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