Free AI-Powered Local Document Interaction – Chat with PDF

Chat with PDF is a Python-based web app that lets you converse with your PDF files using Meta’s Llama 3 model right on your computer.

It uses Retrieval Augmented Generation (RAG) to give precise answers from the PDF’s content. You upload your PDF, ask questions, and receive accurate, AI-powered responses. Your data stays private because everything runs locally.

How it works:

Chat with PDF uses a combination of open-source technologies to deliver a private and effective way to query your PDF documents.

This web app uses Retrieval Augmented Generation (RAG), a technique that combines information retrieval with language model generation. This means the AI doesn’t just generate text; it retrieves relevant information from your uploaded PDF to form its responses. This approach ensures accuracy and contextually appropriate answers.

The backend relies on Llama 3.2, a powerful large language model, accessed through Ollama, a tool for running large language models locally.

Chroma, a vector database, stores embeddings of your PDF content for efficient retrieval.

The entire application is built with Python and uses Reflex, a Python framework for building user interfaces. This framework allows for the easy creation of interactive elements like the file upload and chat interface.

How to use it:

1. Clone the Chat with PDF GitHub repository to your local machine:

git clone https://github.com/Sumanth077/chat_with_pdf.git

2. Navigate to the project directory and install the necessary Python packages using pip install -r requirements.txt. This command installs the libraries listed in the requirements.txt file:

• embedchain: This library handles the knowledge base creation and management, allowing the application to interact with your PDF content.
• reflex: This framework powers the user interface, allowing for interactive elements and real-time updates.
• ollama: This library allows you to run Llama 2 locally and connect to it from the application. It provides the interface for communicating with the Llama 3.2 language model. Ensure Ollama is running on your machine and that the model llama3.2:latest is installed before proceeding.

3. After installation, execute reflex run in your terminal to launch the application. This command starts the Reflex development server and opens the application in your web browser.

FAQs:

Q: What is RAG?
A: RAG stands for Retrieval Augmented Generation. It’s a technique that enhances AI language models by retrieving relevant information from a knowledge base before generating responses, improving accuracy and context-awareness.

Q: What is Reflex App?
A: Reflex is a Python framework for building web applications. It allows developers to create reactive user interfaces using Python code, making it easier to develop full-stack applications without separate frontend and backend code.

Q: Can I use this tool offline?
A: Yes, Chat with PDF runs locally on your machine, allowing you to use it offline once set up.

Q: What types of questions can I ask about my PDF?
A: You can ask any questions related to the content of your uploaded PDF. The AI will attempt to provide relevant answers based on the document’s information.

Q: Is my PDF data kept private?
A: Yes, since the tool runs locally on your machine, your PDF data and questions remain private and are not sent to external servers.