gemini-embedding-001

Model gemini-embedding-001 is used in 11 notebook(s) in this brain.

Concepts: Agent Engine · Agents & ADK · Applied Use Cases · Embeddings & Vector Search · Evaluation · Gemini Capabilities · Getting Started · Open & Partner Models · Prompt Engineering · RAG & Grounding · Vision

Notebooks

• Building a Multimodal Trip Planner with ADK on Vertex AI Agent Engine Memory Bank — Builds and deploys a multimodal ADK trip planner using Vertex AI Agent Engine Memory Bank.
• Use Gemini and OSS Text-Embedding Models Against Your BigQuery Data — Generate BigQuery embeddings with Gemini and a deployed OSS E5 model.
• Visualizing embedding similarity from text documents using t-SNE plots — Embeds 20 Newsgroups text with gemini-embedding-001 and visualizes similarity clusters using t-SNE.
• 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.
• Using “task type” embeddings for improving RAG search quality — Shows how task-type embeddings improve RAG Q&A retrieval quality and MRR.
• 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.
• Getting Started with LangChain 🦜️🔗 + Gemini API in Vertex AI — Introduces LangChain with Gemini on Vertex AI for prompts, embeddings, retrieval, memory, and chains.
• Analyzing movie posters in BigQuery with Gemini — Analyzes movie poster images in BigQuery with Gemini, embeddings, and vector search.
• Text + multimodal embedding generation and vector search in BigQuery — Builds text and image embeddings in BigQuery for semantic product search.

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