Takeaways
- Over 80% of data is unstructured, making it hard to store and search using traditional databases.
- Vector embeddings convert unstructured data into numerical form, enabling similarity-based search.
- A vector database combines vector storage with indexing for fast, scalable retrieval.
- Applications include large language model (LLM) memory extension, semantic search, image/audio similarity, and recommender systems.
- Popular vector database options include Pinecone, Weaviate, Chroma, Redis, Milvus, and Vespa AI.
Summary
Vector databases are specialized systems designed to handle unstructured data by converting it into vector embeddings—a numerical representation generated by machine learning models. These embeddings enable efficient similarity searches, which are crucial when dealing with data like images, text, audio, or video. Traditional relational databases struggle with this kind of content because they rely on structured fields, while unstructured data lacks predefined formats.
A key component of vector databases is the indexing system, which maps embeddings into a structure that allows rapid nearest-neighbor searches. This is essential to avoid performance bottlenecks when querying large-scale datasets.
Common use cases include extending the long-term memory of large language models, performing semantic or meaning-based search instead of keyword matching, identifying similar multimedia content without relying on metadata, and powering recommendation engines. A variety of tools are available in the market, such as Pinecone, Weaviate, Chroma, Redis with vector support, Milvus, and Vespa AI, each offering unique strengths depending on the use case.
