来个 Python 大佬帮忙看看异步同步这块卡住了

[test00001]

import os
from flask import Flask, request, render_template, jsonify, Response
from llama_index.core import StorageContext, VectorStoreIndex, Settings, Document, SimpleDirectoryReader
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
from elasticsearch import AsyncElasticsearch
from llama_index.readers.file import (
    DocxReader, HWPReader, PDFReader, EpubReader, FlatReader, 
    HTMLTagReader, ImageReader, IPYNBReader, MarkdownReader, 
    MboxReader, PptxReader, PandasCSVReader, PyMuPDFReader, 
    XMLReader, PagedCSVReader, CSVReader
)

app = Flask(__name__)
# 配置类
class Ai():
    base_url = "http://localhost:11434"
    es_url = "http://localhost:9200"
    es_index = "spring-ai-index-test"
    model = "qwen2.5:7b"
    embedding = "bge-m3"
    upload_path= "./uploads"

# 确保上传目录存在
os.makedirs(Ai.upload_path, exist_ok=True)

# 解析器映射
PARSERS = {
    ".pdf": PDFReader(),
    ".docx": DocxReader(),
    ".hwp": HWPReader(),
    ".epub": EpubReader(),
    ".txt": FlatReader(),
    ".html": HTMLTagReader(),
    ".jpg": ImageReader(), ".jpeg": ImageReader(), ".png": ImageReader(),
    ".ipynb": IPYNBReader(),
    ".md": MarkdownReader(),
    ".mbox": MboxReader(),
    ".csv": PandasCSVReader(),
    ".xml": XMLReader()
}

# 全局模型配置
Settings.embed_model = OllamaEmbedding(model_name=Ai.embedding, base_url=Ai.base_url)
Settings.llm = Ollama(model=Ai.model, base_url=Ai.base_url)

# 同步策略
es_client = AsyncElasticsearch(Ai.es_url, request_timeout=60)
dense_vector_store = ElasticsearchStore(
    es_client = es_client,
    index_name = Ai.es_index
)

#新增文件入口
@app.route('/add')
def upload_form():
    return render_template('upload.html')

#入口
@app.route('/')
def chat_html():
    return render_template('chat.html')

#聊天入口
@app.route('/chat')
def chat():
    q = request.args.get('q')
    if not q:
        return jsonify({"error": "No query provided"}), 400
    return search(q)

#上传文件方法
@app.route('/upload', methods=['POST'])
def upload_file():
    try:
        if 'file' not in request.files:
            return jsonify({"error": "No file part"}), 400
        
        file = request.files['file']
        
        # 获取文件扩展名
        ext = os.path.splitext(file.filename)[-1].lower()
        if ext not in PARSERS:
            return jsonify({"error": "Unsupported file type"}), 400    

        # 保存文件
        file_path = os.path.join(Ai.upload_path, file.filename)
        file.save(file_path)

        # 解析文件
        parser = PARSERS[ext]
        file_extractor = {ext: parser}
        documents = SimpleDirectoryReader(
            Ai.upload_path, file_extractor=file_extractor
        ).load_data()

        if not documents:
            return jsonify({"error": "No content extracted from file"}), 400
        
        # 确保 documents 是一个列表
        if not isinstance(documents, list):
            documents = [documents]

        # 确保文档类型是 Document ，如果是字符串需要手动包装
        documents = [doc if isinstance(doc, Document) else Document(doc) for doc in documents]

        # 存储到索引
        storage_context = StorageContext.from_defaults(vector_store=dense_vector_store)
        index = VectorStoreIndex.from_documents(
            documents,
            storage_context=storage_context
        )
        # 删除本地上传的文件
        os.remove(file_path)
        
        return jsonify({
            "message": "File processed and stored successfully",
            "filename": file.filename
        }), 200
        
    except UnicodeDecodeError:
        return jsonify({"error": "File must be text encoded in UTF-8"}), 400
    except Exception as e:
        return jsonify({"error": str(e)}), 500

# 查询文档
def search(query: str):
    try:
        storage_context = StorageContext.from_defaults(vector_store=dense_vector_store)
        index = VectorStoreIndex.from_vector_store(
            vector_store=dense_vector_store,
            storage_context=storage_context
        )
        query_engine = index.as_query_engine()
        
        # 直接调用同步 query 方法
        response = query_engine.query(query)
        
        return str(response)
    except Exception as e:
        return f"Error: {str(e)}"


if __name__ == '__main__':  
    app.run(host='0.0.0.0', port=5000, debug=True)

第一次对话正常，第二次就报错 Error: Timeout context manager should be used inside a task

[hellojay]

这么多代码还涉及这么多库，建议找个 ai 问问啊