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Building Applications with Vector Databases

Building Applications with Vector Databases short course promotional banner

Dear learner,

We’re excited to share with you a new course created in collaboration with Pinecone: Building Applications with Vector Databases.

In this course, you'll learn how to use vector databases to create diverse applications quickly and with minimal coding, ranging from retrieval augmented generation (RAG) to facial similarity and hybrid search.

Launch email GIFs

You’ll build unique apps using the following:

  • Semantic Search: Create a search tool that goes beyond keyword matching, focusing on the meaning of content for efficient text-based searches on a user Q/A dataset.
  • RAG: Enhance your LLM applications by incorporating content from sources the model wasn't trained on, like answering questions using the Wikipedia dataset.
  • Recommender System: Develop a system that combines semantic search and RAG to recommend topics, and demonstrate it with a news article dataset.
  • Hybrid Search: Build an application that finds items using both images and descriptive text, using an eCommerce dataset as an example.
  • Facial Similarity: Create an app to compare facial features, using a database of public figures to determine the likeness between them.
  • Anomaly Detection: Learn how to build an anomaly detection app that identifies unusual patterns in network communication logs.

By the end of this course, you'll have a toolkit of ideas for building applications with any vector database.

Learn to build six applications powered by vector databases: semantic search, retrieval augmented generation (RAG), anomaly detection, hybrid search, image similarity search, and recommender systems, each using a different dataset.

  • Learn to create six exciting applications of vector databases and implement them using Pinecone.

  • Build a hybrid search app that combines both text and images for improved multimodal search results.

  • Learn how to build an app that measures and ranks facial similarity.

  • Level: Beginner

  • Instructor: Tim Tully

  • Prerequisite recommendation: Basic Python, machine learning, and large language models knowledge.