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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.