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

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

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

  • Enhanced performance and scalability: 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 Agent Platform Search, which lets you select 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 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.

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

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

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

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

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

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

These code samples show you how to configure 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 Agent Platform Search engine or 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. For more information, see the Agent Platform Python API reference documentation.

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 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 Agent Platform SDK for Python, see Install the Agent Platform 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 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 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 Agent Platform SDK for Python, see Install the Agent Platform 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....
#   ...

What's next