Text2SQL Implementation - Part 2: LangGraph Agent Flow, SQL Retry Logic, Memory & RAGAS Evaluation

This is Part-2 of the Text2SQL implementation series, where we build the core agentic intelligence of the system using LangGraph.

In this video, I implement a production-ready LangGraph flow that safely converts natural language into SQL, executes it, corrects mistakes automatically, and generates final answers — all while tracking memory and evaluating quality using RAGAS.

What’s Covered in This Video

🔹 Query validation node to check user intent & genuineness
🔹 SQL generation node using LLMs
🔹 Secure SQL execution with safety & standard checks
🔹 Retry & correction mechanism for incorrect SQL generation
🔹 Final answer generation from LLM
🔹 RAGAS-based evaluation for correctness & reliability
🔹 Full LangGraph construction: nodes, edges & compilation
🔹 LangGraph memory usage for handling multi-turn queries
🔹 Live testing with different user queries

Everything is implemented step by step with clean, modular, production-style code, making this video ideal for anyone building enterprise-grade Text2SQL or agentic data systems.

👉 This video completes the intelligence layer of the Text2SQL project.
👉 Watch Part-1 if you want to understand the DB & schema setup first.

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🔀 Sorting: https://youtube.com/playlist?list=PL9jefoqM2f-P6Ehn7ZtK9OXFZAuWHUiI1

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🤖 Deep Learning: https://www.youtube.com/playlist?list=PL9jefoqM2f-PSPzQe6DuSeUTtGAftQz7B

Generative AI: https://www.youtube.com/playlist?list=PL9jefoqM2f-PRqJhqaZ5R7WFSzsKHL80x

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🐍 Python Small Projects: https://youtube.com/playlist?list=PL9jefoqM2f-PpdCKjwuWoiDctQcUKeR6S

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Видео Text2SQL Implementation - Part 2: LangGraph Agent Flow, SQL Retry Logic, Memory & RAGAS Evaluation канала At A Glance!