https://github.com/hasan-py/chat-with-pdf-rag
Chat with PDF using LangChain, Streamlit, Ollama (for LLM inference) and PDFPlumber. Overall which is an example of a Retrieval-Augmented Generation (RAG) system with Deepseek r1 model.
https://github.com/hasan-py/chat-with-pdf-rag
Last synced: 2 months ago
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Chat with PDF using LangChain, Streamlit, Ollama (for LLM inference) and PDFPlumber. Overall which is an example of a Retrieval-Augmented Generation (RAG) system with Deepseek r1 model.
- Host: GitHub
- URL: https://github.com/hasan-py/chat-with-pdf-rag
- Owner: hasan-py
- Created: 2025-01-31T13:06:56.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-03T08:17:55.000Z (5 months ago)
- Last Synced: 2025-03-28T22:34:35.876Z (3 months ago)
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 66
- Watchers: 1
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Chat with Your PDFs using RAG
This project allows you to upload a PDF and ask questions about its content using **Deepseek R1** via **Ollama**. The application processes PDFs, extracts text, indexes them into a vector store, and retrieves relevant context to generate concise answers.
## Features
- 📂 **Upload a PDF**: Select a PDF file to process.
- 🔍 **Text Extraction & Indexing**: Extracts content and indexes it for efficient search.
- 💡 **Question-Answering**: Ask questions related to the PDF content and get relevant answers.
- 🚀 **Powered by Ollama & LangChain**: Uses `Deepseek R1` for embeddings and responses.
## Installation
### Prerequisites
- Python 3.8+
- [Ollama](https://ollama.com) installed
- Dependencies installed via pip
### Setup
1. Clone this repository:
```sh
git clone https://github.com/hasan-py/chat-with-pdf-RAG.git
cd chat-with-pdf-RAG
```
Activate your python env and install the dependencies.
2. Install dependencies:
```sh
pip install -r requirements.txt
```
3. Run the Streamlit app:
```sh
streamlit run pdf_rag.py
```
## How It Works
1. **Upload a PDF**: Use the UI to upload a document.
2. **Processing**: The app extracts text and chunks it for indexing.
3. **Ask Questions**: Enter a question in the chat box.
4. **Get Answers**: The system retrieves relevant text and responds concisely.
## How to change model?
To change the model used for inference, you can modify the `LLM` variable in the `pdf_rag.py` file. The `LLM` variable is initialized with the `deepseek-r1:8b` model by default. You can replace it with any other model supported by `Ollama`.
## File Structure
```
chat-with-pdf/
│── pdfs/ # Directory for uploaded PDFs
│── pdf_rag.py # Main Streamlit app
│── requirements.txt # Dependencies
│── README.md # Documentation
│── test_pdf_rag.py # Unit Test
```
## Technologies Used
- **Python**
- **Streamlit** (for UI)
- **LangChain** (for text processing)
- **Ollama** (for LLM inference)
- **PDFPlumber** (for PDF extraction)
## Contributing
Feel free to submit issues and PRs to improve the project! And follow this steps:
- Before submitting PRs, please update the corresponding test cases.
- Please attach a screen recording video to the PR description showing that all functionality is working properly.
## Acknowledgments
Special thanks to the creators of **LangChain**, **Ollama**, **Streamlit** and the **community** for enabling this functionality.