AI Vector Database

Vector databases specialize in storing high-dimensional vectors, crucial for fast and precise similarity searches, particularly in AI domains like natural language processing and computer vision. Large Language Models (LLMs) such as ChatGPT, Gemini, Claude, and Llama rely on vector databases to efficiently manage the vast amount of vectorized data they generate. This reliance is due to the sheer volume and complexity of data handled by such models, making conventional databases less effective.

Text search can be solved with an inverted index and BM25 or TF-IDF ranking, But what if your data is images, audio, or video? Can you find images with textual queries?

With an inverted index, you can represent documents as a sparse vector [1,0,1,1,…,0] But you have two main problems:

  1. Bag-of-words approach representation does not take into account semantic context – “Capital” as the main city or as some monetary value.
  2. Does not respect word order – “Visa from Finland to Canada”, will be the same as “Visa from Canada to Finland”.

You can overcome these problems by searching through vector representations of data to find similar records, using k-nearest-neighbor (KNN) or approximate-nearest neighbor (ANN) algorithms.

Vector search is an information retrieval technique to efficiently retrieve similar vector items from a large dataset. By transforming data into embeddings, each item's unique features are captured, enabling algorithms to measure similarity based on various distance metrics. This approach is particularly in fields like natural language processing and image recognition, where complex data structures can be indexed and searched with remarkable speed and accuracy.

Keyword Search

Example

Query: A bear eating a fish by a river
Result: heron eating a fish by a river

Vector Search

Example

Query: A bear eating a fish by a river
Result vector:
[0.07289, -0.2771, …] A bear catching a salmon in the water

What is a Vector Database?

A Vector Database stores vector embeddings for fast retrieval and similarity search, with capabilities like horizontal and vertical scaling, update/delete operations, metadata storage, and metadata filtering.

Use Cases

Specific

Broad

Vector Databases

How to choose a Vector DB?

There are a few key criteria to evaluate what Vector DB is most suited for your project.

Performance: big models like LLM’s and Computer Vision algorithms require big databases and searching for semantic-based similarity through these great data pools quickly requires an efficient tool. While that might be an obvious requirement for a database, also consider that different indexing techniques offer varying levels of accuracy and speed.

Scalability and adaptability: A good vector database guarantees seamless scalability across numerous nodes as data can expand to millions or even billions of elements. The best vector databases provide flexibility, enabling users to tune the system according to changes in insertion rate, query rate, and underlying hardware.

Security: Keeping your vector database secure is critical. Look for encryption options to scramble data at rest and in transit. User access should be strictly controlled through features like role-based access. If your data is sensitive, consider choosing a DB vendor that offers these features and is committed to regular security updates.

Managed or self-hosted: if you don’t have a dedicated engineering team or if you are in need to push your project to production ASAP, you might want to consider a managed vector database.

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