A Step-by-Step Information to Uncover and Harness the Energy 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
In the direction of Vector Shops
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 scorching matter proper now. Corporations preserve elevating cash to develop their vector databases or so as to add vector search capabilities to their present SQL or NoSQL databases.
Vector Databases make it doable to rapidly search and evaluate giant collections of vectors. That is so attention-grabbing as a result of essentially the most up-to-date embedding fashions are extremely able to understanding the semantics/that means behind phrases and translating them into vectors. This enables us to effectively evaluate sentences with one another.
