All You Need to Know about Vector Databases and How to Use Them to Augment Your LLM Apps
A Step-by-Step Guide to Discover and Harness the Power of Vector Databases
Table of Contents
Intro
What is so special about Vector Databases?
How do we map the meaning of a sentence to a numerical representation?
How does that help our LLM app?
Why can’t we just give the LLM all the data we have?
Hands-On Tutorial — Text to Embeddings and Distance Metrics
1. Text to Embeddings
2. Plot 384 dimensions in 2 using PCA
3. Calculate the distance metrics
Towards Vector Stores
How to accelerate the Similarity Search?
What are the different Vector Stores we can choose from?
Hands-On Tutorial — Set up your first Vector Store
1. Install chroma
2. Get/create a chroma client and collection
3. Add some text documents to the collection
4. Extract all entries from database to excel file
5. Query the collection