Prompt-driven vector search with LLMs

github.com

28 pointstxtai5 comments

Quite similar result than fine tuned gpt with embeddings no?

This example isn't using gpt embeddings but it could. It's using local Hugging Face models (FLAN-T5) with a context provided by a vector store (Faiss).

If I would like to use small but trusted knowledge models with some big language model regardles how silly is the second to get verifiable answers only - are those the ways for doing this ? - or better shall I look for some other approaches - maybe like this one: https://openreview.net/forum?id=Plr5l7r0jY6 Language model with Plug-in Knowldge Memory ?

The goal of this approach is to answer questions with a datasource. The referenced example runs a vector database query to build the context and then runs a prompt to ask the question.

Answer the following question using only the context below. Say 'no answer' when the question can't be answered. Question: {question} Context: {context}

Not sure this prompt works for all scenarios and models. But it can easily be changed and is a starting point.

Link to example: https://neuml.hashnode.dev/prompt-driven-search-with-llms

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