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docs: docs website + installation (#778)
* feat: WIP doc (vibe started and iterated) * install from scratch docs * caddyfile.example * gitignore * authentik script * authentik script * authentik script * llm doc * authentik ongoing * more daily setup logs * doc website * gpu self hosted setup guide (no-mistakes) * doc review round * doc review round * doc review round * update doc site sidebars * feat(docs): add mermaid diagram support * docs polishing * live pipeline doc * move pipeline dev docs to dev docs location * doc pr review iteration * dockerfile healthcheck * docs/pr-comments * remove jwt comment * llm suggestion * pr comments * pr comments * document auto migrations * cleanup docs --------- Co-authored-by: Mathieu Virbel <mat@meltingrocks.com> Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
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docs/docs/installation/overview.md
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docs/docs/installation/overview.md
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---
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sidebar_position: 1
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title: Deployment Guide
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---
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# Deployment Guide
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This guide walks you through deploying Reflector from scratch. Follow these steps in order.
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## What You'll Set Up
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```mermaid
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flowchart LR
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User --> Caddy["Caddy (auto-SSL)"]
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Caddy --> Frontend["Frontend (Next.js)"]
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Caddy --> Backend["Backend (FastAPI)"]
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Backend --> PostgreSQL
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Backend --> Redis
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Backend --> Workers["Celery Workers"]
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Workers --> PostgreSQL
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Workers --> Redis
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Workers --> GPU["GPU Processing<br/>(Modal.com OR Self-hosted)"]
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```
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## Prerequisites
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Before starting, you need:
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- **Production server** - 4+ cores, 8GB+ RAM, public IP
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- **Two domain names** - e.g., `app.example.com` (frontend) and `api.example.com` (backend)
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- **GPU processing** - Choose one:
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- Modal.com account, OR
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- GPU server with NVIDIA GPU (8GB+ VRAM)
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- **HuggingFace account** - Free at https://huggingface.co
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- Accept both Pyannote licenses (required for speaker diarization):
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- https://huggingface.co/pyannote/speaker-diarization-3.1
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- https://huggingface.co/pyannote/segmentation-3.0
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- **LLM API** - For summaries and topic detection. Choose one:
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- OpenAI API key at https://platform.openai.com/account/api-keys, OR
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- Any OpenAI-compatible endpoint (vLLM, LiteLLM, Ollama, etc.)
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- **AWS S3 bucket** - For storing audio files and transcripts (see [S3 Setup](#create-s3-bucket-for-transcript-storage) below)
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### Optional (for live meeting rooms)
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- [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
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- [ ] **AWS S3 bucket + IAM Role** - For Daily.co recording storage (separate from transcript storage)
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---
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## Configure DNS
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```
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Type: A Name: app Value: <your-server-ip>
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Type: A Name: api Value: <your-server-ip>
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```
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---
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## Deploy GPU Processing
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Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
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| | **Modal.com (Cloud)** | **Self-Hosted GPU** |
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|---|---|---|
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| **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
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| **Pricing** | Pay-per-use | Fixed infrastructure cost |
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### Option A: Modal.com (Serverless Cloud GPU)
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#### Accept HuggingFace Licenses
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Visit both pages and click "Accept":
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- https://huggingface.co/pyannote/speaker-diarization-3.1
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- https://huggingface.co/pyannote/segmentation-3.0
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Generate a token at https://huggingface.co/settings/tokens
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#### Deploy to Modal
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There's an install script to help with this setup. It's using modal API to set all necessary moving parts.
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As an alternative, all those operations that script does could be performed in modal settings in modal UI.
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```bash
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uv tool install modal
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modal setup # opens browser for authentication
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git clone https://github.com/monadical-sas/reflector.git
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cd reflector/gpu/modal_deployments
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./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
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```
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**Save the output** - copy the configuration block, you'll need it soon.
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See [Modal Setup](./modal-setup) for troubleshooting and details.
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### Option B: Self-Hosted GPU
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**Location: YOUR GPU SERVER**
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Requires: NVIDIA GPU with 8GB+ VRAM, Ubuntu 22.04+, 40-50GB disk.
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See [Self-Hosted GPU Setup](./self-hosted-gpu-setup) for complete instructions. Quick summary:
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1. Install NVIDIA drivers and Docker
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2. Clone repository: `git clone https://github.com/monadical-sas/reflector.git`
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3. Configure `.env` with HuggingFace token
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4. Start service with Docker compose
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5. Set up Caddy reverse proxy for HTTPS
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**Save your API key and HTTPS URL** - you'll need them soon.
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---
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## Prepare Server
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**Location: dedicated reflector server**
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### Install Docker
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```bash
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ssh user@your-server-ip
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curl -fsSL https://get.docker.com | sh
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sudo usermod -aG docker $USER
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# Log out and back in for group changes
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exit
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ssh user@your-server-ip
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docker --version # verify
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```
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### Firewall
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Ensure ports 80 (HTTP) and 443 (HTTPS) are open for inbound traffic. The method varies by cloud provider and OS configuration.
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**For live transcription without Daily/Whereby rooms**: WebRTC requires UDP port range 49152-65535 for media traffic.
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### Clone Repository
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The Docker images contain all application code. You clone the repository for configuration files and the compose definition:
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```bash
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git clone https://github.com/monadical-sas/reflector.git
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cd reflector
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```
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---
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## Create S3 Bucket for Transcript Storage
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Reflector requires AWS S3 to store audio files during processing.
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### Create Bucket
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```bash
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# Choose a unique bucket name
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BUCKET_NAME="reflector-transcripts-yourname"
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AWS_REGION="us-east-1"
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# Create bucket
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aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
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```
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### Create IAM User
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Create an IAM user with S3 access for Reflector:
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1. Go to AWS IAM Console → Users → Create User
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2. Name: `reflector-transcripts`
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3. Attach policy: `AmazonS3FullAccess` (or create a custom policy for just your bucket)
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4. Create access key (Access key ID + Secret access key)
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Save these credentials - you'll need them in the next step.
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---
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## Configure Environment
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Reflector has two env files:
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- `server/.env` - Backend configuration
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- `www/.env` - Frontend configuration
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### Backend Configuration
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```bash
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cp server/.env.example server/.env
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nano server/.env
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```
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**Required settings:**
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```env
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# Database (defaults work with docker-compose.prod.yml)
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DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
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# Redis
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REDIS_HOST=redis
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CELERY_BROKER_URL=redis://redis:6379/1
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CELERY_RESULT_BACKEND=redis://redis:6379/1
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# Your domains
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BASE_URL=https://api.example.com
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CORS_ORIGIN=https://app.example.com
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CORS_ALLOW_CREDENTIALS=true
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# Secret key - generate with: openssl rand -hex 32
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SECRET_KEY=<your-generated-secret>
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# GPU Processing - choose ONE option:
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# Option A: Modal.com (paste from deploy-all.sh output)
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TRANSCRIPT_BACKEND=modal
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TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
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TRANSCRIPT_MODAL_API_KEY=<from-deploy-all.sh-output>
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DIARIZATION_BACKEND=modal
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DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
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DIARIZATION_MODAL_API_KEY=<from-deploy-all.sh-output>
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# Option B: Self-hosted GPU (use your GPU server URL and API key)
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# TRANSCRIPT_BACKEND=modal
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# TRANSCRIPT_URL=https://gpu.example.com
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# TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
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# DIARIZATION_BACKEND=modal
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# DIARIZATION_URL=https://gpu.example.com
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# DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
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# Storage - where to store audio files and transcripts (requires AWS S3)
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TRANSCRIPT_STORAGE_BACKEND=aws
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TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
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TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
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TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=reflector-media
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TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
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# LLM - for generating titles, summaries, and topics
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LLM_API_KEY=sk-your-openai-api-key
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LLM_MODEL=gpt-4o-mini
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# LLM_URL=https://api.openai.com/v1 # Optional: custom endpoint (vLLM, LiteLLM, Ollama, etc.)
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# Auth - disable for initial setup (see a dedicated step for authentication)
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AUTH_BACKEND=none
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```
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### Frontend Configuration
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```bash
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cp www/.env.example www/.env
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nano www/.env
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```
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**Required settings:**
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```env
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# Your domains
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SITE_URL=https://app.example.com
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API_URL=https://api.example.com
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WEBSOCKET_URL=wss://api.example.com
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SERVER_API_URL=http://server:1250
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# NextAuth
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NEXTAUTH_URL=https://app.example.com
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NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
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# Disable login requirement for initial setup
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FEATURE_REQUIRE_LOGIN=false
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```
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---
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## Configure Caddy
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```bash
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cp Caddyfile.example Caddyfile
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nano Caddyfile
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```
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Replace `example.com` with your domains. The `{$VAR:default}` syntax uses Caddy's env var substitution - you can either edit the file directly or set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables.
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```
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{$FRONTEND_DOMAIN:app.example.com} {
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reverse_proxy web:3000
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}
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{$API_DOMAIN:api.example.com} {
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reverse_proxy server:1250
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}
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```
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---
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## Start Services
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```bash
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docker compose -f docker-compose.prod.yml up -d
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```
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Wait for containers to start (first run may take 1-2 minutes to pull images and initialize).
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---
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## Verify Deployment
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### Check services
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```bash
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docker compose -f docker-compose.prod.yml ps
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# All should show "Up"
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```
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### Test API
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```bash
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curl https://api.example.com/health
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# Should return: {"status":"healthy"}
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```
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### Test Frontend
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- Visit https://app.example.com
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- You should see the Reflector interface
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- Try uploading an audio file to test transcription
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If any verification fails, see [Troubleshooting](#troubleshooting) below.
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---
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## Enable Authentication (Required for Live Rooms)
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By default, Reflector is open (no login required). **Authentication is required if you want to use Live Meeting Rooms.**
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See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
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Quick summary:
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1. Deploy Authentik on your server
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2. Create OAuth provider in Authentik
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3. Extract public key for JWT verification
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4. Update `server/.env`: `AUTH_BACKEND=jwt` + `AUTH_JWT_AUDIENCE`
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5. Update `www/.env`: `FEATURE_REQUIRE_LOGIN=true` + Authentik credentials
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6. Mount JWT keys volume and restart services
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---
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## Enable Live Meeting Rooms
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**Requires: Authentication Step**
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Live rooms require Daily.co and AWS S3. See [Daily.co Setup](./daily-setup) for complete S3/IAM configuration instructions.
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Note that Reflector also supports Whereby as a call provider - this doc doesn't cover its setup yet.
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Quick config - Add to `server/.env`:
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```env
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DEFAULT_VIDEO_PLATFORM=daily
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DAILY_API_KEY=<from-daily.co-dashboard>
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DAILY_SUBDOMAIN=<your-daily-subdomain>
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# S3 for recording storage
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DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket>
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DAILYCO_STORAGE_AWS_REGION=us-east-1
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DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
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```
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Reload env and restart:
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```bash
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docker compose -f docker-compose.prod.yml up -d server worker
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```
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---
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## Troubleshooting
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### Check logs for errors
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```bash
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docker compose -f docker-compose.prod.yml logs server --tail 20
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docker compose -f docker-compose.prod.yml logs worker --tail 20
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```
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### Services won't start
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```bash
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docker compose -f docker-compose.prod.yml logs
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```
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### CORS errors in browser
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- Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
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- Reload env: `docker compose -f docker-compose.prod.yml up -d server`
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### SSL certificate errors
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- Caddy auto-provisions Let's Encrypt certificates
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- Ensure ports 80 and 443 are open
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- Check: `docker compose -f docker-compose.prod.yml logs caddy`
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### Transcription not working
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- Check Modal dashboard: https://modal.com/apps
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- Verify URLs in `server/.env` match deployed functions
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- Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
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### "Login required" but auth not configured
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- Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
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- Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
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### Database migrations or connectivity issues
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Migrations run automatically on server startup. To check database connectivity or debug migration failures:
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```bash
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# Check server logs for migration errors
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docker compose -f docker-compose.prod.yml logs server | grep -i -E "(alembic|migration|database|postgres)"
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# Verify database connectivity
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docker compose -f docker-compose.prod.yml exec server uv run python -c "from reflector.db import engine; print('DB connected')"
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# Manually run migrations (if needed)
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docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
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```
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Reference in New Issue
Block a user