AI-Document-Analyzer-MVP-Example

A simplified reference project architecture for an AI-powered MVP using open-source LLMs (like Mistral-7B or Llama 2) with a modern tech stack: Web app that analyzes text documents and provides summaries + Q&A capabilities.

Project: AI Document Analyzer (MVP Example)

Tech Stack

Frontend (Next.js 14 - App Router)

• Typescript
• Tailwind CSS
• Shadcn/ui components

Backend (Python)

• FastAPI
• LangChain (AI orchestration)
• LlamaIndex (optional for document parsing)

AI Layer

• HuggingFace Transformers (for local models)
• Ollama (for local LLM hosting)
• OR HuggingFace Inference API (cloud)

Database

• SQLite (MVP-friendly)
• Redis (optional for caching)

Minimal Implementation Code

1. Frontend (Next.js) - app/page.tsx typescript

export default function Home() {
async function analyzeDocument(formData: FormData) {
'use server'
const text = formData.get('text')

const res = await fetch('http://localhost:8000/analyze', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ text })
})

return await res.json()
}

return (

<button type="submit" className="bg-blue-500 text-white px-4 py-2 rounded" > Analyze </button> </form> </main> ) } Backend (FastAPI) - main.py python

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from langchain.llms import HuggingFacePipeline
from transformers import AutoTokenizer, pipeline
import torch

app = FastAPI()

Load local model (... Mistral-7B)

model_name = "mistralai/Mistral-7B-Instruct-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
pipe = pipeline(
"text-generation",
model=model_name,
device_map="auto",
torch_dtype=torch.float16
)

llm = HuggingFacePipeline(pipeline=pipe, max_new_tokens=256)

class AnalysisRequest(BaseModel):
text: str

@app.post("/analyze")
async def analyze_document(request: AnalysisRequest):
try:
prompt = f"""
Analyze this document and provide:
1. A 3-sentence summary
2. 3 key insights
3. Answer: What is the main subject?

Document: {request.text}
"""

response = llm(prompt)
return {"analysis": response}

except Exception as e:
raise HTTPException(status_code=500, detail=str(e))

Key Components Explanation

Local Model Setup (Using HuggingFace):

• Use 4-bit quantization for memory efficiency
• Device mapping for GPU/CPU allocation
• Caching models locally after first download

AI Processing Flow:
- mermaid
- flowchart LR
A[User Input] --> B[Text Preprocessing]
B --> C[Prompt Engineering]
C --> D[LLM Inference]
D --> E[Result Post-processing]
E --> F[API Response]

Performance Considerations:

• For CPU-only: Use smaller models (phi-3-mini, TinyLlama)
• Response streaming for longer outputs
• Simple rate limiting on API

MVP Evolution Path

Initial Version (Week 1)

• Local model with basic text analysis
• Simple web interface
• Basic error handling

V1.1 (Week 2)

• File upload support (PDF, DOCX)
• Response formatting (markdown)
• Session history

V1.2 (Week 3)

• Cloud deployment (AWS EC2)
• Model switching capability
• Basic user authentication

To Get Started:

Install requirements: bash pip install fastapi uvicorn langchain transformers torch npm install next react react-dom

Run backend: bash uvicorn main

--reload

Run frontend: bash cd frontend && npm run dev

This architecture gives a foundation for AI data processing while maintaining flexibility. We can swap components (... use OpenAI API instead of local models) by modifying just the AI service layer.