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Future Data Driven 2024 – introduction to vector databases
- Vector databases use vector embeddings to represent data in the form of vectors and store these embeddings in databases.
- Cosine similarity allows an accurate evaluation of similarity between two vectors, which helps categorize unstructured data.
- The use of Pinecone, vector databases and python implementations can help demonstrate how vectors are stored, managed and searched for unstructured data.
- Several techniques are used to create vector embeddings, such as Word2Vec, GloVe, and BERT that generate contextual embeddings.
- Tree-based indexing, approximate nearest neighbors, and quantization are methods used for indexing vectors in vector databases.
- Vectors are multi-dimensional and can be confusing when described using the initial notation. This is overcome by introducing a new notation.
- Cosine similarity is calculated by evaluating the cosine of the angle between two vectors and can range from -1 to 1.
- Azure, AI, CosmosDB, Co-Pilot, Database Watcher were covered during Future Data Driven 2024 event.
- Vector databases index vectors with the help of cosine similarity, making it easier to search for similarities between unstructured data.
- Pinecone is a vector database that is designed to store, manage, and search vectors for similarity search operations and analysis of unstructured data.
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