Introduction As part of my work on CONVAID, a powerful text-to-SQL system, I had the opportunity to enhance its capabilities by integrating Palantir Ontology as the vector database. This transformation introduced a more structured and streamlined approach to SQL query generation….
Naive RAG vs Self-RAG vs Graph RAG: A Comprehensive Overview
Introduction In my journey of building and refining Retrieval-Augmented Generation (RAG) pipelines, I’ve encountered multiple approaches—Naive RAG, Self-RAG, and Graph RAG—each addressing distinct challenges in AI-powered question answering. While working on projects such as CONVAID (my text-to-SQL project) and document Q&A…
FinRAG: Building a Financial Document Query System with RAG
Introduction In the fast-paced world of finance, where decisions hinge on timely and accurate information, I saw an opportunity to enhance how financial documents are queried and analyzed. That’s how FinRAG (Financial Retrieval-Augmented Generation) came to life. I built FinRAG to…
CONVAID’s Evolution in Natural Language to SQL: Exploring Vanna, MAC-SQL, and PET-SQL Frameworks
Introduction When I started working on CONVAID, a system designed to convert natural language into SQL queries, I realized that building an accurate and efficient pipeline required more than just using a large language model. The task involved understanding the intricacies…
RAG vs Fine-Tuning LLM: Enhancing Artificial Intelligence with Contextual Relevance
Custom development of machine learning models tailored to your specific business needs, leveraging algorithms and techniques such as regression, classification, clustering, and deep learning.
Unlock the Future of Marketing: How Evanke’s MarketMate Leverages CLIP for Stunning Image Generation!
Custom development of machine learning models tailored to your specific business needs, leveraging algorithms and techniques such as regression, classification, clustering, and deep learning.
