It does use a vector database (pinecone, weaviate, etc.) to store embeddings. The embeddings are created using OpenAI's text-embedding-ada-002 model, but that's not a requirement. In fact we are looking at embeddings generation through BERT or RoBERTa to benchmark performance.
At prompt time, the plugin retrieves the nearest embeddings to the prompt, and inserts them into a more complete prompt before sending it to the model.
https://github.com/openai/chatgpt-retrieval-plugin#retrieval...
The plugin uses OpenAI's text-embedding-ada-002 embeddings model to generate embeddings of document chunks, and then stores and queries them using a vector database on the backend. As an open-source and self-hosted solution, developers can deploy their own Retrieval Plugin and register it with ChatGPT. The Retrieval Plugin supports several vector database providers, allowing developers to choose their preferred one from a list.