AI Document Chatbot

Intelligent document processing with natural language conversations

Python Flask ChromaDB Sentence Transformers PyPDF2 Vector Search AI/ML HTML/CSS

Project Overview

What is it?

An intelligent document processing chatbot that can analyze PDFs, Word documents, and text files to provide contextual answers and insights through natural language conversations. The system uses advanced AI techniques including vector embeddings and semantic search to understand document content and respond to user queries intelligently.

The chatbot can handle multiple document types, extract meaningful information, and provide accurate responses based on the uploaded content. It's designed to be a powerful tool for document analysis, research assistance, and information retrieval.

Key Features

✓ Multi-format document support (PDF, DOCX, TXT)
✓ Intelligent text extraction and processing
✓ Vector-based semantic search
✓ Natural language conversation interface
✓ Real-time document analysis and insights
✓ Responsive web interface

Technical Implementation

Architecture

Document Processing Pipeline

Documents are processed through a multi-stage pipeline: extraction → chunking → embedding → storage

Vector Database

ChromaDB stores document embeddings for efficient semantic search and retrieval

Web Interface

Flask-based web application with real-time chat interface and document upload capabilities

Key Technologies

AI/ML Stack

Sentence Transformers for embeddings, semantic similarity for intelligent matching

Document Processing

PyPDF2 for PDF extraction, python-docx for Word documents, comprehensive text processing

Backend Framework

Flask web framework with RESTful API design and efficient request handling

Code Structure

agentchatbot/
├── document_processor.py    # Core document processing logic
├── main.py                 # Flask application entry point
├── templates/
│   └── chat.html          # Web interface template
├── documents/             # Sample documents for testing
├── processed/             # Processed document storage
└── requirements.txt       # Python dependencies

Features & Capabilities

📄

Document Processing

Supports PDF, Word documents, and text files with intelligent text extraction and formatting preservation.

🧠

AI-Powered Search

Vector-based semantic search using sentence transformers for contextually relevant responses.

💬

Natural Conversations

Intuitive chat interface that understands natural language queries and provides contextual answers.

⚡

Real-time Processing

Fast document upload and processing with immediate availability for querying and analysis.

🔍

Smart Insights

Extracts key information and provides intelligent insights based on document content and context.

📊

Document Analytics

Comprehensive document summary and analytics with processing statistics and metadata tracking.

Application Screenshots

Home Interface - Document Upload & Chat

File Processing & Document Analysis

Development Process

Research & Planning

Researched vector databases, document processing libraries, and AI embedding techniques. Designed the architecture for efficient document processing and retrieval.

Core Development

Built the document processor with support for multiple file formats, implemented vector embedding system using ChromaDB, and created the Flask web application.

Testing & Optimization

Tested with various document types, optimized embedding generation, and refined the chat interface for better user experience and response accuracy.

Deployment & Documentation

Set up the application for local deployment, created comprehensive documentation, and prepared the codebase for open-source contribution.

Challenges & Solutions

Key Challenges

⚠
Dependency Conflicts: Managing compatibility between sentence-transformers, huggingface-hub, and transformers libraries
⚠
Document Processing: Handling different file formats and extracting meaningful text content
⚠
Vector Search: Implementing efficient semantic search with proper chunking and embedding
⚠
Performance Optimization: Balancing processing speed with accuracy and memory usage

Solutions Implemented

✓
Version Management: Resolved dependency conflicts by using compatible versions and proper installation strategies
✓
Multi-format Support: Implemented robust document processors for PDF, DOCX, and TXT files
✓
Efficient Search: Used ChromaDB for vector storage and implemented smart chunking strategies
✓
Optimized Architecture: Designed modular system with efficient processing pipeline and caching

Future Enhancements

Planned Features

🔮 Multi-language document support
🔮 Advanced document summarization
🔮 User authentication and document privacy
🔮 Integration with cloud storage services
🔮 Real-time collaborative document analysis

Technical Improvements

⚡ Enhanced vector search algorithms
⚡ Improved document processing speed
⚡ Better memory management and caching
⚡ Advanced error handling and logging
⚡ Scalable microservices architecture