text-embedding-005

Model text-embedding-005 is used in 26 notebook(s) in this brain.

Concepts: Agent Engine · Agents & ADK · Applied Use Cases · Embeddings & Vector Search · Function Calling & Tools · Gemini Capabilities · Getting Started · MLOps & Deployment · Multimodal Live API · Open & Partner Models · Prompt Engineering · RAG & Grounding · Tuning & Customization · Vertex AI Search · Vision

Notebooks

• Get started with Vertex AI Memory Bank — Builds a Vertex AI Memory Bank hotel concierge that stores and retrieves guest preferences across sessions.
• Customizing Memory Topics — Customizes Vertex AI Memory Bank topics for a financial advisor assistant and compares default vs custom extraction.
• Governance with Vertex AI Memory Bank — Builds a governed Vertex AI Memory Bank with TTL, topics, revision history, rollback, and cleanup.
• Get started with embeddings tuning on Agent Platform — Tunes text-embedding-005 for retrieval using synthetic Gemini queries and Document AI PDF chunks.
• Log Anomaly Detection & Investigation with Text Embeddings + BigQuery Vector Search — Detects audit log anomalies using text-embedding-005 embeddings and BigQuery VECTOR_SEARCH.
• Anomaly Detection of Infrastructure Logs using Gemini and BigQuery Vector Search — Detects HDFS log anomalies with Gemini summaries, text embeddings, and BigQuery Vector Search.
• Building a Multi-Agent RAG Application with LangGraph and Agent Engine — Builds and deploys a LangGraph multi-agent RAG app on Vertex AI Agent Engine with Cloud SQL vector stores.
• 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.
• Real-time Retrieval Augmented Generation (RAG) using the Multimodal Live API with Gemini 2.0 — Builds a retail RAG pipeline with Gemini Multimodal Live API for grounded text and audio answers.
• LlamaIndex RAG Workflows using Gemini and Firestore — Builds a LlamaIndex RAG workflow with Gemini, Vertex embeddings, and Firestore storage.
• 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.
• RAG Based on Sensitive Data Protection using Faker — Builds a RAG flow that anonymizes PII with Cloud DLP, Faker, Firestore, Chroma, and Gemini.
• Intra Knowledge QnA — Builds a Vertex AI and LangChain RAG Q&A app over an IRS PDF using Chroma embeddings.
• 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.
• Running a Gemma 2-based agentic RAG with Ollama on Vertex AI and LangGraph — Deploys a Gemma 2 Ollama container on Vertex AI and uses it in a LangGraph SQL RAG agent.
• Cloud Run GPU Inference: Gemma 2 RAG Q&A with Ollama and LangChain — Deploys Gemma 2 on Cloud Run GPU with Ollama and builds a LangChain RAG Q&A chain.
• Getting started with Google Generative AI using the Gen AI SDK — Introduces Google Gen AI SDK on Vertex AI for Gemini prompts, tools, caching, batches, and embeddings.
• Custom Embeddings with Vertex AI Search — Builds a Vertex AI Search app using custom text-embedding-005 embeddings from Stack Overflow data.

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