All You Need to Know about Vector Databases and How to Use Them to Augment Your LLM Apps | by Dominik Polzer | Sep, 2023

A Step-by-Step Guide to Discover and Harness the Power of Vector Databases

Dominik Polzer
Towards Data Science

Dominik Polzer


Why we need Vector Stores for LLM apps — Image by the author


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


Vector databases are a hot topic right now. Companies keep raising money to develop their vector databases or to add vector search capabilities to their existing SQL or NoSQL databases.

Vector Store Funding — Image by the author (Chroma, 2023; Cook, 2022; Miller, 2022)

Vector Databases make it possible to quickly search and compare large collections of vectors. This is so interesting because the most up-to-date embedding models are highly capable of understanding the semantics/meaning behind words and translating them into vectors. This allows us to efficiently compare sentences with each other.

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This post originally appeared on TechToday.

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