🧠 用 Flask + HuggingFace + OpenRouter 打造一个免费的 RAG 问答系统（完整教学）
✅ 项目介绍
我们要做一个网页，可以上传 .txt 文档，然后问它问题，它会回答你。
这个功能背后用了 AI 模型，但我们不会花钱，全程使用免费的模型和服务！
🧱 步骤一：准备环境
1. 创建一个新文件夹
mkdir rag_flask_app
cd rag_flask_app
2. 创建并激活虚拟环境（可选但推荐）
conda create -n rag_env python=3.10 -y
conda activate rag_env
3. 安装依赖
创建一个 requirements.txt 文件：
flask
llama-index>=0.10.0
sentence-transformers
llama-index-embeddings-huggingface
openai
安装依赖：
pip install -r requirements.txt
📁 步骤二：构建项目结构
mkdir templates
mkdir uploads
touch app.py templates/index.html
🧠 步骤三：写入核心代码
🔹 app.py（主逻辑）
from flask import Flask, render_template, request
import os
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.settings import Settings
# 初始化 Flask
app = Flask(__name__)
UPLOAD_FOLDER = 'uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
# 设置模型
embed_model = HuggingFaceEmbedding(model_name="all-MiniLM-L6-v2") # 本地嵌入模型
llm = OpenAI(
api_base="https://openrouter.ai/api/v1",
api_key="sk-or-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", # 👉 填你的 OpenRouter Key
model="gpt-3.5-turbo"
)
# 应用到 LlamaIndex 设置
Settings.embed_model = embed_model
Settings.llm = llm
index = None
@app.route('/', methods=['GET', 'POST'])
def index_page():
global index
response = ""
if request.method == 'POST':
if 'file' in request.files:
file = request.files['file']
filepath = os.path.join(UPLOAD_FOLDER, file.filename)
file.save(filepath)
documents = SimpleDirectoryReader(UPLOAD_FOLDER).load_data()
index = VectorStoreIndex.from_documents(documents)
response = "Document uploaded and indexed!"
elif 'question' in request.form and index is not None:
question = request.form['question']
query_engine = index.as_query_engine()
response = query_engine.query(question).response
return render_template("index.html", response=response)
if __name__ == '__main__':
app.run(debug=True)
🔹 templates/index.html（网页界面）
<!DOCTYPE html>
<html>
<head>
<title>RAG Web App</title>
</head>
<body>
<h1>🧠 RAG + LLM Document QA</h1>
<form method="POST" enctype="multipart/form-data">
<p><strong>Upload a .txt document:</strong></p>
<input type="file" name="file" required>
<input type="submit" value="Upload">
</form>
<form method="POST">
<p><strong>Ask a question about the document:</strong></p>
<input type="text" name="question" required style="width:300px;">
<input type="submit" value="Ask">
</form>
<h2>Answer:</h2>
<p>{{ response }}</p>
</body>
</html>
🔑 步骤四：获取免费 OpenRouter Key
登录后点击右上角头像 →
API Keys创建新 key，会像这样：
sk-or-v1-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx在
app.py中粘贴进去替换
🚀 步骤五：运行你的应用！
在终端里运行：
python app.py
打开浏览器访问： 👉 http://127.0.0.1:5000
🧪 测试流程
上传一个
.txt文档（例如一段英文文章）输入问题，比如：
“What is the main topic?”
“Who is the author?”
系统会返回基于文档的智能回答 ✅