Create a RAG Application with BigQueryGoogle Cloud Skills Boost Lab Solution

Follow along with this video to solve the lab on your own!
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Try the Lab:
​​https://www.cloudskillsboost.google/c...

What is the lab about? 🤖

Concerned about AI hallucinations? While AI can be a valuable resource, it sometimes generates inaccurate, outdated, or overly general responses - a phenomenon known as "hallucination." This lab teaches you how to implement a Retrieval Augmented Generation (RAG) pipeline to address this issue. RAG improves large language models (LLMs) like Gemini by grounding their output in contextually relevant information from a specific dataset.

Assume you are helping Coffee-on-Wheels, a pioneering mobile coffee vendor, analyze customer feedback on its services. Without access to the latest data, Gemini's responses might be inaccurate. To solve this problem, you decide to build a RAG pipeline that includes three steps:☕

Generate embeddings: Convert customer feedback text into vector embeddings, which are numerical representations of data that capture semantic meaning.
Search vector space: Create an index of these vectors, search for similar items, and retrieve them.
Generate improved answers: Augment Gemini with the retrieved information to produce more accurate and relevant responses.

BigQuery allows seamless connection to remote generative AI models on Vertex AI. It also provides various functions for embeddings, vector search, and text generation directly through SQL queries or Python notebooks.

What you'll learn
Create a source connection and grant IAM permissions.
Generate embeddings and convert text data to vector embeddings.
Search the vector space and retrieve similar items.
Generate an improved answer by augmenting Gemini with the search results.


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