Summary of Vector Databases simply explained! (Embeddings & Indexes)

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  Vector databases have recently gained popularity in the tech world, with companies investing heavily in them for AI applications. While they offer unique benefits for tasks like long-term memory storage for large language models, they may be unnecessary for simpler projects where traditional databases or arrays suffice. These databases excel at handling unstructured data like social media content, images, and audio, which are challenging to organize in relational databases. They work by storing vector embeddings, which are numerical representations of the data that allow for efficient retrieval and similarity searches using indexing techniques.

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  Indexes are essential for efficient search in vector databases, which are used for various purposes such as equipping large language models with long-term memory, semantic search, similarity search for images/audio/video data, and as a ranking/recommendation engine for online retailers. Different vector databases available include Pinecone, vv8, Chroma, Redis, Cool.trans, Milvus, and Vespa AI. For a detailed comparison of these options, a separate video can be requested. Overall, vector databases offer a range of functionalities and applications, making them valuable tools in AI and data management.