Using Typesense for Embeddings Search

Using Typesense for Embeddings Search

This notebook takes you through a simple flow to download some data, embed it, and then index and search it using a selection of vector databases. This is a common requirement for customers who want to store and search our embeddings with their own data in a secure environment to support production use cases such as chatbots, topic modelling and more.

What is a Vector Database

A vector database is a database made to store, manage and search embedding vectors. The use of embeddings to encode unstructured data (text, audio, video and more) as vectors for consumption by machine-learning models has exploded in recent years, due to the increasing effectiveness of AI in solving use cases involving natural language, image recognition and other unstructured forms of data. Vector databases have emerged as an effective solution for enterprises to deliver and scale these use cases.

Why use a Vector Database

Vector databases enable enterprises to take many of the embeddings use cases we've shared in this repo (question and answering, chatbot and recommendation services, for example), and make use of them in a secure, scalable environment. Many of our customers make embeddings solve their problems at small scale but performance and security hold them back from going into production - we see vector databases as a key component in solving that, and in this guide we'll walk through the basics of embedding text data, storing it in a vector database and using it for semantic search.

Demo Flow

The demo flow is:

• Setup: Import packages and set any required variables
• Load data: Load a dataset and embed it using OpenAI embeddings
• Typesense
  • Setup: Set up the Typesense Python client. For more details go here
  • Index Data: We'll create a collection and index it for both titles and content.
  • Search Data: Run a few example queries with various goals in mind.

Once you've run through this notebook you should have a basic understanding of how to setup and use vector databases, and can move on to more complex use cases making use of our embeddings.

Setup

Import the required libraries and set the embedding model that we'd like to use.

Load data

In this section we'll load embedded data that we've prepared previous to this session.

Typesense

The next vector store we'll look at is Typesense, which is an open source, in-memory search engine, that you can either self-host or run on Typesense Cloud.

Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults. It also lets you combine attribute-based filtering together with vector queries.

For this example, we will set up a local docker-based Typesense server, index our vectors in Typesense and then do some nearest-neighbor search queries. If you use Typesense Cloud, you can skip the docker setup part and just obtain the hostname and API keys from your cluster dashboard.

Setup

To run Typesense locally, you'll need Docker. Following the instructions contained in the Typesense documentation here, we created an example docker-compose.yml file in this repo saved at ./typesense/docker-compose.yml.

After starting Docker, you can start Typesense locally by navigating to the examples/vector_databases/typesense/ directory and running docker-compose up -d.

The default API key is set to xyz in the Docker compose file, and the default Typesense port to 8108.

Index data

To index vectors in Typesense, we'll first create a Collection (which is a collection of Documents) and turn on vector indexing for a particular field. You can even store multiple vector fields in a single document.

Search Data

Now that we've imported the vectors into Typesense, we can do a nearest neighbor search on the title_vector or content_vector field.

Thanks for following along, you're now equipped to set up your own vector databases and use embeddings to do all kinds of cool things - enjoy! For more complex use cases please continue to work through other cookbook examples in this repo.