How to build a custom GPT enabled full-stack app for real-time data

1

Streamlit

Inspired by this article around enterprise search, our sample app should expose an HTTP REST API endpoint in Python to answer user queries about current sales by retrieving the latest deals from various sources (CSV, Jsonlines, API, message brokers, or databases), filters and presents deals based on user queries or chosen data sources, leverages OpenAI API Embeddings and Chat Completion endpoints to generate AI assistant responses and offers user-friendly UI with Streamlit.

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2

ChatGPT Plugins by SamurAI

As you can see, you get the expected output and this is quite simple to achieve since ChatGPT is context-aware now. However, the issue with this method is that the model’s context is restricted (gpt-4 maximum text length is 8,192 tokens). This strategy will quickly become problematic when input data is huge you may expect thousands of items discovered in sales and you can not provide this large amount of data as an input message. Also, once you have collected your data, you may want to clean, format, and preprocess data to ensure data quality and relevancy. If you utilize the OpenAI Chat Completion endpoint or build custom plugins for ChatGPT, it introduces other problems as follows:.

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