RAG with LM Studio

RAG with LM Studio, local LLMs

Main Notebook for RAG based on a set of PDFs: PDF_RAG_with_LMstudio.ipynb

This notebook performs the following operations:

• Reads all PDFs in a given folder
• Extracts text using GROBID
• Stores text elements in SQLite3 database
• Handles recursive chunks
• Embeds text
• Vectorizes extracted data
• Retrieval Methods
  1. Standard Retrieval
  2. LangChain: MultiQueryRetriever
• OpenAI-based chat using LM Studio
• Displays:
  • Query
  • Prompt Information
  • Answer: Dashboard Browser Tab
• Retrieval and QA chain based chat using LM Studio
• Displays results in a new Browser Tab

Installation & Usage

• Additional information is within the Notebook, as some Markdown cells describe requirements and usage details.

Prerequisites

1. Install LM Studio
   • Follow the official LM Studio installation guide for your operating system.
2. Download LLM Model from Hugging Face
   • You can download a pre-trained model from Hugging Face using the Hugging Face Model Hub. Follow the instructions on their site to use the desired model.
3. Install Docker for GROBID
   • Make sure Docker is installed on your machine. You can follow the installation instructions from the Docker website.
   • After installing Docker, pull the GROBID Docker image by running:

     docker pull lfoppiano/grobid

Running the Notebook

1. Clone the repository.
2. Install the required dependencies.
3. Run the PDFs_RAG_with_LMstudio.ipynb notebook to begin processing your PDFs.