyncio 来避免事件循环冲突
|
||
try:
|
||
import nest_asyncio
|
||
nest_asyncio.apply()
|
||
except ImportError:
|
||
pass
|
||
|
||
# 尝试获取现有事件循环，如果不存在则创建新的
|
||
try:
|
||
loop = asyncio.get_event_loop()
|
||
if loop.is_closed():
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
except RuntimeError:
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
|
||
result = loop.run_until_complete(_run())
|
||
elapsed_time = time.time() - start_time
|
||
result["elapsed_time"] = elapsed_time
|
||
result["task_id"] = self.request.id
|
||
|
||
return result
|
||
except Exception as e:
|
||
elapsed_time = time.time() - start_time
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": str(e),
|
||
"elapsed_time": elapsed_time,
|
||
"task_id": self.request.id
|
||
} |