Reading
A practical guide from data swamp to natural language, demonstrating how to build a Text2SQL agent using n8n, Supabase, and LLM, including best practices for semantic layer setup, RAG, and SQL execution.
Section 01
This article introduces a practical guide that demonstrates how to build a Text2SQL agent using n8n, Supabase, and LLM to address the data democratization dilemma. It includes best practices for semantic layer setup, RAG, and SQL execution, helping business users access databases without SQL skills.
Section 02
In modern enterprises, massive structured data is locked in databases, accessible only to SQL engineers. Business users rely on data teams, slowing down decision-making. Text2SQL promises natural language queries for users, but implementation requires solving issues like understanding complex business logic, handling ambiguous intentions, and ensuring SQL accuracy.
Section 03
This project provides a complete solution for building a Text2SQL agent. The tech stack includes: n8n (open-source workflow automation platform with visual orchestration), Supabase (open-source Firebase alternative based on PostgreSQL + pgvector vector storage), and LLM (semantic understanding and SQL generation). The core value is to demonstrate the transformation path from data swamp to natural language interface.
Section 04
Semantic Layer: 1. Glossary: Mapping business terms to database fields (e.g., "active users" corresponds to records in the users table where last_login is within the past 30 days); 2. Few-shot examples: Storing "question-SQL" pairs, using RAG to retrieve similar examples to assist LLM in SQL generation.
Four-Stage Workflow: Basic version (direct SQL generation by LLM), Schema-enhanced version (injecting detailed table structure), RAG-enhanced version (vector retrieval of examples), Full version (integrating semantic layer closed loop).
Section 05
Section 06
Section 07
Limitations: Accuracy of complex analytical queries (multi-table JOINs, window functions) needs improvement; cross-database federated queries and real-time data stream queries are not covered.
Future Directions: Query intent classification, multi-turn dialogue support, visualization integration, A/B testing framework to optimize generation quality.
Section 08
Text2SQL is a system engineering involving data understanding, semantic mapping, and security control. This project demonstrates implementation capabilities through the combination of n8n+Supabase+LLM, providing a reference paradigm for teams aiming to lower data access barriers and improve organizational data literacy.
Keep going with more reads from the same topic.
Nornir MCP Server is an enterprise-level server based on the Model Context Protocol (MCP). It seamlessly integrates large language models (such as Claude) with the Nornir network automation framework, supporting natural language orchestration for multi-vendor network devices (Cisco, Arista, Juniper, etc.), and providing production-grade features like a dual-engine architecture (NAPALM + Netmiko), intelligent filtering, and a secure sandbox.
Bibliothèque Française LLM is a structured indexing and annotation project for French public domain literature designed specifically for large language models (LLMs). It integrates multiple authoritative sources such as DraCor, Common Corpus, and Wikisource, providing metadata indexing categorized by genre, author, and era, as well as in-depth annotations for dramatic texts (including characters, lines, stage directions, etc.). Its aim is to enable LLMs to efficiently read and understand classic French literary works.
Splinter is a minimalist, high-performance key-value (KV) and vector storage system enabling zero-latency inter-process communication via shared memory and atomic operations. With only 766 lines of core code, it supports millions of operations per second and 768-dimensional vector storage, offering a new architectural approach for local LLM inference and data-intensive applications.
libmlxforge is an embeddable MLX large language model (LLM) inference engine designed specifically for Apple Silicon. It provides a unified C ABI interface, supports calls from Node.js, Swift, and Rust, and features continuous batching, streaming output, JSON-constrained structured output, and embedding vector generation.