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    RAG Engine API

    The Vertex AI RAG Engine is a component of the Vertex AI platform, which facilitates Retrieval-Augmented Generation (RAG). RAG Engine enables Large Language Models (LLMs) to access and incorporate data from external knowledge sources, such as documents and databases. By using RAG, LLMs can generate more accurate and informative LLM responses.

    Parameters list

    This section lists the following:

    Parameters Examples
    See Corpus management parameters . See Corpus management examples .
    See File management parameters . See File management examples .
    See Project management parameters . See Project management examples .

    Corpus management parameters

    For information about a RAG corpus, see Corpus management .

    Create a RAG corpus

    This table lists the parameters used to create a RAG corpus.

    Body Request
    Parameters

    corpus_type_config

    Optional: Immutable.

    RagCorpus.CorpusTypeConfig

    The configuration to specify the corpus type.

    display_name

    Required: string

    The display name of the RAG corpus.

    description

    Optional: string

    The description of the RAG corpus.

    encryption_spec

    Optional: Immutable: string

    The CMEK key name is used to encrypt at-rest data that's related to the RAG corpus. The key name is only applicable to the RagManaged option for the vector database. When the corpus is created, this field can be set and can't be updated or deleted.

    Format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{key_name}

    vector_db_config

    Optional: Immutable: RagVectorDbConfig

    The configuration for the vector databases.

    vertex_ai_search_config.serving_config

    Optional: string

    The configuration for the Vertex AI Search.

    Format: projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config} or projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/servingConfigs/{serving_config}

    CorpusTypeConfig
    Parameters

    document_corpus

    oneof RagCorpus.CorpusTypeConfig.DocumentCorpus

    The default value of corpus_type_config , which represents a conventional document-based RAG corpus.

    memory_corpus

    oneof RagCorpus.CorpusTypeConfig.MemoryCorpus

    If you set this type, the RAG corpus is a MemoryCorpus that can be used with the Gemini Live API as a memory store.

    For more information, see Use Vertex AI RAG Engine as the memory store .

    memory_corpus.llm_parser

    oneof RagFileParsingConfig.LlmParser

    The LLM parser that's used to parse and store session contexts from the Gemini Live API. You can build memories for indexing.
    RagVectorDbConfig
    Parameters

    rag_managed_db

    oneof vector_db : RagVectorDbConfig.RagManagedDb

    If no vector database is specified, rag_managed_db is the default vector database.

    rag_managed_db.knn

    oneof retrieval_strategy : KNN

    Default.

    Finds the exact nearest neighbors by comparing all data points in your RAG corpus.

    If you don't specify a strategy during the creation of your RAG corpus, KNN is the default retrieval strategy used.

    rag_managed_db.ann

    oneof retrieval_strategy : ANN

    tree_depth

    Determines the number of layers or levels in the tree.

    If you have O(10K) RAG files in the RAG corpus, set thi value to 2.
    • If more layers or levels are required, set this value to 3.
    • If the number of layers or levels isn't specified, Vertex AI RAG Engine assigns a default value of 2 for this parameter.

    leaf_count

    Determines the number of leaf nodes in the tree-based structure.

    • The recommended value is 10 * sqrt(num of RAG files in your RAG corpus) .
    • If not specified, Vertex AI RAG Engine assigns a default value of 500 for this parameter.

    rebuild_ann_index

    • Vertex AI RAG Engine rebuilds your ANN index.
    • Set to true in your ImportRagFiles API request.
    • Before you query the RAG corpus, it's required to rebuild the ANN index once.
    • Only one concurrent index rebuild is supported on a project in each location.

    weaviate

    oneof vector_db : RagVectorDbConfig.Weaviate

    Specifies your Weaviate instance.

    weaviate.http_endpoint

    string

    The Weaviate instance's HTTP endpoint.

    This value can't be changed after it's set. You can leave it empty in the CreateRagCorpus API call, and set it with a non-empty value in a follow up UpdateRagCorpus API call.

    weaviate.collection_name

    string

    The Weaviate collection that the RAG corpus maps to.

    This value can't be changed after it's set. You can leave it empty in the CreateRagCorpus API call, and set it with a non-empty value in a follow up UpdateRagCorpus API call.

    pinecone

    oneof vector_db : RagVectorDbConfig.Pinecone

    Specifies your Pinecone instance.

    pinecone.index_name

    string

    This is the name used to create the Pinecone index that's used with the RAG corpus.

    This value can't be changed after it's set. You can leave it empty in the CreateRagCorpus API call, and set it with a non-empty value in a follow up UpdateRagCorpus API call.

    vertex_feature_store

    oneof vector_db : RagVectorDbConfig.VertexFeatureStore

    Specifies your Vertex AI Feature Store instance.

    vertex_feature_store.feature_view_resource_name

    string

    The Vertex AI Feature Store FeatureView that the RAG corpus maps to.

    Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

    This value can't be changed after it's set. You can leave it empty in the CreateRagCorpus API call, and set it with a non-empty value in a follow up UpdateRagCorpus API call.

    vertex_vector_search

    oneof vector_db : RagVectorDbConfig.VertexVectorSearch

    Specifies your Vertex Vector Search instance.

    vertex_vector_search.index

    string

    This is the resource name of the Vector Search index that's used with the RAG corpus.

    Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

    This value can't be changed after it's set. You can leave it empty in the CreateRagCorpus API call, and set it with a non-empty value in a follow up UpdateRagCorpus API call.

    vertex_vector_search.index_endpoint

    string

    This is the resource name of the Vector Search index endpoint that's used with the RAG corpus.

    Format: projects/{project}/locations/{location}/indexes/{index}

    This value can't be changed after it's set. You can leave it empty in the CreateRagCorpus API call, and set it with a non-empty value in a follow up UpdateRagCorpus API call.

    api_auth.api_key_config.api_key_secret_version

    string

    This the full resource name of the secret that is stored in Secret Manager, which contains your Weaviate or Pinecone API key that depends on your choice of vector database.

    Format: projects/{PROJECT_NUMBER}/secrets/{SECRET_ID}/versions/{VERSION_ID}

    You can leave it empty in the CreateRagCorpus API call, and set it with a non-empty value in a follow up UpdateRagCorpus API call.

    rag_embedding_model_config.vertex_prediction_endpoint.endpoint

    Optional: Immutable: string

    The embedding model to use for the RAG corpus. This value can't be changed after it's set. If you leave it empty, we use text-embedding-005 as the embedding model.

    Update a RAG corpus

    This table lists the parameters used to update a RAG corpus.

    Body Request
    Parameters

    display_name

    Optional: string

    The display name of the RAG corpus.

    description

    Optional: string

    The description of the RAG corpus.

    rag_vector_db.weaviate.http_endpoint

    string

    The Weaviate instance's HTTP endpoint.

    If your RagCorpus was created with a Weaviate configuration, and this field has never been set before, then you can update the Weaviate instance's HTTP endpoint.

    rag_vector_db.weaviate.collection_name

    string

    The Weaviate collection that the RAG corpus maps to.

    If your RagCorpus was created with a Weaviate configuration, and this field has never been set before, then you can update the Weaviate instance's collection name.

    rag_vector_db.pinecone.index_name

    string

    This is the name used to create the Pinecone index that's used with the RAG corpus.

    If your RagCorpus was created with a Pinecone configuration, and this field has never been set before, then you can update the Pinecone instance's index name.

    rag_vector_db.vertex_feature_store.feature_view_resource_name

    string

    The Vertex AI Feature Store FeatureView that the RAG corpus maps to.

    Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

    If your RagCorpus was created with a Vertex AI Feature Store configuration, and this field has never been set before, then you can update it.

    rag_vector_db.vertex_vector_search.index

    string

    This is the resource name of the Vector Search index that's used with the RAG corpus.

    Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

    If your RagCorpus was created with a Vector Search configuration, and this field has never been set before, then you can update it.

    rag_vector_db.vertex_vector_search.index_endpoint

    string

    This is the resource name of the Vector Search index endpoint that's used with the RAG corpus.

    Format: projects/{project}/locations/{location}/indexes/{index}

    If your RagCorpus was created with a Vector Search configuration, and this field has never been set before, then you can update it.

    rag_vector_db.api_auth.api_key_config.api_key_secret_version

    string

    The full resource name of the secret that is stored in Secret Manager, which contains your Weaviate or Pinecone API key depends on your choice of vector database.

    Format: projects/{PROJECT_NUMBER}/secrets/{SECRET_ID}/versions/{VERSION_ID}

    List RAG corpora

    This table lists the parameters used to list RAG corpora.

    Parameters

    page_size

    Optional: int

    The standard list page size.

    page_token

    Optional: string

    The standard list page token. Typically obtained from [ListRagCorporaResponse.next_page_token][] of the previous [VertexRagDataService.ListRagCorpora][] call.

    Get a RAG corpus

    This table lists parameters used to get a RAG corpus.

    Parameters

    name

    string

    The name of the RagCorpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}

    Delete a RAG corpus

    This table lists parameters used to delete a RAG corpus.

    Parameters

    name

    string

    The name of the RagCorpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}

    File management parameters

    For information about a RAG file, see File management .

    Upload a RAG file

    This table lists parameters used to upload a RAG file.

    Body Request
    Parameters

    parent

    string

    The name of the RagCorpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}

    rag_file

    Required: RagFile

    The file to upload.

    upload_rag_file_config

    Required: UploadRagFileConfig

    The configuration for the RagFile to be uploaded into the RagCorpus .
    RagFile

    display_name

    Required: string

    The display name of the RAG file.

    description

    Optional: string

    The description of the RAG file.
    UploadRagFileConfig

    rag_file_transformation_config.rag_file_chunking_config.fixed_length_chunking.chunk_size

    int32

    Number of tokens each chunk has.

    rag_file_transformation_config.rag_file_chunking_config.fixed_length_chunking.chunk_overlap

    int32

    The overlap between chunks.

    Import RAG files

    This table lists parameters used to import a RAG file.

    Parameters

    parent

    Required: string

    The name of the RagCorpus resource.

    Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}

    gcs_source

    oneof import_source : GcsSource

    Cloud Storage location.

    Supports importing individual files as well as entire Cloud Storage directories.

    gcs_source.uris

    list of string

    Cloud Storage URI that contains the upload file.

    google_drive_source

    oneof import_source : GoogleDriveSource

    Google Drive location.

    Supports importing individual files as well as Google Drive folders.

    slack_source

    oneof import_source : SlackSource

    The slack channel where the file is uploaded.

    jira_source

    oneof import_source : JiraSource

    The Jira query where the file is uploaded.

    share_point_sources

    oneof import_source : SharePointSources

    The SharePoint sources where the file is uploaded.

    rag_file_transformation_config.rag_file_chunking_config.fixed_length_chunking.chunk_size

    int32

    Number of tokens each chunk has.

    rag_file_transformation_config.rag_file_chunking_config.fixed_length_chunking.chunk_overlap

    int32

    The overlap between chunks.

    rag_file_parsing_config

    Optional: RagFileParsingConfig

    Specifies the parsing configuration for RagFiles .

    If this field isn't set, RAG uses the default parser.

    max_embedding_requests_per_min

    Optional: int32

    The maximum number of queries per minute that this job is allowed to make to the embedding model specified on the corpus. This value is specific to this job and not shared across other import jobs. Consult the Quotas page on the project to set an appropriate value.

    If unspecified, a default value of 1,000 QPM is used.
    GoogleDriveSource

    resource_ids.resource_id

    Required: string

    The ID of the Google Drive resource.

    resource_ids.resource_type

    Required: string

    The type of the Google Drive resource.
    SlackSource

    channels.channels

    Repeated: SlackSource.SlackChannels.SlackChannel

    Slack channel information, include ID and time range to import.

    channels.channels.channel_id

    Required: string

    The Slack channel ID.

    channels.channels.start_time

    Optional: google.protobuf.Timestamp

    The starting timestamp for messages to import.

    channels.channels.end_time

    Optional: google.protobuf.Timestamp

    The ending timestamp for messages to import.

    channels.api_key_config.api_key_secret_version

    Required: string

    The full resource name of the secret that is stored in Secret Manager, which contains a Slack channel access token that has access to the slack channel IDs.See: https://api.slack.com/tutorials/tracks/getting-a-token.

    Format: projects/{PROJECT_NUMBER}/secrets/{SECRET_ID}/versions/{VERSION_ID}

    JiraSource

    jira_queries.projects

    Repeated: string

    A list of Jira projects to import in their entirety.

    jira_queries.custom_queries

    Repeated: string

    A list of custom Jira queries to import. For information about JQL (Jira Query Language), see Jira Support

    jira_queries.email

    Required: string

    The Jira email address.

    jira_queries.server_uri

    Required: string

    The Jira server URI.

    jira_queries.api_key_config.api_key_secret_version

    Required: string

    The full resource name of the secret that is stored in Secret Manager, which contains Jira API key that has access to the slack channel IDs.See: https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/

    Format: projects/{PROJECT_NUMBER}/secrets/{SECRET_ID}/versions/{VERSION_ID}

    SharePointSources

    share_point_sources.sharepoint_folder_path

    oneof in folder_source : string

    The path of the SharePoint folder to download from.

    share_point_sources.sharepoint_folder_id

    oneof in folder_source : string

    The ID of the SharePoint folder to download from.

    share_point_sources.drive_name

    oneof in drive_source : string

    The name of the drive to download from.

    share_point_sources.drive_id

    oneof in drive_source : string

    The ID of the drive to download from.

    share_point_sources.client_id

    string

    The Application ID for the app registered in Microsoft Azure Portal.The application must also be configured with MS Graph permissions "Files.ReadAll", "Sites.ReadAll" and BrowserSiteLists.Read.All.

    share_point_sources.client_secret.api_key_secret_version

    Required: string

    The full resource name of the secret that is stored in Secret Manager, which contains the application secret for the app registered in Azure.

    Format: projects/{PROJECT_NUMBER}/secrets/{SECRET_ID}/versions/{VERSION_ID}

    share_point_sources.tenant_id

    string

    Unique identifier of the Azure Active Directory Instance.

    share_point_sources.sharepoint_site_name

    string

    The name of the SharePoint site to download from. This can be the site name or the site id.
    RagFileParsingConfig

    layout_parser

    oneof parser : RagFileParsingConfig.LayoutParser

    The Layout Parser to use for RagFile s.

    layout_parser.processor_name

    string

    The full resource name of a Document AI processor or processor version.

    Format:
    projects/{project_id}/locations/{location}/processors/{processor_id}
    projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}

    layout_parser.max_parsing_requests_per_min

    string

    The maximum number of requests the job is allowed to make to the Document AI processor per minute.

    Consult https://cloud.google.com/document-ai/quotas and the Quota page for your project to set an appropriate value here. If unspecified, a default value of 120 QPM is used.

    llm_parser

    oneof parser : RagFileParsingConfig.LlmParser

    The LLM parser to use for RagFile s.

    llm_parser.model_name

    string

    The resource name of an LLM model.

    Format: projects/{project_id}/locations/{location}/publishers/{publisher}/models/{model}

    llm_parser.max_parsing_requests_per_min

    string

    The maximum number of requests the job is allowed to make to the LLM model per minute.

    To set an appropriate value for your project, see model quota section and the Quota page for your project to set an appropriate value here. If unspecified, a default value of 5000 QPM is used.

    Get a RAG file

    This table lists parameters used to get a RAG file.

    Parameters

    name

    string

    The name of the RagFile resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_file_id}

    Delete a RAG file

    This table lists parameters used to delete a RAG file.

    Parameters

    name

    string

    The name of the RagFile resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_file_id}

    Retrieval and prediction parameters

    This section lists the retrieval and prediction parameters.

    Retrieval parameters

    This table lists parameters for retrieveContexts API.

    Parameters

    parent

    Required: string

    The resource name of the Location to retrieve RagContexts .
    The users must have permission to make a call in the project.

    Format: projects/{project}/locations/{location}

    vertex_rag_store

    VertexRagStore

    The data source for Vertex RagStore.

    query

    Required: RagQuery

    Single RAG retrieve query.
    VertexRagStore
    VertexRagStore

    rag_resources

    list: RagResource

    The representation of the RAG source. It can be used to specify the corpus only or RagFile s. Only support one corpus or multiple files from one corpus.

    rag_resources.rag_corpus

    Optional: string

    RagCorpora resource name.

    Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

    rag_resources.rag_file_ids

    list: string

    A list of RagFile resources.

    Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}

    RagQuery

    text

    string

    The query in text format to get relevant contexts.

    rag_retrieval_config

    Optional: RagRetrievalConfig

    The retrieval configuration for the query.

    RagRetrievalConfig

    top_k

    Optional: int32

    The number of contexts to retrieve.

    hybrid_search.alpha

    Optional: float

    Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], where 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5, which balances sparse and dense vector search equally.

    Hybrid Search is only available for Weaviate.

    filter.vector_distance_threshold

    oneof vector_db_threshold : double

    Only returns contexts with a vector distance smaller than the threshold.

    filter.vector_similarity_threshold

    oneof vector_db_threshold : double

    Only returns contexts with vector similarity larger than the threshold.

    ranking.rank_service.model_name

    Optional: string

    The model name of the rank service.

    Example: semantic-ranker-512@latest

    ranking.llm_ranker.model_name

    Optional: string

    The model name used for ranking.

    Example: gemini-2.5-flash

    Prediction parameters

    This table lists prediction parameters.

    GenerateContentRequest

    tools.retrieval.vertex_rag_store

    VertexRagStore

    Set to use a data source powered by Vertex AI RAG store.

    See VertexRagStore for details.

    Project management parameters

    This table lists project-level parameters.

    RagEngineConfig
    Parameters
    RagManagedDbConfig.scaled This tier offers production-scale performance along with autoscaling functionality.
    RagManagedDbConfig.basic This tier offers a cost-effective and low-compute tier.
    RagManagedDbConfig.unprovisioned This tier deletes the RagManagedDb and its underlying Spanner instance.

    Corpus management examples

    This section provides examples of how to use the API to manage your RAG corpus.

    Create a RAG corpus example

    This code sample demonstrates how to create a RAG corpus.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • CORPUS_DISPLAY_NAME : The display name of the RagCorpus .
    • CORPUS_DESCRIPTION : The description of the RagCorpus .

    HTTP method and URL:

    POST https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora

    Request JSON body:

    {
  "display_name" : " CORPUS_DISPLAY_NAME 
",
  "description": " CORPUS_DESCRIPTION 
",
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora"

    PowerShell

    Save the request body in a file named request.json , and execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora" | Select-Object -Expand Content
    You should receive a successful status code (2xx).

    The following example demonstrates how to create a RAG corpus by using the REST API.

       
 PROJECT_ID 
:  
Your  
project  
ID.  
 LOCATION 
:  
The  
region  
to  
process  
the  
request.  
 CORPUS_DISPLAY_NAME 
:  
The  
display  
name  
of  
the  
<code>RagCorpus</code>. 

       
//  
CreateRagCorpus  
//  
Input:  
LOCATION,  
PROJECT_ID,  
CORPUS_DISPLAY_NAME  
//  
Output:  
CreateRagCorpusOperationMetadata  
curl  
-X  
POST  
 \ 
  
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
  
-H  
 "Content-Type: application/json" 
  
 \ 
  
https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora  
 \ 
  
-d  
 '{ 
 "display_name" : " CORPUS_DISPLAY_NAME 
" 
 }'

    Update a RAG corpus example

    You can update your RAG corpus with a new display name, description, and vector database configuration. However, you can't change the following parameters in your RAG corpus:

    • The vector database type. For example, you can't change the vector database from Weaviate to Vertex AI Feature Store.
    • If you're using the managed database option, you can't update the vector database configuration.

    These examples demonstrate how to update a RAG corpus.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • CORPUS_ID : The corpus ID of your RAG corpus.
    • CORPUS_DISPLAY_NAME : The display name of the RagCorpus .
    • CORPUS_DESCRIPTION : The description of the RagCorpus .
    • INDEX_NAME : The resource name of the Vector Search Index . Format: projects/{project}/locations/{location}/indexes/{index}
    • INDEX_ENDPOINT_NAME : The resource name of the Vector Search Index Endpoint . Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

    HTTP method and URL:

    PATCH https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ CORPUS_ID

    Request JSON body:

    {
  "display_name" : " CORPUS_DISPLAY_NAME 
",
  "description": " CORPUS_DESCRIPTION 
",
  "rag_vector_db_config": {
     "vertex_vector_search": {
         "index": " INDEX_NAME 
",
         "index_endpoint": " INDEX_ENDPOINT_NAME 
",
     }
  }
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    curl -X PATCH \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ CORPUS_ID 
"

    PowerShell

    Save the request body in a file named request.json , and execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method PATCH `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ CORPUS_ID 
" | Select-Object -Expand Content
    You should receive a successful status code (2xx).

    List RAG corpora example

    This code sample demonstrates how to list all of the RAG corpora.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • PAGE_SIZE : The standard list page size. You may adjust the number of RagCorpora to return per page by updating the page_size parameter.
    • PAGE_TOKEN : The standard list page token. Obtained typically using ListRagCorporaResponse.next_page_token of the previous VertexRagDataService.ListRagCorpora call.

    HTTP method and URL:

    GET https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora?page_size= PAGE_SIZE 
&page_token= PAGE_TOKEN

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora?page_size= PAGE_SIZE 
&page_token= PAGE_TOKEN 
"

    PowerShell

    Execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora?page_size= PAGE_SIZE 
&page_token= PAGE_TOKEN 
" | Select-Object -Expand Content
    You should receive a successful status code (`2xx`) and a list of RagCorpora under the given PROJECT_ID .

    Get a RAG corpus example

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • RAG_CORPUS_ID : The ID of the RagCorpus resource.

    HTTP method and URL:

    GET https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
"

    PowerShell

    Execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
" | Select-Object -Expand Content
    A successful response returns the RagCorpus resource.

    The get and list commands are used in an example to demonstrate how RagCorpus uses the rag_embedding_model_config field with in the vector_db_config , which points to the embedding model you have chosen.

       
 PROJECT_ID 
:  
Your  
project  
ID.  
 LOCATION 
:  
The  
region  
to  
process  
the  
request.  
 RAG_CORPUS_ID 
:  
The  
corpus  
ID  
of  
your  
RAG  
corpus. 

     //  
GetRagCorpus
//  
Input:  
LOCATION,  
PROJECT_ID,  
RAG_CORPUS_ID
//  
Output:  
RagCorpus
curl  
-X  
GET  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
//  
ListRagCorpora
curl  
-sS  
-X  
GET  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/

    Delete a RAG corpus example

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • RAG_CORPUS_ID : The ID of the RagCorpus resource.

    HTTP method and URL:

    DELETE https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X DELETE \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
"

    PowerShell

    Execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
" | Select-Object -Expand Content
    A successful response returns the DeleteOperationMetadata .

    File management examples

    This section provides examples of how to use the API to manage RAG files.

    Upload a RAG file example

    REST

    Before using any of the request data, make the following replacements: 
       
 PROJECT_ID 
:  
Your  
project  
ID.  
 LOCATION 
:  
The  
region  
to  
process  
the  
request.  
 RAG_CORPUS_ID 
:  
The  
corpus  
ID  
of  
your  
RAG  
corpus.  
 LOCAL_FILE_PATH 
:  
The  
 local 
  
path  
to  
the  
file  
to  
be  
uploaded.  
 DISPLAY_NAME 
:  
The  
display  
name  
of  
the  
RAG  
file.  
 DESCRIPTION 
:  
The  
description  
of  
the  
RAG  
file.

    To send your request, use the following command:

       
curl  
-X  
POST  
 \ 
  
-H  
 "X-Goog-Upload-Protocol: multipart" 
  
 \ 
  
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
  
-F  
 metadata 
 = 
 "{'rag_file': {'display_name':' DISPLAY_NAME 
', 'description':' DESCRIPTION 
'}}" 
  
 \ 
  
-F  
 file 
 = 
@ LOCAL_FILE_PATH 
  
 \ 
  
 "https:// LOCATION 
-aiplatform.googleapis.com/upload/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles:upload"

    Import RAG files example

    Files and folders can be imported from Drive or Cloud Storage.

    The response.skipped_rag_files_count refers to the number of files that were skipped during import. A file is skipped when the following conditions are met:

    1. The file has already been imported.
    2. The file hasn't changed.
    3. The chunking configuration for the file hasn't changed.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • RAG_CORPUS_ID : The ID of the RagCorpus resource.
    • GCS_URIS : A list of Cloud Storage locations. Example: gs://my-bucket1, gs://my-bucket2 .
    • CHUNK_SIZE : Optional: Number of tokens each chunk should have.
    • CHUNK_OVERLAP : Optional: Number of tokens overlap between chunks.

    HTTP method and URL:

    POST https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles:import

    Request JSON body:

    {
  "import_rag_files_config": {
    "gcs_source": {
      "uris": " GCS_URIS 
"
    },
    "rag_file_chunking_config": {
      "chunk_size": CHUNK_SIZE 
,
      "chunk_overlap": CHUNK_OVERLAP 
}
  }
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles:import"

    PowerShell

    Save the request body in a file named request.json , and execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles:import" | Select-Object -Expand Content
    A successful response returns the ImportRagFilesOperationMetadata resource.

    The following sample demonstrates how to import a file from Cloud Storage. Use the max_embedding_requests_per_min control field to limit the rate at which RAG Engine calls the embedding model during the ImportRagFiles indexing process. The field has a default value of 1000 calls per minute.

       
 PROJECT_ID 
:  
Your  
project  
ID.  
 LOCATION 
:  
The  
region  
to  
process  
the  
request.  
 RAG_CORPUS_ID 
:  
The  
corpus  
ID  
of  
your  
RAG  
corpus.  
 GCS_URIS 
:  
A  
list  
of  
Cloud  
Storage  
locations.  
Example:  
gs://my-bucket1.  
 CHUNK_SIZE 
:  
Number  
of  
tokens  
each  
chunk  
should  
have.  
 CHUNK_OVERLAP 
:  
Number  
of  
tokens  
overlap  
between  
chunks.  
 EMBEDDING_MODEL_QPM_RATE 
:  
The  
QPM  
rate  
to  
limit  
RAGs  
access  
to  
your  
embedding  
model.  
Example:  
 1000 
. 

     //  
ImportRagFiles
//  
Import  
a  
single  
Cloud  
Storage  
file  
or  
all  
files  
 in 
  
a  
Cloud  
Storage  
bucket.
//  
Input:  
LOCATION,  
PROJECT_ID,  
RAG_CORPUS_ID,  
GCS_URIS
//  
Output:  
ImportRagFilesOperationMetadataNumber
//  
Use  
ListRagFiles  
to  
find  
the  
server-generated  
rag_file_id.
curl  
-X  
POST  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles:import  
 \ 
-d  
 '{ 
 "import_rag_files_config": { 
 "gcs_source": { 
 "uris": " GCS_URIS 
" 
 }, 
 "rag_file_chunking_config": { 
 "chunk_size": CHUNK_SIZE 
, 
 "chunk_overlap": CHUNK_OVERLAP 
 
 }, 
 "max_embedding_requests_per_min": EMBEDDING_MODEL_QPM_RATE 
 
 } 
 }' 
//  
Poll  
the  
operation  
status.
//  
The  
response  
contains  
the  
number  
of  
files  
imported. OPERATION_ID 
:  
The  
operation  
ID  
you  
get  
from  
the  
response  
of  
the  
previous  
command.
poll_op_wait  
 OPERATION_ID

    The following sample demonstrates how to import a file from Drive. Use the max_embedding_requests_per_min control field to limit the rate at which RAG Engine calls the embedding model during the ImportRagFiles indexing process. The field has a default value of 1000 calls per minute.

       
 PROJECT_ID 
:  
Your  
project  
ID.  
 LOCATION 
:  
The  
region  
to  
process  
the  
request.  
 RAG_CORPUS_ID 
:  
The  
corpus  
ID  
of  
your  
RAG  
corpus.  
 FOLDER_RESOURCE_ID 
:  
The  
resource  
ID  
of  
your  
Google  
Drive  
folder.  
 CHUNK_SIZE 
:  
Number  
of  
tokens  
each  
chunk  
should  
have.  
 CHUNK_OVERLAP 
:  
Number  
of  
tokens  
overlap  
between  
chunks.  
 EMBEDDING_MODEL_QPM_RATE 
:  
The  
QPM  
rate  
to  
limit  
RAGs  
access  
to  
your  
embedding  
model.  
Example:  
 1000 
. 

     //  
ImportRagFiles
//  
Import  
all  
files  
 in 
  
a  
Google  
Drive  
folder.
//  
Input:  
LOCATION,  
PROJECT_ID,  
RAG_CORPUS_ID,  
FOLDER_RESOURCE_ID
//  
Output:  
ImportRagFilesOperationMetadataNumber
//  
Use  
ListRagFiles  
to  
find  
the  
server-generated  
rag_file_id.
curl  
-X  
POST  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles:import  
 \ 
-d  
 '{ 
 "import_rag_files_config": { 
 "google_drive_source": { 
 "resource_ids": { 
 "resource_id": " FOLDER_RESOURCE_ID 
", 
 "resource_type": "RESOURCE_TYPE_FOLDER" 
 } 
 }, 
 "max_embedding_requests_per_min": EMBEDDING_MODEL_QPM_RATE 
 
 } 
 }' 
//  
Poll  
the  
operation  
status.
//  
The  
response  
contains  
the  
number  
of  
files  
imported. OPERATION_ID 
:  
The  
operation  
ID  
you  
get  
from  
the  
response  
of  
the  
previous  
command.
poll_op_wait  
 OPERATION_ID

    List RAG files example

    This code sample demonstrates how to list RAG files.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • RAG_CORPUS_ID : The ID of the RagCorpus resource.
    • PAGE_SIZE : The standard list page size. You may adjust the number of RagFiles to return per page by updating the page_size parameter.
    • PAGE_TOKEN : The standard list page token. Obtained typically using ListRagFilesResponse.next_page_token of the previous VertexRagDataService.ListRagFiles call.

    HTTP method and URL:

    GET https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles?page_size= PAGE_SIZE 
&page_token= PAGE_TOKEN

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles?page_size= PAGE_SIZE 
&page_token= PAGE_TOKEN 
"

    PowerShell

    Execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles?page_size= PAGE_SIZE 
&page_token= PAGE_TOKEN 
" | Select-Object -Expand Content
    You should receive a successful status code (2xx) along with a list of RagFiles under the given RAG_CORPUS_ID .

    Get a RAG file example

    This code sample demonstrates how to get a RAG file.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • RAG_CORPUS_ID : The ID of the RagCorpus resource.
    • RAG_FILE_ID : The ID of the RagFile resource.

    HTTP method and URL:

    GET https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles/ RAG_FILE_ID

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles/ RAG_FILE_ID 
"

    PowerShell

    Execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles/ RAG_FILE_ID 
" | Select-Object -Expand Content
    A successful response returns the RagFile resource.

    Delete a RAG file example

    This code sample demonstrates how to delete a RAG file.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • RAG_CORPUS_ID : The ID of the RagCorpus resource.
    • RAG_FILE_ID : The ID of the RagFile resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file_id} .

    HTTP method and URL:

    DELETE https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles/ RAG_FILE_ID

    To send your request, choose one of these options:

    curl

    Execute the following command:

    curl -X DELETE \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles/ RAG_FILE_ID 
"

    PowerShell

    Execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora/ RAG_CORPUS_ID 
/ragFiles/ RAG_FILE_ID 
" | Select-Object -Expand Content
    A successful response returns the DeleteOperationMetadata resource.

    Retrieval query example

    When a user asks a question or provides a prompt, the retrieval component in RAG searches through its knowledge base to find information that is relevant to the query.

    REST

    Before using any of the request data, make the following replacements:

    • LOCATION : The region to process the request.
    • PROJECT_ID : Your project ID .
    • RAG_CORPUS_RESOURCE : The name of the RagCorpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus} .
    • VECTOR_DISTANCE_THRESHOLD : Only contexts with a vector distance smaller than the threshold are returned.
    • TEXT : The query text to get relevant contexts.
    • SIMILARITY_TOP_K : The number of top contexts to retrieve.

    HTTP method and URL:

    POST https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
:retrieveContexts

    Request JSON body:

    {
 "vertex_rag_store": {
    "rag_resources": {
      "rag_corpus": " RAG_CORPUS_RESOURCE 
"
    },
    "vector_distance_threshold": VECTOR_DISTANCE_THRESHOLD 
},
  "query": {
   "text": " TEXT 
",
   "similarity_top_k": SIMILARITY_TOP_K 
}
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
:retrieveContexts"

    PowerShell

    Save the request body in a file named request.json , and execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
:retrieveContexts" | Select-Object -Expand Content
    You should receive a successful status code (2xx) and a list of related RagFiles .

    Generation example

    The LLM generates a grounded response using the retrieved contexts.

    REST

    Before using any of the request data, make the following replacements:

    • PROJECT_ID : Your project ID .
    • LOCATION : The region to process the request.
    • MODEL_ID : LLM model for content generation. Example: gemini-2.5-flash
    • GENERATION_METHOD : LLM method for content generation. Options: generateContent , streamGenerateContent
    • INPUT_PROMPT : The text sent to the LLM for content generation. Try to use a prompt relevant to the uploaded rag Files.
    • RAG_CORPUS_RESOURCE : The name of the RagCorpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus} .
    • SIMILARITY_TOP_K : Optional: The number of top contexts to retrieve.
    • VECTOR_DISTANCE_THRESHOLD : Optional: Contexts with a vector distance smaller than the threshold are returned.

    HTTP method and URL:

    POST https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
: GENERATION_METHOD

    Request JSON body:

    {
 "contents": {
  "role": "user",
  "parts": {
    "text": " INPUT_PROMPT 
"
  }
 },
 "tools": {
  "retrieval": {
   "disable_attribution": false,
   "vertex_rag_store": {
    "rag_resources": {
      "rag_corpus": " RAG_CORPUS_RESOURCE 
"
    },
    "similarity_top_k": SIMILARITY_TOP_K 
,
    "vector_distance_threshold": VECTOR_DISTANCE_THRESHOLD 
}
  }
 }
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
: GENERATION_METHOD 
"

    PowerShell

    Save the request body in a file named request.json , and execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred" }

    Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https:// LOCATION 
-aiplatform.googleapis.com/v1beta1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
: GENERATION_METHOD 
" | Select-Object -Expand Content
    A successful response returns the generated content with citations.

    Project management examples

    The tier is a project-level setting available under the RagEngineConfig resource and impacts RAG corpora using RagManagedDb . To get the tier configuration, use GetRagEngineConfig . To update the tier configuration, use UpdateRagEngineConfig .

    For more information on managing your tier configuration, see Manage your tier .

    Get project configuration

    The following sample code demonstrates how to read your RagEngineConfig :

    Console

    1. In the Google Cloud console, go to the RAG Engine page.

      Go to RAG Engine

    2. Select the region in which your RAG Engine is running. Your list of RAG corpora is updated.
    3. Click Configure RAG Engine . The Configure RAG Engine pane appears. You can see the tier that's selected for your RAG Engine.
    4. Click Cancel .

    Python

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
  vertexai 
 
 PROJECT_ID 
 = 
  YOUR_PROJECT_ID 
 
 LOCATION 
 = 
  YOUR_RAG_ENGINE_LOCATION 
 
 # Initialize Vertex AI API once per session 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 LOCATION 
 ) 
 rag_engine_config 
 = 
 rag 
 . 
 rag_data 
 . 
 get_rag_engine_config 
 ( 
 name 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/ 
 { 
 LOCATION 
 } 
 /ragEngineConfig" 
 ) 
 print 
 ( 
 rag_engine_config 
 )

    REST

     curl  
-X  
GET  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https:// ${ 
 LOCATION 
 } 
-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_ID 
 } 
/locations/ ${ 
 LOCATION 
 } 
/ragEngineConfig

    Update project configuration

    This section provides code samples to demonstrate how to change your tier in the configuration.

    Update your RagEngineConfig to the Scaled tier

    The following code samples demonstrate how to set the RagEngineConfig to the Scaled tier:

    Console

    1. In the Google Cloud console, go to the RAG Engine page.

      Go to RAG Engine

    2. Select the region in which your RAG Engine is running. Your list of RAG corpora is updated.
    3. Click Configure RAG Engine . The Configure RAG Engine pane appears.
    4. Select the tier that you want to run your RAG Engine.
    5. Click Save .

    Python

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
  vertexai 
 
 PROJECT_ID 
 = 
  YOUR_PROJECT_ID 
 
 LOCATION 
 = 
  YOUR_RAG_ENGINE_LOCATION 
 
 # Initialize Vertex AI API once per session 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 LOCATION 
 ) 
 rag_engine_config_name 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/ 
 { 
 LOCATION 
 } 
 /ragEngineConfig" 
 new_rag_engine_config 
 = 
 rag 
 . 
 RagEngineConfig 
 ( 
 name 
 = 
 rag_engine_config_name 
 , 
 rag_managed_db_config 
 = 
 rag 
 . 
 RagManagedDbConfig 
 ( 
 tier 
 = 
 rag 
 . 
 Scaled 
 ()), 
 ) 
 updated_rag_engine_config 
 = 
 rag 
 . 
 rag_data 
 . 
 update_rag_engine_config 
 ( 
 rag_engine_config 
 = 
 new_rag_engine_config 
 ) 
 print 
 ( 
 updated_rag_engine_config 
 )

    REST

     curl  
-X  
PATCH  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https:// ${ 
 LOCATION 
 } 
-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_ID 
 } 
/locations/ ${ 
 LOCATION 
 } 
/ragEngineConfig  
-d  
 "{'ragManagedDbConfig': {'scaled': {}}}"

    Update your RagEngineConfig to the Basic tier

    The following code samples demonstrate how to set the RagEngineConfig to the Basic tier:

    If you have a large amount of data in your RagManagedDb across your RAG corpora, downgrading to a Basic tier can fail due to insufficient compute and storage capacity.

    Console

    1. In the Google Cloud console, go to the RAG Engine page.

      Go to RAG Engine

    2. Select the region in which your RAG Engine is running. Your list of RAG corpora is updated.
    3. Click Configure RAG Engine . The Configure RAG Engine pane appears.
    4. Select the tier that you want to run your RAG Engine.
    5. Click Save .

    Python

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
  vertexai 
 
 PROJECT_ID 
 = 
  YOUR_PROJECT_ID 
 
 LOCATION 
 = 
  YOUR_RAG_ENGINE_LOCATION 
 
 # Initialize Vertex AI API once per session 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 LOCATION 
 ) 
 rag_engine_config_name 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/ 
 { 
 LOCATION 
 } 
 /ragEngineConfig" 
 new_rag_engine_config 
 = 
 rag 
 . 
 RagEngineConfig 
 ( 
 name 
 = 
 rag_engine_config_name 
 , 
 rag_managed_db_config 
 = 
 rag 
 . 
 RagManagedDbConfig 
 ( 
 tier 
 = 
 rag 
 . 
 Basic 
 ()), 
 ) 
 updated_rag_engine_config 
 = 
 rag 
 . 
 rag_data 
 . 
 update_rag_engine_config 
 ( 
 rag_engine_config 
 = 
 new_rag_engine_config 
 ) 
 print 
 ( 
 updated_rag_engine_config 
 )

    REST

     curl  
-X  
PATCH  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https:// ${ 
 LOCATION 
 } 
-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_ID 
 } 
/locations/ ${ 
 LOCATION 
 } 
/ragEngineConfig  
-d  
 "{'ragManagedDbConfig': {'basic': {}}}"

    Update your RagEngineConfig to the Unprovisioned tier

    The following code samples demonstrate how to set the RagEngineConfig to the Unprovisioned tier:

    Console

    1. In the Google Cloud console, go to the RAG Engine page.

      Go to RAG Engine

    2. Select the region in which your RAG Engine is running. Your list of RAG corpora is updated.
    3. Click Configure RAG Engine . The Configure RAG Engine pane appears.
    4. Click Delete RAG Engine . A confirmation dialog appears.
    5. Verify that you're about to delete your data in RAG Engine by typing delete , then click Confirm .
    6. Click Save .

    Python

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
  vertexai 
 
 PROJECT_ID 
 = 
  YOUR_PROJECT_ID 
 
 LOCATION 
 = 
  YOUR_RAG_ENGINE_LOCATION 
 
 # Initialize Vertex AI API once per session 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 LOCATION 
 ) 
 rag_engine_config_name 
 = 
 f 
 "projects/ 
 { 
 PROJECT_ID 
 } 
 /locations/ 
 { 
 LOCATION 
 } 
 /ragEngineConfig" 
 new_rag_engine_config 
 = 
 rag 
 . 
 RagEngineConfig 
 ( 
 name 
 = 
 rag_engine_config_name 
 , 
 rag_managed_db_config 
 = 
 rag 
 . 
 RagManagedDbConfig 
 ( 
 tier 
 = 
 rag 
 . 
 Unprovisioned 
 ()), 
 ) 
 updated_rag_engine_config 
 = 
 rag 
 . 
 rag_data 
 . 
 update_rag_engine_config 
 ( 
 rag_engine_config 
 = 
 new_rag_engine_config 
 ) 
 print 
 ( 
 updated_rag_engine_config 
 )

    REST

     curl  
-X  
PATCH  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https:// ${ 
 LOCATION 
 } 
-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_ID 
 } 
/locations/ ${ 
 LOCATION 
 } 
/ragEngineConfig  
-d  
 "{'ragManagedDbConfig': {'unprovisioned': {}}}"

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