Top 5 Vector Database Videos of 2023 🎥
Tags: beginners, tutorial, database, vectordatabase
Published: 2023-12-21 16:27:41

If you're looking to learn the fundamentals of vector databases, check out [Zilliz's](https://zilliz.com/?utm_source=referral&utm_medium=partner&utm_campaign=2023-12-19_blog_top-5-videos_devto) top 5 videos of 2023. Let's countdown, starting at number 5. 

##5: IVF Vector Index 
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/oupsoaj91k359bzeph1q.png)

IVF, or [Inverted File index](https://zilliz.com/learn/vector-index?utm_source=referral&utm_medium=partner&utm_campaign=2023-12-19_blog_top-5-videos_devto), is probably the most intuitive vector indexing method from a machine learning standpoint. It asks us, how can we easily cluster these points? and comes up with K-Means. When we query, we first look for the closest centroids, then into their clusters, massively reducing the number of computations from a brute for method. 

🎥 [Watch now](https://www.youtube.com/watch?v=VOerTAir9SU)

##4: ANNOY Vector Index

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/m6xrsd98gk0n9k5h45s7.png)

ANNOY stands for [Approximate Nearest Neighbors, Oh Yeah](https://zilliz.com/learn/approximate-nearest-neighbor-oh-yeah-ANNOY?utm_source=referral&utm_medium=partner&utm_campaign=2023-12-19_blog_top-5-videos_devto). It's a vector search algorithm that came out of Spotify. It is a rougher version of IVF. It uses hyperplanes to divide the space in half between two points, and does this over and over again. When we query, we are essentially querying a binary tree. 

🎥 [Watch now](https://www.youtube.com/watch?v=fhzDrXCpIRQ)

##3: How to Add Conversational Memory to an LLM Using LangChain


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ei5hpzbqa6vlzrr9o3cw.png)

One of the main differences between talking to a person and talking to ChatGPT is memory. People have memories of their conversations. You can give this memory to ChatGPT using LangChain with a vector database. In this tutorial, we cover how.

🎥 [Watch now](https://www.youtube.com/watch?v=m3WwttSBDvo)

##2: Persistent Vector Storage with LlamaIndex


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/5vymytzrytm0l3zfsr14.png)

Learn how to use [Milvus](https://milvus.io/?utm_source=referral&utm_medium=partner&utm_campaign=2023-12-19_blog_top-5-videos_devto) as persistent vector storage [with LlamaIndex](https://github.com/run-llama/llama_index/tree/main/examples/paul_graham_essay/data) in under 5 minutes.

🎥 [Watch now](https://www.youtube.com/watch?v=S4GmdrlqVKc)

##1: HNSW Vector Index

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/fwpjntn8xqbtotxm3dge.png)

[Hierarchical Navigable Small Worlds](https://zilliz.com/learn/hierarchical-navigable-small-worlds-HNSW?utm_source=referral&utm_medium=partner&utm_campaign=2023-12-19_blog_top-5-videos_devto) is a graph based index. When you are inserting the points, they are placed in some layers of the graph based on a uniform random number variable. At query time, the layers are queried top down. 

🎥 [Watch now](https://www.youtube.com/watch?v=v1AiTcwZaRY)

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