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Chloe Williams for Zilliz

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Top 5 Vector Database Videos of 2023 🎥

If you're looking to learn the fundamentals of vector databases, check out Zilliz's top 5 videos of 2023. Let's countdown, starting at number 5.

5: IVF Vector Index

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IVF, or Inverted File index, 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.

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4: ANNOY Vector Index

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ANNOY stands for Approximate Nearest Neighbors, Oh Yeah. 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.

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

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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.

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2: Persistent Vector Storage with LlamaIndex

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Learn how to use Milvus as persistent vector storage with LlamaIndex in under 5 minutes.

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1: HNSW Vector Index

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Hierarchical Navigable Small Worlds 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.

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🚀 pgai Vectorizer: SQLAlchemy and LiteLLM Make Vector Search Simple

We built pgai Vectorizer to simplify embedding management for AI applications—without needing a separate database or complex infrastructure. Since launch, developers have created over 3,000 vectorizers on Timescale Cloud, with many more self-hosted.

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