How to Use Retrieval-Augmented Generation (RAG) with Vultr Serverless Inference

Retrieval-Augmented Generation (RAG) enhances AI models by combining real-time information retrieval with generative capabilities. When using Vultr Serverless Inference, RAG enables chat models to incorporate external, domain-specific knowledge from vector store collections. This improves the contextual accuracy of responses, reduces hallucinations, and ensures that the output is based on the most relevant and up-to-date information.

Follow this guide to use RAG-based chat completion on Vultr Serverless Inference using the Vultr API.

1. Send a GET request to the List Collections endpoint and note the target collection's ID.

   console Copy

   $ curl "https://api.vultrinference.com/v1/vector_store" \
       -X GET \
       -H "Authorization: Bearer ${INFERENCE_API_KEY}"
   

2. Send a GET request to the List Models endpoint and note the preferred inference model's ID.

   console Copy

   $ curl "https://api.vultrinference.com/v1/models" \
       -X GET \
       -H "Authorization: Bearer ${INFERENCE_API_KEY}"
   

3. Send a POST request to the RAG Chat Completion endpoint to generate responses using Retrieval-Augmented Generation (RAG).

   console Copy

   $ curl "https://api.vultrinference.com/v1/chat/completions/RAG" \
       -X POST \
       -H "Authorization: Bearer ${INFERENCE_API_KEY}" \
       -H "Content-Type: application/json" \
       --data '{
           "collection": "{collection-id}",
           "model": "{model-id}",
           "messages": [
               {
                   "role": "user",
                   "content": "{user-input}"
               }
           ],
           "max_tokens": 512
       }'
   

   Visit the RAG Chat Completion endpoint to view additional attributes you can apply for greater control when interacting with the preferred inference model.