Retrieval-Augmented Generation (RAG)
AZURE AI RAG
I developed a Python-based pipeline to generate an Azure AI Assistant using Retrieval-Augmented Generation (RAG). The process involved unzipping a file, chunking the text, vectorizing the data into a FAISS index, and uploading it to Azure AI Search. The assistant was then created, and the indexed files were linked to enable retrieval. Finally, a prompt was crafted to interact with the data. RAG is essential because it allows the AI to provide answers based on custom data, rather than relying solely on the pre-trained LLM, ensuring more accurate and relevant responses.
Want to download the Python code: 
Get the Python AI Assistant & RAG Framework
This chatbot is designed to help you explore key financial and operational details from three annual reports:
NACCO Industries – Focused on mining operations
AAON – A leader in HVAC manufacturing
ArcBest – An integrated logistics company
You can ask questions about income, expenses, financial statements, and other details from these reports. Whether you need insights on revenue trends, cost breakdowns, or key financial metrics, the assistant will provide accurate answers based on the uploaded data.
Try asking:
“What was NACCO Industries’ net income last year?”
“How much did AAON spend on R&D?”
“What are ArcBest’s major expense categories?”
Start exploring now!
Key Features of the Approach:
By leveraging RAG and real-world constraints, this chatbot delivers accurate, data-driven insights for education, finance, and research. 🚀
01.
The chatbot follows a structured approach to financial analysis:
1️⃣ Identify the focus (e.g., revenue, expenses, profitability).
2️⃣ Use only relevant financial data (from reports, statements, and filings).
3️⃣ Ensure insights are data-driven and aligned with key financial metrics
02.
RAG can enhance various domains by retrieving and synthesizing relevant data.
- Healthcare, it answers medical queries using clinical guidelines and research.
- Legal, it provides case law insights and regulatory guidance.
- E-commerce, it analyzes pricing and recommends products based on sales data.
- Education, it generates tailored explanations from textbooks and academic papers.
- Customer support, it retrieves troubleshooting steps from internal knowledge bases.
By adapting RAG, organizations ensure responses are contextually relevant, data-driven, and highly accurate.
03.
- Sales Training: Generate ideas for programs tailored to a company’s products.
- Customer Relations: Offer real-time suggestions for handling difficult customer interactions.
- Idea Generation: Assist marketers, educators, or business owners in brainstorming tailored strategies.