Building an Interactive Q&A Chatbot for PDFs Using Mistral and RAG
The idea is to pass a book in pdf format to a chatbot that we can ask questions and talk about.
The first step is choosing a pre-trained LLM to customize and train with our own data which are pdf files in this example. GPT4, LLAMA 3.1, T5, ... come to mind when considering text-to-text llms but in this example I'm going to use Mistralai for several reasons such as being more convenient to use since it does not require access approval like Meta's LLAMA and is open-source unlike gpt4.
We receive the pdf file and break it into smaller chunks then we use Hugging face Embeddings and chromedb to vectorize the text and the embeddings to implement Retrieval-Augmented Generation (RAG). After that load the LLM and create a Q/A chain for the end user to interact with.
We can also integrate fine-tuning with RAG and use a model with richer tokens to enhance the Q/A procedure.
Conclusion
In summary, building an interactive chatbot for PDFs involves using pre-trained models like Mistral and techniques like Retrieval-Augmented Generation (RAG). By combining open-source NLP tools and embedding methods, we create a dynamic Q&A system that enables users to gain direct, context-aware insights from PDF documents efficiently.