Getting Started with Vector Databases in Node.js

2023/07/10

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Vector databases are gaining popularity in the programming community, especially when combined with AI tools like ChatGPT. These databases enable semantic similarity search, allowing developers to find things that are similar to a given text. Unlike basic search engines, vector databases interpret the semantics of the text rather than just matching keywords.

One practical application of semantic search is topic classification. By using Node.js and a vector database called Chroma, developers can build a toy topic classification tool. To get started with Chroma, clone the Chroma GitHub repo and run their docker-compose file. Additionally, you'll need an OpenAI key to store embeddings for sentences in Chroma.

To handle topic classification, developers can insert an embedding for each topic with sample text into Chroma. Then, for any given text, they can find the closest vector to determine its category. The article provides code examples for generating embeddings and inserting them into Chroma.

Vector databases are not limited to text classification. Developers can also create embeddings for images and audio. This powerful combination of vector databases and AI-generated embeddings opens up a wide range of possibilities for developers.