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Arthur DANJOU
5a4a4f380f
- Created CLAUDE.md to provide development commands, architecture overview, and environment variables for the Nuxt 3 portfolio website. - Refactored project pages to remove unused color mappings and improve project filtering logic. - Updated content.config.ts to enforce stricter project type definitions and added short descriptions for projects. - Deleted outdated project files and added new projects related to hackathons and academic research. - Enhanced existing project descriptions with short summaries for better clarity.
56 lines
3.0 KiB
Markdown
56 lines
3.0 KiB
Markdown
---
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slug: hackathon-natixis
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title: "Natixis Hackathon: Generative SQL Analytics"
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type: Hackathon
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description: An intensive 4-week challenge to build an AI-powered data assistant. Our team developed a GenAI agent that transforms natural language into executable SQL queries, interactive visualizations, and natural language insights.
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shortDescription: A team-based project building an NL-to-SQL agent with Nuxt, Ollama, and Vercel AI SDK.
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publishedAt: 2026-03-07
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readingTime: 4
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status: completed
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tags:
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- Nuxt
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- Ollama
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- Vercel AI SDK
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- PostgreSQL
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- ETL
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icon: i-ph-database-duotone
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---
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## The Challenge
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Organized by **Natixis**, this hackathon followed a unique high-intensity format: **three consecutive Saturdays** of on-site development, bridged by two full weeks of remote collaboration.
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Working in a **team of four**, our goal was to bridge the gap between non-technical stakeholders and complex financial databases by creating an autonomous "Data Talk" agent.
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## Core Features
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### 1. Data Engineering & Schema Design
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Before building the AI layer, we had to handle a significant data migration task. I led the effort to:
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* **ETL Pipeline:** Convert fragmented datasets from **.xlsx** and **.csv** formats into a structured **SQL database**.
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* **Schema Optimization:** Design robust SQL schemas that allow an LLM to easily understand relationships (foreign keys, indexing) for accurate query generation.
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### 2. Natural Language to SQL (NL-to-SQL)
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Using the **Vercel AI SDK** and **Ollama**, we implemented an agentic workflow:
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* **Prompt Engineering:** Fine-tuning the agent to translate complex business questions (e.g., "What was our highest growth margin last quarter?") into valid, optimized SQL.
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* **Self-Correction:** If a query fails, the agent analyzes the SQL error and self-corrects the syntax before returning a result.
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### 3. Automated Insights & Visualization
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Data is only useful if it’s readable. Our Nuxt application goes beyond raw tables:
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* **Dynamic Charts:** The agent automatically determines the best visualization type (Bar, Line, Pie) based on the query result and renders it using interactive components.
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* **Narrative Explanations:** A final LLM pass summarizes the data findings in plain English, highlighting anomalies or key trends.
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## Technical Stack
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* **Frontend/API:** **Nuxt 3** for a seamless, reactive user interface.
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* **Orchestration:** **Vercel AI SDK** to manage streams and tool-calling logic.
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* **Inference:** **Ollama** for running LLMs locally, ensuring data privacy during development.
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* **Storage:** **PostgreSQL** for the converted data warehouse.
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## Impact & Results
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This project demonstrated that a modern stack (Nuxt + local LLMs) can drastically reduce the time needed for data discovery. By the final Saturday, our team successfully presented a working prototype capable of handling multi-table joins and generating real-time financial dashboards from simple chat prompts.
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---
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*Curious about the ETL logic or the prompt structure we used? I'd be happy to discuss how we optimized the LLM's SQL accuracy.*
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