Exploring RAG Implementation with Metadata Filters — llama_Index
Exploring Metadata Filters in RAG with Llama-Index: A Practical Guide
Hey everyone! Today, I’m diving into an intriguing feature of RAG (Retrieval-Augmented Generation) and how it works with Llama-Index’s metadata filters. If you’ve played around with LLMs and Llama-Index before, this post will show you how to leverage metadata filters to get more out of your queries.
What’s RAG All About?
For those who might be new, RAG combines pre-trained language models with your own documents, which can come from various sources, including the web. If you’re somewhat familiar with LLMs and Llama-Index, you’re in the right place.
Focus on Metadata Filters
Today, I’ll guide you through creating and using metadata filters in Llama-Index. These filters can really fine-tune your results and make your queries more effective. Even though I’m using version 0.9.34/48 of Llama-Index and have run into a few hiccups — like the OR operation not working as expected — there’s still a lot we can explore. If you’re on version 0.10.*, let me know if you’re seeing better results with the OR condition.
What to Expect
Here’s what we’ll cover:
- Creating Metadata: I’ll show you how to set up metadata effectively.
- Applying Filters: Learn how to use these filters in your queries to get precise results.
