# Detailed Plan for Data Preparation Pipeline

## Tech Stack

1. **Node.js**: Server-side JavaScript runtime.
2. **Express.js**: Web framework for Node.js.
3. **MongoDB**: NoSQL database for storing raw and processed data.
4. **Mongoose**: ODM for MongoDB to manage data schemas.
5. **Python**: For data processing and preparation.
6. **Pandas**: Python library for data manipulation and analysis.
7. **OpenAI API**: For interacting with the LLM (Language Model).
8. **Docker**: Containerization for consistent environments.
9. **Jest**: Testing framework for JavaScript.
10. **Swagger**: API documentation.

## Architecture

1. **Data Ingestion**

   - **API Endpoint**: An Express.js endpoint to receive raw data.
   - **Storage**: Save raw data in MongoDB.

2. **Data Processing**

   - **Python Script**: Use Pandas to clean and preprocess data.
   - **Scheduler**: Cron job to run the Python script periodically.

3. **Data Transformation**

   - **API Endpoint**: Another Express.js endpoint to trigger data transformation.
   - **OpenAI API**: Send preprocessed data to OpenAI API and receive JSON results.

4. **Data Storage**

   - **MongoDB**: Store the processed JSON results.

5. **API Documentation**
   - **Swagger**: Document the API endpoints.

## Detailed Steps

1. **Setup Express.js Server**

   - Create an Express.js server with endpoints for data ingestion and transformation.
   - Use Mongoose to define schemas for raw and processed data.

2. **Data Ingestion Endpoint**

   - Create a POST endpoint to receive raw data and store it in MongoDB.

3. **Data Processing Script**

   - Write a Python script using Pandas to clean and preprocess the raw data.
   - Schedule the script to run periodically using a cron job.

4. **Data Transformation Endpoint**

   - Create a POST endpoint to trigger data transformation.
   - Use the OpenAI API to send preprocessed data and receive JSON results.
   - Store the JSON results in MongoDB.

5. **API Documentation**
   - Use Swagger to document the API endpoints.

## Example Code Snippets

**Express.js Server Setup**

```javascript
const express = require("express");
const mongoose = require("mongoose");
const bodyParser = require("body-parser");

const app = express();
app.use(bodyParser.json());

mongoose.connect("mongodb://localhost:27017/vr_expo", {
  useNewUrlParser: true,
  useUnifiedTopology: true,
});

const rawDataSchema = new mongoose.Schema({ data: Object });
const processedDataSchema = new mongoose.Schema({ data: Object });

const RawData = mongoose.model("RawData", rawDataSchema);
const ProcessedData = mongoose.model("ProcessedData", processedDataSchema);

app.post("/ingest", async (req, res) => {
  const rawData = new RawData({ data: req.body });
  await rawData.save();
  res.send("Data ingested");
});

app.post("/transform", async (req, res) => {
  // Call Python script and OpenAI API here
  res.send("Data transformed");
});

app.listen(3000, () => {
  console.log("Server is running on port 3000");
});
```

**Python Data Processing Script**

```python
import pandas as pd
from pymongo import MongoClient

client = MongoClient('mongodb://localhost:27017/')
db = client.vr_expo
raw_data_collection = db.rawdatas
processed_data_collection = db.processeddatas

def process_data():
    raw_data = list(raw_data_collection.find())
    df = pd.DataFrame(raw_data)
    # Data cleaning and preprocessing
    processed_data = df.to_dict(orient='records')
    processed_data_collection.insert_many(processed_data)

if __name__ == "__main__":
    process_data()
```

**Swagger Documentation**

```javascript
const swaggerUi = require("swagger-ui-express");
const swaggerJsDoc = require("swagger-jsdoc");

const swaggerOptions = {
  swaggerDefinition: {
    info: {
      title: "VR Expo API",
      version: "1.0.0",
    },
  },
  apis: ["server.js"],
};

const swaggerDocs = swaggerJsDoc(swaggerOptions);
app.use("/api-docs", swaggerUi.serve, swaggerUi.setup(swaggerDocs));
```
