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Check out REX - AI Decision Intelligenceย
My open-source platform with a ton of micro-apps and tooling's for AI driven analytics
Text to SQL / connect to ANY data-warehouse on the fly/ direct file upload to data-warehouse table / create temporary database on the fly / python charts / statistical analysis
Realtime-voice connected to database - OpenAI new WebRTC API & Eleven Labs
And more ....
3rd part of the series on LLM Analytics Assistant Apps
Demonstrating data transformation and analysis on AWS MySQL via an LLM App. The app is deployed on my public website (outside of GPT Store, access-controlled section).
I cover 3 areas:
๐๐๐ ๐๐ฃ๐ฃ ๐๐๐ ๐ข
๐๐ฎ๐๐ฎ ๐ช๐ฟ๐ฎ๐ป๐ด๐น๐ถ๐ป๐ด & ๐๐ป๐ฎ๐น๐๐๐ถ๐: prototype customer table and transaction table with a million to 10 million records, creating summaries and merging data into new tables with additional variables... analyzing and creating customer profiles. All instructions in natural language... sometimes fuzzy and unclear... and sometimes with spellos...
๐๐๐ฆ๐๐ ๐๐ฅ๐๐๐๐ง๐๐๐ง๐จ๐ฅ๐
Similar to one that I am currently using on a live client project.
๐๐๐ ๐๐ฝ๐ฝ ๐๐๐ถ๐น๐ฑ ๐ฎ๐ป๐ฑ ๐จ๐: using Flowise AI. Open-source. Allows for rapid deployment. Powerful capabilities. Many other options - e.g. custom build with React/Next.js that can link up to company SSO and authentications.
๐ ๐ผ๐ฑ๐ฒ๐น ๐๐ต๐ผ๐ถ๐ฐ๐ฒ: trade-offs between pricing, speed, response quality, and security/privacy. Premium model vs. open-source on-prem solution.
๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ ๐๐น๐ฒ๐ ๐ถ๐ฏ๐ถ๐น๐ถ๐๐: FastAPI processing server. Separate from the main system, making it reusable with different UI apps and backend databases.
๐๐ข๐ฆ๐ง ๐๐ข๐ก๐ฆ๐๐๐๐ฅ๐๐ง๐๐ข๐ก๐ฆ
๐๐ผ๐๐ ๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ: ran 478 API requests/queries over 10 hours with GPT-3.5, costing around $1... working with the 1 million-10 million dataset referred to above... also discuss optimization strategies...
๐๐ต๐ผ๐ผ๐๐ถ๐ป๐ด ๐๐๐ ๐บ๐ผ๐ฑ๐ฒ๐น๐: depends on use case. e.g. Multi-LLM option...for difficult tasks, use an expensive model, and for simpler tasks, use a lower cost model.... or On-Prem solution for specific use cases.
๐๐๐น๐น ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ฒ๐๐๐ถ๐ผ๐ป by the LLM model is not always necessary... can significantly increase costs... potentially increasing by 100 times or more. For many use cases, processing can be done separately, and the LLM only passes SQL queries/Python commands.
๐ฆ๐ฝ๐น๐ถ๐ ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ ๐๐ฝ๐ฝ๐ฟ๐ผ๐ฎ๐ฐ๐ต: for scenarios requiring full data ingestion, split the workflow into multiple modules. LLM to only ingest the necessary and smallest amount of data directly... process the rest of the data separately.
๐จ๐ฃ๐๐ข๐ ๐๐ก๐ ๐ฉ๐๐๐๐ข๐ฆ ๐๐ก๐ ๐ฃ๐ข๐ฆ๐ง๐ฆ
Currently preparing detailed tutorials and step-by-step guides covering code, tips, and leveraging GPTs to develop apps. In future videos and posts, I will also cover areas like : processing with on-prem solutions, multiple LLM approaches, segregation of Python processing vs. MySQL processing, machine learning model builds, selective accesses, and more.