# LLM-based E-commerce Intelligent Analytics Assistant: Fusion Practice of RAG and NL-to-SQL

> An e-commerce data analysis system integrating LLM, Retrieval-Augmented Generation (RAG), and Natural Language to SQL (NL-to-SQL) technologies, enabling business personnel to obtain in-depth data insights using natural language.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-06T18:45:21.000Z
- 最近活动: 2026-06-06T18:47:45.070Z
- 热度: 153.0
- 关键词: LLM, RAG, NL-to-SQL, 电商数据分析, 自然语言查询, FAISS, Groq, Streamlit, 智能分析
- 页面链接: https://www.zingnex.cn/en/forum/thread/ragnl-to-sql
- Canonical: https://www.zingnex.cn/forum/thread/ragnl-to-sql
- Markdown 来源: floors_fallback

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## Introduction: E-commerce Intelligent Analytics Assistant Integrating LLM, RAG, and NL-to-SQL

This article introduces an e-commerce data analysis system that combines Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and Natural Language to SQL (NL-to-SQL) technologies, aiming to enable business personnel to obtain in-depth data insights through natural language. The project is maintained by Rihanashariff and was published on the GitHub platform on June 6, 2026, with the original title "LLM-Powered-Analytics-Assistant-with-RAG."

## Project Background: Pain Points and Needs of E-commerce Data Analysis

In e-commerce operations, business personnel face two major challenges: First, massive structured data (transactions, user behavior) requires complex SQL skills to extract value, and relying on the data team leads to long waiting cycles; second, unstructured data such as customer reviews is difficult to perform semantic understanding and sentiment analysis through traditional BI tools. The need is to build a natural language interaction system that allows business personnel to directly ask questions to the data and get visual results.

## System Architecture and Technology Selection

### Data Layer
Uses the public dataset of Brazilian e-commerce Olist (about 100,000 orders, 8 tables, 40,000 customer reviews, 2016-2018), stored in SQLite.

### Core Engine
- NL-to-SQL module: Converts English questions to SQL
- RAG retrieval module: Processes unstructured data based on FAISS vector database
- Hybrid reasoning module: Intelligently coordinates SQL and RAG
- Visualization module: Automatically generates Plotly charts

### Technology Stack
LLM API (Groq), embedding model (Sentence Transformers), vector database (FAISS), frontend (Streamlit), data processing (Pandas/NumPy)

## Detailed Explanation of Three Query Modes

### Structured Data Query (SQL Mode)
For metrics like order volume and sales, automatically generates SQL, e.g.:
- Top 5 product categories with the highest revenue
- Average order delivery time

### Unstructured Data Analysis (RAG Mode)
Processes customer reviews through FAISS semantic retrieval + LLM summary/sentiment analysis, e.g.:
- Customer reviews on delivery services
- Main reasons for low-rated reviews

### Hybrid Analysis Mode (HYBRID Mode)
Combines structured and unstructured data, e.g.:
- Impact of delivery time on customer satisfaction
- Product categories with low sales and poor reviews

### Hybrid Analysis Mode (HYBRID Mode)
Combines structured and unstructured data, e.g.:
- Impact of delivery time on customer satisfaction
- Product categories with low sales and poor reviews

## Intelligent Routing and Visualization Capabilities

### Intelligent Routing
LLM automatically judges the query type:
- Only needs structured data → SQL mode
- Only needs text analysis → RAG mode
- Combination of both → HYBRID mode

### Visualization
Automatically recommends chart types (bar charts, line charts, etc.), generates Plotly interactive charts, and achieves the experience of "get charts immediately after asking questions."

## Project Structure and Practical Value

### Project Structure
- data/: Database and vector indexes
- sql/: NL-to-SQL conversion
- rag/: Embedding generation and semantic retrieval
- llm/: Routing, sentiment analysis, result synthesis, chart generation
- app.py: Streamlit interface

### Practical Value
1. Natural language interface lowers the threshold for data analysis
2. RAG effectively handles unstructured text
3. Hybrid reasoning answers complex business questions
4. Open-source tech stack (Groq replaces OpenAI, FAISS replaces commercial vector databases) controls costs

## Summary and Insights

This project provides a practical reference implementation for e-commerce data analysis, showing the complete path from data preparation to interface design. With the improvement of LLM capabilities and cost reduction, conversational data analysis is expected to become a standard configuration of enterprise BI tools, and its open-source implementation provides valuable practical experience for the industry.