AI Support Agent Template

Production-ready AI support-agent template built with FastAPI, Pydantic, Markdown knowledge bases, retrieval pipelines, session memory and optional backend worker integration.

This repository is designed as a reusable public template. Replace the knowledge-base structure, prompts and configuration with your own product documentation and support workflows.

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Features

• FastAPI backend architecture
• Pydantic request and response contracts
• AI support-agent orchestration
• Markdown knowledge-base support
• Retrieval and context scoring pipeline
• Prompt management
• Intent classification
• Safety and grounding policies
• Session memory and summarization
• Optional WebSocket worker integration
• Dockerized deployment

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Tech Stack

• Python
• FastAPI
• Pydantic
• Uvicorn
• Docker
• Docker Compose

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Repository Structure

app/
  routes/          API routes
  schemas/         Pydantic models and contracts
  services/        Agent orchestration, retrieval and policies
  utils/           Shared helpers

kb/
  README.md        KB structure overview
  01-routing/      Intent routing rules
  02-product-core/ Product documentation
  03-onboarding/   Onboarding guides
  04-auth/         Authentication flows
  05-billing/      Billing and subscriptions
  06-workflows/    Automation workflows
  07-security/     Security and safety rules
  08-support/      Support and escalation rules
  09-troubleshooting/ Common issues and fixes

Dockerfile
docker-compose.yml
.env.example
requirements.txt

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Knowledge Base

The kb/ directory contains a reusable template structure for organizing support documentation.

You can:

• rename folders;
• remove sections;
• add your own documentation;
• reorganize the hierarchy completely.

Recommended content:

• onboarding guides;
• FAQ;
• troubleshooting flows;
• billing rules;
• integration guides;
• response templates;
• escalation policies.

Do not store:

• API keys;
• tokens;
• passwords;
• customer data;
• private credentials.

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Quick Start

Docker

cp .env.example .env
docker compose up --build

Health check:

curl http://127.0.0.1:8011/health

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Local Development

Linux / macOS

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
python -m uvicorn app.main:app --host 127.0.0.1 --port 8011

Windows PowerShell

py -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
Copy-Item .env.example .env -Force
python -m uvicorn app.main:app --host 127.0.0.1 --port 8011

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Environment Variables

Use .env.example as a safe template.

Main variables:

Variable	Description
LLM_PROVIDER	Provider label
LLM_BASE_URL	OpenAI-compatible API endpoint
LLM_API_KEY	Provider API key
LLM_MODEL	Model name
KB_DIR	Knowledge-base directory
WORKER_ENABLED	Enables worker mode
BACKEND_API_BASE_URL	Optional backend API
BACKEND_API_TOKEN	Optional backend token

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Customization

1. Copy .env.example to .env.
2. Configure your provider credentials.
3. Replace KB templates with your own documentation.
4. Adjust prompts and intent routing if needed.
5. Run locally with Docker or Uvicorn.

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Security Notes

Before deployment:

• do not commit .env;
• do not commit API keys or tokens;
• do not include private customer data;
• review prompts and KB before production usage.

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License

MIT