AI-Agent Document Analyzer

This project is an AI-powered document analysis bot that processes and extracts information from PDF documents. It uses LlamaIndex, LangChain, and Ollama models to parse, analyze, and interact with document content. The bot intelligently handles user queries, retrieving only relevant information from the PDFs.

🚀 Why I Built This

1. To deepen my understanding of LLMs by building a practical, hands-on project.
2. To implement Retrieval-Augmented Generation (RAG), reducing hallucinations by feeding the model curated, factual data.
3. To create a tool I wish I had during my thesis—an AI that could compare and summarize research papers interactively.

This project is still a work in progress. I am continuously enhancing the bot by adding new tools and improving the clarity and readability of responses. Running a local Ollama model ensures privacy and eliminates API costs, allowing unrestricted experimentation.

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✨ Features

• 📄 PDF Parsing – Extracts and interprets text from PDFs using LlamaParse.
• 🔍 Intelligent Querying – Uses embeddings and vector search to find only the most relevant sections of documents.
• 📊 Vector Database – Stores and retrieves information efficiently.
• 🤖 ReAct Agent – Selects the appropriate tools to answer user queries dynamically.
• 💬 Interactive Chatbot – Engages in real-time conversations about the documents.
• 🔗 Smart Link Extraction – Extracts only the links relevant to the user’s query, not all links.

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⚙️ Prerequisites

Before running the project, ensure you’ve set up the environment:

1️⃣ Install Ollama (No API Key Needed)

• Download and install Ollama (free & local).
• Verify Ollama is running:

  ollama list

2️⃣ Get a LlamaParse API Key (Free)

• Sign up for a key: LlamaParse
• Add it to your .env file:

  LLAMA_PARSE_API_KEY=your_api_key
  

3️⃣ Use Local Embeddings (No API Key Required)

• Ensure the embedding model is accessible:

  local:BAAI/bge-m3

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📥 Installation

1. Clone the Repository:

   git clone https://github.com/your-username/AI-Agent-Document-Analyzer.git
   cd AI-Agent-Document-Analyzer

2. Install Dependencies:

   pip install -r requirements.txt

3. Set Up Environment Variables:

   • Create a .env file.
   • Add your LlamaParse API key.

4. Prepare Data:

   • Place your PDF documents inside the data directory.

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▶️ Usage

Start the document analysis bot:

python main.py

📝 Example Queries

"Summarize this document." "What are the key concepts discussed?" "What is the objective of this research paper?"

🛠️ Code Overview

Component	Functionality
LlamaParse	Parses PDFs into a structured format.
VectorStoreIndex	Converts document text into embeddings for search.
Query Engine	Finds and retrieves only the most relevant sections.
ReAct Agent	Dynamically decides which tools to use based on queries.
Intent Classifier	Determines if a query needs general info or links.
Contextual Link Finder	Extracts only the relevant links based on user queries.

🛠️ Future Improvements

• Improve response formatting for better clarity.
• Enhance multi-document support.
• Integrate better summarization techniques for long documents.