Vertex AI RAG Engine supported models

This page lists Gemini models, self-deployed models, and models with managed APIs on Vertex AI that support Vertex AI RAG Engine.

Gemini models

The following models support Vertex AI RAG Engine:

Fine-tuned Gemini models are unsupported when the Gemini models use Vertex AI RAG Engine.

Self-deployed models

Vertex AI RAG Engine supports all models in Model Garden.

Use Vertex AI RAG Engine with your self-deployed open model endpoints.

Replace the variables used in the code sample:

  • PROJECT_ID: Your project ID.
  • LOCATION: The region to process your request.

  • ENDPOINT_ID: Your endpoint ID.

     # Create a model instance with your self-deployed open model endpoint rag_model = GenerativeModel( "projects/PROJECT_ID/locations/LOCATION/endpoints/ENDPOINT_ID", tools=[rag_retrieval_tool] )

Models with managed APIs on Vertex AI

The models with managed APIs on Vertex AI that support Vertex AI RAG Engine include the following:

The following code sample demonstrates how to use the Gemini GenerateContent API to create a generative model instance. The model ID, /publisher/meta/models/llama-3.1-405B-instruct-maas, is found in the model card.

Replace the variables used in the code sample:

  • PROJECT_ID: Your project ID.
  • LOCATION: The region to process your request.

  • RAG_RETRIEVAL_TOOL: Your RAG retrieval tool.

     # Create a model instance with Llama 3.1 MaaS endpoint rag_model = GenerativeModel( "projects/PROJECT_ID/locations/LOCATION/publisher/meta/models/llama-3.1-405B-instruct-maas", tools=RAG_RETRIEVAL_TOOL )

The following code sample demonstrates how to use the OpenAI compatible ChatCompletions API to generate a model response.

Replace the variables used in the code sample:

  • PROJECT_ID: Your project ID.
  • LOCATION: The region to process your request.
  • MODEL_ID: LLM model for content generation. For example, meta/llama-3.1-405b-instruct-maas.
  • INPUT_PROMPT: The text sent to the LLM for content generation. Use a prompt relevant to the documents in Vertex AI Search.
  • RAG_CORPUS_ID: The ID of the RAG corpus resource.
  • ROLE: Your role.
  • USER: Your username.

  • CONTENT: Your content.

     # Generate a response with Llama 3.1 MaaS endpoint response = client.chat.completions.create( model="MODEL_ID", messages=[{"ROLE": "USER", "content": "CONTENT"}], extra_body={ "extra_body": { "google": { "vertex_rag_store": { "rag_resources": { "rag_corpus": "RAG_CORPUS_ID" }, "similarity_top_k": 10 } } } }, )

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