LLM Powered GST Finder
The LLM Powered RAG Application for Finding GST Information is designed to simplify the process of identifying GST rates for Indian goods and services based solely on product descriptions. The project integrates Retrieval Augmented Generation (RAG) with FAISS vector indexing and OpenAI embeddings to build a scalable, intelligent query system. By combining efficient data cleaning, vector-based search, and a user-friendly Streamlit interface, the system provides a practical solution for businesses, policymakers, and individuals dealing with complex GST classifications.
Client
Personal
Client
Personal
Technology
LLM, RAG, Python, Streamlit, FAISS, genAI
Technology
LLM, RAG, Python, Streamlit, FAISS, genAI
Timeline
2 weeks
Timeline
2 weeks
The main objective of this project is to make GST information retrieval efficient, accurate, and cost-effective by:
Cleaning and preprocessing the official GST dataset, removing null and irrelevant entries.
Embedding product descriptions and indexing them in FAISS for fast similarity-based retrieval.
Using RAG architecture to locate the top relevant matches and then mapping them back to the original dataset for complete GST details.
Delivering results via a Streamlit dashboard that allows users to input product descriptions and quickly receive GST classifications and rates.
Enhancing usability for B2B businesses, retailers, and tax consultants who need instant GST lookups without manual searching.
The project successfully demonstrates how LLMs combined with RAG and vector databases can solve a real-world compliance and taxation challenge. By leveraging semantic similarity retrieval instead of traditional keyword search, the system improves accuracy and reduces errors in GST classification. The Streamlit-based interface makes the tool accessible to non-technical users, while the modular backend ensures scalability.
Looking ahead, the application can be extended by:
Integrating real-time GST database updates to keep information current.
Expanding the system with multi-language support for Indian regional languages.
Embedding advanced filtering (HSN codes, categories, tax slabs) for professional use.
Deploying as an API service to integrate with ERP and accounting software.
Overall, this project shows how AI-powered retrieval systems can reduce complexity in government and taxation processes, making compliance faster and more reliable.
Check out some of my recent projects.
LLM Powered GST Finder
The LLM Powered RAG Application for Finding GST Information is designed to simplify the process of identifying GST rates for Indian goods and services based solely on product descriptions. The project integrates Retrieval Augmented Generation (RAG) with FAISS vector indexing and OpenAI embeddings to build a scalable, intelligent query system. By combining efficient data cleaning, vector-based search, and a user-friendly Streamlit interface, the system provides a practical solution for businesses, policymakers, and individuals dealing with complex GST classifications.
Client
Personal
Client
Personal
Technology
LLM, RAG, Python, Streamlit, FAISS, genAI
Technology
LLM, RAG, Python, Streamlit, FAISS, genAI
Timeline
2 weeks
Timeline
2 weeks
The main objective of this project is to make GST information retrieval efficient, accurate, and cost-effective by:
Cleaning and preprocessing the official GST dataset, removing null and irrelevant entries.
Embedding product descriptions and indexing them in FAISS for fast similarity-based retrieval.
Using RAG architecture to locate the top relevant matches and then mapping them back to the original dataset for complete GST details.
Delivering results via a Streamlit dashboard that allows users to input product descriptions and quickly receive GST classifications and rates.
Enhancing usability for B2B businesses, retailers, and tax consultants who need instant GST lookups without manual searching.
The project successfully demonstrates how LLMs combined with RAG and vector databases can solve a real-world compliance and taxation challenge. By leveraging semantic similarity retrieval instead of traditional keyword search, the system improves accuracy and reduces errors in GST classification. The Streamlit-based interface makes the tool accessible to non-technical users, while the modular backend ensures scalability.
Looking ahead, the application can be extended by:
Integrating real-time GST database updates to keep information current.
Expanding the system with multi-language support for Indian regional languages.
Embedding advanced filtering (HSN codes, categories, tax slabs) for professional use.
Deploying as an API service to integrate with ERP and accounting software.
Overall, this project shows how AI-powered retrieval systems can reduce complexity in government and taxation processes, making compliance faster and more reliable.
Check out some of my recent projects.
LLM Powered GST Finder
The LLM Powered RAG Application for Finding GST Information is designed to simplify the process of identifying GST rates for Indian goods and services based solely on product descriptions. The project integrates Retrieval Augmented Generation (RAG) with FAISS vector indexing and OpenAI embeddings to build a scalable, intelligent query system. By combining efficient data cleaning, vector-based search, and a user-friendly Streamlit interface, the system provides a practical solution for businesses, policymakers, and individuals dealing with complex GST classifications.
Client
Personal
Client
Personal
Technology
LLM, RAG, Python, Streamlit, FAISS, genAI
Technology
LLM, RAG, Python, Streamlit, FAISS, genAI
Timeline
2 weeks
Timeline
2 weeks
The main objective of this project is to make GST information retrieval efficient, accurate, and cost-effective by:
Cleaning and preprocessing the official GST dataset, removing null and irrelevant entries.
Embedding product descriptions and indexing them in FAISS for fast similarity-based retrieval.
Using RAG architecture to locate the top relevant matches and then mapping them back to the original dataset for complete GST details.
Delivering results via a Streamlit dashboard that allows users to input product descriptions and quickly receive GST classifications and rates.
Enhancing usability for B2B businesses, retailers, and tax consultants who need instant GST lookups without manual searching.
The project successfully demonstrates how LLMs combined with RAG and vector databases can solve a real-world compliance and taxation challenge. By leveraging semantic similarity retrieval instead of traditional keyword search, the system improves accuracy and reduces errors in GST classification. The Streamlit-based interface makes the tool accessible to non-technical users, while the modular backend ensures scalability.
Looking ahead, the application can be extended by:
Integrating real-time GST database updates to keep information current.
Expanding the system with multi-language support for Indian regional languages.
Embedding advanced filtering (HSN codes, categories, tax slabs) for professional use.
Deploying as an API service to integrate with ERP and accounting software.
Overall, this project shows how AI-powered retrieval systems can reduce complexity in government and taxation processes, making compliance faster and more reliable.