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.
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.
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.
The demo flow is:
- Setup: Import packages and set any required variables
- Load data: Load a dataset and embed it using OpenAI embeddings
- Setup: Here we'll set up the Python client for Qdrant. For more details go here
- Index Data: We'll create a collection with vectors for titles and content
- Search Data: We'll run a few searches to confirm it works
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.