Platform Overview
About Simple Chat
Simple Chat combines Azure-hosted chat, grounded retrieval, workspace management, and optional agent orchestration into one application surface.
- Azure-native by design
- Workspace-first document grounding
- Optional agents and automation
Simple Chat is an enterprise-ready Flask application for teams that want conversational AI grounded in their own data, with admin controls that stay practical instead of sprawling.
Grounded Chat
Users can chat directly with models or switch into retrieval-backed conversations that cite files from personal, group, or public workspaces.
Document Workflows
Uploads are processed through document extraction, chunking, embeddings, and search so teams can move from raw files to usable retrieval quickly.
Admin Control Surface
Admins configure branding, models, workspaces, search, safety, logging, and scale features from one settings experience.
What the docs are optimized for
The documentation is organized to help with three common paths: deploy the platform, configure the services behind it, and teach end users how to get value from workspaces, search, and agents.
Follow the right path
Deploy
Use the guided deployment path if you want the fastest route into a working environment.
Understand capabilities
Review how chat, workspaces, safety, citations, multimedia, and agents fit together before expanding the deployment.
Operate and tune
Use the admin, workflow, scaling, and troubleshooting guides when the platform moves from proof of concept into production support.
Teach the team
The tutorial track is written for people who need to go from first login to useful document-grounded chat without reading every reference page first.
Built as an open repo
The project lives in the open and includes application code, deployment assets, functional tests, UI tests, and deep change documentation under the docs tree.
Source and contribution flow
Contributors can inspect the application, deployment scripts, docs site, and supporting tools in one repository.
Release-by-release traceability
Feature explanations, fixes, and release notes are captured inside the docs site so changes remain searchable after deployment work is done.