How RAG‑Powered DB‑GPT Enables Intelligent Marine‑Environment Queries with Text2SQL

1. Business Background and Pain Points

Environmental governance in China is shifting from coarse to fine‑grained management, with generative AI becoming a key technology for the entire lifecycle of ecological protection. In marine‑environment supervision, three core pain points exist:

Low utilization of massive unstructured knowledge (PDF, Word, Excel, images) makes manual lookup inefficient.

Cross‑source information retrieval and comprehensive judgment are difficult because staff must switch between multiple systems and rely on data‑development teams.

Public‑cloud large models pose data‑privacy risks and suffer from hallucinations due to lack of domain‑specific knowledge.

2. Technical Selection and Solution

2.1 Industry Solution Comparison

A survey of mainstream AI‑application development frameworks concluded that a low‑code, privately deployable platform best fits the project’s budget and security requirements.

2.2 Choice of DB‑GPT Private Deployment

All evaluated platforms provide mature RAG‑based knowledge‑base capabilities, but only DB‑GPT offers seamless Text2SQL conversion for direct database dialogue, making it the decisive choice.

2.3 Solution and Implementation

Pain point 1 – Low utilization of unstructured knowledge: Use RAG‑enhanced vector retrieval in DB‑GPT to build a domain‑specific knowledge base that supports multi‑format uploads and plug‑in crawling, allowing continuous knowledge updates.

Pain point 2 – Cross‑source query difficulty: Employ the AWEL workflow and data‑driven multi‑agent collaboration to integrate heterogeneous data sources, enabling natural‑language‑driven data access, analysis, and automated report generation.

Pain point 3 – Data‑security and domain‑adaptation issues: Deploy DB‑GPT in a private environment with local model, agent‑level data masking, and direct database connections to keep all data on‑premise and avoid hallucinations.

3. Core Implementation Process

3.1 Data Preparation and Access

All source documents are converted to Markdown, uniformly formatted, and uploaded as vector embeddings (using the bge‑m3 model). Manual verification ensures balanced, semantically meaningful chunks. The MinerU tool is recommended for secure local document processing.

3.2 Local Model Deployment

The solution adopts the Qwen series large model for on‑premise inference, combined with the vLLM engine for high‑concurrency dynamic batching, offering superior performance compared with TensorRT‑LLM and TGI.

3.3 Application Construction

Two applications are built:

Knowledge‑base QA using the Summarizer agent, bound to each classified knowledge base.

Database QA using the DataScientist agent with Text2SQL, bound to the target database and enriched prompts.

3.4 Intent‑Recognition Prompt Construction

App codes are embedded in intent‑recognition prompts to direct the Intent Recognition Expert agent to the appropriate application, with prompts stored as .txt files in the intent‑recognition knowledge base.

3.5 Workflow Orchestration

The workflow orchestrates two agents: Intent Recognition Expert determines user intent, and AppLauncher launches the corresponding application. A fallback branch retrieves answers from the full knowledge base when intent detection fails.

4. Results

Product launch: an intelligent marine‑environment Q&A assistant.

Efficiency boost: single‑query time reduced from an average of 30 minutes to 1‑3 minutes, a >90 % speed increase.

Work simplification: lowered operational thresholds and eliminated error‑prone manual comparison.

Remaining challenges: current design separates data analysis and knowledge Q&A into different intents, preventing fully integrated reporting; future versions (DB‑GPT 0.8.0) will support multi‑source data fusion.