GenAI App | LLM Analytics Assistant: Simplifying Data Transformation & Insights. AWS & Azure MySQL DW Example
Published: July 27, 2024
NEW
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:
LLM APP DEMO
Data Wrangling & Analysis: 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...
BASIC ARCHITECTURE
Similar to one that I am currently using on a live client project.
LLM App Build and UI: 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.
Model Choice: trade-offs between pricing, speed, response quality, and security/privacy. Premium model vs. open-source on-prem solution.
Architecture Flexibility: FastAPI processing server. Separate from the main system, making it reusable with different UI apps and backend databases.
COST CONSIDERATIONS
Cost Example: 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...
Choosing LLM models: 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.
Full Data Ingestion 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.
Split Workflow Approach: 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.
UPCOMING VIDEOS AND POSTS
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.