AI-Code-Mastery (Episode 5): Zero-Shot document question answering with Flan-ULv2
Let’s create a zero-shot document question answering system using Flan-ULv2, a powerful encoder-decoder language model from Google based on T5. This system can surpass the performance of other models like GPT-3 and T5 in various downstream tasks. We’ll utilize the chainlang library to build an embedding database, search the database for the most similar documents to a given query, and then use the language model to retrieve the most likely answers. This approach allows for scaling up to thousands of documents for question answering.
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