PDFConverse

Upload your PDF and talk to it in natural language to gain insights and knowledge, just as you would talk to an LLM such as ChatGPT or Bard.

Note: The app will only respond to questions related to the loaded PDF.

How It Works

1. PDF is divided up into small chunks
2. Chunks are embedded using an embedding model
3. Embeddings are stored in a vector store
4. User asks a question
5. Question is embedded using the same embedding model
6. Similarity search of the embedded question is performed with docs in the vector store
7. Question + similar docs are sent to LLM
8. LLM answers the question, which is shown to the user

Demo

Setup

This project is written in python 3.10.10

• Copy .env.example and rename to .env
  
• Add the OPENAI_API_KEY to .env
  
• To use hugging face models add the HUGGINGFACEHUB_API_TOKEN to .env

Install requirements:

pip install -r requirements.txt

In order to use hugging face models, uncomment the following in requirements.txt

• huggingface-hub
• InstructorEmbedding
• sentence-transformers

and run:

pip install -r requirements.txt

Usage

streamlit run app.py

To use hugging face models:

streamlit run app.py -- --hf

Using --hf downloads the embedding model on your machine and the embeddings are performed locally. The LLM used is accessed via the Hugging Face Inference API.