Hyper-v

Vector databases are so hot right now. WTF are they?

Vector databases are so hot right now. WTF are they?

#Vector #databases #hot #WTF

“Fireship”

Vector databases are rapidly growing in popularity as a way to add long-term memory to LLMs like GPT-4, LLaMDA, and LLaMA. Learn how popular vector databases like Pinecone and Weaviate can store ML embeddings to integrate with tools like ChatGPT.

#programming #tech #TheCodeReport

💬 Chat…

source

 

To see the full content, share this page by clicking one of the buttons below

Related Articles

25 Comments

  1. Most simply a vector has two features: a direction and a magnitude. I think knowing this would help a lot.

    Like, embedding is basically grouping of vectors based on similar direction (and possibly magnitude too). Not necessarily distance. And often embedding is in a lower dimension.

  2. Fireship senpai, plis do vector database vid with integration of codellama or wizardcoder into own projects, holy shet I totally missed this video and this looks pretty lit

  3. The idea of vector database seems interesting but you should know there is no such a thing as a relational database since none of the commercial products that sells themselves as relational actually properly implemented the relational data model. A vector database by the way is by definition not a data model.

  4. Just trying to think through use cases. Would a user create multiple vector databases for different problems? Embeddings of the same document would differ based on how it was trained, correct? Would embeddings within a use case change over time given more documents?

  5. Can't you store vectors in any database? I don't see any difference than me storing it in a good ol sql database and solving these vector similarity re-ranking related questions with the regular tooling such as elasticsearch or faiss

Leave a Reply