Vector Search

Vertex AI Vector Search (formerly Matching Engine): low-latency approximate nearest-neighbor search over embeddings at scale. · Official docs

Related concepts

• Getting Started · Gemini Capabilities · Prompt Engineering · Function Calling & Tools · Agents & ADK · RAG & Grounding · Embeddings & Vector Search · Vision · Multimodal Live API · Evaluation · MLOps & Deployment · Applied Use Cases

Used in 25 notebooks

Embeddings & Vector Search (9)

• 🛡️ AI Brand Safety: Three-Tier Agent Anomaly Detection — Builds ADK agent anomaly detection with Gemini baselines, Vector Search scoring, and tiered audits.
• Import from BigQuery into Vector Search — Imports BigQuery embedding rows into a Vertex AI Vector Search index using the import REST API.
• Combining Semantic & Keyword Search: A Hybrid Search Tutorial with Agent Platform Vector Search — Builds sparse and hybrid Vector Search indexes for Google Merch Shop product search.
• Getting Started with Text Embeddings + Agent Platform Vector Search — Builds text embeddings from Stack Overflow titles and serves semantic search with Vector Search.
• Handling large-scale embedding generation for Agent Platform Vector Search — Generates large-scale text and image embeddings for Agent Platform Vector Search with throttling and checkpointing.
• Log Anomaly Detection & Investigation with Text Embeddings + BigQuery Vector Search — Detects audit log anomalies using text-embedding-005 embeddings and BigQuery VECTOR_SEARCH.
• Introduction to Agent Platform Vector Search 2.0 — Builds an e-commerce product search demo with Agent Platform Vector Search 2.0 and auto-embeddings.
• Vector Search 2.0 Public Preview Quickstart — Creates a Vector Search 2.0 movie collection with CRUD, filters, semantic search, and ANN indexes.
• Agent Platform Vector Search Quickstart — Builds and queries a streaming-update Vector Search index from product embeddings.

Gemini (16)

• Interactive Loan Application Assistant (Financial Services) — Builds a Gemini 2.0 loan document assistant with RAG, large context, audio, Vertex AI Search, and Vector Search.
• Getting Started with LangChain 🦜️🔗 + Gemini API in Vertex AI — Introduces LangChain with Gemini on Vertex AI for prompts, embeddings, retrieval, memory, and chains.
• Vertex AI RAG Engine with Vertex AI Vector Search — Builds a Vertex AI RAG Engine corpus backed by Vertex AI Vector Search and queries it with Gemini.
• AI-Assisted Data Science Workflows in BigQuery — Builds a BigQuery multimodal housing workflow with Gemini enrichment, BQML clustering, and vector search.
• Text + multimodal embedding generation and vector search in BigQuery — Builds text and image embeddings in BigQuery for semantic product search.
• Performing Semantic Search in BigQuery — Builds semantic search over Stack Overflow questions in BigQuery with Vertex AI text embeddings.
• Use Retrieval Augmented Generation (RAG) with Gemini API — Builds a LangChain RAG pipeline over GitHub code notebooks using Gemini and Vertex AI embeddings.
• Comparing LlamaIndex and LlamaParse for Dense Document Questioning Answering on Vertex AI — Compares LlamaIndex and LlamaParse RAG parsing methods for dense 10-Q document QA on Vertex AI.
• Data Curation Pipeline: Splitting and Transcoding — Deduplicates video clips using video embeddings and BigQuery vector search.
• Leverage LlamaIndex with Vertex AI Vector Search to perform question answering RAG — Builds LlamaIndex RAG on Vertex AI Vector Search, compares prompts, and adds multi-document agents.
• Retrieval Augmented Generation(RAG) with AlloyDB — Builds a RAG workflow over patent abstracts using AlloyDB vector search and Gemini on Vertex AI.
• Building a Gen AI RAG application with Vertex AI Feature Store and BigQuery — Builds a LangChain RAG Q&A app using BigQuery Vector Search and Vertex AI Feature Store.
• Augment Gemini Output with Vector Embeddings from BigQuery — Builds BigQuery vector-search RAG over patent abstracts and uses Gemini to generate project ideas.
• Run RAG Pipelines in BigQuery with BQML and Vector Search — Builds a BigQuery RAG pipeline over a PDF using Document AI, embeddings, vector search, and Gemini.
• Building a Multimodal Chatbot for Warranty Claims using Gemini and Vector Search in Vertex AI — Builds a multimodal warranty-claims chatbot with Gemini, RAG, Vector Search, and function calling.
• Production & Scalable RAG Pipeline Using BigFrames — Builds a scalable BigFrames RAG pipeline over Stack Overflow data with BigQuery, Vertex AI, and LangChain.

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