What is Vertex AI RAG Engine?
Vertex AI RAG Engine is a fully managed orchestration runtime for retrieval-augmented generation (RAG) on Google Cloud. The service handles building and operating a complete RAG pipeline for you and enriches the responses of large language models with your own private data. As a result, models like Gemini answer more accurately and hallucinations are reduced, because generation builds on verifiable sources rather than the model’s training knowledge alone.
Vertex AI RAG Engine follows a six-step pipeline: data ingestion, transformation with chunking, embedding, indexing into a corpus, retrieval, and grounded generation. Chunk size and overlap are configurable, so you can tune retrieval quality for your use case. The service is natively integrated with the Gemini API as a retrieval tool and additionally draws on hundreds of models from the Vertex AI Model Garden, including Gemini, Claude, and Llama.
Core Features
- Six-step RAG pipeline: Data ingestion, transformation and chunking, embedding, corpus indexing, retrieval, and grounded generation as a managed, end-to-end flow with configurable chunk size and overlap.
- Native Gemini integration: Available as a retrieval tool of the Gemini API (VertexRagStore) and with access to hundreds of generation models from the Model Garden such as Gemini, Claude, and Llama.
- Pluggable vector databases: Choose between RagManagedDb (default, backed by Spanner Enterprise), Vertex AI Vector Search, Vertex AI Feature Store, Pinecone, Weaviate, and Cloud Spanner.
- Broad data-source connectivity: Ingest from Cloud Storage, Google Drive, BigQuery datasets, local files, and websites, as well as through connectors such as Jira and Slack.
Typical Use Cases
Knowledge-based chatbots and assistants: RAG Engine grounds responses on internal documents, manuals, and knowledge bases. This lets assistants answer questions about company-specific content that no general model knows.
Question-answering systems with source citations: By grounding on a corpus, responses can be traced back to concrete sources. This improves traceability in areas such as support, legal, or compliance.
RAG backends for agents and search: RAG Engine serves as the retrieval layer for Vertex AI agents and search applications, delivering the relevant context that agents need to complete tasks.
Benefits
- Fully managed pipeline with no need to operate your own embedding, index, and retrieval infrastructure.
- Flexible choice of vector database and data sources, so you avoid lock-in to a single storage solution.
- Pay-per-use with composed costs: you pay only for the components you use, and the default parser for data sources is free.
Integration with innFactory
As a certified Google Cloud Partner, innFactory supports you with the adoption and operation of this service.
Typical Use Cases
Frequently Asked Questions
What is Vertex AI RAG Engine?
Vertex AI RAG Engine is a managed orchestration runtime for retrieval-augmented generation on Google Cloud. The service runs the complete pipeline: data ingestion, chunking, embedding, indexing into a corpus, retrieval, and grounded generation. It enriches LLM responses with your own data to reduce hallucinations.
When should I use Vertex AI RAG Engine?
Use RAG Engine when you want to ground Gemini or other model responses on internal documents, knowledge bases, or structured data. Typical scenarios are knowledge-based chatbots, question-answering systems with source citations, and RAG backends for agents where you do not want to operate the pipeline yourself.
How much does Vertex AI RAG Engine cost?
RAG Engine bills pay-per-use with composed costs. Accessing data sources through the default parser is free, while LLM Parser calls, vector storage (such as the underlying Spanner instance for RagManagedDb), embedding, and generation model usage are billed separately. There is no flat fee.
Which vector databases and data sources does Vertex AI RAG Engine support?
Supported vector stores include RagManagedDb (default, backed by Spanner Enterprise), Vertex AI Vector Search, Vertex AI Feature Store, Pinecone, Weaviate, and Cloud Spanner. Supported data sources include Cloud Storage, Google Drive, BigQuery datasets, local files, and websites, plus connectors such as Jira and Slack.
