LlamaIndex Hands-On Examples Using Synthetic Data.
I’m convinced that the most effective way to learn is through hands-on experiments. Engaging with actual code allows you to grasp how things function and assess the strengths and weaknesses of various options and approaches.
The examples will be published as a series of Python notebooks. We’ll cover topics related to using LLMs with LlamaIndex, including:
- Loading Data: Processing and ingesting data.
- Indexing: Creating data structures for LLM querying.
- Querying: Making prompt calls to an LLM.
- Evaluation: To improve you have to continuously be benchmarking and measuring the performance of your solutions.
These examples draw inspiration from the LlamaIndex Bottoms-Up Development video series.
The goal of the examples in this folder is to utilize the bubl-ai environment (container + library) to experiment with ideas and simultaneously learn LlamaIndex.
The synthetic dataset employed in these examples is the Williams Family Tree:
This post is licensed under CC BY 4.0 by the author.