Q&A Document Retrieval With DPR
▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB

The third video in building our Stoic Q&A app.

In open-domain question answering, we typically design a model architecture that contains a data source, retriever, and reader/generator.

The first of these components is typically a document store. The two most popular stores we use here are Elasticsearch and FAISS.

Next up is our retriever — the topic of this video. The job of the retriever is to filter through our document store for relevant chunks of information (the documents) and pass them to the reader/generator model.

DPR (dense passage retriever) is a dense vector retriever that is trained on question-context pairs. Encoding both accordingly - enabling super accurate similarity indexing.

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If you're interested in learning more about DPR, I wrote about it on Medium here:
https://towardsdatascience.com/how-to-create-an-answer-from-a-question-with-dpr-d76e29cc5d60

(Free link):
https://towardsdatascience.com/how-to-create-an-answer-from-a-question-with-dpr-d76e29cc5d60?sk=1bdd7c1bff80bf51410962691c690c69

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