GraphRAG with Ollama - Install Local Models for RAG - Easiest Tutorial
AI Summary
Video Summary: Using Microsoft Graph RAG with OLAMA Locally
- Introduction
- The video demonstrates using Microsoft Graph RAG locally with OLAMA.
- Previous videos cover Graph RAG installation with OpenAI and its architecture.
- Viewers are assumed to have OLAMA installed, which is suitable for running large language models locally.
- Installation Guides
- For Windows, download the executable and follow the installation prompts.
- For Linux, use the provided command in the terminal to install OLAMA.
- Graph RAG Overview
- Graph RAG (Retrieval Augmented Generation) adds personal or business information to language models.
- It involves chunking text, converting to vectors, storing in a Vector store, and indexing for retrieval.
- Microsoft’s Graph RAG creates a graph of entities and relationships from text data, improving context and relevance.
- Setup and Configuration
- The video provides a step-by-step guide to configuring Graph RAG with OLAMA.
- Changes are made to the settings.yml file to use local models instead of OpenAI’s API.
- A hack is shown to modify the code to work with OLAMA, which is not officially supported.
- Running Graph RAG
- The pipeline is run to chunk, convert, and store text data.
- A question is asked to test the setup, and the system successfully retrieves information from the local text file.
- Closing Remarks
- The process is demonstrated in real-time with some hiccups.
- The video concludes with encouragement to subscribe and share the content.
- Sponsorship and Discounts
- Mast Compute is acknowledged for sponsoring the VM and GPU used in the video.
- A coupon code for a 50% discount on GPU rentals is provided.
- Commands and Files
- All commands and files used in the video will be available in the video description for easy access.