PDF Analyzer with Local Ollama

A powerful tool to analyze PDF documents and answer questions using Retrieval-Augmented Generation (RAG) with your local Ollama installation. This tool extracts content only from the provided PDF and doesn't use any external knowledge sources.

Features

• PDF Text Extraction: Uses pdfplumber for robust text extraction, including Arabic text
• Semantic Search: Creates embeddings and uses FAISS for fast similarity search
• Local AI: Uses your local Ollama installation for answering questions
• Multi-language Support: Works with Arabic, English, and other languages
• Two Interfaces: Both web UI (Streamlit) and command-line interface
• Source Citation: Shows which parts of the PDF were used to generate answers

Installation

1. Install Python dependencies:

pip install -r requirements.txt

2. Install and setup Ollama:

   • Download Ollama from https://ollama.ai
   • Install and start the Ollama service
   • Pull a model (e.g., ollama pull llama3.2)

3. Verify Ollama is running:

ollama list

Usage

Web Interface (Recommended)

1. Start the Streamlit app:

streamlit run pdf_analyzer.py

2. Open your browser to http://localhost:8501

3. Upload your PDF and click "Process PDF"

4. Ask questions about the PDF content

Command Line Interface

1. Run the CLI:

python cli.py "path/to/your/pdf/file.pdf"

2. Ask questions interactively

Advanced Options

python cli.py "document.pdf" --model llama3.2 --chunk-size 500 --overlap 100 --top-k 5

Configuration

Edit config.py to customize:

• Ollama model name
• Chunk size and overlap
• Embedding model
• Number of relevant chunks to retrieve

How It Works

1. Text Extraction: Extracts text from PDF using pdfplumber
2. Text Chunking: Splits text into overlapping chunks for better context
3. Embedding Creation: Creates vector embeddings using SentenceTransformers
4. Vector Storage: Stores embeddings in FAISS index for fast similarity search
5. Question Processing:
   • Converts question to embedding
   • Finds most similar text chunks
   • Sends relevant context to Ollama
   • Returns AI-generated answer based only on PDF content

Supported Models

Any Ollama model can be used. Popular choices:

• llama3.2 (recommended for general use)
• mistral
• codellama (for code-related documents)
• qwen2.5 (good for multilingual content)

Make sure to pull the model first: ollama pull model-name

Troubleshooting

Ollama Connection Issues

• Ensure Ollama is running: ollama serve
• Check if model is available: ollama list
• Verify the model name in configuration

PDF Processing Issues

• Ensure the PDF contains extractable text (not just images)
• Try with a different PDF to isolate the issue
• Check file permissions

Memory Issues

• Reduce chunk size in configuration
• Use a smaller embedding model
• Process smaller PDFs

Example Questions

For an Arabic legal document:

• "ما هو موضوع هذا النظام؟" (What is the subject of this system?)
• "ما هي المواد المتعلقة بالحكم؟" (What are the articles related to governance?)

For English documents:

• "What is the main topic of this document?"
• "Summarize the key points"
• "What are the requirements mentioned?"

File Structure

pdf_analyzer/
├── pdf_analyzer.py      # Main Streamlit application
├── cli.py              # Command-line interface
├── config.py           # Configuration settings
├── requirements.txt    # Python dependencies
├── README.md          # This file
└── cache/             # Cached indexes (created automatically)

License

This project is open source and available under the MIT License.