Perform semantic search and retrieval-augmented generation
👉 This tutorial guides you through the end-to-end process of creating and using text embeddings, including using vector indexes to improve search performance.
This tutorial covers the following tasks:
🖥 Creating a BigQuery ML remote model over a Vertex AI embedding model.
🖥 Using the remote model with the ML.GENERATE_EMBEDDING function to generate embeddings from text in a BigQuery table.
🖥 Creating a vector index to index the embeddings.
🖥 Using the VECTOR_SEARCH function with the embeddings to search for similar text.
🖥 Perform retrieval-augmented generation (RAG) by generating text with the ML.GENERATE_TEXT function, and using vector search results to augment the prompt input and improve results.
This tutorial uses the BigQuery public table patents-public-data.google_patents_research.publications.
Perform semantic search and retrieval-augmented generation
👉 This tutorial guides you through the end-to-end process of creating and using text embeddings, including using vector indexes to improve search performance.
This tutorial covers the following tasks:
🖥 Creating a BigQuery ML remote model over a Vertex AI embedding model.
🖥 Using the remote model with the ML.GENERATE_EMBEDDING function to generate embeddings from text in a BigQuery table.
🖥 Creating a vector index to index the embeddings.
🖥 Using the VECTOR_SEARCH function with the embeddings to search for similar text.
🖥 Perform retrieval-augmented generation (RAG) by generating text with the ML.GENERATE_TEXT function, and using vector search results to augment the prompt input and improve results.
This tutorial uses the BigQuery public table patents-public-data.google_patents_research.publications.
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