Merge branch 'develop' into fix/memory-enduser-config
This commit is contained in:
Ke Sun
483
api/app/tasks.py
483
api/app/tasks.py
@@ -7,6 +7,8 @@ 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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@@ -16,8 +18,13 @@ import trio
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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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@@ -29,7 +36,9 @@ from app.core.rag.vdb.elasticsearch.elasticsearch_vector import (
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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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@@ -382,6 +391,480 @@ def build_graphrag_for_kb(kb_id: uuid.UUID):
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db.close()
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@celery_app.task(name="app.core.rag.tasks.sync_knowledge_for_kb")
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def sync_knowledge_for_kb(kb_id: uuid.UUID):
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"""
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sync knowledge document and 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_knowledge = None
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try:
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db_knowledge = db.query(Knowledge).filter(Knowledge.id == kb_id).first()
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# 1. get vector_service
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vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
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# 2. sync data
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match db_knowledge.type:
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case "Web": # Crawl webpages in batches through a web crawler
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entry_url = db_knowledge.parser_config.get("entry_url", "")
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max_pages = db_knowledge.parser_config.get("max_pages", 20)
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delay_seconds = db_knowledge.parser_config.get("delay_seconds", 1.0)
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timeout_seconds = db_knowledge.parser_config.get("timeout_seconds", 10)
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user_agent = db_knowledge.parser_config.get("user_agent", "KnowledgeBaseCrawler/1.0")
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# Create crawler
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crawler = WebCrawler(
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entry_url=entry_url,
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max_pages=max_pages,
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delay_seconds=delay_seconds,
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timeout_seconds=timeout_seconds,
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user_agent=user_agent
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)
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try:
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# 初始化存储已爬取 URLs 的集合
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file_urls = set()
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# crawl entry_url by yield
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for crawled_document in crawler.crawl():
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file_urls.add(crawled_document.url)
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db_file = db.query(File).filter(File.kb_id == db_knowledge.id,
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File.file_url == crawled_document.url).first()
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if db_file:
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if db_file.file_size == crawled_document.content_length: # same
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continue
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else: # --update
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if crawled_document.content_length:
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# 1. update file
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db_file.file_name = f"{crawled_document.title}.txt"
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db_file.file_ext=".txt"
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db_file.file_size=crawled_document.content_length
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db.commit()
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db.refresh(db_file)
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# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
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save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id), str(db_knowledge.parent_id))
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Path(save_dir).mkdir(parents=True, exist_ok=True) # Ensure that the directory exists
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save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
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# update file
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if os.path.exists(save_path):
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os.remove(save_path) # Delete a single file
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content_bytes = crawled_document.content.encode('utf-8')
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with open(save_path, "wb") as f:
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f.write(content_bytes)
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# 2. update a document
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db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
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Document.file_id == db_file.id).first()
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if db_document:
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db_document.file_name = db_file.file_name
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db_document.file_ext = db_file.file_ext
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db_document.file_size = db_file.file_size
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db_document.updated_at = datetime.now()
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db.commit()
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db.refresh(db_document)
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# 3. Document parsing, vectorization, and storage
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parse_document(file_path=save_path, document_id=db_document.id)
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else: # --add
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if crawled_document.content_length:
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# 1. upload file
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upload_file = file_schema.FileCreate(
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kb_id=db_knowledge.id,
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created_by=db_knowledge.created_by,
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parent_id=db_knowledge.id,
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file_name=f"{crawled_document.title}.txt",
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file_ext=".txt",
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file_size=crawled_document.content_length,
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file_url=crawled_document.url,
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)
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db_file = File(**upload_file.model_dump())
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db.add(db_file)
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db.commit()
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# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
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save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id), str(db_knowledge.id))
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Path(save_dir).mkdir(parents=True, exist_ok=True) # Ensure that the directory exists
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save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
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# Save file
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content_bytes = crawled_document.content.encode('utf-8')
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with open(save_path, "wb") as f:
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f.write(content_bytes)
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# 2. Create a document
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create_document_data = document_schema.DocumentCreate(
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kb_id=db_knowledge.id,
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created_by=db_knowledge.created_by,
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file_id=db_file.id,
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file_name=db_file.file_name,
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file_ext=db_file.file_ext,
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file_size=db_file.file_size,
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file_meta={},
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parser_id="naive",
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parser_config={
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"layout_recognize": "DeepDOC",
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"chunk_token_num": 128,
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"delimiter": "\n",
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"auto_keywords": 0,
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"auto_questions": 0,
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"html4excel": "false"
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}
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)
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db_document = Document(**create_document_data.model_dump())
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db.add(db_document)
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db.commit()
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# 3. Document parsing, vectorization, and storage
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parse_document(file_path=save_path, document_id=db_document.id)
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db_files = db.query(File).filter(File.kb_id == db_knowledge.id, File.file_url.notin_(file_urls)).all()
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if db_files: # --delete
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for db_file in db_files:
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db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
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Document.file_id == db_file.id).first()
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if db_document:
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# 1. Delete vector index
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vector_service.delete_by_metadata_field(key="document_id", value=str(db_document.id))
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# 2. Delete document
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db.delete(db_document)
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# 3. Delete file
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file_path = Path(
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settings.FILE_PATH,
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str(db_file.kb_id),
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str(db_file.parent_id),
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f"{db_file.id}{db_file.file_ext}"
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)
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if file_path.exists():
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file_path.unlink() # Delete a single file
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db.delete(db_file)
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# commit transaction
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db.commit()
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except Exception as e:
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print(f"\n\nError during crawl: {e}")
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case "Third-party": # Integration of knowledge bases from three parties
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yuque_user_id = db_knowledge.parser_config.get("yuque_user_id", "")
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feishu_app_id = db_knowledge.parser_config.get("feishu_app_id", "")
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if yuque_user_id: # Yuque Knowledge Base
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yuque_token = db_knowledge.parser_config.get("yuque_token", "")
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# Create yuqueAPIClient
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api_client = YuqueAPIClient(
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user_id=yuque_user_id,
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token=yuque_token
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)
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try:
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# 初始化存储获取语雀 URLs 的集合
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file_urls = set()
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# Get all files from all repos
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async def async_get_files(api_client: YuqueAPIClient):
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async with api_client as client:
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print("\n=== Fetching repositories ===")
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repos = await client.get_user_repos()
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print(f"Found {len(repos)} repositories:")
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all_files = []
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for repo in repos:
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# Get documents from repository
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print(f"\n=== Fetching documents from '{repo.name}' ===")
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docs = await client.get_repo_docs(repo.id)
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all_files.extend(docs)
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return all_files
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files = asyncio.run(async_get_files(api_client))
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for doc in files:
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file_urls.add(doc.slug)
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db_file = db.query(File).filter(File.kb_id == db_knowledge.id,
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File.file_url == doc.slug).first()
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if db_file:
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if db_file.created_at == doc.updated_at: # same
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continue
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else: # --update
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# 1. update file
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# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
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save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id), str(db_knowledge.parent_id))
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Path(save_dir).mkdir(parents=True, exist_ok=True) # Ensure that the directory exists
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# download document from Feishu FileInfo
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async def async_download_document(api_client: YuqueAPIClient, doc: YuqueDocInfo, save_dir: str):
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async with api_client as client:
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file_path = await client.download_document(doc, save_dir)
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return file_path
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file_path = asyncio.run(async_download_document(api_client, doc, save_dir))
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save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
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# update file
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if os.path.exists(save_path):
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os.remove(save_path) # Delete a single file
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shutil.copyfile(file_path, save_path)
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# update db_file
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file_name = os.path.basename(file_path)
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_, file_extension = os.path.splitext(file_name)
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file_size = os.path.getsize(file_path)
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db_file.file_name = file_name
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db_file.file_ext = file_extension.lower()
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db_file.file_size = file_size
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db_file.created_at = doc.updated_at
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db.commit()
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db.refresh(db_file)
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# 2. update a document
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db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
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Document.file_id == db_file.id).first()
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if db_document:
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db_document.file_name = db_file.file_name
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db_document.file_ext = db_file.file_ext
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db_document.file_size = db_file.file_size
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db_document.created_at = db_file.created_at
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db_document.updated_at = datetime.now()
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db.commit()
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db.refresh(db_document)
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# 3. Document parsing, vectorization, and storage
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parse_document(file_path=save_path, document_id=db_document.id)
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else: # --add
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# 1. update file
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# Construct a save path：/files/{kb_id}/{parent_id}/{file.id}{file_extension}
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save_dir = os.path.join(settings.FILE_PATH, str(db_knowledge.id), str(db_knowledge.parent_id))
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Path(save_dir).mkdir(parents=True, exist_ok=True) # Ensure that the directory exists
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# download document from Feishu FileInfo
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async def async_download_document(api_client: YuqueAPIClient, doc: YuqueDocInfo, save_dir: str):
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async with api_client as client:
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file_path = await client.download_document(doc, save_dir)
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return file_path
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file_path = asyncio.run(async_download_document(api_client, doc, save_dir))
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# add db_file
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file_name = os.path.basename(file_path)
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_, file_extension = os.path.splitext(file_name)
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file_size = os.path.getsize(file_path)
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upload_file = file_schema.FileCreate(
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kb_id=db_knowledge.id,
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created_by=db_knowledge.created_by,
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parent_id=db_knowledge.id,
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file_name=file_name,
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file_ext=file_extension.lower(),
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file_size=file_size,
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file_url=doc.slug,
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created_at=doc.updated_at
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)
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db_file = File(**upload_file.model_dump())
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db.add(db_file)
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db.commit()
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# Save file
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save_path = os.path.join(save_dir, f"{db_file.id}{db_file.file_ext}")
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# update file
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if os.path.exists(save_path):
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os.remove(save_path) # Delete a single file
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shutil.copyfile(file_path, save_path)
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# 2. Create a document
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create_document_data = document_schema.DocumentCreate(
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kb_id=db_knowledge.id,
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created_by=db_knowledge.created_by,
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file_id=db_file.id,
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file_name=db_file.file_name,
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file_ext=db_file.file_ext,
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file_size=db_file.file_size,
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file_meta={},
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parser_id="naive",
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parser_config={
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"layout_recognize": "DeepDOC",
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"chunk_token_num": 128,
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"delimiter": "\n",
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"auto_keywords": 0,
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"auto_questions": 0,
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"html4excel": "false"
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}
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)
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db_document = Document(**create_document_data.model_dump())
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db.add(db_document)
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db.commit()
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# 3. Document parsing, vectorization, and storage
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parse_document(file_path=save_path, document_id=db_document.id)
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db_files = db.query(File).filter(File.kb_id == db_knowledge.id,
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File.file_url.notin_(file_urls)).all()
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if db_files: # --delete
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for db_file in db_files:
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db_document = db.query(Document).filter(Document.kb_id == db_knowledge.id,
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Document.file_id == db_file.id).first()
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if db_document:
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# 1. Delete vector index
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vector_service.delete_by_metadata_field(key="document_id",
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value=str(db_document.id))
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# 2. Delete document
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db.delete(db_document)
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# 3. Delete file
|
||||
file_path = Path(
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settings.FILE_PATH,
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str(db_file.kb_id),
|
||||
str(db_file.parent_id),
|
||||
f"{db_file.id}{db_file.file_ext}"
|
||||
)
|
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if file_path.exists():
|
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file_path.unlink() # Delete a single file
|
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db.delete(db_file)
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# commit transaction
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||||
db.commit()
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n\nError during fetch feishu: {e}")
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||||
if feishu_app_id: # Feishu Knowledge Base
|
||||
feishu_app_secret = db_knowledge.parser_config.get("feishu_app_secret", "")
|
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feishu_folder_token = db_knowledge.parser_config.get("feishu_folder_token", "")
|
||||
# Create feishuAPIClient
|
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api_client = FeishuAPIClient(
|
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app_id=feishu_app_id,
|
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app_secret=feishu_app_secret
|
||||
)
|
||||
try:
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||||
# 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:
|
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files = await client.list_all_folder_files(feishu_folder_token, recursive=True)
|
||||
return files
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||||
files = asyncio.run(async_get_files(api_client, feishu_folder_token))
|
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# 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.parent_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.parent_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": 128,
|
||||
"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]:
|
||||
|
||||
|
||||
Reference in New Issue
Block a user