Many people are now starting to think about how to bring Gen AI and large language models (LLMs) to production services. You may be wondering

Vertex AI Embeddings for Text: Grounding LLMs made easy

submited by

Style Pass

2023-05-26 15:30:06

Many people are now starting to think about how to bring Gen AI and large language models (LLMs) to production services. You may be wondering "How to integrate LLMs or AI chatbots with existing IT systems, databases and business data?", "We have thousands of products. How can I let LLM memorize them all precisely?", or "How to handle the hallucination issues in AI chatbots to build a reliable service?". Here is a quick solution: grounding with embeddings and vector search.

What is grounding? What are embedding and vector search? In this post, we will learn these crucial concepts to build reliable Gen AI services for enterprise use. But before we dive deeper, here is an example:

This demo is available as a public live demo here. Select "STACKOVERFLOW" and enter any coding question as a query, so it runs a text search on 8 million questions posted on Stack Overflow.

LLM-enabled semantic search: The 8 million Stack Overflow questions and query text are both interpreted by Vertex AI Generative AI models. The model understands the meaning and intent (semantics) of the text and code snippets in the question body at librarian-level precision. The demo leverages this ability for finding highly relevant questions and goes far beyond simple keyword search in terms of user experience. For example, if you enter "How do I write a class that instantiates only once", then the demo shows "How to create a singleton class" at the top, as the model knows their meanings are the same in the context of computer programming.