EASILY Build RAG Application with Ollama, Mistral & Llama Index
AI Summary
Summary: Creating a RAG Application with Mistral, Ollama, and Llama Index
- Introduction
- Excitement about demonstrating the creation of a RAG application.
- Encouragement to subscribe to the YouTube channel for AI-related content.
- Overview of the Process
- Indexing, saving, and loading data using Mistral and Ollama.
- Data stored in Quadrant Vector Store, consisting of JSON-formatted tweets.
- Analysis of Star Trek-related tweets.
- System and Setup
- Utilizing an NVIDIA RTX 5000 with 32 GB GPU on a Linux machine.
- Installation of Ollama and necessary Python packages.
- Coding the Application (
app.py)
- Importing modules from Llama Index, Quadrant Client, and others.
- Loading JSON data and initializing Quadrant Client and Vector Store.
- Setting up service context with language model and embedding model.
- Creating Vector Store Index and Query Engine.
- Performing a query about Star Trek and printing the response.
- Running the Application
- Executing the code to index, store, and query data.
- Using Mistal’s 7 billion parameter model for responses.
- Additional File (
index.py)
- Importing similar modules and initializing Quadrant Client and Vector Store.
- Loading the previously saved index instead of re-indexing.
- Running the code to query the loaded index and print the response.
- Conclusion
- Successfully created a RAG application using Mistral, Ollama, Llama Index, and Quadrant Vector Store.
- Ability to index, save, load, and query data.
- Invitation to watch more videos and subscribe to the channel.