Integrating RAG API with Vertex AI Vector Search for Enhanced LLM Grounding

Retrieval-Augmented Generation (RAG) combines the power of retrieval systems with generative models to answer queries based on both pre-trained knowledge and external datasets. By pairing the RAG API with Vertex AI Vector Search, developers can create scalable, efficient, and highly accurate systems for semantic retrieval and grounded generation.

In this guide, we walk you through the process of integrating the RAG API with Vertex AI Vector Search, leveraging Google Cloud's powerful tools for creating enhanced LLM applications.

Prerequisites

Before diving into the integration, ensure you have:

• Access to a Google Cloud project.
• Google Cloud SDK installed and authenticated.
• APIs enabled: aiplatform.googleapis.com and compute.googleapis.com.
• Python installed with the Vertex AI SDK.
• Basic understanding of embeddings, vector search, and generative AI concepts.

1. Setting Up the Environment

Install and Initialize Vertex AI SDK

First, set up the Vertex AI SDK to interact with the platform:

pip install google-cloud-aiplatform        

In your Python environment:

from google.cloud import aiplatform

PROJECT_ID = "your-project-id"
LOCATION = "your-location"

aiplatform.init(project=PROJECT_ID, location=LOCATION)        

Authenticate

If working in Google Colab, authenticate with:

from google.colab import auth
auth.authenticate_user()        

Enable the necessary APIs:

! gcloud services enable compute.googleapis.com aiplatform.googleapis.com --project "$PROJECT_ID"        

2. Create a Vertex AI Vector Search Index

The Vector Search index acts as the database for storing vector embeddings. It enables efficient similarity searches for documents or other data representations. Create the index with the following parameters:

my_index = aiplatform.MatchingEngineIndex.create_tree_ah_index(
    display_name="my-index",
    description="Index for RAG",
    dimensions=768,  # Match embedding dimensions of your model
    distance_measure_type="DOT_PRODUCT_DISTANCE",
    index_update_method="STREAM_UPDATE",
    approximate_neighbors_count=10,
    leaf_node_embedding_count=500,
    leaf_nodes_to_search_percent=7,
)        

Key Parameters:

• Dimensions: Ensure this matches the output size of your embedding model.
• Distance Measure Type: Choose based on the similarity metric (e.g., cosine or dot product).
• Index Update Method: Allows real-time streaming updates to your index.

3. Deploy the Index to an Endpoint

Next, create an endpoint to query the index. Endpoints provide a way to expose your index for integration with other applications:

my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(
    display_name="my-index-endpoint",
    public_endpoint_enabled=True
)

DEPLOYED_INDEX_ID = "my-deployed-index"

my_index_endpoint.deploy_index(index=my_index, deployed_index_id=DEPLOYED_INDEX_ID)        

Note: Deployment may take up to 30 minutes initially. You can check the status in the Google Cloud Console under the “Index endpoints” tab. After the first deployment, subsequent updates are processed much faster.

4. Setting Up the RAG Corpus

A RAG corpus acts as the bridge between your data and the generative AI model. It structures and manages the data to be retrieved during generation.

Create and Link a RAG Corpus

from google.cloud.aiplatform.experimental import rag

CORPUS_DISPLAY_NAME = "my-rag-corpus"
vector_db = rag.VertexVectorSearch(
    index=my_index.resource_name,
    index_endpoint=my_index_endpoint.resource_name
)

rag_corpus = rag.create_corpus(display_name=CORPUS_DISPLAY_NAME, vector_db=vector_db)        

Alternatively, create an empty RAG corpus to update later:

rag_corpus = rag.create_corpus(display_name=CORPUS_DISPLAY_NAME)        

Update the Corpus with Vector Search Information

rag.update_corpus(corpus_name=rag_corpus.name, vector_db=vector_db)        

5. Importing Files into the RAG Corpus

Add your datasets to the RAG corpus for use during generation. This can include PDFs, text files, or other structured data. Use the ImportRagFiles API to import documents from Google Cloud Storage or Google Drive:

GCS_BUCKET = "your-bucket-name"
response = rag.import_files(
    corpus_name=rag_corpus.name,
    paths=[f"gs://{GCS_BUCKET}/your-file.pdf"],
    chunk_size=512,  # Adjust chunk size based on your use case
    chunk_overlap=100,  # Optional
)        

Tips for Importing:

• Chunk Size and Overlap: Fine-tune these parameters to balance granularity and context.
• File Types: Ensure supported file formats such as PDFs, text files, or JSON.

6. Querying the RAG Corpus

After importing the data, you can query the corpus to retrieve relevant contexts for specific questions or inputs.

RETRIEVAL_QUERY = "Your search query here"
response = rag.retrieval_query(
    rag_resources=[rag.RagResource(rag_corpus=rag_corpus.name)],
    text=RETRIEVAL_QUERY,
    similarity_top_k=10,  # Optional
    vector_distance_threshold=0.3,  # Optional
)

print(response)        

Understanding the Response:

• Contexts: Contains text snippets or metadata matching the query.
• Distance Scores: Lower values indicate higher similarity.

7. Grounding LLMs with RAG and Vertex AI

Grounding LLMs involves providing them with external data to improve their accuracy and relevance. By integrating RAG with Vertex AI, you can ensure your generative models are contextually aware.

Setting Up the Integration

from vertexai.preview.generative_models import GenerativeModel, Tool

tool = Tool.from_retrieval(
    retrieval=rag.Retrieval(
        source=rag.VertexRagStore(
            rag_resources=[rag.RagResource(rag_corpus=rag_corpus.name)],
            similarity_top_k=10,
        ),
    )
)

model = GenerativeModel(model_name="gemini-1.5-flash-001", tools=[tool])        

Generating Grounded Responses

PROMPT = "What is the cargo capacity of Cymbal Starlight?"
response = model.generate_content(PROMPT)
print(response.text)        

Example Use Cases:

• Customer support systems grounded in product manuals.
• Research assistants retrieving and synthesizing academic papers.
• Domain-specific chatbots enriched with proprietary data.

By integrating RAG API with Vertex AI Vector Search, you can build powerful, scalable systems for semantic search and retrieval-augmented generation. This combination allows your applications to handle large data volumes while delivering precise, contextually grounded responses.

Key Takeaways:

• Utilize Vertex AI’s robust infrastructure for scalable and low-latency vector search.
• Fine-tune parameters like chunk size, overlap, and similarity thresholds for optimal performance.
• Leverage grounding to make LLMs more relevant and accurate in specialized domains.