2231 lines
101 KiB
Python
2231 lines
101 KiB
Python
import asyncio
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from concurrent.futures import ThreadPoolExecutor
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import json
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import os
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import re
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import time
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import uuid
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from uuid import UUID
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from datetime import datetime, timezone
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from math import ceil
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from pathlib import Path
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import shutil
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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.crawler.web_crawler import WebCrawler
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from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb
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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.integrations.feishu.client import FeishuAPIClient
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from app.core.rag.integrations.feishu.models import FileInfo
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from app.core.rag.integrations.yuque.client import YuqueAPIClient
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from app.core.rag.integrations.yuque.models import YuqueDocInfo
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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.llm.embedding_model import OpenAIEmbed
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from app.core.rag.llm.sequence2txt_model import QWenSeq2txt
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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, get_db_context
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from app.models.document_model import Document
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from app.models.file_model import File
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from app.models.knowledge_model import Knowledge
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from app.schemas import file_schema, document_schema
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from app.services.memory_agent_service import MemoryAgentService
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from app.utils.config_utils import resolve_config_id
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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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# Force re-importing Trio in child processes (to avoid inheriting the state of the parent process)
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import trio
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import importlib
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importlib.reload(trio)
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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、embedding_model、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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embedding_model = OpenAIEmbed(
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key=db_knowledge.embedding.api_keys[0].api_key,
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model_name=db_knowledge.embedding.api_keys[0].model_name,
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base_url=db_knowledge.embedding.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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if re.search(r"\.(da|wave|wav|mp3|aac|flac|ogg|aiff|au|midi|wma|realaudio|vqf|oggvorbis|ape?)$", file_path,
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re.IGNORECASE):
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vision_model = QWenSeq2txt(
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key=os.getenv("QWEN3_OMNI_API_KEY", ""),
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model_name=os.getenv("QWEN3_OMNI_MODEL_NAME", "qwen3-omni-flash"),
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lang="Chinese",
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base_url=os.getenv("QWEN3_OMNI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
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)
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elif re.search(r"\.(png|jpeg|jpg|gif|bmp|svg|mp4|mov|avi|flv|mpeg|mpg|webm|wmv|3gp|3gpp|mkv?)$", file_path,
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re.IGNORECASE):
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vision_model = QWenCV(
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key=os.getenv("QWEN3_OMNI_API_KEY", ""),
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model_name=os.getenv("QWEN3_OMNI_MODEL_NAME", "qwen3-omni-flash"),
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lang="Chinese",
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base_url=os.getenv("QWEN3_OMNI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
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)
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else:
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print(file_path)
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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",
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{"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",
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{"topn": topn})
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chunks.append(
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DocumentChunk(page_content=f"question: {cached} answer: {item['content_with_weight']}",
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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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# using graphrag
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if db_knowledge.parser_config and db_knowledge.parser_config.get("graphrag", {}).get("use_graphrag", False):
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graphrag_conf = db_knowledge.parser_config.get("graphrag", {})
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with_resolution = graphrag_conf.get("resolution", False)
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with_community = graphrag_conf.get("community", False)
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def callback(*args, msg=None, **kwargs):
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nonlocal progress_msg
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message = msg or (args[0] if args else "No message")
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} run graphrag msg: {message}.\n"
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Start to run graphrag.\n"
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start_time = time.time()
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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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task = {
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"id": str(db_document.id),
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"workspace_id": str(db_knowledge.workspace_id),
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"kb_id": str(db_knowledge.id),
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"parser_config": db_knowledge.parser_config,
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}
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# init_graphrag
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vts, _ = embedding_model.encode(["ok"])
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vector_size = len(vts[0])
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init_graphrag(task, vector_size)
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async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service,
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chat_model, embedding_model, callback, with_resolution: bool = True,
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with_community: bool = True, ) -> dict:
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await trio.sleep(5) # Delay for 10 seconds
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nonlocal progress_msg # Declare the use of an external progress_msg variable
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result = await run_graphrag_for_kb(
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row=row,
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document_ids=document_ids,
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language=language,
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parser_config=parser_config,
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vector_service=vector_service,
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chat_model=chat_model,
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embedding_model=embedding_model,
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callback=callback,
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with_resolution=with_resolution,
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with_community=with_community,
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)
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task result for task {task}:\n{result}\n"
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return result
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try:
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trio.run(
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lambda: _run(
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row=task,
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document_ids=[str(db_document.id)],
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language="Chinese",
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parser_config=db_knowledge.parser_config,
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vector_service=vector_service,
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chat_model=chat_model,
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embedding_model=embedding_model,
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callback=callback,
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with_resolution=with_resolution,
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with_community=with_community,
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)
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)
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except Exception as e:
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task failed for task {task}:\n{str(e)}\n"
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progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Knowledge Graph done ({time.time() - start_time}s)"
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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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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.rag.tasks.build_graphrag_for_kb")
|
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def build_graphrag_for_kb(kb_id: uuid.UUID):
|
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"""
|
||
build knowledge graph
|
||
"""
|
||
# Force re-importing Trio in child processes (to avoid inheriting the state of the parent process)
|
||
import trio
|
||
import importlib
|
||
importlib.reload(trio)
|
||
db = next(get_db()) # Manually call the generator
|
||
db_documents = None
|
||
db_knowledge = None
|
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try:
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db_documents = db.query(Document).filter(Document.kb_id == kb_id).all()
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db_knowledge = db.query(Knowledge).filter(Knowledge.id == kb_id).first()
|
||
# 1. Prepare to configure chat_mdl、embedding_model、vision_model information
|
||
chat_model = Base(
|
||
key=db_knowledge.llm.api_keys[0].api_key,
|
||
model_name=db_knowledge.llm.api_keys[0].model_name,
|
||
base_url=db_knowledge.llm.api_keys[0].api_base
|
||
)
|
||
embedding_model = OpenAIEmbed(
|
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key=db_knowledge.embedding.api_keys[0].api_key,
|
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model_name=db_knowledge.embedding.api_keys[0].model_name,
|
||
base_url=db_knowledge.embedding.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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# 2. get all document_ids from knowledge base
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||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
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total, items = vector_service.search_by_segment(document_id=None, query=None, pagesize=9999, page=1, asc=True)
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document_ids = [str(item.id) for item in db_documents]
|
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|
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# 2. using graphrag
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if db_knowledge.parser_config and db_knowledge.parser_config.get("graphrag", {}).get("use_graphrag", False):
|
||
graphrag_conf = db_knowledge.parser_config.get("graphrag", {})
|
||
with_resolution = graphrag_conf.get("resolution", False)
|
||
with_community = graphrag_conf.get("community", False)
|
||
|
||
def callback(*args, msg=None, **kwargs):
|
||
message = msg or (args[0] if args else "No message")
|
||
print(f"{datetime.now().strftime('%H:%M:%S')} run graphrag msg: {message}.\n")
|
||
|
||
start_time = time.time()
|
||
task = {
|
||
"id": str(db_knowledge.id),
|
||
"workspace_id": str(db_knowledge.workspace_id),
|
||
"kb_id": str(db_knowledge.id),
|
||
"parser_config": db_knowledge.parser_config,
|
||
}
|
||
|
||
# init_graphrag
|
||
vts, _ = embedding_model.encode(["ok"])
|
||
vector_size = len(vts[0])
|
||
init_graphrag(task, vector_size)
|
||
|
||
async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service,
|
||
chat_model, embedding_model, callback, with_resolution: bool = True,
|
||
with_community: bool = True, ) -> dict:
|
||
result = await run_graphrag_for_kb(
|
||
row=row,
|
||
document_ids=document_ids,
|
||
language=language,
|
||
parser_config=parser_config,
|
||
vector_service=vector_service,
|
||
chat_model=chat_model,
|
||
embedding_model=embedding_model,
|
||
callback=callback,
|
||
with_resolution=with_resolution,
|
||
with_community=with_community,
|
||
)
|
||
print(f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task result for task {task}:\n{result}\n")
|
||
return result
|
||
|
||
def sync_task():
|
||
trio.run(
|
||
lambda: _run(
|
||
row=task,
|
||
document_ids=document_ids,
|
||
language="Chinese",
|
||
parser_config=db_knowledge.parser_config,
|
||
vector_service=vector_service,
|
||
chat_model=chat_model,
|
||
embedding_model=embedding_model,
|
||
callback=callback,
|
||
with_resolution=with_resolution,
|
||
with_community=with_community,
|
||
)
|
||
)
|
||
try:
|
||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||
future = executor.submit(sync_task)
|
||
future.result() # Blocks until the task completes
|
||
except Exception as e:
|
||
print(f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task failed for task {task}:\n{str(e)}\n")
|
||
finally:
|
||
if db:
|
||
db.close()
|
||
print(f"{datetime.now().strftime('%H:%M:%S')} Knowledge Graph done ({time.time() - start_time}s)")
|
||
|
||
result = f"build knowledge graph '{db_knowledge.name}' processed successfully."
|
||
return result
|
||
except Exception as e:
|
||
if 'db_knowledge' in locals():
|
||
print(f"Failed to build knowledge grap:{str(e)}\n")
|
||
result = f"build knowledge grap '{db_knowledge.name}' failed."
|
||
return result
|
||
finally:
|
||
if db:
|
||
db.close()
|
||
|
||
|
||
@celery_app.task(name="app.core.rag.tasks.sync_knowledge_for_kb")
|
||
def sync_knowledge_for_kb(kb_id: uuid.UUID):
|
||
"""
|
||
sync knowledge document and Document parsing, vectorization, and storage
|
||
"""
|
||
db = next(get_db()) # Manually call the generator
|
||
db_knowledge = None
|
||
try:
|
||
db_knowledge = db.query(Knowledge).filter(Knowledge.id == kb_id).first()
|
||
# 1. get vector_service
|
||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||
|
||
# 2. sync data
|
||
match db_knowledge.type:
|
||
case "Web": # Crawl webpages in batches through a web crawler
|
||
entry_url = db_knowledge.parser_config.get("entry_url", "")
|
||
max_pages = db_knowledge.parser_config.get("max_pages", 20)
|
||
delay_seconds = db_knowledge.parser_config.get("delay_seconds", 1.0)
|
||
timeout_seconds = db_knowledge.parser_config.get("timeout_seconds", 10)
|
||
user_agent = db_knowledge.parser_config.get("user_agent", "KnowledgeBaseCrawler/1.0")
|
||
# Create crawler
|
||
crawler = WebCrawler(
|
||
entry_url=entry_url,
|
||
max_pages=max_pages,
|
||
delay_seconds=delay_seconds,
|
||
timeout_seconds=timeout_seconds,
|
||
user_agent=user_agent
|
||
)
|
||
try:
|
||
# 初始化存储已爬取 URLs 的集合
|
||
file_urls = set()
|
||
# crawl entry_url by yield
|
||
for crawled_document in crawler.crawl():
|
||
file_urls.add(crawled_document.url)
|
||
db_file = db.query(File).filter(File.kb_id == db_knowledge.id,
|
||
File.file_url == crawled_document.url).first()
|
||
if db_file:
|
||
if db_file.file_size == crawled_document.content_length: # same
|
||
continue
|
||
else: # --update
|
||
if crawled_document.content_length:
|
||
# 1. update file
|
||
db_file.file_name = f"{crawled_document.title}.txt"
|
||
db_file.file_ext = ".txt"
|
||
db_file.file_size = crawled_document.content_length
|
||
db.commit()
|
||
db.refresh(db_file)
|
||
# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
|
||
save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id),
|
||
str(db_knowledge.id))
|
||
Path(save_dir).mkdir(parents=True,
|
||
exist_ok=True) # Ensure that the directory exists
|
||
save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
|
||
# update file
|
||
if os.path.exists(save_path):
|
||
os.remove(save_path) # Delete a single file
|
||
content_bytes = crawled_document.content.encode('utf-8')
|
||
with open(save_path, "wb") as f:
|
||
f.write(content_bytes)
|
||
# 2. update a document
|
||
db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
|
||
Document.file_id == db_file.id).first()
|
||
if db_document:
|
||
db_document.file_name = db_file.file_name
|
||
db_document.file_ext = db_file.file_ext
|
||
db_document.file_size = db_file.file_size
|
||
db_document.updated_at = datetime.now()
|
||
db.commit()
|
||
db.refresh(db_document)
|
||
# 3. Document parsing, vectorization, and storage
|
||
parse_document(file_path=save_path, document_id=db_document.id)
|
||
else: # --add
|
||
if crawled_document.content_length:
|
||
# 1. upload file
|
||
upload_file = file_schema.FileCreate(
|
||
kb_id=db_knowledge.id,
|
||
created_by=db_knowledge.created_by,
|
||
parent_id=db_knowledge.id,
|
||
file_name=f"{crawled_document.title}.txt",
|
||
file_ext=".txt",
|
||
file_size=crawled_document.content_length,
|
||
file_url=crawled_document.url,
|
||
)
|
||
db_file = File(**upload_file.model_dump())
|
||
db.add(db_file)
|
||
db.commit()
|
||
# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
|
||
save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id), str(db_knowledge.id))
|
||
Path(save_dir).mkdir(parents=True, exist_ok=True) # Ensure that the directory exists
|
||
save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
|
||
# Save file
|
||
content_bytes = crawled_document.content.encode('utf-8')
|
||
with open(save_path, "wb") as f:
|
||
f.write(content_bytes)
|
||
# 2. Create a document
|
||
create_document_data = document_schema.DocumentCreate(
|
||
kb_id=db_knowledge.id,
|
||
created_by=db_knowledge.created_by,
|
||
file_id=db_file.id,
|
||
file_name=db_file.file_name,
|
||
file_ext=db_file.file_ext,
|
||
file_size=db_file.file_size,
|
||
file_meta={},
|
||
parser_id="naive",
|
||
parser_config={
|
||
"layout_recognize": "DeepDOC",
|
||
"chunk_token_num": 130,
|
||
"delimiter": "\n",
|
||
"auto_keywords": 0,
|
||
"auto_questions": 0,
|
||
"html4excel": "false"
|
||
}
|
||
)
|
||
db_document = Document(**create_document_data.model_dump())
|
||
db.add(db_document)
|
||
db.commit()
|
||
# 3. Document parsing, vectorization, and storage
|
||
parse_document(file_path=save_path, document_id=db_document.id)
|
||
db_files = db.query(File).filter(File.kb_id == db_knowledge.id,
|
||
File.file_url.notin_(file_urls)).all()
|
||
if db_files: # --delete
|
||
for db_file in db_files:
|
||
db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
|
||
Document.file_id == db_file.id).first()
|
||
if db_document:
|
||
# 1. Delete vector index
|
||
vector_service.delete_by_metadata_field(key="document_id", value=str(db_document.id))
|
||
# 2. Delete document
|
||
db.delete(db_document)
|
||
# 3. Delete file
|
||
file_path = Path(
|
||
settings.FILE_PATH,
|
||
str(db_file.kb_id),
|
||
str(db_file.parent_id),
|
||
f"{db_file.id}{db_file.file_ext}"
|
||
)
|
||
if file_path.exists():
|
||
file_path.unlink() # Delete a single file
|
||
db.delete(db_file)
|
||
# commit transaction
|
||
db.commit()
|
||
|
||
except Exception as e:
|
||
print(f"\n\nError during crawl: {e}")
|
||
case "Third-party": # Integration of knowledge bases from three parties
|
||
yuque_user_id = db_knowledge.parser_config.get("yuque_user_id", "")
|
||
feishu_app_id = db_knowledge.parser_config.get("feishu_app_id", "")
|
||
if yuque_user_id: # Yuque Knowledge Base
|
||
yuque_token = db_knowledge.parser_config.get("yuque_token", "")
|
||
# Create yuqueAPIClient
|
||
api_client = YuqueAPIClient(
|
||
user_id=yuque_user_id,
|
||
token=yuque_token
|
||
)
|
||
try:
|
||
# 初始化存储获取语雀 URLs 的集合
|
||
file_urls = set()
|
||
|
||
# Get all files from all repos
|
||
async def async_get_files(api_client: YuqueAPIClient):
|
||
async with api_client as client:
|
||
print("\n=== Fetching repositories ===")
|
||
repos = await client.get_user_repos()
|
||
print(f"Found {len(repos)} repositories:")
|
||
all_files = []
|
||
for repo in repos:
|
||
# Get documents from repository
|
||
print(f"\n=== Fetching documents from '{repo.name}' ===")
|
||
docs = await client.get_repo_docs(repo.id)
|
||
all_files.extend(docs)
|
||
return all_files
|
||
|
||
files = asyncio.run(async_get_files(api_client))
|
||
for doc in files:
|
||
file_urls.add(doc.slug)
|
||
db_file = db.query(File).filter(File.kb_id == db_knowledge.id,
|
||
File.file_url == doc.slug).first()
|
||
if db_file:
|
||
if db_file.created_at == doc.updated_at: # same
|
||
continue
|
||
else: # --update
|
||
# 1. update file
|
||
# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
|
||
save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id),
|
||
str(db_knowledge.id))
|
||
Path(save_dir).mkdir(parents=True,
|
||
exist_ok=True) # Ensure that the directory exists
|
||
|
||
# download document from Feishu FileInfo
|
||
async def async_download_document(api_client: YuqueAPIClient, doc: YuqueDocInfo,
|
||
save_dir: str):
|
||
async with api_client as client:
|
||
file_path = await client.download_document(doc, save_dir)
|
||
return file_path
|
||
|
||
file_path = asyncio.run(async_download_document(api_client, doc, save_dir))
|
||
|
||
save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
|
||
# update file
|
||
if os.path.exists(save_path):
|
||
os.remove(save_path) # Delete a single file
|
||
shutil.copyfile(file_path, save_path)
|
||
# update db_file
|
||
file_name = os.path.basename(file_path)
|
||
_, file_extension = os.path.splitext(file_name)
|
||
file_size = os.path.getsize(file_path)
|
||
db_file.file_name = file_name
|
||
db_file.file_ext = file_extension.lower()
|
||
db_file.file_size = file_size
|
||
db_file.created_at = doc.updated_at
|
||
db.commit()
|
||
db.refresh(db_file)
|
||
# 2. update a document
|
||
db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
|
||
Document.file_id == db_file.id).first()
|
||
if db_document:
|
||
db_document.file_name = db_file.file_name
|
||
db_document.file_ext = db_file.file_ext
|
||
db_document.file_size = db_file.file_size
|
||
db_document.created_at = db_file.created_at
|
||
db_document.updated_at = datetime.now()
|
||
db.commit()
|
||
db.refresh(db_document)
|
||
# 3. Document parsing, vectorization, and storage
|
||
parse_document(file_path=save_path, document_id=db_document.id)
|
||
else: # --add
|
||
# 1. update file
|
||
# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
|
||
save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id),
|
||
str(db_knowledge.id))
|
||
Path(save_dir).mkdir(parents=True, exist_ok=True) # Ensure that the directory exists
|
||
|
||
# download document from Feishu FileInfo
|
||
async def async_download_document(api_client: YuqueAPIClient, doc: YuqueDocInfo,
|
||
save_dir: str):
|
||
async with api_client as client:
|
||
file_path = await client.download_document(doc, save_dir)
|
||
return file_path
|
||
|
||
file_path = asyncio.run(async_download_document(api_client, doc, save_dir))
|
||
# add db_file
|
||
file_name = os.path.basename(file_path)
|
||
_, file_extension = os.path.splitext(file_name)
|
||
file_size = os.path.getsize(file_path)
|
||
upload_file = file_schema.FileCreate(
|
||
kb_id=db_knowledge.id,
|
||
created_by=db_knowledge.created_by,
|
||
parent_id=db_knowledge.id,
|
||
file_name=file_name,
|
||
file_ext=file_extension.lower(),
|
||
file_size=file_size,
|
||
file_url=doc.slug,
|
||
created_at=doc.updated_at
|
||
)
|
||
db_file = File(**upload_file.model_dump())
|
||
db.add(db_file)
|
||
db.commit()
|
||
# Save file
|
||
save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
|
||
# update file
|
||
if os.path.exists(save_path):
|
||
os.remove(save_path) # Delete a single file
|
||
shutil.copyfile(file_path, save_path)
|
||
# 2. Create a document
|
||
create_document_data = document_schema.DocumentCreate(
|
||
kb_id=db_knowledge.id,
|
||
created_by=db_knowledge.created_by,
|
||
file_id=db_file.id,
|
||
file_name=db_file.file_name,
|
||
file_ext=db_file.file_ext,
|
||
file_size=db_file.file_size,
|
||
file_meta={},
|
||
parser_id="naive",
|
||
parser_config={
|
||
"layout_recognize": "DeepDOC",
|
||
"chunk_token_num": 130,
|
||
"delimiter": "\n",
|
||
"auto_keywords": 0,
|
||
"auto_questions": 0,
|
||
"html4excel": "false"
|
||
}
|
||
)
|
||
db_document = Document(**create_document_data.model_dump())
|
||
db.add(db_document)
|
||
db.commit()
|
||
# 3. Document parsing, vectorization, and storage
|
||
parse_document(file_path=save_path, document_id=db_document.id)
|
||
db_files = db.query(File).filter(File.kb_id == db_knowledge.id,
|
||
File.file_url.notin_(file_urls)).all()
|
||
if db_files: # --delete
|
||
for db_file in db_files:
|
||
db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
|
||
Document.file_id == db_file.id).first()
|
||
if db_document:
|
||
# 1. Delete vector index
|
||
vector_service.delete_by_metadata_field(key="document_id",
|
||
value=str(db_document.id))
|
||
# 2. Delete document
|
||
db.delete(db_document)
|
||
# 3. Delete file
|
||
file_path = Path(
|
||
settings.FILE_PATH,
|
||
str(db_file.kb_id),
|
||
str(db_file.parent_id),
|
||
f"{db_file.id}{db_file.file_ext}"
|
||
)
|
||
if file_path.exists():
|
||
file_path.unlink() # Delete a single file
|
||
db.delete(db_file)
|
||
# commit transaction
|
||
db.commit()
|
||
|
||
except Exception as e:
|
||
print(f"\n\nError during fetch feishu: {e}")
|
||
if feishu_app_id: # Feishu Knowledge Base
|
||
feishu_app_secret = db_knowledge.parser_config.get("feishu_app_secret", "")
|
||
feishu_folder_token = db_knowledge.parser_config.get("feishu_folder_token", "")
|
||
# Create feishuAPIClient
|
||
api_client = FeishuAPIClient(
|
||
app_id=feishu_app_id,
|
||
app_secret=feishu_app_secret
|
||
)
|
||
try:
|
||
# 初始化存储获取飞书 URLs 的集合
|
||
file_urls = set()
|
||
|
||
# Get all files from folder
|
||
async def async_get_files(api_client: FeishuAPIClient, feishu_folder_token: str):
|
||
async with api_client as client:
|
||
files = await client.list_all_folder_files(feishu_folder_token, recursive=True)
|
||
return files
|
||
|
||
files = asyncio.run(async_get_files(api_client, feishu_folder_token))
|
||
# Filter out folders, only sync documents
|
||
documents = [f for f in files if f.type in ["doc", "docx", "sheet", "bitable", "file"]]
|
||
for doc in documents:
|
||
file_urls.add(doc.url)
|
||
db_file = db.query(File).filter(File.kb_id == db_knowledge.id,
|
||
File.file_url == doc.url).first()
|
||
if db_file:
|
||
if db_file.created_at == doc.modified_time: # same
|
||
continue
|
||
else: # --update
|
||
# 1. update file
|
||
# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
|
||
save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id),
|
||
str(db_knowledge.id))
|
||
Path(save_dir).mkdir(parents=True,
|
||
exist_ok=True) # Ensure that the directory exists
|
||
|
||
# download document from Feishu FileInfo
|
||
async def async_download_document(api_client: FeishuAPIClient, doc: FileInfo,
|
||
save_dir: str):
|
||
async with api_client as client:
|
||
file_path = await client.download_document(document=doc, save_dir=save_dir)
|
||
return file_path
|
||
|
||
file_path = asyncio.run(async_download_document(api_client, doc, save_dir))
|
||
|
||
save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
|
||
# update file
|
||
if os.path.exists(save_path):
|
||
os.remove(save_path) # Delete a single file
|
||
shutil.copyfile(file_path, save_path)
|
||
# update db_file
|
||
file_name = os.path.basename(file_path)
|
||
_, file_extension = os.path.splitext(file_name)
|
||
file_size = os.path.getsize(file_path)
|
||
db_file.file_name = file_name
|
||
db_file.file_ext = file_extension.lower()
|
||
db_file.file_size = file_size
|
||
db_file.created_at = doc.modified_time
|
||
db.commit()
|
||
db.refresh(db_file)
|
||
# 2. update a document
|
||
db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
|
||
Document.file_id == db_file.id).first()
|
||
if db_document:
|
||
db_document.file_name = db_file.file_name
|
||
db_document.file_ext = db_file.file_ext
|
||
db_document.file_size = db_file.file_size
|
||
db_document.created_at = db_file.created_at
|
||
db_document.updated_at = datetime.now()
|
||
db.commit()
|
||
db.refresh(db_document)
|
||
# 3. Document parsing, vectorization, and storage
|
||
parse_document(file_path=save_path, document_id=db_document.id)
|
||
else: # --add
|
||
# 1. update file
|
||
# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
|
||
save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id),
|
||
str(db_knowledge.id))
|
||
Path(save_dir).mkdir(parents=True, exist_ok=True) # Ensure that the directory exists
|
||
|
||
# download document from Feishu FileInfo
|
||
async def async_download_document(api_client: FeishuAPIClient, doc: FileInfo,
|
||
save_dir: str):
|
||
async with api_client as client:
|
||
file_path = await client.download_document(document=doc, save_dir=save_dir)
|
||
return file_path
|
||
|
||
file_path = asyncio.run(async_download_document(api_client, doc, save_dir))
|
||
# add db_file
|
||
file_name = os.path.basename(file_path)
|
||
_, file_extension = os.path.splitext(file_name)
|
||
file_size = os.path.getsize(file_path)
|
||
upload_file = file_schema.FileCreate(
|
||
kb_id=db_knowledge.id,
|
||
created_by=db_knowledge.created_by,
|
||
parent_id=db_knowledge.id,
|
||
file_name=file_name,
|
||
file_ext=file_extension.lower(),
|
||
file_size=file_size,
|
||
file_url=doc.url,
|
||
created_at=doc.modified_time
|
||
)
|
||
db_file = File(**upload_file.model_dump())
|
||
db.add(db_file)
|
||
db.commit()
|
||
# Save file
|
||
save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
|
||
# update file
|
||
if os.path.exists(save_path):
|
||
os.remove(save_path) # Delete a single file
|
||
shutil.copyfile(file_path, save_path)
|
||
# 2. Create a document
|
||
create_document_data = document_schema.DocumentCreate(
|
||
kb_id=db_knowledge.id,
|
||
created_by=db_knowledge.created_by,
|
||
file_id=db_file.id,
|
||
file_name=db_file.file_name,
|
||
file_ext=db_file.file_ext,
|
||
file_size=db_file.file_size,
|
||
file_meta={},
|
||
parser_id="naive",
|
||
parser_config={
|
||
"layout_recognize": "DeepDOC",
|
||
"chunk_token_num": 130,
|
||
"delimiter": "\n",
|
||
"auto_keywords": 0,
|
||
"auto_questions": 0,
|
||
"html4excel": "false"
|
||
}
|
||
)
|
||
db_document = Document(**create_document_data.model_dump())
|
||
db.add(db_document)
|
||
db.commit()
|
||
# 3. Document parsing, vectorization, and storage
|
||
parse_document(file_path=save_path, document_id=db_document.id)
|
||
db_files = db.query(File).filter(File.kb_id == db_knowledge.id,
|
||
File.file_url.notin_(file_urls)).all()
|
||
if db_files: # --delete
|
||
for db_file in db_files:
|
||
db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
|
||
Document.file_id == db_file.id).first()
|
||
if db_document:
|
||
# 1. Delete vector index
|
||
vector_service.delete_by_metadata_field(key="document_id",
|
||
value=str(db_document.id))
|
||
# 2. Delete document
|
||
db.delete(db_document)
|
||
# 3. Delete file
|
||
file_path = Path(
|
||
settings.FILE_PATH,
|
||
str(db_file.kb_id),
|
||
str(db_file.parent_id),
|
||
f"{db_file.id}{db_file.file_ext}"
|
||
)
|
||
if file_path.exists():
|
||
file_path.unlink() # Delete a single file
|
||
db.delete(db_file)
|
||
# commit transaction
|
||
db.commit()
|
||
|
||
except Exception as e:
|
||
print(f"\n\nError during fetch feishu: {e}")
|
||
case _: # General
|
||
print(f"General: No synchronization needed\n")
|
||
|
||
result = f"sync knowledge '{db_knowledge.name}' processed successfully."
|
||
return result
|
||
except Exception as e:
|
||
if 'db_knowledge' in locals():
|
||
print(f"Failed to sync knowledge:{str(e)}\n")
|
||
result = f"sync knowledge '{db_knowledge.name}' failed."
|
||
return result
|
||
finally:
|
||
db.close()
|
||
|
||
|
||
@celery_app.task(name="app.core.memory.agent.read_message", bind=True)
|
||
def read_message_task(self, end_user_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]:
|
||
"""Celery task to process a read message via MemoryAgentService.
|
||
|
||
Args:
|
||
end_user_id: Group ID for the memory agent (also used as end_user_id)
|
||
message: User message to process
|
||
history: Conversation history
|
||
search_switch: Search switch parameter
|
||
config_id: Configuration ID as string (will be converted to UUID)
|
||
|
||
Returns:
|
||
Dict containing the result and metadata
|
||
|
||
Raises:
|
||
Exception on failure
|
||
"""
|
||
start_time = time.time()
|
||
|
||
# Convert config_id string to UUID
|
||
actual_config_id = None
|
||
if config_id:
|
||
try:
|
||
with get_db_context() as db:
|
||
actual_config_id = resolve_config_id(config_id, db)
|
||
except (ValueError, AttributeError):
|
||
# If conversion fails, leave as None and try to resolve
|
||
pass
|
||
|
||
# Resolve config_id if None
|
||
if actual_config_id is None:
|
||
try:
|
||
from app.services.memory_agent_service import get_end_user_connected_config
|
||
db = next(get_db())
|
||
try:
|
||
connected_config = get_end_user_connected_config(end_user_id, db)
|
||
actual_config_id = connected_config.get("memory_config_id")
|
||
finally:
|
||
db.close()
|
||
except Exception:
|
||
# Log but continue - will fail later with proper error
|
||
pass
|
||
|
||
async def _run() -> str:
|
||
db = next(get_db())
|
||
try:
|
||
service = MemoryAgentService()
|
||
return await service.read_memory(end_user_id, message, history, search_switch, actual_config_id, db,
|
||
storage_type, user_rag_memory_id)
|
||
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
|
||
|
||
return {
|
||
"status": "SUCCESS",
|
||
"result": result,
|
||
"end_user_id": end_user_id,
|
||
"config_id": config_id,
|
||
"elapsed_time": elapsed_time,
|
||
"task_id": self.request.id
|
||
}
|
||
except BaseException as e:
|
||
elapsed_time = time.time() - start_time
|
||
# Handle ExceptionGroup from TaskGroup
|
||
if hasattr(e, 'exceptions'):
|
||
error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions]
|
||
detailed_error = "; ".join(error_messages)
|
||
else:
|
||
detailed_error = str(e)
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": detailed_error,
|
||
"end_user_id": end_user_id,
|
||
"config_id": config_id,
|
||
"elapsed_time": elapsed_time,
|
||
"task_id": self.request.id
|
||
}
|
||
|
||
|
||
@celery_app.task(name="app.core.memory.agent.write_message", bind=True)
|
||
def write_message_task(self, end_user_id: str, message: list[dict], config_id: str | int, storage_type: str, user_rag_memory_id: str,
|
||
language: str = "zh") -> Dict[str, Any]:
|
||
"""Celery task to process a write message via MemoryAgentService.
|
||
Args:
|
||
end_user_id: Group ID for the memory agent (also used as end_user_id)
|
||
message: Message to write
|
||
config_id: Configuration ID (can be UUID string, integer, or config_id_old)
|
||
storage_type: Storage type (neo4j or rag)
|
||
user_rag_memory_id: User RAG memory ID
|
||
language: 语言类型 ("zh" 中文, "en" 英文)
|
||
|
||
Returns:
|
||
Dict containing the result and metadata
|
||
|
||
Raises:
|
||
Exception on failure
|
||
"""
|
||
from app.core.logging_config import get_logger
|
||
logger = get_logger(__name__)
|
||
|
||
logger.info(f"[CELERY WRITE] Starting write task - end_user_id={end_user_id}, config_id={config_id} (type: {type(config_id).__name__}), storage_type={storage_type}, language={language}")
|
||
start_time = time.time()
|
||
|
||
# Convert config_id to UUID
|
||
actual_config_id = None
|
||
|
||
if config_id:
|
||
try:
|
||
with get_db_context() as db:
|
||
actual_config_id = resolve_config_id(config_id, db)
|
||
print(100*'-')
|
||
print(actual_config_id)
|
||
print(100*'-')
|
||
logger.info(
|
||
f"[CELERY WRITE] Converted config_id to UUID: {actual_config_id} (type: {type(actual_config_id).__name__})")
|
||
except (ValueError, AttributeError) as e:
|
||
logger.error(f"[CELERY WRITE] Invalid config_id format: {config_id} (type: {type(config_id).__name__}), error: {e}")
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": f"Invalid config_id format: {config_id} - {str(e)}",
|
||
"end_user_id": end_user_id,
|
||
"config_id": str(config_id),
|
||
"elapsed_time": 0.0,
|
||
"task_id": self.request.id
|
||
}
|
||
|
||
# Resolve config_id if None
|
||
if actual_config_id is None:
|
||
try:
|
||
from app.services.memory_agent_service import get_end_user_connected_config
|
||
db = next(get_db())
|
||
try:
|
||
connected_config = get_end_user_connected_config(end_user_id, db)
|
||
actual_config_id = connected_config.get("memory_config_id")
|
||
finally:
|
||
db.close()
|
||
except Exception:
|
||
# Log but continue - will fail later with proper error
|
||
pass
|
||
|
||
async def _run() -> str:
|
||
db = next(get_db())
|
||
try:
|
||
logger.info(
|
||
f"[CELERY WRITE] Executing MemoryAgentService.write_memory with config_id={actual_config_id} (type: {type(actual_config_id).__name__}), language={language}")
|
||
service = MemoryAgentService()
|
||
result = await service.write_memory(end_user_id, message, actual_config_id, db, storage_type,
|
||
user_rag_memory_id, language)
|
||
logger.info(f"[CELERY WRITE] Write completed successfully: {result}")
|
||
return result
|
||
except Exception as e:
|
||
logger.error(f"[CELERY WRITE] Write failed: {e}", exc_info=True)
|
||
raise
|
||
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
|
||
|
||
logger.info(
|
||
f"[CELERY WRITE] Task completed successfully - elapsed_time={elapsed_time:.2f}s, task_id={self.request.id}")
|
||
|
||
return {
|
||
"status": "SUCCESS",
|
||
"result": result,
|
||
"end_user_id": end_user_id,
|
||
"config_id": config_id,
|
||
"elapsed_time": elapsed_time,
|
||
"task_id": self.request.id
|
||
}
|
||
except BaseException as e:
|
||
elapsed_time = time.time() - start_time
|
||
# Handle ExceptionGroup from TaskGroup
|
||
if hasattr(e, 'exceptions'):
|
||
error_messages = [f"{type(sub_e).__name__}: {str(sub_e)}" for sub_e in e.exceptions]
|
||
detailed_error = "; ".join(error_messages)
|
||
else:
|
||
detailed_error = str(e)
|
||
|
||
logger.error(f"[CELERY WRITE] Task failed - elapsed_time={elapsed_time:.2f}s, error={detailed_error}",
|
||
exc_info=True)
|
||
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": detailed_error,
|
||
"end_user_id": end_user_id,
|
||
"config_id": config_id,
|
||
"elapsed_time": elapsed_time,
|
||
"task_id": self.request.id
|
||
}
|
||
|
||
|
||
def reflection_engine() -> None:
|
||
"""Empty function placeholder for timed background reflection.
|
||
|
||
Intentionally left blank; replace with real reflection logic later.
|
||
"""
|
||
import asyncio
|
||
|
||
from app.core.memory.utils.self_reflexion_utils.self_reflexion import self_reflexion
|
||
|
||
host_id = uuid.UUID("2f6ff1eb-50c7-4765-8e89-e4566be19122")
|
||
asyncio.run(self_reflexion(host_id))
|
||
|
||
|
||
@celery_app.task(name="app.core.memory.agent.reflection.timer")
|
||
def reflection_timer_task() -> None:
|
||
"""Periodic Celery task that invokes reflection_engine.
|
||
|
||
Raises an exception on failure.
|
||
"""
|
||
reflection_engine()
|
||
|
||
|
||
# unused task
|
||
# @celery_app.task(name="app.core.memory.agent.health.check_read_service")
|
||
# def check_read_service_task() -> Dict[str, str]:
|
||
# """Call read_service and write latest status to Redis.
|
||
|
||
# Returns status data dict that gets written to Redis.
|
||
# """
|
||
# client = redis.Redis(
|
||
# host=settings.REDIS_HOST,
|
||
# port=settings.REDIS_PORT,
|
||
# db=settings.REDIS_DB,
|
||
# password=settings.REDIS_PASSWORD if settings.REDIS_PASSWORD else None
|
||
# )
|
||
# try:
|
||
# api_url = f"http://{settings.SERVER_IP}:8000/api/memory/read_service"
|
||
# payload = {
|
||
# "user_id": "健康检查",
|
||
# "apply_id": "健康检查",
|
||
# "group_id": "健康检查",
|
||
# "message": "你好",
|
||
# "history": [],
|
||
# "search_switch": "2",
|
||
# }
|
||
# resp = requests.post(api_url, json=payload, timeout=15)
|
||
# ok = resp.status_code == 200
|
||
# status = "Success" if ok else "Fail"
|
||
# msg = "接口请求成功" if ok else f"接口请求失败: {resp.status_code}"
|
||
# error = "" if ok else resp.text
|
||
# code = 0 if ok else 500
|
||
# except Exception as e:
|
||
# status = "Fail"
|
||
# msg = "接口请求失败"
|
||
# error = str(e)
|
||
# code = 500
|
||
|
||
# data = {
|
||
# "status": status,
|
||
# "msg": msg,
|
||
# "error": error,
|
||
# "code": str(code),
|
||
# "time": str(int(time.time())),
|
||
# }
|
||
|
||
# client.hset("memsci:health:read_service", mapping=data)
|
||
# client.expire("memsci:health:read_service", int(settings.HEALTH_CHECK_SECONDS))
|
||
|
||
# return data
|
||
|
||
|
||
@celery_app.task(name="app.controllers.memory_storage_controller.search_all")
|
||
def write_total_memory_task(workspace_id: str) -> Dict[str, Any]:
|
||
"""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
|
||
|
||
Args:
|
||
workspace_id: 工作空间ID
|
||
|
||
Returns:
|
||
包含任务执行结果的字典
|
||
"""
|
||
start_time = time.time()
|
||
|
||
async def _run() -> Dict[str, Any]:
|
||
from app.models.app_model import App
|
||
from app.models.end_user_model import EndUser
|
||
from app.repositories.memory_increment_repository import write_memory_increment
|
||
from app.services.memory_storage_service import search_all
|
||
|
||
with get_db_context() as db:
|
||
try:
|
||
workspace_uuid = uuid.UUID(workspace_id)
|
||
|
||
# 1. 查询当前workspace下的所有app（仅未删除的）
|
||
apps = db.query(App).filter(
|
||
App.workspace_id == workspace_uuid,
|
||
App.is_active.is_(True)
|
||
).all()
|
||
|
||
if not apps:
|
||
# 如果没有app，总量为0
|
||
memory_increment = write_memory_increment(
|
||
db=db,
|
||
workspace_id=workspace_uuid,
|
||
total_num=0
|
||
)
|
||
return {
|
||
"status": "SUCCESS",
|
||
"workspace_id": workspace_id,
|
||
"total_num": 0,
|
||
"end_user_count": 0,
|
||
"memory_increment_id": str(memory_increment.id),
|
||
"created_at": memory_increment.created_at.isoformat(),
|
||
}
|
||
|
||
# 2. 查询所有app下的end_user_id（去重）
|
||
app_ids = [app.id for app in apps]
|
||
end_users = db.query(EndUser.id).filter(
|
||
EndUser.app_id.in_(app_ids)
|
||
).distinct().all()
|
||
|
||
# 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(),
|
||
}
|
||
except Exception as e:
|
||
raise e
|
||
|
||
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.regenerate_memory_cache",
|
||
bind=True,
|
||
ignore_result=True,
|
||
max_retries=0,
|
||
acks_late=False,
|
||
time_limit=3600,
|
||
soft_time_limit=3300,
|
||
)
|
||
def regenerate_memory_cache(self) -> Dict[str, Any]:
|
||
"""定时任务：为所有用户重新生成记忆洞察和用户摘要缓存
|
||
|
||
遍历所有活动工作空间的所有终端用户，为每个用户重新生成记忆洞察和用户摘要。
|
||
实现错误隔离，单个用户失败不影响其他用户的处理。
|
||
|
||
Returns:
|
||
包含任务执行结果的字典，包括：
|
||
- status: 任务状态 (SUCCESS/FAILURE)
|
||
- message: 执行消息
|
||
- workspace_count: 处理的工作空间数量
|
||
- total_users: 总用户数
|
||
- successful: 成功生成的用户数
|
||
- failed: 失败的用户数
|
||
- workspace_results: 每个工作空间的详细结果
|
||
- elapsed_time: 执行耗时（秒）
|
||
- task_id: 任务ID
|
||
"""
|
||
start_time = time.time()
|
||
|
||
async def _run() -> Dict[str, Any]:
|
||
from app.core.logging_config import get_logger
|
||
from app.repositories.end_user_repository import EndUserRepository
|
||
from app.services.user_memory_service import UserMemoryService
|
||
|
||
logger = get_logger(__name__)
|
||
logger.info("开始执行记忆缓存重新生成定时任务")
|
||
|
||
service = UserMemoryService()
|
||
|
||
total_users = 0
|
||
successful = 0
|
||
failed = 0
|
||
workspace_results = []
|
||
|
||
with get_db_context() as db:
|
||
try:
|
||
# 获取所有活动工作空间
|
||
repo = EndUserRepository(db)
|
||
workspaces = repo.get_all_active_workspaces()
|
||
logger.info(f"找到 {len(workspaces)} 个活动工作空间")
|
||
|
||
# 遍历每个工作空间
|
||
for workspace_id in workspaces:
|
||
logger.info(f"开始处理工作空间: {workspace_id}")
|
||
workspace_start_time = time.time()
|
||
|
||
try:
|
||
# 获取工作空间的所有终端用户
|
||
end_users = repo.get_all_by_workspace(workspace_id)
|
||
workspace_user_count = len(end_users)
|
||
total_users += workspace_user_count
|
||
|
||
logger.info(f"工作空间 {workspace_id} 有 {workspace_user_count} 个终端用户")
|
||
|
||
workspace_successful = 0
|
||
workspace_failed = 0
|
||
workspace_errors = []
|
||
|
||
# 遍历每个用户并生成缓存
|
||
for end_user in end_users:
|
||
end_user_id = str(end_user.id)
|
||
|
||
try:
|
||
# 生成记忆洞察
|
||
insight_result = await service.generate_and_cache_insight(db, end_user_id)
|
||
|
||
# 生成用户摘要
|
||
summary_result = await service.generate_and_cache_summary(db, end_user_id)
|
||
|
||
# 检查是否都成功
|
||
if insight_result["success"] and summary_result["success"]:
|
||
workspace_successful += 1
|
||
successful += 1
|
||
logger.info(f"成功为终端用户 {end_user_id} 重新生成缓存")
|
||
else:
|
||
workspace_failed += 1
|
||
failed += 1
|
||
error_info = {
|
||
"end_user_id": end_user_id,
|
||
"insight_error": insight_result.get("error"),
|
||
"summary_error": summary_result.get("error")
|
||
}
|
||
workspace_errors.append(error_info)
|
||
logger.warning(f"终端用户 {end_user_id} 的缓存重新生成部分失败: {error_info}")
|
||
|
||
except Exception as e:
|
||
# 单个用户失败不影响其他用户（错误隔离）
|
||
workspace_failed += 1
|
||
failed += 1
|
||
error_info = {
|
||
"end_user_id": end_user_id,
|
||
"error": str(e)
|
||
}
|
||
workspace_errors.append(error_info)
|
||
logger.error(f"为终端用户 {end_user_id} 重新生成缓存时出错: {str(e)}")
|
||
|
||
workspace_elapsed = time.time() - workspace_start_time
|
||
|
||
# 记录工作空间处理结果
|
||
workspace_result = {
|
||
"workspace_id": str(workspace_id),
|
||
"total_users": workspace_user_count,
|
||
"successful": workspace_successful,
|
||
"failed": workspace_failed,
|
||
"errors": workspace_errors[:10], # 只保留前10个错误
|
||
"elapsed_time": workspace_elapsed
|
||
}
|
||
workspace_results.append(workspace_result)
|
||
|
||
logger.info(
|
||
f"工作空间 {workspace_id} 处理完成: "
|
||
f"总数={workspace_user_count}, 成功={workspace_successful}, "
|
||
f"失败={workspace_failed}, 耗时={workspace_elapsed:.2f}秒"
|
||
)
|
||
|
||
except Exception as e:
|
||
# 工作空间处理失败，记录错误并继续处理下一个
|
||
logger.error(f"处理工作空间 {workspace_id} 时出错: {str(e)}")
|
||
workspace_results.append({
|
||
"workspace_id": str(workspace_id),
|
||
"error": str(e),
|
||
"total_users": 0,
|
||
"successful": 0,
|
||
"failed": 0,
|
||
"errors": []
|
||
})
|
||
|
||
# 记录总体统计信息
|
||
logger.info(
|
||
f"记忆缓存重新生成定时任务完成: "
|
||
f"工作空间数={len(workspaces)}, 总用户数={total_users}, "
|
||
f"成功={successful}, 失败={failed}"
|
||
)
|
||
|
||
return {
|
||
"status": "SUCCESS",
|
||
"message": f"成功处理 {len(workspaces)} 个工作空间，总共 {successful}/{total_users} 个用户缓存重新生成成功",
|
||
"workspace_count": len(workspaces),
|
||
"total_users": total_users,
|
||
"successful": successful,
|
||
"failed": failed,
|
||
"workspace_results": workspace_results
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"记忆缓存重新生成定时任务执行失败: {str(e)}")
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": str(e),
|
||
"workspace_count": len(workspace_results),
|
||
"total_users": total_users,
|
||
"successful": successful,
|
||
"failed": failed,
|
||
"workspace_results": workspace_results
|
||
}
|
||
|
||
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
|
||
}
|
||
|
||
|
||
@celery_app.task(
|
||
name="app.tasks.workspace_reflection_task",
|
||
bind=True,
|
||
ignore_result=True,
|
||
max_retries=0,
|
||
acks_late=False,
|
||
time_limit=300,
|
||
soft_time_limit=240,
|
||
)
|
||
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()
|
||
|
||
with get_db_context() as 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['memory_configs'] == []:
|
||
continue
|
||
|
||
releases = data['releases']
|
||
memory_configs = data['memory_configs']
|
||
end_users = data['end_users']
|
||
|
||
for base, config, user in zip(releases, memory_configs, end_users):
|
||
if str(base['config']) == str(config['config_id']) and str(base['app_id']) == str(
|
||
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:
|
||
db.rollback() # Rollback failed transaction to allow next query
|
||
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": []
|
||
}
|
||
|
||
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
|
||
}
|
||
|
||
|
||
@celery_app.task(
|
||
name="app.tasks.run_forgetting_cycle_task",
|
||
bind=True,
|
||
ignore_result=True,
|
||
max_retries=0,
|
||
acks_late=False,
|
||
time_limit=7200,
|
||
soft_time_limit=7000,
|
||
)
|
||
def run_forgetting_cycle_task(self, config_id: Optional[uuid.UUID] = None) -> Dict[str, Any]:
|
||
"""定时任务：运行遗忘周期
|
||
|
||
定期执行遗忘周期，识别并融合低激活值的知识节点。
|
||
|
||
Args:
|
||
config_id: 配置ID（可选，如果为None则使用默认配置）
|
||
|
||
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.services.memory_forget_service import MemoryForgetService
|
||
|
||
api_logger = get_api_logger()
|
||
|
||
with get_db_context() as db:
|
||
try:
|
||
api_logger.info(f"开始执行遗忘周期定时任务，config_id: {config_id}")
|
||
|
||
forget_service = MemoryForgetService()
|
||
|
||
# 运行遗忘周期
|
||
report = await forget_service.trigger_forgetting(
|
||
db=db,
|
||
end_user_id=None, # 处理所有组
|
||
config_id=config_id
|
||
)
|
||
|
||
duration = time.time() - start_time
|
||
|
||
api_logger.info(
|
||
f"遗忘周期定时任务完成: "
|
||
f"融合 {report['merged_count']} 对节点, "
|
||
f"失败 {report['failed_count']} 对, "
|
||
f"耗时 {duration:.2f} 秒"
|
||
)
|
||
|
||
return {
|
||
"status": "SUCCESS",
|
||
"message": "遗忘周期执行成功",
|
||
"report": report,
|
||
"duration_seconds": duration
|
||
}
|
||
|
||
except Exception as e:
|
||
duration = time.time() - start_time
|
||
api_logger.error(f"遗忘周期定时任务失败: {str(e)}", exc_info=True)
|
||
|
||
return {
|
||
"status": "FAILED",
|
||
"message": f"遗忘周期执行失败: {str(e)}",
|
||
"duration_seconds": duration
|
||
}
|
||
|
||
# 运行异步函数
|
||
loop = asyncio.new_event_loop()
|
||
asyncio.set_event_loop(loop)
|
||
try:
|
||
result = loop.run_until_complete(_run())
|
||
return result
|
||
finally:
|
||
loop.close()
|
||
|
||
# =============================================================================
|
||
# Long-term Memory Storage Tasks (Batched Write Strategies)
|
||
# =============================================================================
|
||
|
||
# @celery_app.task(name="app.core.memory.agent.long_term_storage.time", bind=True)
|
||
# def long_term_storage_time_task(
|
||
# self,
|
||
# end_user_id: str,
|
||
# config_id: str,
|
||
# time_window: int = 5
|
||
# ) -> Dict[str, Any]:
|
||
# """Celery task for time-based long-term memory storage.
|
||
|
||
# Retrieves recent sessions from Redis within time window and writes to Neo4j.
|
||
|
||
# Args:
|
||
# end_user_id: End user identifier
|
||
# config_id: Memory configuration ID
|
||
# time_window: Time window in minutes for retrieving recent sessions
|
||
|
||
# Returns:
|
||
# Dict containing task status and metadata
|
||
# """
|
||
# from app.core.logging_config import get_logger
|
||
# logger = get_logger(__name__)
|
||
|
||
# logger.info(f"[LONG_TERM_TIME] Starting task - end_user_id={end_user_id}, time_window={time_window}")
|
||
# start_time = time.time()
|
||
|
||
# async def _run() -> Dict[str, Any]:
|
||
# from app.core.memory.agent.langgraph_graph.routing.write_router import memory_long_term_storage
|
||
# from app.services.memory_config_service import MemoryConfigService
|
||
|
||
# db = next(get_db())
|
||
# try:
|
||
# # Load memory config
|
||
# config_service = MemoryConfigService(db)
|
||
# memory_config = config_service.load_memory_config(
|
||
# config_id=config_id,
|
||
# service_name="LongTermStorageTask"
|
||
# )
|
||
|
||
# # Execute time-based storage
|
||
# await memory_long_term_storage(end_user_id, memory_config, time_window)
|
||
|
||
# return {"status": "SUCCESS", "strategy": "time", "time_window": time_window}
|
||
# finally:
|
||
# db.close()
|
||
|
||
# try:
|
||
# import nest_asyncio
|
||
# nest_asyncio.apply()
|
||
# except ImportError:
|
||
# pass
|
||
|
||
# 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)
|
||
|
||
# try:
|
||
# result = loop.run_until_complete(_run())
|
||
# elapsed_time = time.time() - start_time
|
||
|
||
# logger.info(f"[LONG_TERM_TIME] Task completed - elapsed_time={elapsed_time:.2f}s")
|
||
|
||
# return {
|
||
# **result,
|
||
# "end_user_id": end_user_id,
|
||
# "config_id": config_id,
|
||
# "elapsed_time": elapsed_time,
|
||
# "task_id": self.request.id
|
||
# }
|
||
# except Exception as e:
|
||
# elapsed_time = time.time() - start_time
|
||
# logger.error(f"[LONG_TERM_TIME] Task failed - error={str(e)}", exc_info=True)
|
||
|
||
# return {
|
||
# "status": "FAILURE",
|
||
# "strategy": "time",
|
||
# "error": str(e),
|
||
# "end_user_id": end_user_id,
|
||
# "config_id": config_id,
|
||
# "elapsed_time": elapsed_time,
|
||
# "task_id": self.request.id
|
||
# }
|
||
|
||
|
||
# @celery_app.task(name="app.core.memory.agent.long_term_storage.aggregate", bind=True)
|
||
# def long_term_storage_aggregate_task(
|
||
# self,
|
||
# end_user_id: str,
|
||
# langchain_messages: List[Dict[str, Any]],
|
||
# config_id: str
|
||
# ) -> Dict[str, Any]:
|
||
# """Celery task for aggregate-based long-term memory storage.
|
||
|
||
# Uses LLM to determine if new messages describe the same event as history.
|
||
# Only writes to Neo4j if messages represent new information (not duplicates).
|
||
|
||
# Args:
|
||
# end_user_id: End user identifier
|
||
# langchain_messages: List of messages [{"role": "user/assistant", "content": "..."}]
|
||
# config_id: Memory configuration ID
|
||
|
||
# Returns:
|
||
# Dict containing task status, is_same_event flag, and metadata
|
||
# """
|
||
# from app.core.logging_config import get_logger
|
||
# logger = get_logger(__name__)
|
||
|
||
# logger.info(f"[LONG_TERM_AGGREGATE] Starting task - end_user_id={end_user_id}")
|
||
# start_time = time.time()
|
||
|
||
# async def _run() -> Dict[str, Any]:
|
||
# from app.core.memory.agent.langgraph_graph.routing.write_router import aggregate_judgment
|
||
# from app.core.memory.agent.langgraph_graph.tools.write_tool import chat_data_format
|
||
# from app.core.memory.agent.utils.redis_tool import write_store
|
||
# from app.services.memory_config_service import MemoryConfigService
|
||
|
||
# db = next(get_db())
|
||
# try:
|
||
# # Save to Redis buffer first
|
||
# write_store.save_session_write(end_user_id, await chat_data_format(langchain_messages))
|
||
|
||
# # Load memory config
|
||
# config_service = MemoryConfigService(db)
|
||
# memory_config = config_service.load_memory_config(
|
||
# config_id=config_id,
|
||
# service_name="LongTermStorageTask"
|
||
# )
|
||
|
||
# # Execute aggregate judgment
|
||
# result = await aggregate_judgment(end_user_id, langchain_messages, memory_config)
|
||
|
||
# return {
|
||
# "status": "SUCCESS",
|
||
# "strategy": "aggregate",
|
||
# "is_same_event": result.get("is_same_event", False),
|
||
# "wrote_to_neo4j": not result.get("is_same_event", False)
|
||
# }
|
||
# finally:
|
||
# db.close()
|
||
|
||
# try:
|
||
# import nest_asyncio
|
||
# nest_asyncio.apply()
|
||
# except ImportError:
|
||
# pass
|
||
|
||
# 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)
|
||
|
||
# try:
|
||
# result = loop.run_until_complete(_run())
|
||
# elapsed_time = time.time() - start_time
|
||
|
||
# logger.info(f"[LONG_TERM_AGGREGATE] Task completed - is_same_event={result.get('is_same_event')}, elapsed_time={elapsed_time:.2f}s")
|
||
|
||
# return {
|
||
# **result,
|
||
# "end_user_id": end_user_id,
|
||
# "config_id": config_id,
|
||
# "elapsed_time": elapsed_time,
|
||
# "task_id": self.request.id
|
||
# }
|
||
# except Exception as e:
|
||
# elapsed_time = time.time() - start_time
|
||
# logger.error(f"[LONG_TERM_AGGREGATE] Task failed - error={str(e)}", exc_info=True)
|
||
|
||
# return {
|
||
# "status": "FAILURE",
|
||
# "strategy": "aggregate",
|
||
# "error": str(e),
|
||
# "end_user_id": end_user_id,
|
||
# "config_id": config_id,
|
||
# "elapsed_time": elapsed_time,
|
||
# "task_id": self.request.id
|
||
# }
|
||
|
||
|
||
# =============================================================================
|
||
# 隐性记忆和情绪数据更新定时任务
|
||
# =============================================================================
|
||
|
||
@celery_app.task(
|
||
name="app.tasks.update_implicit_emotions_storage",
|
||
bind=True,
|
||
ignore_result=True,
|
||
max_retries=0,
|
||
acks_late=False,
|
||
time_limit=7200, # 2小时硬超时
|
||
soft_time_limit=6900, # 1小时55分钟软超时
|
||
)
|
||
def update_implicit_emotions_storage(self) -> Dict[str, Any]:
|
||
"""定时任务：更新所有用户的隐性记忆画像和情绪建议数据
|
||
|
||
遍历数据库中所有已存在数据的用户，为每个用户重新生成隐性记忆画像和情绪建议。
|
||
实现错误隔离，单个用户失败不影响其他用户的处理。
|
||
|
||
Returns:
|
||
包含任务执行结果的字典，包括：
|
||
- status: 任务状态 (SUCCESS/FAILURE)
|
||
- message: 执行消息
|
||
- total_users: 总用户数
|
||
- successful_implicit: 成功更新隐性记忆的用户数
|
||
- successful_emotion: 成功更新情绪建议的用户数
|
||
- failed: 失败的用户数
|
||
- user_results: 每个用户的详细结果
|
||
- elapsed_time: 执行耗时（秒）
|
||
- task_id: 任务ID
|
||
"""
|
||
start_time = time.time()
|
||
|
||
async def _run() -> Dict[str, Any]:
|
||
from app.core.logging_config import get_logger
|
||
from app.repositories.implicit_emotions_storage_repository import ImplicitEmotionsStorageRepository
|
||
from app.models.implicit_emotions_storage_model import ImplicitEmotionsStorage
|
||
from sqlalchemy import select, func
|
||
from app.services.implicit_memory_service import ImplicitMemoryService
|
||
from app.services.emotion_analytics_service import EmotionAnalyticsService
|
||
|
||
logger = get_logger(__name__)
|
||
logger.info("开始执行隐性记忆和情绪数据更新定时任务")
|
||
|
||
total_users = 0
|
||
successful_implicit = 0
|
||
successful_emotion = 0
|
||
failed = 0
|
||
user_results = []
|
||
|
||
with get_db_context() as db:
|
||
try:
|
||
# 获取所有已存储数据的用户ID（分批次处理）
|
||
repo = ImplicitEmotionsStorageRepository(db)
|
||
|
||
# 先统计总数用于日志
|
||
from sqlalchemy import func
|
||
total_users = db.execute(
|
||
select(func.count()).select_from(ImplicitEmotionsStorage)
|
||
).scalar() or 0
|
||
logger.info(f"找到 {total_users} 个需要更新的用户")
|
||
|
||
# 遍历每个用户并更新数据（分批次，避免一次性加载所有ID）
|
||
for end_user_id in repo.get_all_user_ids(batch_size=100):
|
||
logger.info(f"开始处理用户: {end_user_id}")
|
||
user_start_time = time.time()
|
||
|
||
implicit_success = False
|
||
emotion_success = False
|
||
errors = []
|
||
|
||
try:
|
||
# 更新隐性记忆画像
|
||
try:
|
||
implicit_service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
|
||
profile_data = await implicit_service.generate_complete_profile(user_id=end_user_id)
|
||
await implicit_service.save_profile_cache(
|
||
end_user_id=end_user_id,
|
||
profile_data=profile_data,
|
||
db=db
|
||
)
|
||
implicit_success = True
|
||
logger.info(f"成功更新用户 {end_user_id} 的隐性记忆画像")
|
||
except Exception as e:
|
||
error_msg = f"隐性记忆更新失败: {str(e)}"
|
||
errors.append(error_msg)
|
||
logger.error(f"用户 {end_user_id} {error_msg}")
|
||
|
||
# 更新情绪建议
|
||
try:
|
||
emotion_service = EmotionAnalyticsService()
|
||
suggestions_data = await emotion_service.generate_emotion_suggestions(
|
||
end_user_id=end_user_id,
|
||
db=db,
|
||
language="zh"
|
||
)
|
||
await emotion_service.save_suggestions_cache(
|
||
end_user_id=end_user_id,
|
||
suggestions_data=suggestions_data,
|
||
db=db
|
||
)
|
||
emotion_success = True
|
||
logger.info(f"成功更新用户 {end_user_id} 的情绪建议")
|
||
except Exception as e:
|
||
error_msg = f"情绪建议更新失败: {str(e)}"
|
||
errors.append(error_msg)
|
||
logger.error(f"用户 {end_user_id} {error_msg}")
|
||
|
||
# 统计结果
|
||
if implicit_success:
|
||
successful_implicit += 1
|
||
if emotion_success:
|
||
successful_emotion += 1
|
||
if not implicit_success and not emotion_success:
|
||
failed += 1
|
||
|
||
user_elapsed = time.time() - user_start_time
|
||
|
||
# 记录用户处理结果
|
||
user_result = {
|
||
"end_user_id": end_user_id,
|
||
"implicit_success": implicit_success,
|
||
"emotion_success": emotion_success,
|
||
"errors": errors,
|
||
"elapsed_time": user_elapsed
|
||
}
|
||
user_results.append(user_result)
|
||
|
||
logger.info(
|
||
f"用户 {end_user_id} 处理完成: "
|
||
f"隐性记忆={'成功' if implicit_success else '失败'}, "
|
||
f"情绪建议={'成功' if emotion_success else '失败'}, "
|
||
f"耗时={user_elapsed:.2f}秒"
|
||
)
|
||
|
||
except Exception as e:
|
||
# 单个用户失败不影响其他用户（错误隔离）
|
||
failed += 1
|
||
user_elapsed = time.time() - user_start_time
|
||
error_info = {
|
||
"end_user_id": end_user_id,
|
||
"implicit_success": False,
|
||
"emotion_success": False,
|
||
"errors": [str(e)],
|
||
"elapsed_time": user_elapsed
|
||
}
|
||
user_results.append(error_info)
|
||
logger.error(f"处理用户 {end_user_id} 时出错: {str(e)}")
|
||
|
||
# ---- 处理增量用户（当天新增、尚未初始化的用户）----
|
||
new_users_initialized = 0
|
||
new_users_failed = 0
|
||
logger.info("开始处理当天新增的增量用户初始化")
|
||
|
||
for end_user_id in repo.get_new_user_ids_today(batch_size=100):
|
||
logger.info(f"开始初始化新用户: {end_user_id}")
|
||
user_start_time = time.time()
|
||
implicit_success = False
|
||
emotion_success = False
|
||
errors = []
|
||
|
||
try:
|
||
try:
|
||
implicit_service = ImplicitMemoryService(db=db, end_user_id=end_user_id)
|
||
profile_data = await implicit_service.generate_complete_profile(user_id=end_user_id)
|
||
await implicit_service.save_profile_cache(
|
||
end_user_id=end_user_id,
|
||
profile_data=profile_data,
|
||
db=db
|
||
)
|
||
implicit_success = True
|
||
logger.info(f"成功初始化新用户 {end_user_id} 的隐性记忆画像")
|
||
except Exception as e:
|
||
error_msg = f"隐性记忆初始化失败: {str(e)}"
|
||
errors.append(error_msg)
|
||
logger.error(f"新用户 {end_user_id} {error_msg}")
|
||
|
||
try:
|
||
emotion_service = EmotionAnalyticsService()
|
||
suggestions_data = await emotion_service.generate_emotion_suggestions(
|
||
end_user_id=end_user_id,
|
||
db=db,
|
||
language="zh"
|
||
)
|
||
await emotion_service.save_suggestions_cache(
|
||
end_user_id=end_user_id,
|
||
suggestions_data=suggestions_data,
|
||
db=db
|
||
)
|
||
emotion_success = True
|
||
logger.info(f"成功初始化新用户 {end_user_id} 的情绪建议")
|
||
except Exception as e:
|
||
error_msg = f"情绪建议初始化失败: {str(e)}"
|
||
errors.append(error_msg)
|
||
logger.error(f"新用户 {end_user_id} {error_msg}")
|
||
|
||
if implicit_success or emotion_success:
|
||
new_users_initialized += 1
|
||
else:
|
||
new_users_failed += 1
|
||
|
||
user_elapsed = time.time() - user_start_time
|
||
user_results.append({
|
||
"end_user_id": end_user_id,
|
||
"type": "init",
|
||
"implicit_success": implicit_success,
|
||
"emotion_success": emotion_success,
|
||
"errors": errors,
|
||
"elapsed_time": user_elapsed
|
||
})
|
||
|
||
except Exception as e:
|
||
new_users_failed += 1
|
||
user_elapsed = time.time() - user_start_time
|
||
user_results.append({
|
||
"end_user_id": end_user_id,
|
||
"type": "init",
|
||
"implicit_success": False,
|
||
"emotion_success": False,
|
||
"errors": [str(e)],
|
||
"elapsed_time": user_elapsed
|
||
})
|
||
logger.error(f"初始化新用户 {end_user_id} 时出错: {str(e)}")
|
||
|
||
logger.info(
|
||
f"增量用户初始化完成: 成功={new_users_initialized}, 失败={new_users_failed}"
|
||
)
|
||
# ---- 增量用户处理结束 ----
|
||
|
||
# 记录总体统计信息
|
||
logger.info(
|
||
f"隐性记忆和情绪数据更新定时任务完成: "
|
||
f"存量用户总数={total_users}, "
|
||
f"隐性记忆成功={successful_implicit}, "
|
||
f"情绪建议成功={successful_emotion}, "
|
||
f"存量失败={failed}, "
|
||
f"增量初始化成功={new_users_initialized}, "
|
||
f"增量初始化失败={new_users_failed}"
|
||
)
|
||
|
||
return {
|
||
"status": "SUCCESS",
|
||
"message": (
|
||
f"存量用户 {total_users} 个，隐性记忆 {successful_implicit} 个成功，情绪建议 {successful_emotion} 个成功；"
|
||
f"增量新用户初始化 {new_users_initialized} 个成功，{new_users_failed} 个失败"
|
||
),
|
||
"total_users": total_users,
|
||
"successful_implicit": successful_implicit,
|
||
"successful_emotion": successful_emotion,
|
||
"failed": failed,
|
||
"new_users_initialized": new_users_initialized,
|
||
"new_users_failed": new_users_failed,
|
||
"user_results": user_results[:50] # 只保留前50个用户的详细结果
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"隐性记忆和情绪数据更新定时任务执行失败: {str(e)}")
|
||
return {
|
||
"status": "FAILURE",
|
||
"error": str(e),
|
||
"total_users": total_users,
|
||
"successful_implicit": successful_implicit,
|
||
"successful_emotion": successful_emotion,
|
||
"failed": failed,
|
||
"new_users_initialized": 0,
|
||
"new_users_failed": 0,
|
||
"user_results": user_results[:50]
|
||
}
|
||
|
||
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
|
||
}
|