Document Search

What will I learn?

Semantic search allows users to perform searches based on the meaning or similarity of the data rather than exact matches. Learn how to use KDB.AI to run semantic search on unstructured text documents.

Agenda

In this example, we’ll be using a research paper that presents information on the formation of Interstellar Objects in the Milky Way. We will then create some vector embeddings based on the information within these sentences and store these in our KDB.AI vector database. Finally, we will perform semantic search to retrieve similar records.

Who is this suitable for?

Aimed at complete beginners to KDB.AI. No prior experience required but python knowledge will make it easier to follow along.

To access your own KDB.AI instance as shown in the video simply click the blue button above or else signup here.

The Jupyter notebook shown in the video is available to download here. Note, you will need to install the requirements.txt file in the repository to make sure you have all the relevant Python packages needed to run the code.

Got a question? Ask here on our Slack channel here!