Build Your Own GPT-4 Powered Chatbot with Custom Training & Email Login (Next.js & Vercel)

Build Your Own GPT-4 Powered Chatbot with Custom Training & Email Login (Next.js & Vercel)

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4 min read

To customize the prompt and train the GPT-4 model with your own data, such as PDFs and Word documents, you will need to integrate additional services and tools to handle data ingestion, processing, and training. Here's an updated tech stack and workflow to achieve these features:

Updated Tech Stack

Frontend:

  • Framework: Next.js

  • UI Library: Tailwind CSS or Material-UI

  • State Management: Redux or Zustand

  • Authentication: NextAuth.js

  • Form Handling: React Hook Form or Formik

Backend:

  • Serverless Functions: Vercel's serverless functions or AWS Lambda

  • API: OpenAI API for GPT-4 integration

  • Database: MongoDB (using MongoDB Atlas for serverless deployment) or PostgreSQL

  • Authentication: Firebase Authentication or Auth0

  • Data Storage: AWS S3, Google Cloud Storage, or Azure Blob Storage for storing documents

  • Data Processing: Python scripts for parsing and processing PDF/Word documents

Data Processing and Training:

  • Data Extraction: Apache Tika, PyMuPDF, or Python-docx for extracting text from PDFs and Word documents

  • Embedding Storage: Pinecone, Weaviate, or Milvus for storing and searching document embeddings

  • Fine-tuning GPT-4: OpenAI’s fine-tuning capabilities (when available) or using embeddings and custom retrieval-augmented generation (RAG) techniques

DevOps:

  • Deployment: Vercel

  • CI/CD: GitHub Actions (integrated with Vercel)

  • Monitoring: Sentry or LogRocket

  1. Next.js ChatGPT Example:

  2. NextAuth.js Example:

  3. Next.js with Tailwind CSS:

  4. Document Processing with Python:

  5. Embedding and Vector Search:

Steps to Get Started

  1. Set up Next.js Project:

     npx create-next-app@latest my-chatgpt-app
     cd my-chatgpt-app
     npm install
    
  2. Integrate Tailwind CSS: Follow the setup guide from the Next.js with Tailwind CSS example.

  3. Set up Authentication with NextAuth.js: Follow the NextAuth.js example to configure email login.

  4. Add OpenAI API Integration: Create a serverless function in the api directory to handle OpenAI API requests.

  5. Set Up Data Storage for Documents: Choose a cloud storage service (AWS S3, Google Cloud Storage, or Azure Blob Storage) and set up a bucket for storing your documents.

  6. Extract Text from Documents: Use Python scripts with libraries like PyMuPDF, python-docx, and Apache Tika to extract text from PDFs and Word documents. Example script for extracting text from PDFs:

    ```plaintext

    import fitz # PyMuPDF

    def extract_text_from_pdf(file_path): doc = fitz.open(file_path) text = "" for page in doc: text += page.get_text() return text


7. **Store Document Embeddings**: Use a service like Pinecone, Weaviate, or Milvus to store and search embeddings of your documents. Example using Pinecone:

    ```plaintext
    import pinecone
    from sentence_transformers import SentenceTransformer

    pinecone.init(api_key='YOUR_PINECONE_API_KEY', environment='YOUR_ENVIRONMENT')
    index = pinecone.Index('document-embeddings')

    model = SentenceTransformer('all-MiniLM-L6-v2')

    def store_document_embeddings(doc_text, doc_id):
        embeddings = model.encode([doc_text])
        index.upsert([(doc_id, embeddings[0])])
  1. Customize Prompt and Retrieval-Augmented Generation (RAG): Implement a retrieval mechanism to fetch relevant document embeddings and include them in your prompt for GPT-4. Example:

    ```plaintext // pages/api/generate-response.js import { Configuration, OpenAIApi } from 'openai'; import pinecone from 'pinecone-client';

    const configuration = new Configuration({ apiKey: process.env.OPENAI_API_KEY, }); const openai = new OpenAIApi(configuration);

    export default async function handler(req, res) { if (req.method === 'POST') { const { prompt } = req.body;

    // Retrieve relevant document embeddings const index = pinecone.Index('document-embeddings'); const query_embeddings = model.encode([prompt]); const results = await index.query(query_embeddings[0], topK=5);

    // Include retrieved text in the prompt const context = results.matches.map( match => match.metadata.text).join('\n'); const full_prompt = ${context}\n\n${prompt};

    const response = await openai.createCompletion({ model: 'gpt-4', prompt: full_prompt, max_tokens: 100, }); res.status(200).json(response.data); } else { res.status(405).end(); // Method Not Allowed } }

```

  1. Deploy to Vercel:

    • Push your code to GitHub.

    • Connect your GitHub repository to Vercel.

    • Configure environment variables in Vercel for OPENAI_API_KEY and any other necessary secrets.

By following these steps, you will be able to build a ChatGPT-like interface that allows for email login, customizes prompts, and utilizes your own data from PDFs and Word documents, all built with Next.js and deployed on Vercel.