RAG et connaissances
DB-GPT
An agentic AI data assistant - chat with your databases, run SQL and code, get reports
★ 20,1 k étoilesMIT
Voir le guide →SQLBot est un projet de RAG et connaissances open source, créé par dataease (dataease/SQLBot sur GitHub). Il compte 6 875 étoiles sur GitHub et 893 forks, il est publié sous la licence GPL-3.0 et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.
SQLBot — DataEase's conversational analytics: ask in natural language, get SQL answers via LLMs plus RAG.
Choose the LLM you will connect and the first data source, and plan your workspace and permission boundaries before inviting users.
Clone dataease/SQLBot from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Run the documented Docker container with its ports and data volumes, then open the web UI to configure your model and data source.
Add one table you know well, ask a question in natural language, and verify the generated SQL and answer against a hand-written query.
Natural-language questions become SQL using LLM understanding plus RAG, with no complex development to get started.
Workspace-level resource isolation and fine-grained data permissions keep access bounded and compliant.
Embed via web, popup or MCP calls, and integrate quickly with n8n, Dify, MaxKB, DataEase and similar apps.
Custom prompts, a terminology library and SQL examples calibrate it to your business, and interaction data drives continued tuning.
Only a model and a data source need configuring before users can start asking questions.
From the team behind DataEase, designed to give existing BI-style deployments a conversational query front end.
Get the source from the official GitHub repository for SQLBot.
git clone https://github.com/dataease/SQLBot.git
cd SQLBotStart the container with the documented ports and data volumes from the README.
docker run -d --name sqlbot --restart unless-stopped -p 8000:8000 -p 8001:8001 -v ./data/sqlbot/excel:/opt/sqlbot/data/excel -v ./data/sqlbot/file:/opt/sqlbot/data/file -v ./data/sqlbot/images:/opt/sqlbot/images -v ./data/sqlbot/logs:/opt/sqlbot/app/logs -v ./data/postgresql:/var/lib/postgresql/data --privileged=true dataease/sqlbotOpen the web access address from the README, configure your large model and data source, then ask your first question.
# Open the SQLBot web UI (ports above), add model + data source — see README access sectiondataease (FIT2CLOUD), through the dataease/SQLBot repository, with the site at sqlbot.org.
Yes — the project is open source under the GPL-3.0 license as recorded for this listing. See the license terms for any conditions on redistribution.
You configure your own large model for it to call, so model usage is under your credentials; SQLBot itself is open source under its FIT2CLOUD license (GPLv3-based, per its README).
Generic chat tools can generate SQL, but SQLBot is purpose-built for querying: workspace isolation, fine-grained data permissions, terminology/SQL-example calibration and embed/MCP delivery for analytics products.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — add one table you know well, ask a question in natural language, and verify the generated SQL and answer against a hand-written query.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.