RAG quickstart for Python

This page shows you how to use the Vertex AI SDK to run LlamaIndex on Vertex AI for RAG tasks.

Prepare your Google Cloud console

To use LlamaIndex on Vertex AI for RAG, do the following:

  1. Install the Vertex AI SDK for Python.

  2. Run this command in the Google Cloud console to set up your project.

    gcloud config set {project}

  3. Run this command to authorize your login.

    gcloud auth application-default login

Run LlamaIndex on Vertex AI for RAG

Copy and paste this sample code into the Google Cloud console to run LlamaIndex on Vertex AI for RAG.

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.preview import rag
from vertexai.preview.generative_models import GenerativeModel, Tool
import vertexai

# Create a RAG Corpus, Import Files, and Generate a response

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# display_name = "test_corpus"
# paths = ["https://drive.google.com/file/d/123", "gs://my_bucket/my_files_dir"]  # Supports Google Cloud Storage and Google Drive Links

# Initialize Vertex AI API once per session
vertexai.init(project=PROJECT_ID, location="us-central1")

# Create RagCorpus
# Configure embedding model, for example "text-embedding-004".
embedding_model_config = rag.EmbeddingModelConfig(
    publisher_model="publishers/google/models/text-embedding-004"
)

rag_corpus = rag.create_corpus(
    display_name=display_name,
    embedding_model_config=embedding_model_config,
)

# Import Files to the RagCorpus
response = rag.import_files(
    rag_corpus.name,
    paths,
    chunk_size=512,  # Optional
    chunk_overlap=100,  # Optional
    max_embedding_requests_per_min=900,  # Optional
)

# Direct context retrieval
response = rag.retrieval_query(
    rag_resources=[
        rag.RagResource(
            rag_corpus=rag_corpus.name,
            # Optional: supply IDs from `rag.list_files()`.
            # rag_file_ids=["rag-file-1", "rag-file-2", ...],
        )
    ],
    text="What is RAG and why it is helpful?",
    similarity_top_k=10,  # Optional
    vector_distance_threshold=0.5,  # Optional
)
print(response)

# Enhance generation
# Create a RAG retrieval tool
rag_retrieval_tool = Tool.from_retrieval(
    retrieval=rag.Retrieval(
        source=rag.VertexRagStore(
            rag_resources=[
                rag.RagResource(
                    rag_corpus=rag_corpus.name,  # Currently only 1 corpus is allowed.
                    # Optional: supply IDs from `rag.list_files()`.
                    # rag_file_ids=["rag-file-1", "rag-file-2", ...],
                )
            ],
            similarity_top_k=3,  # Optional
            vector_distance_threshold=0.5,  # Optional
        ),
    )
)
# Create a gemini-pro model instance
rag_model = GenerativeModel(
    model_name="gemini-1.5-flash-001", tools=[rag_retrieval_tool]
)

# Generate response
response = rag_model.generate_content("What is RAG and why it is helpful?")
print(response.text)

What's next