Creating a Q&A Chatbot with GPT and Embeddings: Tips for Accuracy
This article is a summary of a YouTube video "Building a Q&A Chatbot using GPT and embeddings" by Jeremy Pinto
TLDR Chatbots like Buster can use embeddings and similarity scores to retrieve and parse documentation for easy organization and generate answers through text completion using GPT, but proper configuration and prompt engineering are crucial for accuracy.
Buster, a chatbot, uses embeddings to collect and parse documentation from Hugging Face and other projects for easy organization.
Using parser scripts and cosine similarity, it's possible to retrieve relevant documentation by measuring the distance between embeddings of a user's question and available sources.
Comparing user questions to documents using similarity scores and different models can generate answers through text completion.
GPT can be prompted with context to generate responses, and prompt engineering is key to its success, including formatting answers in markdown and adding relevant URLs for reference.
The bot uses GPT to respond to prompts, but requires proper configuration and a minimum cosine similarity score to ensure accurate responses.
Buster is not always perfect, but Gradio can help deploy a chatbot web app for testing, while the GPT model may have been updated without the speaker's knowledge.
Use the Buster library and CSV database for embeddings, and the vice Library for similarity search to scale to millions of documents efficiently and cheaply.
OpenAI API is exciting for potential open source models.