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=Plr5l7r0jY6Language 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.
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[ 2.1 ms ] story [ 17.7 ms ] threadAnswer 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.