This page describes how you can manage your corpus for RAG tasks by performing corpus management and file management.
Corpus management
A corpus, also referred to as an index, is a collection of documents or source of information. The corpus can be queried to retrieve relevant contexts for response generation. When creating a corpus for the first time, the process might take an additional minute.
The following corpus operations are supported:
Operation | Description | Parameters | Examples |
---|---|---|---|
Create a RAG corpus. | Create an index to import or upload documents. | Create parameters | Create example |
Update a RAG corpus. | Update a previously-created index to import or upload documents. | Update parameters | Update example |
List a RAG corpus. | List all of the indexes. | List parameters | List example |
Get a RAG corpus. | Get the metadata describing the index. | Get parameters | Get example |
Delete a RAG corpus. | Delete the index. | Delete parameters | Delete example |
Concurrent operations on corpora aren't supported. For more information, see the RAG API reference.
File management
The following file operations are supported:
Operation | Description | Parameters | Examples |
---|---|---|---|
Upload a RAG file. | Upload a file from local storage with additional information that provides context to the LLM to generate more accurate responses. | Upload parameters | Upload example |
Import RAG files. | Import a set of files from some other storage into a storage location. | Import parameters | Import example |
Get a RAG file. | Get details about a RAG file for use by the LLM. | Get parameters | Get example |
Delete a RAG file. | Upload a file from local storage with additional information that provides context to the LLM to generate more accurate responses. | Delete parameters | Delete example |
For more information, see the RAG API reference.
What's next
- To learn about the file size limits, see Supported document types.
- To learn about quotas related to RAG Engine, see RAG Engine quotas.
- To learn about customizing parameters, see Retrieval parameters.
- To learn more about the RAG API, see RAG Engine API.
- To learn more about grounding, see Grounding overview.
- To learn more about the difference between grounding and RAG, see Ground responses using RAG.
- To learn more about Generative AI on Vertex AI, see Overview of Generative AI on Vertex AI.
- To learn more about the RAG architecture, see
Infrastructure for a RAG-capable generative AI application using Vertex AI.