Use Agent Search as a retrieval backend using Vertex AI RAG Engine

This page introduces Agent Search integration with the Vertex AI RAG Engine.

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

  • Enhanced performance and scalability: Agent 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: Vertex AI provides built-in integration with Agent Search, which lets you select Agent 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 Agent 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.

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

To set up a Agent Search, do the following:

  1. Create a search data store.
  2. Create a search application.

Use the Agent Search as a retrieval backend for Vertex AI RAG Engine

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

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

These code samples show you how to configure Agent 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 Agent Search engine or Agent 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 Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, 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 Agent 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 Vertex AI Gemini API

REST

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