In the fast-paced world of AI and data, the ability to extract meaningful insights from
structured databases seamlessly is becoming a game-changer. Inspired by this
challenge, I set out to explore Retrieval-Augmented Generation (RAG) and
developed a robust system that combines cutting-edge AI with efficient data
querying.
Here’s how I tackled it:
Mission
The goal was clear:
1. Automate data ingestion from AWS S3 into Frappe, creating a streamlined
flow for structured information.
2. Leverage Large Language Models (LLMs) to convert natural language
questions into meaningful answers from MariaDB.
With this, I aimed to create a system where users—irrespective of technical
expertise—could query databases effortlessly.
How It Works
The architecture follows a two-phase RAG approach:
1. Retrieval Phase
This is where the groundwork happens:
The system intelligently queries structured datasets like SQL and fetches only
the relevant data chunks required for the user's question.
2. Augmented Generation Phase
Here, the magic unfolds:
An LLM processes the retrieved data along with the user query, generating
precise, human-readable, and context-aware answers.
This dual-phase approach ensures the system not only retrieves data but also
understands and presents it meaningfully.
Key Scenarios and Learnings
Scenario 1: The Ideal Query
When posed with, "What is the Invoice Number for SKU ID 10009 in the Sales
Register?", the system delivered a perfect response—showcasing its ability to
handle structured and accurate queries effectively.
Scenario 2: The Challenging Query
However, with an invalid SKU ID (100), the system faltered, highlighting the need for:
Stronger input validation to identify incorrect data.
Enhanced error-handling mechanisms to manage edge cases.
Takeaways from the Journey
1. Automation Triumph: Automating the connection between AWS S3 and
Frappe simplified data ingestion and preparation.
2. RAG in Action: The blend of retrieval and generation enabled dynamic
interactions, proving the versatility of RAG architecture.
3. Growth Opportunities: This project revealed areas for improvement, such as
refining the error-handling layer and enhancing LLM training for complex
scenarios.
The Road Ahead
This is just the beginning! Future improvements include:
Advanced error correction for invalid or ambiguous inputs.
Scaling to include more diverse and complex datasets.
Deepening LLM capabilities to handle multi-layered queries.
The potential of RAG in reshaping enterprise-grade solutions is immense, and I’m
excited to see where this journey leads.
Let’s Talk AI!
This experience has opened up a new world of possibilities for simplifying data-
driven workflows. Have you explored similar AI innovations? Let’s connect and share
ideas!
P.S. - We have used Leonardo.ai for the Featured Image in this blog
Written by - Shivani Vichare
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