Towards AI Can Help your Team Adopt AI: Corporate Training, Consulting, and Talent Solutions.

Publication

How To Quickly Build A Semantic Search System With txtai And Weaviate
Latest   Machine Learning

How To Quickly Build A Semantic Search System With txtai And Weaviate

Last Updated on July 25, 2023 by Editorial Team

Author(s): ___

Originally published on Towards AI.

Step 1: Define the image for the txtai API server

This member-only story is on us. Upgrade to access all of Medium.

In this article, I will explain how to quickly build a machine learning-based semantic search engine using docker compose and the following tools/libraries:

txtai: A framework to run machine-learning workflows to transform data and build AI-powered semantic search applications.weaviate: A vector search enginetxt-weaviate: A small library to make it easy to integrate weaviate with txtai

The code to reproduce this solution can be found here.

We only need to follow 3 steps to get started:

Step 1: Define the image for the txtai API serverStep 2: Define the txtai configurationStep 3: Deploy with… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓