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    Use Gemini Enterprise Agent Platform Search as a retrieval backend using Gemini Enterprise Agent Platform RAG Engine

    This page introduces Gemini Enterprise Agent Platform Search integration with the Gemini Enterprise Agent Platform RAG Engine.

    Gemini Enterprise Agent Platform Search provides a solution for retrieving and managing data within your Gemini Enterprise Agent Platform RAG applications. By using Gemini Enterprise Agent Platform Search as your retrieval backend, you can improve performance, scalability, and ease of integration.

    • Enhanced performance and scalability: Gemini Enterprise Agent Platform Search is designed to handle large volumes of data with exceptionally low latency. This translates to faster response times and improved performance for your RAG applications, especially when dealing with complex or extensive knowledge bases.

    • Simplified data management: Import your data from various sources, such as websites, BigQuery datasets, and Cloud Storage buckets, that can streamline your data ingestion process .

    • Seamless integration: Agent Platform provides built-in integration with Gemini Enterprise Agent Platform Search, which lets you select Gemini Enterprise Agent Platform Search as the corpus backend for your RAG application. This simplifies the integration process and helps to ensure optimal compatibility between components.

    • Improved LLM output quality: By using the retrieval capabilities of Gemini Enterprise Agent Platform Search, you can help to ensure that your RAG application retrieves the most relevant information from your corpus, which leads to more accurate and informative LLM-generated outputs.

    Gemini Enterprise Agent Platform Search brings together deep information retrieval, natural-language processing, and the latest features in large language model (LLM) processing, which helps to understand user intent and to return the most relevant results for the user.

    With Gemini Enterprise Agent Platform Search, you can build a Google-quality search application using data that you control.

    To set up a Gemini Enterprise Agent Platform Search, do the following:

    1. Create a search data store .

    2. Create a search application .

    Use the Gemini Enterprise Agent Platform Search as a retrieval backend for Gemini Enterprise Agent Platform RAG Engine

    Once the Gemini Enterprise Agent Platform Search is set up, follow these steps to set it as the retrieval backend for the RAG application.

    Set the Gemini Enterprise Agent Platform Search as the retrieval backend to create a RAG corpus

    These code samples show you how to configure Gemini Enterprise Agent Platform Search as the retrieval backend for a RAG corpus.

    REST

    To use the command line to create a RAG corpus, do the following:

    1. Create a RAG corpus

      Replace the following variables used in the code sample:

      • PROJECT_ID : The ID of your Google Cloud project.
      • LOCATION : The region to process the request.
      • DISPLAY_NAME : The display name of the RAG corpus that you want to create.
      • ENGINE_NAME : The full resource name of the Gemini Enterprise Agent Platform Search engine or Gemini Enterprise Agent Platform Search Datastore. For example,

        projects/ PROJECT_NUMBER /locations/ LOCATION /collections/default_collection/engines/ ENGINE_NAME /servingConfigs/default_search 

       curl  
-X  
POST  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
 "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/ragCorpora" 
  
 \ 
-d  
 '{ 
 "display_name" : " DISPLAY_NAME 
", 
 "vertex_ai_search_config" : { 
 "serving_config": " ENGINE_NAME 
/servingConfigs/default_search" 
 } 
 }'

    2. Monitor progress

      Replace the following variables used in the code sample:

      • PROJECT_ID : The ID of your Google Cloud project.
      • LOCATION : The region to process the request.
      • OPERATION_ID : The ID of the RAG corpus create operation.
       curl  
-X  
GET  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
 "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/operations/ OPERATION_ID 
"

    Python

    Before trying this sample, follow the Python setup instructions in the Agent Platform quickstart using client libraries .

    To authenticate to Agent Platform, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
  vertexai 
 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # vertex_ai_search_engine_name = "projects/{PROJECT_ID}/locations/{LOCATION}/collections/default_collection/engines/{ENGINE_ID}" 
 # display_name = "test_corpus" 
 # description = "Corpus Description" 
 # Initialize Vertex AI API once per session 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 # Configure Search 
 vertex_ai_search_config 
 = 
 rag 
 . 
 VertexAiSearchConfig 
 ( 
 serving_config 
 = 
 f 
 " 
 { 
 vertex_ai_search_engine_name 
 } 
 /servingConfigs/default_search" 
 , 
 ) 
 corpus 
 = 
 rag 
 . 
 create_corpus 
 ( 
 display_name 
 = 
 display_name 
 , 
 description 
 = 
 description 
 , 
 vertex_ai_search_config 
 = 
 vertex_ai_search_config 
 , 
 ) 
 print 
 ( 
 corpus 
 ) 
 # Example response: 
 # RagCorpus(name='projects/1234567890/locations/us-central1/ragCorpora/1234567890', 
 # display_name='test_corpus', description='Corpus Description'. 
 # ...

    Retrieve contexts using the RAG API

    After the RAG corpus creation, relevant contexts can be retrieved from Gemini Enterprise Agent Platform Search through the RetrieveContexts API.

    REST

    This code sample demonstrates how to retrieve contexts using REST.

    Replace the following variables used in the code sample:

    • PROJECT_ID : The ID of your Google Cloud project.
    • LOCATION : The region to process the request.
    • RAG_CORPUS_RESOURCE : The name of the RAG corpus resource.

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

    • TEXT : The query text to get relevant contexts.
     curl  
-X  
POST  
 \ 
  
-H  
 "Content-Type: application/json" 
  
 \ 
  
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
 "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
:retrieveContexts" 
  
 \ 
  
-d  
 '{ 
 "vertex_rag_store": { 
 "rag_resources": { 
 "rag_corpus": " RAG_CORPUS_RESOURCE 
" 
 } 
 }, 
 "query": { 
 "text": " TEXT 
" 
 } 
 }'

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
 vertexai 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # corpus_name = "projects/[PROJECT_ID]/locations/us-central1/ragCorpora/[rag_corpus_id]" 
 # Initialize Vertex AI API once per session 
 vertexai 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 response 
 = 
 rag 
 . 
 retrieval_query 
 ( 
 rag_resources 
 = 
 [ 
 rag 
 . 
 RagResource 
 ( 
 rag_corpus 
 = 
 corpus_name 
 , 
 # Optional: supply IDs from `rag.list_files()`. 
 # rag_file_ids=["rag-file-1", "rag-file-2", ...], 
 ) 
 ], 
 text 
 = 
 "Hello World!" 
 , 
 rag_retrieval_config 
 = 
 rag 
 . 
 RagRetrievalConfig 
 ( 
 top_k 
 = 
 10 
 , 
 filter 
 = 
 rag 
 . 
 utils 
 . 
 resources 
 . 
 Filter 
 ( 
 vector_distance_threshold 
 = 
 0.5 
 ), 
 ), 
 ) 
 print 
 ( 
 response 
 ) 
 # Example response: 
 # contexts { 
 #   contexts { 
 #     source_uri: "gs://your-bucket-name/file.txt" 
 #     text: ".... 
 #   ....

    Generate content using Agent Platform Gemini API

    REST

    To generate content using Gemini models, make a call to the Agent Platform GenerateContent API. By specifying the RAG_CORPUS_RESOURCE in the request, it automatically retrieves data from Gemini Enterprise Agent Platform Search.

    Replace the following variables used in the sample code:

    • PROJECT_ID : The ID of your Google Cloud project.

    • LOCATION : The region to process the request.

    • MODEL_ID : LLM model for content generation. For example, gemini-2.0-flash .

    • GENERATION_METHOD : LLM method for content generation. For example, generateContent , streamGenerateContent .

    • INPUT_PROMPT : The text that is sent to the LLM for content generation. Try to use a prompt relevant to the documents in Gemini Enterprise Agent Platform Search.

    • RAG_CORPUS_RESOURCE : The name of the RAG corpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus} .

    • SIMILARITY_TOP_K : Optional: The number of top contexts to retrieve.

       curl  
-X  
POST  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
 "https:// LOCATION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ LOCATION 
/publishers/google/models/ MODEL_ID 
: GENERATION_METHOD 
" 
  
 \ 
-d  
 '{ 
 "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 
 
 } 
 } 
 } 
 }'

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      from 
  
 vertexai 
  
 import 
 rag 
 from 
  
 vertexai.generative_models 
  
 import 
 GenerativeModel 
 , 
 Tool 
 import 
  
 vertexai 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # corpus_name = "projects/{PROJECT_ID}/locations/us-central1/ragCorpora/{rag_corpus_id}" 
 # Initialize Vertex AI API once per session 
 vertexai 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 rag_retrieval_tool 
 = 
 Tool 
 . 
 from_retrieval 
 ( 
 retrieval 
 = 
 rag 
 . 
 Retrieval 
 ( 
 source 
 = 
 rag 
 . 
 VertexRagStore 
 ( 
 rag_resources 
 = 
 [ 
 rag 
 . 
 RagResource 
 ( 
 rag_corpus 
 = 
 corpus_name 
 , 
 # Optional: supply IDs from `rag.list_files()`. 
 # rag_file_ids=["rag-file-1", "rag-file-2", ...], 
 ) 
 ], 
 rag_retrieval_config 
 = 
 rag 
 . 
 RagRetrievalConfig 
 ( 
 top_k 
 = 
 10 
 , 
 filter 
 = 
 rag 
 . 
 utils 
 . 
 resources 
 . 
 Filter 
 ( 
 vector_distance_threshold 
 = 
 0.5 
 ), 
 ), 
 ), 
 ) 
 ) 
 rag_model 
 = 
 GenerativeModel 
 ( 
 model_name 
 = 
 "gemini-2.0-flash-001" 
 , 
 tools 
 = 
 [ 
 rag_retrieval_tool 
 ] 
 ) 
 response 
 = 
 rag_model 
 . 
 generate_content 
 ( 
 "Why is the sky blue?" 
 ) 
 print 
 ( 
 response 
 . 
 text 
 ) 
 # Example response: 
 #   The sky appears blue due to a phenomenon called Rayleigh scattering. 
 #   Sunlight, which contains all colors of the rainbow, is scattered 
 #   by the tiny particles in the Earth's atmosphere.... 
 #   ...

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