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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:
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
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 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
Deployment: Vercel
CI/CD: GitHub Actions (integrated with Vercel)
Monitoring: Sentry or LogRocket
Next.js ChatGPT Example:
NextAuth.js Example:
Next.js with Tailwind CSS:
Document Processing with Python:
miso-belica/awesome-py-pdf: Collection of Python PDF libraries and resources.
python-openxml/python-docx: A Python library for creating and updating Microsoft Word (.docx) files.
Embedding and Vector Search:
pinecone-io/examples: Examples of how to use Pinecone for vector search.
weaviate/weaviate: An open-source vector search engine.
Set up Next.js Project:
npx create-next-app@latest my-chatgpt-app
cd my-chatgpt-app
npm install
Integrate Tailwind CSS: Follow the setup guide from the Next.js with Tailwind CSS example.
Set up Authentication with NextAuth.js: Follow the NextAuth.js example to configure email login.
Add OpenAI API Integration: Create a serverless function in the api directory to handle OpenAI API requests.
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.
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])])
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 } }
```
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.