feat: new parakeet v3 implementation

Multi languages, but less performant than v2. I tried on french, and it was switching from english to french. Maybe some configuration is required to get it right, but at the moment we cannot select any kind of source translation from the UI, only target translation
2026-03-22 07:06:47 +00:00 · 2025-08-21 20:41:56 -06:00
502 changed files with 15720 additions and 87712 deletions
--- a/.github/workflows/db_migrations.yml
+++ b/.github/workflows/db_migrations.yml
@@ -2,8 +2,6 @@ name: Test Database Migrations

 on:
  push:
-    branches:
-      - main
    paths:
      - "server/migrations/**"
      - "server/reflector/db/**"
@@ -19,9 +17,6 @@ on:
 jobs:
  test-migrations:
    runs-on: ubuntu-latest
-    concurrency:
-      group: db-ubuntu-latest-${{ github.ref }}
-      cancel-in-progress: true
    services:
      postgres:
        image: postgres:17
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -0,0 +1,90 @@
+name: Deploy to Amazon ECS
+
+on: [workflow_dispatch]
+
+env:
+  # 950402358378.dkr.ecr.us-east-1.amazonaws.com/reflector
+  AWS_REGION: us-east-1
+  ECR_REPOSITORY: reflector
+
+jobs:
+  build:
+    strategy:
+      matrix:
+        include:
+          - platform: linux/amd64
+            runner: linux-amd64
+            arch: amd64
+          - platform: linux/arm64
+            runner: linux-arm64
+            arch: arm64
+
+    runs-on: ${{ matrix.runner }}
+
+    permissions:
+      contents: read
+
+    outputs:
+      registry: ${{ steps.login-ecr.outputs.registry }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ env.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v2
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Build and push ${{ matrix.arch }}
+        uses: docker/build-push-action@v5
+        with:
+          context: server
+          platforms: ${{ matrix.platform }}
+          push: true
+          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest-${{ matrix.arch }}
+          cache-from: type=gha,scope=${{ matrix.arch }}
+          cache-to: type=gha,mode=max,scope=${{ matrix.arch }}
+          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
+          provenance: false
+
+  create-manifest:
+    runs-on: ubuntu-latest
+    needs: [build]
+
+    permissions:
+      deployments: write
+      contents: read
+
+    steps:
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ env.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        uses: aws-actions/amazon-ecr-login@v2
+
+      - name: Create and push multi-arch manifest
+        run: |
+          # Get the registry URL (since we can't easily access job outputs in matrix)
+          ECR_REGISTRY=$(aws ecr describe-registry --query 'registryId' --output text).dkr.ecr.${{ env.AWS_REGION }}.amazonaws.com
+
+          docker manifest create \
+            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest \
+            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-amd64 \
+            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-arm64
+
+          docker manifest push $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest
+
+          echo "✅ Multi-arch manifest pushed: $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest"
--- a/.github/workflows/dockerhub-backend.yml
+++ b/.github/workflows/dockerhub-backend.yml
@@ -1,53 +0,0 @@
-name: Build and Push Backend Docker Image (Docker Hub)
-
-on:
-  push:
-    tags:
-      - "v*"
-  workflow_dispatch:
-
-env:
-  REGISTRY: docker.io
-  IMAGE_NAME: monadicalsas/reflector-backend
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: monadicalsas
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-          tags: |
-            type=ref,event=branch
-            type=ref,event=tag
-            type=raw,value=latest,enable={{is_default_branch}}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v5
-        with:
-          context: ./server
-          file: ./server/Dockerfile
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          platforms: linux/amd64,linux/arm64
--- a/.github/workflows/dockerhub-frontend.yml
+++ b/.github/workflows/dockerhub-frontend.yml
@@ -1,70 +0,0 @@
-name: Build and Push Frontend Docker Image
-
-on:
-  push:
-    tags:
-      - "v*"
-  workflow_dispatch:
-
-env:
-  REGISTRY: docker.io
-  IMAGE_NAME: monadicalsas/reflector-frontend
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: monadicalsas
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-          tags: |
-            type=ref,event=branch
-            type=ref,event=tag
-            type=raw,value=latest,enable={{is_default_branch}}
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v5
-        with:
-          context: ./www
-          file: ./www/Dockerfile
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          platforms: linux/amd64,linux/arm64
-
-  deploy:
-    needs: build-and-push
-    runs-on: ubuntu-latest
-    if: success()
-    strategy:
-      matrix:
-        environment: [reflector-monadical, reflector-media]
-    environment: ${{ matrix.environment }}
-    steps:
-      - name: Trigger Coolify deployment
-        run: |
-          curl -X POST "${{ secrets.COOLIFY_WEBHOOK_URL }}" \
-            -H "Content-Type: application/json" \
-            -H "Authorization: Bearer ${{ secrets.COOLIFY_WEBHOOK_TOKEN }}" \
-            -f || (echo "Failed to trigger Coolify deployment for ${{ matrix.environment }}" && exit 1)
--- a/.github/workflows/selfhost-script.yml
+++ b/.github/workflows/selfhost-script.yml
@@ -1,36 +0,0 @@
-# Validates the self-hosted setup script: runs with --cpu and --garage,
-# brings up services, runs health checks, then tears down.
-name: Selfhost script (CPU + Garage)
-
-on:
-  workflow_dispatch: {}
-  push:
-    branches:
-      - main
-  pull_request: {}
-
-jobs:
-  selfhost-cpu-garage:
-    runs-on: ubuntu-latest
-    timeout-minutes: 25
-    concurrency:
-      group: selfhost-${{ github.ref }}
-      cancel-in-progress: true
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Run setup-selfhosted.sh (CPU + Garage)
-        run: |
-          ./scripts/setup-selfhosted.sh --cpu --garage
-
-      - name: Quick health checks
-        run: |
-          curl -sf http://localhost:1250/health && echo " Server OK"
-          curl -sf http://localhost:3000 > /dev/null && echo " Frontend OK"
-          curl -sf http://localhost:3903/metrics > /dev/null && echo " Garage admin OK"
-
-      - name: Teardown
-        if: always()
-        run: |
-          docker compose -f docker-compose.selfhosted.yml --profile cpu --profile garage down -v --remove-orphans 2>/dev/null || true
--- a/.github/workflows/test_next_server.yml
+++ b/.github/workflows/test_next_server.yml
@@ -1,45 +0,0 @@
-name: Test Next Server
-
-on:
-  pull_request:
-    paths:
-      - "www/**"
-  push:
-    branches:
-      - main
-    paths:
-      - "www/**"
-
-jobs:
-  test-next-server:
-    runs-on: ubuntu-latest
-
-    defaults:
-      run:
-        working-directory: ./www
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Setup Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-
-    - name: Install pnpm
-      uses: pnpm/action-setup@v4
-      with:
-        version: 8
-
-    - name: Setup Node.js cache
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        cache: 'pnpm'
-        cache-dependency-path: './www/pnpm-lock.yaml'
-
-    - name: Install dependencies
-      run: pnpm install
-
-    - name: Run tests
-      run: pnpm test
--- a/.github/workflows/test_server.yml
+++ b/.github/workflows/test_server.yml
@@ -5,17 +5,12 @@ on:
    paths:
      - "server/**"
  push:
-    branches:
-      - main
    paths:
      - "server/**"

 jobs:
  pytest:
    runs-on: ubuntu-latest
-    concurrency:
-      group: pytest-${{ github.ref }}
-      cancel-in-progress: true
    services:
      redis:
        image: redis:6
@@ -34,10 +29,7 @@ jobs:
          uv run -m pytest -v tests

  docker-amd64:
-    runs-on: [linux-amd64]
-    concurrency:
-      group: docker-amd64-${{ github.ref }}
-      cancel-in-progress: true
+    runs-on: linux-amd64
    steps:
      - uses: actions/checkout@v4
      - name: Set up Docker Buildx
@@ -52,14 +44,9 @@ jobs:
          github-token: ${{ secrets.GHA_CACHE_TOKEN }}

  docker-arm64:
-    runs-on: [linux-arm64]
-    concurrency:
-      group: docker-arm64-${{ github.ref }}
-      cancel-in-progress: true
+    runs-on: linux-arm64
    steps:
      - uses: actions/checkout@v4
-      - name: Wait for Docker daemon
-        run: while ! docker version; do sleep 1; done
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Build ARM64
--- a/.gitignore
+++ b/.gitignore
@@ -1,9 +1,6 @@
 .DS_Store
 server/.env
-server/.env.production
 .env
-Caddyfile
-.env.hatchet
 server/exportdanswer
 .vercel
 .env*.local
@@ -17,11 +14,4 @@ data/
 www/REFACTOR.md
 www/reload-frontend
 server/test.sqlite
-CLAUDE.local.md
-www/.env.development
-www/.env.production
-.playwright-mcp
-.secrets
-opencode.json
-
-vibedocs/
+CLAUDE.local.md
--- a/.gitleaksignore
+++ b/.gitleaksignore
@@ -1,7 +0,0 @@
-b9d891d3424f371642cb032ecfd0e2564470a72c:server/tests/test_transcripts_recording_deletion.py:generic-api-key:15
-docs/docs/installation/auth-setup.md:curl-auth-header:250
-docs/docs/installation/daily-setup.md:curl-auth-header:277
-gpu/self_hosted/DEV_SETUP.md:curl-auth-header:74
-gpu/self_hosted/DEV_SETUP.md:curl-auth-header:83
-server/reflector/worker/process.py:generic-api-key:465
-server/reflector/worker/process.py:generic-api-key:594
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ repos:
      - id: format
        name: run format
        language: system
-        entry: bash -c 'if [ -f "$HOME/.nvm/nvm.sh" ]; then source "$HOME/.nvm/nvm.sh"; fi; cd www && pnpm format'
+        entry: bash -c 'cd www && pnpm format'
        pass_filenames: false
        files: ^www/

@@ -27,8 +27,3 @@ repos:
        files: ^server/
      - id: ruff-format
        files: ^server/
-
-  - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.28.0
-    hooks:
-      - id: gitleaks
--- a/.secrets.example
+++ b/.secrets.example
@@ -1,24 +0,0 @@
-# Example secrets file for GitHub Actions workflows
-# Copy this to .secrets and fill in your values
-# These secrets should be configured in GitHub repository settings:
-# Settings > Secrets and variables > Actions
-
-# DockerHub Configuration (required for frontend and backend deployment)
-# Create a Docker Hub access token at https://hub.docker.com/settings/security
-# Username: monadicalsas
-DOCKERHUB_TOKEN=your-dockerhub-access-token
-
-# GitHub Token (required for frontend and backend deployment)
-# Used by docker/metadata-action for extracting image metadata
-# Can use the default GITHUB_TOKEN or create a personal access token
-GITHUB_TOKEN=your-github-token-or-use-default-GITHUB_TOKEN
-
-# Coolify Deployment Webhook (required for frontend deployment)
-# Used to trigger automatic deployment after image push
-# Configure these secrets in GitHub Environments:
-# Each environment should have:
-#   - COOLIFY_WEBHOOK_URL: The webhook URL for that specific deployment
-#   - COOLIFY_WEBHOOK_TOKEN: The webhook token (can be the same for both if using same token)
-
-# Optional: GitHub Actions Cache Token (for local testing with act)
-GHA_CACHE_TOKEN=your-github-token-or-empty
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,474 +1,5 @@
 # Changelog

-## [0.38.1](https://github.com/GreyhavenHQ/reflector/compare/v0.38.0...v0.38.1) (2026-03-06)
-
-
-### Bug Fixes
-
-* pin hatchet sdk version ([#903](https://github.com/GreyhavenHQ/reflector/issues/903)) ([504ca74](https://github.com/GreyhavenHQ/reflector/commit/504ca74184211eda9020d0b38ba7bd2b55d09991))
-
-## [0.38.0](https://github.com/GreyhavenHQ/reflector/compare/v0.37.0...v0.38.0) (2026-03-06)
-
-
-### Features
-
-* 3-mode selfhosted refactoring (--gpu, --cpu, --hosted) + audio token auth fallback ([#896](https://github.com/GreyhavenHQ/reflector/issues/896)) ([a682846](https://github.com/GreyhavenHQ/reflector/commit/a6828466456407c808302e9eb8dc4b4f0614dd6f))
-
-
-### Bug Fixes
-
-* improve hatchet workflow reliability ([#900](https://github.com/GreyhavenHQ/reflector/issues/900)) ([c155f66](https://github.com/GreyhavenHQ/reflector/commit/c155f669825e8e2a6e929821a1ef0bd94237dc11))
-
-## [0.37.0](https://github.com/GreyhavenHQ/reflector/compare/v0.36.0...v0.37.0) (2026-03-03)
-
-
-### Features
-
-* enable daily co in selfhosted + only schedule tasks when necessary ([#883](https://github.com/GreyhavenHQ/reflector/issues/883)) ([045eae8](https://github.com/GreyhavenHQ/reflector/commit/045eae8ff2014a7b83061045e3c8cb25cce9d60a))
-
-
-### Bug Fixes
-
-* aws storage construction ([#895](https://github.com/GreyhavenHQ/reflector/issues/895)) ([f5ec2d2](https://github.com/GreyhavenHQ/reflector/commit/f5ec2d28cfa2de9b2b4aeec81966737b740689c2))
-* remaining dependabot security issues ([#890](https://github.com/GreyhavenHQ/reflector/issues/890)) ([0931095](https://github.com/GreyhavenHQ/reflector/commit/0931095f49e61216e651025ce92be460e6a9df9e))
-* test selfhosted script ([#892](https://github.com/GreyhavenHQ/reflector/issues/892)) ([4d915e2](https://github.com/GreyhavenHQ/reflector/commit/4d915e2a9fe9f05f31cbd0018d9c2580daf7854f))
-* upgrade to nextjs 16 ([#888](https://github.com/GreyhavenHQ/reflector/issues/888)) ([f6cc032](https://github.com/GreyhavenHQ/reflector/commit/f6cc03286baf3e3a115afd3b22ae993ad7a4b7e3))
-
-## [0.35.1](https://github.com/GreyhavenHQ/reflector/compare/v0.35.0...v0.35.1) (2026-02-25)
-
-
-### Bug Fixes
-
-* enable sentry on frontend ([#876](https://github.com/GreyhavenHQ/reflector/issues/876)) ([bc6bb63](https://github.com/GreyhavenHQ/reflector/commit/bc6bb63c32dc84be5d3b00388618d53f04f64e35))
-* switch structured output to tool-call with reflection retry ([#879](https://github.com/GreyhavenHQ/reflector/issues/879)) ([5d54758](https://github.com/GreyhavenHQ/reflector/commit/5d547586ef0f54514d1d65aacca8e57869013a82))
-
-## [0.35.0](https://github.com/Monadical-SAS/reflector/compare/v0.34.0...v0.35.0) (2026-02-23)
-
-
-### Features
-
-* Add Single User authentication to Selfhosted ([#870](https://github.com/Monadical-SAS/reflector/issues/870)) ([c8db373](https://github.com/Monadical-SAS/reflector/commit/c8db37362b6cfd8f772aee8857de2909f283c029))
-
-## [0.34.0](https://github.com/Monadical-SAS/reflector/compare/v0.33.0...v0.34.0) (2026-02-20)
-
-
-### Features
-
-* add Caddy reverse proxy with auto HTTPS for LAN access and auto-derive WebSocket URL ([#863](https://github.com/Monadical-SAS/reflector/issues/863)) ([7f2a401](https://github.com/Monadical-SAS/reflector/commit/7f2a4013cbb3d3ee3e76885f28d73331dcaf325c))
-* add change_seq to transcripts for ingestion support ([#868](https://github.com/Monadical-SAS/reflector/issues/868)) ([d4cc6be](https://github.com/Monadical-SAS/reflector/commit/d4cc6be1fed56ea7fba06acb8d50c9de43b26b07))
-* local llm support + standalone-script doc/draft ([#856](https://github.com/Monadical-SAS/reflector/issues/856)) ([b468427](https://github.com/Monadical-SAS/reflector/commit/b468427f1bb12634f5840990e9d64b2c145d7c1a))
-* remove network_mode host for standalone WebRTC ([#864](https://github.com/Monadical-SAS/reflector/issues/864)) ([9dbf155](https://github.com/Monadical-SAS/reflector/commit/9dbf155be4de7c059035a75f90c7bf0845344b74))
-* standalone frontend uses production build instead of dev server ([#862](https://github.com/Monadical-SAS/reflector/issues/862)) ([5bca925](https://github.com/Monadical-SAS/reflector/commit/5bca92510a5c33f8baeeaac2c346fb1978366ac8))
-
-
-### Bug Fixes
-
-* auto-rebuild standalone images and blank Hatchet vars ([3d13e5d](https://github.com/Monadical-SAS/reflector/commit/3d13e5d42fc53ce3c005841265ed1e8735a61518))
-* check compose version output, not just exit code ([e57c618](https://github.com/Monadical-SAS/reflector/commit/e57c6186f92d66e4525786e56b018c08cf792d2f))
-* check for Docker BuildKit (buildx) before building images ([14a8b58](https://github.com/Monadical-SAS/reflector/commit/14a8b5808e5aed860e55aaed35a0fdf8b2f4afa3))
-* check for Docker Compose plugin before running standalone setup ([36a8dae](https://github.com/Monadical-SAS/reflector/commit/36a8daee61c2b7a0937fd0914d51fb4ea8212ae7))
-* live flow real-time updates during processing ([#861](https://github.com/Monadical-SAS/reflector/issues/861)) ([972a52d](https://github.com/Monadical-SAS/reflector/commit/972a52d22f989f9e2c6f52362b3f1a4e17773663))
-* remove max_tokens cap to support thinking models (Kimi-K2.5) ([#869](https://github.com/Monadical-SAS/reflector/issues/869)) ([527a069](https://github.com/Monadical-SAS/reflector/commit/527a069ba9eff6717ccd4bb1e839674edebffceb))
-* standalone on ubuntu ([#865](https://github.com/Monadical-SAS/reflector/issues/865)) ([a8ad237](https://github.com/Monadical-SAS/reflector/commit/a8ad237d8571d5ef5c78fb4427c538592d6a7b43))
-* standalone server networking and setup diagnostics ([695f3c4](https://github.com/Monadical-SAS/reflector/commit/695f3c49285254869f6a6cbd5f860d1169fa4daa))
-
-## [0.33.0](https://github.com/Monadical-SAS/reflector/compare/v0.32.2...v0.33.0) (2026-02-05)
-
-
-### Features
-
-* Daily+hatchet default ([#846](https://github.com/Monadical-SAS/reflector/issues/846)) ([15ab2e3](https://github.com/Monadical-SAS/reflector/commit/15ab2e306eacf575494b4b5d2b2ad779d44a1c7f))
-
-
-### Bug Fixes
-
-* websocket tests ([#825](https://github.com/Monadical-SAS/reflector/issues/825)) ([1ce1c7a](https://github.com/Monadical-SAS/reflector/commit/1ce1c7a910b6c374115d2437b17f9d288ef094dc))
-
-## [0.32.2](https://github.com/Monadical-SAS/reflector/compare/v0.32.1...v0.32.2) (2026-02-03)
-
-
-### Bug Fixes
-
-* increase TIMEOUT_MEDIUM from 2m to 5m for LLM tasks ([#843](https://github.com/Monadical-SAS/reflector/issues/843)) ([4acde4b](https://github.com/Monadical-SAS/reflector/commit/4acde4b7fdef88cc02ca12cf38c9020b05ed96ac))
-* make caddy optional ([#841](https://github.com/Monadical-SAS/reflector/issues/841)) ([a2ed7d6](https://github.com/Monadical-SAS/reflector/commit/a2ed7d60d557b551a5b64e4dfd909b63a791d9fc))
-* use Daily API recording.duration as master source for transcript duration ([#844](https://github.com/Monadical-SAS/reflector/issues/844)) ([8707c66](https://github.com/Monadical-SAS/reflector/commit/8707c6694a80c939b6214bbc13331741f192e082))
-
-## [0.32.1](https://github.com/Monadical-SAS/reflector/compare/v0.32.0...v0.32.1) (2026-01-30)
-
-
-### Bug Fixes
-
-* daily multitrack pipeline finalze dependency fix ([23eb137](https://github.com/Monadical-SAS/reflector/commit/23eb1371cb9348c4b81eb12ad506b582f8a4799e))
-* match httpx pad with hatchet audio timeout ([c05d1f0](https://github.com/Monadical-SAS/reflector/commit/c05d1f03cd8369fc06efd455527e50246887efd0))
-
-## [0.32.0](https://github.com/Monadical-SAS/reflector/compare/v0.31.0...v0.32.0) (2026-01-30)
-
-
-### Features
-
-* modal padding ([#837](https://github.com/Monadical-SAS/reflector/issues/837)) ([7fde64e](https://github.com/Monadical-SAS/reflector/commit/7fde64e2529a1d37b0f7507c62d983a7bd0b5b89))
-
-## [0.31.0](https://github.com/Monadical-SAS/reflector/compare/v0.30.0...v0.31.0) (2026-01-23)
-
-
-### Features
-
-* mixdown optional ([#834](https://github.com/Monadical-SAS/reflector/issues/834)) ([fc3ef6c](https://github.com/Monadical-SAS/reflector/commit/fc3ef6c8933231c731fad84e7477a476a6220a5e))
-
-## [0.30.0](https://github.com/Monadical-SAS/reflector/compare/v0.29.0...v0.30.0) (2026-01-23)
-
-
-### Features
-
-* brady bunch ([#816](https://github.com/Monadical-SAS/reflector/issues/816)) ([6c175a1](https://github.com/Monadical-SAS/reflector/commit/6c175a11d8a3745095bfad06a4ad3ccdfd278433))
-
-## [0.29.0](https://github.com/Monadical-SAS/reflector/compare/v0.28.1...v0.29.0) (2026-01-21)
-
-
-### Features
-
-* set hatchet as default for multitracks ([#822](https://github.com/Monadical-SAS/reflector/issues/822)) ([c723752](https://github.com/Monadical-SAS/reflector/commit/c723752b7e15aa48a41ad22856f147a5517d3f46))
-
-## [0.28.1](https://github.com/Monadical-SAS/reflector/compare/v0.28.0...v0.28.1) (2026-01-21)
-
-
-### Bug Fixes
-
-* ics non-sync bugfix ([#823](https://github.com/Monadical-SAS/reflector/issues/823)) ([23d2bc2](https://github.com/Monadical-SAS/reflector/commit/23d2bc283d4d02187b250d2055103e0374ee93d6))
-
-## [0.28.0](https://github.com/Monadical-SAS/reflector/compare/v0.27.0...v0.28.0) (2026-01-20)
-
-
-### Features
-
-* worker affinity ([#819](https://github.com/Monadical-SAS/reflector/issues/819)) ([3b6540e](https://github.com/Monadical-SAS/reflector/commit/3b6540eae5b597449f98661bdf15483b77be3268))
-
-## [0.27.0](https://github.com/Monadical-SAS/reflector/compare/v0.26.0...v0.27.0) (2025-12-26)
-
-
-### Features
-
-* devex/hatchet log progress track ([#813](https://github.com/Monadical-SAS/reflector/issues/813)) ([2d0df48](https://github.com/Monadical-SAS/reflector/commit/2d0df487674e5486208cd599e3338ebff8b6e470))
-
-
-### Bug Fixes
-
-* webhook parity, pipeline rename, waveform constant fix ([#806](https://github.com/Monadical-SAS/reflector/issues/806)) ([5f7b1ff](https://github.com/Monadical-SAS/reflector/commit/5f7b1ff1a68ebbb907684c7c5f55c1f82dac8550))
-
-## [0.26.0](https://github.com/Monadical-SAS/reflector/compare/v0.25.0...v0.26.0) (2025-12-23)
-
-
-### Features
-
-* parallelize hatchet ([#804](https://github.com/Monadical-SAS/reflector/issues/804)) ([594bcc0](https://github.com/Monadical-SAS/reflector/commit/594bcc09e0ca744163de2f1525ebbf7c52a68448))
-
-## [0.25.0](https://github.com/Monadical-SAS/reflector/compare/v0.24.0...v0.25.0) (2025-12-22)
-
-
-### Features
-
-* consent disable feature ([#799](https://github.com/Monadical-SAS/reflector/issues/799)) ([2257834](https://github.com/Monadical-SAS/reflector/commit/225783496f2e265d5cb58e3539a20bf6b55589b8))
-* durable ([#794](https://github.com/Monadical-SAS/reflector/issues/794)) ([1dac999](https://github.com/Monadical-SAS/reflector/commit/1dac999b56997582ce400e7d56e915adc1e4728d))
-* increase daily recording max duration ([#801](https://github.com/Monadical-SAS/reflector/issues/801)) ([f580b99](https://github.com/Monadical-SAS/reflector/commit/f580b996eef49cce16433c505abfc6454dd45de1))
-
-
-### Bug Fixes
-
-* logout redirect ([#802](https://github.com/Monadical-SAS/reflector/issues/802)) ([f0ee7b5](https://github.com/Monadical-SAS/reflector/commit/f0ee7b531a0911f214ccbb84d399e9a6c9b700c0))
-
-## [0.24.0](https://github.com/Monadical-SAS/reflector/compare/v0.23.2...v0.24.0) (2025-12-18)
-
-
-### Features
-
-* identify action items ([#790](https://github.com/Monadical-SAS/reflector/issues/790)) ([964cd78](https://github.com/Monadical-SAS/reflector/commit/964cd78bb699d83d012ae4b8c96565df25b90a5d))
-
-
-### Bug Fixes
-
-* automatically reprocess daily recordings ([#797](https://github.com/Monadical-SAS/reflector/issues/797)) ([5f458aa](https://github.com/Monadical-SAS/reflector/commit/5f458aa4a7ec3d00ca5ec49d62fcc8ad232b138e))
-* daily video optimisation ([#789](https://github.com/Monadical-SAS/reflector/issues/789)) ([16284e1](https://github.com/Monadical-SAS/reflector/commit/16284e1ac3faede2b74f0d91b50c0b5612af2c35))
-* main menu login ([#800](https://github.com/Monadical-SAS/reflector/issues/800)) ([0bc971b](https://github.com/Monadical-SAS/reflector/commit/0bc971ba966a52d719c8c240b47dc7b3bdea4391))
-* retry on workflow timeout ([#798](https://github.com/Monadical-SAS/reflector/issues/798)) ([5f7dfad](https://github.com/Monadical-SAS/reflector/commit/5f7dfadabd3e8017406ad3720ba495a59963ee34))
-
-## [0.23.2](https://github.com/Monadical-SAS/reflector/compare/v0.23.1...v0.23.2) (2025-12-11)
-
-
-### Bug Fixes
-
-* build on push tags ([#785](https://github.com/Monadical-SAS/reflector/issues/785)) ([d7f140b](https://github.com/Monadical-SAS/reflector/commit/d7f140b7d1f4660d5da7a0da1357f68869e0b5cd))
-
-## [0.23.1](https://github.com/Monadical-SAS/reflector/compare/v0.23.0...v0.23.1) (2025-12-11)
-
-
-### Bug Fixes
-
-* populate room_name in transcript GET endpoint ([#783](https://github.com/Monadical-SAS/reflector/issues/783)) ([0eba147](https://github.com/Monadical-SAS/reflector/commit/0eba1470181c7b9e0a79964a1ef28c09bcbdd9d7))
-
-## [0.23.0](https://github.com/Monadical-SAS/reflector/compare/v0.22.4...v0.23.0) (2025-12-10)
-
-
-### Features
-
-* dockerhub ci ([#772](https://github.com/Monadical-SAS/reflector/issues/772)) ([00549f1](https://github.com/Monadical-SAS/reflector/commit/00549f153ade922cf4cb6c5358a7d11a39c426d2))
-* llm retries ([#739](https://github.com/Monadical-SAS/reflector/issues/739)) ([61f0e29](https://github.com/Monadical-SAS/reflector/commit/61f0e29d4c51eab54ee67af92141fbb171e8ccaa))
-
-
-### Bug Fixes
-
-* celery inspect bug sidestep in restart script ([#766](https://github.com/Monadical-SAS/reflector/issues/766)) ([ec17ed7](https://github.com/Monadical-SAS/reflector/commit/ec17ed7b587cf6ee143646baaee67a7c017044d4))
-* deploy frontend to coolify ([#779](https://github.com/Monadical-SAS/reflector/issues/779)) ([91650ec](https://github.com/Monadical-SAS/reflector/commit/91650ec65f65713faa7ee0dcfb75af427b7c4ba0))
-* hide rooms settings instead of disabling ([#763](https://github.com/Monadical-SAS/reflector/issues/763)) ([3ad78be](https://github.com/Monadical-SAS/reflector/commit/3ad78be7628c0d029296b301a0e87236c76b7598))
-* return participant emails from transcript endpoint ([#769](https://github.com/Monadical-SAS/reflector/issues/769)) ([d3a5cd1](https://github.com/Monadical-SAS/reflector/commit/d3a5cd12d2d0d9c32af2d5bd9322e030ef69b85d))
-
-## [0.22.4](https://github.com/Monadical-SAS/reflector/compare/v0.22.3...v0.22.4) (2025-12-02)
-
-
-### Bug Fixes
-
-* Multitrack mixdown optimisation 2 ([#764](https://github.com/Monadical-SAS/reflector/issues/764)) ([bd5df1c](https://github.com/Monadical-SAS/reflector/commit/bd5df1ce2ebf35d7f3413b295e56937a9a28ef7b))
-
-## [0.22.3](https://github.com/Monadical-SAS/reflector/compare/v0.22.2...v0.22.3) (2025-12-02)
-
-
-### Bug Fixes
-
-* align daily room settings ([#759](https://github.com/Monadical-SAS/reflector/issues/759)) ([28f87c0](https://github.com/Monadical-SAS/reflector/commit/28f87c09dc459846873d0dde65b03e3d7b2b9399))
-
-## [0.22.2](https://github.com/Monadical-SAS/reflector/compare/v0.22.1...v0.22.2) (2025-12-02)
-
-
-### Bug Fixes
-
-* daily auto refresh fix ([#755](https://github.com/Monadical-SAS/reflector/issues/755)) ([fe47c46](https://github.com/Monadical-SAS/reflector/commit/fe47c46489c5aa0cc538109f7559cc9accb35c01))
-* Skip mixdown for multitrack ([#760](https://github.com/Monadical-SAS/reflector/issues/760)) ([b51b7aa](https://github.com/Monadical-SAS/reflector/commit/b51b7aa9176c1a53ba57ad99f5e976c804a1e80c))
-
-## [0.22.1](https://github.com/Monadical-SAS/reflector/compare/v0.22.0...v0.22.1) (2025-11-27)
-
-
-### Bug Fixes
-
-* participants update from daily ([#749](https://github.com/Monadical-SAS/reflector/issues/749)) ([7f0b728](https://github.com/Monadical-SAS/reflector/commit/7f0b728991c1b9f9aae702c96297eae63b561ef5))
-
-## [0.22.0](https://github.com/Monadical-SAS/reflector/compare/v0.21.0...v0.22.0) (2025-11-26)
-
-
-### Features
-
-* Multitrack segmentation ([#747](https://github.com/Monadical-SAS/reflector/issues/747)) ([d63040e](https://github.com/Monadical-SAS/reflector/commit/d63040e2fdc07e7b272e85a39eb2411cd6a14798))
-
-## [0.21.0](https://github.com/Monadical-SAS/reflector/compare/v0.20.0...v0.21.0) (2025-11-26)
-
-
-### Features
-
-* add transcript format parameter to GET endpoint ([#709](https://github.com/Monadical-SAS/reflector/issues/709)) ([f6ca075](https://github.com/Monadical-SAS/reflector/commit/f6ca07505f34483b02270a2ef3bd809e9d2e1045))
-
-## [0.20.0](https://github.com/Monadical-SAS/reflector/compare/v0.19.0...v0.20.0) (2025-11-25)
-
-
-### Features
-
-* link transcript participants ([#737](https://github.com/Monadical-SAS/reflector/issues/737)) ([9bec398](https://github.com/Monadical-SAS/reflector/commit/9bec39808fc6322612d8b87e922a6f7901fc01c1))
-* transcript restart script ([#742](https://github.com/Monadical-SAS/reflector/issues/742)) ([86d5e26](https://github.com/Monadical-SAS/reflector/commit/86d5e26224bb55a0f1cc785aeda52065bb92ee6f))
-
-## [0.19.0](https://github.com/Monadical-SAS/reflector/compare/v0.18.0...v0.19.0) (2025-11-25)
-
-
-### Features
-
-* dailyco api module ([#725](https://github.com/Monadical-SAS/reflector/issues/725)) ([4287f8b](https://github.com/Monadical-SAS/reflector/commit/4287f8b8aeee60e51db7539f4dcbda5f6e696bd8))
-* dailyco poll ([#730](https://github.com/Monadical-SAS/reflector/issues/730)) ([8e438ca](https://github.com/Monadical-SAS/reflector/commit/8e438ca285152bd48fdc42767e706fb448d3525c))
-* multitrack cli ([#735](https://github.com/Monadical-SAS/reflector/issues/735)) ([11731c9](https://github.com/Monadical-SAS/reflector/commit/11731c9d38439b04e93b1c3afbd7090bad11a11f))
-
-
-### Bug Fixes
-
-* default platform fix ([#736](https://github.com/Monadical-SAS/reflector/issues/736)) ([c442a62](https://github.com/Monadical-SAS/reflector/commit/c442a627873ca667656eeaefb63e54ab10b8d19e))
-* parakeet vad not getting the end timestamp ([#728](https://github.com/Monadical-SAS/reflector/issues/728)) ([18ed713](https://github.com/Monadical-SAS/reflector/commit/18ed7133693653ef4ddac6c659a8c14b320d1657))
-* start raw tracks recording ([#729](https://github.com/Monadical-SAS/reflector/issues/729)) ([3e47c2c](https://github.com/Monadical-SAS/reflector/commit/3e47c2c0573504858e0d2e1798b6ed31f16b4a5d))
-
-## [0.18.0](https://github.com/Monadical-SAS/reflector/compare/v0.17.0...v0.18.0) (2025-11-14)
-
-
-### Features
-
-* daily QOL: participants dictionary ([#721](https://github.com/Monadical-SAS/reflector/issues/721)) ([b20cad7](https://github.com/Monadical-SAS/reflector/commit/b20cad76e69fb6a76405af299a005f1ddcf60eae))
-
-
-### Bug Fixes
-
-* add proccessing page to file upload and reprocessing ([#650](https://github.com/Monadical-SAS/reflector/issues/650)) ([28a7258](https://github.com/Monadical-SAS/reflector/commit/28a7258e45317b78e60e6397be2bc503647eaace))
-* copy transcript ([#674](https://github.com/Monadical-SAS/reflector/issues/674)) ([a9a4f32](https://github.com/Monadical-SAS/reflector/commit/a9a4f32324f66c838e081eee42bb9502f38c1db1))
-
-## [0.17.0](https://github.com/Monadical-SAS/reflector/compare/v0.16.0...v0.17.0) (2025-11-13)
-
-
-### Features
-
-* add API key management UI ([#716](https://github.com/Monadical-SAS/reflector/issues/716)) ([372202b](https://github.com/Monadical-SAS/reflector/commit/372202b0e1a86823900b0aa77be1bfbc2893d8a1))
-* daily.co support as alternative to whereby ([#691](https://github.com/Monadical-SAS/reflector/issues/691)) ([1473fd8](https://github.com/Monadical-SAS/reflector/commit/1473fd82dc472c394cbaa2987212ad662a74bcac))
-
-## [0.16.0](https://github.com/Monadical-SAS/reflector/compare/v0.15.0...v0.16.0) (2025-10-24)
-
-
-### Features
-
-* search date filter ([#710](https://github.com/Monadical-SAS/reflector/issues/710)) ([962c40e](https://github.com/Monadical-SAS/reflector/commit/962c40e2b6428ac42fd10aea926782d7a6f3f902))
-
-## [0.15.0](https://github.com/Monadical-SAS/reflector/compare/v0.14.0...v0.15.0) (2025-10-20)
-
-
-### Features
-
-* api tokens  ([#705](https://github.com/Monadical-SAS/reflector/issues/705)) ([9a258ab](https://github.com/Monadical-SAS/reflector/commit/9a258abc0209b0ac3799532a507ea6a9125d703a))
-
-## [0.14.0](https://github.com/Monadical-SAS/reflector/compare/v0.13.1...v0.14.0) (2025-10-08)
-
-
-### Features
-
-* Add calendar event data to transcript webhook payload ([#689](https://github.com/Monadical-SAS/reflector/issues/689)) ([5f6910e](https://github.com/Monadical-SAS/reflector/commit/5f6910e5131b7f28f86c9ecdcc57fed8412ee3cd))
-* container build for www / github ([#672](https://github.com/Monadical-SAS/reflector/issues/672)) ([969bd84](https://github.com/Monadical-SAS/reflector/commit/969bd84fcc14851d1a101412a0ba115f1b7cde82))
-* docker-compose for production frontend ([#664](https://github.com/Monadical-SAS/reflector/issues/664)) ([5bf64b5](https://github.com/Monadical-SAS/reflector/commit/5bf64b5a41f64535e22849b4bb11734d4dbb4aae))
-
-
-### Bug Fixes
-
-* restore feature boolean logic ([#671](https://github.com/Monadical-SAS/reflector/issues/671)) ([3660884](https://github.com/Monadical-SAS/reflector/commit/36608849ec64e953e3be456172502762e3c33df9))
-* security review ([#656](https://github.com/Monadical-SAS/reflector/issues/656)) ([5d98754](https://github.com/Monadical-SAS/reflector/commit/5d98754305c6c540dd194dda268544f6d88bfaf8))
-* update transcript list on reprocess ([#676](https://github.com/Monadical-SAS/reflector/issues/676)) ([9a71af1](https://github.com/Monadical-SAS/reflector/commit/9a71af145ee9b833078c78d0c684590ab12e9f0e))
-* upgrade nemo toolkit ([#678](https://github.com/Monadical-SAS/reflector/issues/678)) ([eef6dc3](https://github.com/Monadical-SAS/reflector/commit/eef6dc39037329b65804297786d852dddb0557f9))
-
-## [0.13.1](https://github.com/Monadical-SAS/reflector/compare/v0.13.0...v0.13.1) (2025-09-22)
-
-
-### Bug Fixes
-
-* TypeError on not all arguments converted during string formatting in logger ([#667](https://github.com/Monadical-SAS/reflector/issues/667)) ([565a629](https://github.com/Monadical-SAS/reflector/commit/565a62900f5a02fc946b68f9269a42190ed70ab6))
-
-## [0.13.0](https://github.com/Monadical-SAS/reflector/compare/v0.12.1...v0.13.0) (2025-09-19)
-
-
-### Features
-
-* room form edit with enter ([#662](https://github.com/Monadical-SAS/reflector/issues/662)) ([47716f6](https://github.com/Monadical-SAS/reflector/commit/47716f6e5ddee952609d2fa0ffabdfa865286796))
-
-
-### Bug Fixes
-
-* invalid cleanup call ([#660](https://github.com/Monadical-SAS/reflector/issues/660)) ([0abcebf](https://github.com/Monadical-SAS/reflector/commit/0abcebfc9491f87f605f21faa3e53996fafedd9a))
-
-## [0.12.1](https://github.com/Monadical-SAS/reflector/compare/v0.12.0...v0.12.1) (2025-09-17)
-
-
-### Bug Fixes
-
-* production blocked because having existing meeting with room_id null ([#657](https://github.com/Monadical-SAS/reflector/issues/657)) ([870e860](https://github.com/Monadical-SAS/reflector/commit/870e8605171a27155a9cbee215eeccb9a8d6c0a2))
-
-## [0.12.0](https://github.com/Monadical-SAS/reflector/compare/v0.11.0...v0.12.0) (2025-09-17)
-
-
-### Features
-
-* calendar integration ([#608](https://github.com/Monadical-SAS/reflector/issues/608)) ([6f680b5](https://github.com/Monadical-SAS/reflector/commit/6f680b57954c688882c4ed49f40f161c52a00a24))
-* self-hosted gpu api ([#636](https://github.com/Monadical-SAS/reflector/issues/636)) ([ab859d6](https://github.com/Monadical-SAS/reflector/commit/ab859d65a6bded904133a163a081a651b3938d42))
-
-
-### Bug Fixes
-
-* ignore player hotkeys for text inputs ([#646](https://github.com/Monadical-SAS/reflector/issues/646)) ([fa049e8](https://github.com/Monadical-SAS/reflector/commit/fa049e8d068190ce7ea015fd9fcccb8543f54a3f))
-
-## [0.11.0](https://github.com/Monadical-SAS/reflector/compare/v0.10.0...v0.11.0) (2025-09-16)
-
-
-### Features
-
-* remove profanity filter that was there for conference ([#652](https://github.com/Monadical-SAS/reflector/issues/652)) ([b42f7cf](https://github.com/Monadical-SAS/reflector/commit/b42f7cfc606783afcee792590efcc78b507468ab))
-
-
-### Bug Fixes
-
-* zulip and consent handler on the file pipeline ([#645](https://github.com/Monadical-SAS/reflector/issues/645)) ([5f143fe](https://github.com/Monadical-SAS/reflector/commit/5f143fe3640875dcb56c26694254a93189281d17))
-* zulip stream and topic selection in share dialog ([#644](https://github.com/Monadical-SAS/reflector/issues/644)) ([c546e69](https://github.com/Monadical-SAS/reflector/commit/c546e69739e68bb74fbc877eb62609928e5b8de6))
-
-## [0.10.0](https://github.com/Monadical-SAS/reflector/compare/v0.9.0...v0.10.0) (2025-09-11)
-
-
-### Features
-
-* replace nextjs-config with environment variables ([#632](https://github.com/Monadical-SAS/reflector/issues/632)) ([369ecdf](https://github.com/Monadical-SAS/reflector/commit/369ecdff13f3862d926a9c0b87df52c9d94c4dde))
-
-
-### Bug Fixes
-
-* anonymous users transcript permissions ([#621](https://github.com/Monadical-SAS/reflector/issues/621)) ([f81fe99](https://github.com/Monadical-SAS/reflector/commit/f81fe9948a9237b3e0001b2d8ca84f54d76878f9))
-* auth post ([#624](https://github.com/Monadical-SAS/reflector/issues/624)) ([cde99ca](https://github.com/Monadical-SAS/reflector/commit/cde99ca2716f84ba26798f289047732f0448742e))
-* auth post ([#626](https://github.com/Monadical-SAS/reflector/issues/626)) ([3b85ff3](https://github.com/Monadical-SAS/reflector/commit/3b85ff3bdf4fb053b103070646811bc990c0e70a))
-* auth post ([#627](https://github.com/Monadical-SAS/reflector/issues/627)) ([962038e](https://github.com/Monadical-SAS/reflector/commit/962038ee3f2a555dc3c03856be0e4409456e0996))
-* missing follow_redirects=True on modal endpoint ([#630](https://github.com/Monadical-SAS/reflector/issues/630)) ([fc363bd](https://github.com/Monadical-SAS/reflector/commit/fc363bd49b17b075e64f9186e5e0185abc325ea7))
-* sync backend and frontend token refresh logic ([#614](https://github.com/Monadical-SAS/reflector/issues/614)) ([5a5b323](https://github.com/Monadical-SAS/reflector/commit/5a5b3233820df9536da75e87ce6184a983d4713a))
-
-## [0.9.0](https://github.com/Monadical-SAS/reflector/compare/v0.8.2...v0.9.0) (2025-09-06)
-
-
-### Features
-
-* frontend openapi react query ([#606](https://github.com/Monadical-SAS/reflector/issues/606)) ([c4d2825](https://github.com/Monadical-SAS/reflector/commit/c4d2825c81f81ad8835629fbf6ea8c7383f8c31b))
-
-
-### Bug Fixes
-
-* align whisper transcriber api with parakeet ([#602](https://github.com/Monadical-SAS/reflector/issues/602)) ([0663700](https://github.com/Monadical-SAS/reflector/commit/0663700a615a4af69a03c96c410f049e23ec9443))
-* kv use tls explicit ([#610](https://github.com/Monadical-SAS/reflector/issues/610)) ([08d88ec](https://github.com/Monadical-SAS/reflector/commit/08d88ec349f38b0d13e0fa4cb73486c8dfd31836))
-* source kind for file processing ([#601](https://github.com/Monadical-SAS/reflector/issues/601)) ([dc82f8b](https://github.com/Monadical-SAS/reflector/commit/dc82f8bb3bdf3ab3d4088e592a30fd63907319e1))
-* token refresh locking ([#613](https://github.com/Monadical-SAS/reflector/issues/613)) ([7f5a4c9](https://github.com/Monadical-SAS/reflector/commit/7f5a4c9ddc7fd098860c8bdda2ca3b57f63ded2f))
-
-## [0.8.2](https://github.com/Monadical-SAS/reflector/compare/v0.8.1...v0.8.2) (2025-08-29)
-
-
-### Bug Fixes
-
-* search-logspam ([#593](https://github.com/Monadical-SAS/reflector/issues/593)) ([695d1a9](https://github.com/Monadical-SAS/reflector/commit/695d1a957d4cd862753049f9beed88836cabd5ab))
-
-## [0.8.1](https://github.com/Monadical-SAS/reflector/compare/v0.8.0...v0.8.1) (2025-08-29)
-
-
-### Bug Fixes
-
-* make webhook secret/url allowing null ([#590](https://github.com/Monadical-SAS/reflector/issues/590)) ([84a3812](https://github.com/Monadical-SAS/reflector/commit/84a381220bc606231d08d6f71d4babc818fa3c75))
-
-## [0.8.0](https://github.com/Monadical-SAS/reflector/compare/v0.7.3...v0.8.0) (2025-08-29)
-
-
-### Features
-
-* **cleanup:** add automatic data retention for public instances ([#574](https://github.com/Monadical-SAS/reflector/issues/574)) ([6f0c7c1](https://github.com/Monadical-SAS/reflector/commit/6f0c7c1a5e751713366886c8e764c2009e12ba72))
-* **rooms:** add webhook for transcript completion ([#578](https://github.com/Monadical-SAS/reflector/issues/578)) ([88ed7cf](https://github.com/Monadical-SAS/reflector/commit/88ed7cfa7804794b9b54cad4c3facc8a98cf85fd))
-
-
-### Bug Fixes
-
-* file pipeline status reporting and websocket updates ([#589](https://github.com/Monadical-SAS/reflector/issues/589)) ([9dfd769](https://github.com/Monadical-SAS/reflector/commit/9dfd76996f851cc52be54feea078adbc0816dc57))
-* Igor/evaluation ([#575](https://github.com/Monadical-SAS/reflector/issues/575)) ([124ce03](https://github.com/Monadical-SAS/reflector/commit/124ce03bf86044c18313d27228a25da4bc20c9c5))
-* optimize parakeet transcription batching algorithm ([#577](https://github.com/Monadical-SAS/reflector/issues/577)) ([7030e0f](https://github.com/Monadical-SAS/reflector/commit/7030e0f23649a8cf6c1eb6d5889684a41ce849ec))
-
-## [0.7.3](https://github.com/Monadical-SAS/reflector/compare/v0.7.2...v0.7.3) (2025-08-22)
-
-
-### Bug Fixes
-
-* cleaned repo, and get git-leaks clean ([359280d](https://github.com/Monadical-SAS/reflector/commit/359280dd340433ba4402ed69034094884c825e67))
-* restore previous behavior on live pipeline + audio downscaler ([#561](https://github.com/Monadical-SAS/reflector/issues/561)) ([9265d20](https://github.com/Monadical-SAS/reflector/commit/9265d201b590d23c628c5f19251b70f473859043))
-
-## [0.7.2](https://github.com/Monadical-SAS/reflector/compare/v0.7.1...v0.7.2) (2025-08-21)
-
-
-### Bug Fixes
-
-* docker image not loading libgomp.so.1 for torch ([#560](https://github.com/Monadical-SAS/reflector/issues/560)) ([773fccd](https://github.com/Monadical-SAS/reflector/commit/773fccd93e887c3493abc2e4a4864dddce610177))
-* include shared rooms to search ([#558](https://github.com/Monadical-SAS/reflector/issues/558)) ([499eced](https://github.com/Monadical-SAS/reflector/commit/499eced3360b84fb3a90e1c8a3b554290d21adc2))
-
 ## [0.7.1](https://github.com/Monadical-SAS/reflector/compare/v0.7.0...v0.7.1) (2025-08-21)


--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -6,7 +6,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co

 Reflector is an AI-powered audio transcription and meeting analysis platform with real-time processing capabilities. The system consists of:

- **Frontend**: Next.js 16 React application (`www/`) with Chakra UI, real-time WebSocket integration
+- **Frontend**: Next.js 14 React application (`www/`) with Chakra UI, real-time WebSocket integration
 - **Backend**: Python FastAPI server (`server/`) with async database operations and background processing
 - **Processing**: GPU-accelerated ML pipeline for transcription, diarization, summarization via Modal.com
 - **Infrastructure**: Redis, PostgreSQL/SQLite, Celery workers, WebRTC streaming
@@ -66,6 +66,7 @@ pnpm install

 # Copy configuration templates
 cp .env_template .env
+cp config-template.ts config.ts
 ```

 **Development:**
@@ -151,7 +152,7 @@ All endpoints prefixed `/v1/`:

 **Frontend** (`www/.env`):
 - `NEXTAUTH_URL`, `NEXTAUTH_SECRET` - Authentication configuration
- `REFLECTOR_API_URL` - Backend API endpoint
+- `NEXT_PUBLIC_REFLECTOR_API_URL` - Backend API endpoint
 - `REFLECTOR_DOMAIN_CONFIG` - Feature flags and domain settings

 ## Testing Strategy
--- a/Caddyfile.example
+++ b/Caddyfile.example
@@ -1,24 +0,0 @@
-# Reflector Caddyfile (optional reverse proxy)
-# Use this only when you run Caddy via: docker compose -f docker-compose.prod.yml --profile caddy up -d
-# If Coolify, Traefik, or nginx already use ports 80/443, do NOT start Caddy; point your proxy at web:3000 and server:1250.
-#
-# Replace example.com with your actual domains. CORS is handled by the backend - Caddy just proxies.
-#
-# For environment variable substitution, set:
-#   FRONTEND_DOMAIN=app.example.com
-#   API_DOMAIN=api.example.com
-#   AUTHENTIK_DOMAIN=authentik.example.com  (optional, for authentication)
-# Or edit this file directly with your domains.
-
-{$FRONTEND_DOMAIN:app.example.com} {
-	reverse_proxy web:3000
-}
-
-{$API_DOMAIN:api.example.com} {
-	reverse_proxy server:1250
-}
-
-# Uncomment if using Authentik for authentication (see auth-setup.md)
-# {$AUTHENTIK_DOMAIN:authentik.example.com} {
-# 	reverse_proxy authentik-server-1:9000
-# }
--- a/Caddyfile.selfhosted.example
+++ b/Caddyfile.selfhosted.example
@@ -1,25 +0,0 @@
-# Reflector self-hosted production — HTTPS via Caddy reverse proxy
-# Copy to Caddyfile: cp Caddyfile.selfhosted.example Caddyfile
-# Run: ./scripts/setup-selfhosted.sh --ollama-gpu --garage --caddy
-#
-# DOMAIN defaults to localhost (self-signed cert).
-# Set to your real domain for automatic Let's Encrypt:
-#   export DOMAIN=reflector.example.com
-#
-# TLS_MODE defaults to "internal" (self-signed).
-# Set to "" for automatic Let's Encrypt (requires real domain + ports 80/443 open):
-#   export TLS_MODE=""
-
-{$DOMAIN:localhost} {
-    tls {$TLS_MODE:internal}
-
-    handle /v1/* {
-        reverse_proxy server:1250
-    }
-    handle /health {
-        reverse_proxy server:1250
-    }
-    handle {
-        reverse_proxy web:3000
-    }
-}
--- a/Caddyfile.standalone.example
+++ b/Caddyfile.standalone.example
@@ -1,42 +0,0 @@
-# Reflector standalone — HTTPS via Caddy (droplet / IP access)
-# Copy to Caddyfile: cp Caddyfile.standalone.example Caddyfile
-# Run: docker compose -f docker-compose.standalone.yml --profile ollama-cpu up -d
-#
-# :443 = catch-all inside container; Docker maps host port 3043 → container 443
-# on_demand = generate self-signed cert for IP/SNI on first request (required for bare IP access)
-# Browser will warn. Click Advanced → Proceed.
-# Access at https://localhost:3043 (or https://YOUR_IP:3043 on droplet)
-# Update www/.env.local with: API_URL=https://YOUR_IP:3043, WEBSOCKET_URL=wss://YOUR_IP:3043, SITE_URL=https://YOUR_IP:3043, NEXTAUTH_URL=https://YOUR_IP:3043
-
-:443 {
-    tls internal {
-        on_demand
-    }
-    handle /v1/* {
-        reverse_proxy server:1250
-    }
-    handle /health {
-        reverse_proxy server:1250
-    }
-    handle {
-        reverse_proxy web:3000
-    }
-}
-
-# Option B: localhost (comment Option A, uncomment this)
-# app.localhost {
-#     tls internal
-#     reverse_proxy web:3000
-# }
-# api.localhost {
-#     tls internal
-#     reverse_proxy server:1250
-# }
-
-# Option C: Real domain (uncomment and replace example.com)
-# app.example.com {
-#     reverse_proxy web:3000
-# }
-# api.example.com {
-#     reverse_proxy server:1250
-# }
--- a/README.md
+++ b/README.md
@@ -1,145 +1,48 @@
 <div align="center">
-<img width="100" alt="image" src="https://github.com/user-attachments/assets/66fb367b-2c89-4516-9912-f47ac59c6a7f"/>

 # Reflector

-Reflector is an AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, translation and summarization for audio content and live meetings. It works 100% with local models (whisper/parakeet, pyannote, seamless-m4t, and your local llm like phi-4).
+Reflector Audio Management and Analysis is a cutting-edge web application under development by Monadical. It utilizes AI to record meetings, providing a permanent record with transcripts, translations, and automated summaries.

-[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml)
+[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml)
 [![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
 </div>
-</div>
+
+## Screenshots
 <table>
  <tr>
    <td>
-      <a href="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97" />
+      <a href="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3" />
      </a>
    </td>
    <td>
-      <a href="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c" />
+      <a href="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33" />
      </a>
    </td>
    <td>
-      <a href="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897" />
-      </a>
-    </td>
-    <td>
-      <a href="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4" />
+      <a href="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc" />
      </a>
    </td>
  </tr>
 </table>

-<p align="center" style="font-size: 1.5em; font-weight: bold;">By <a href="https://greyhaven.co">Greyhaven</a></p>
+## Background

-## What is Reflector?
+The project architecture consists of three primary components:

-Reflector is a web application that utilizes local models to process audio content, providing:
+- **Front-End**: NextJS React project hosted on Vercel, located in `www/`.
+- **Back-End**: Python server that offers an API and data persistence, found in `server/`.
+- **GPU implementation**: Providing services such as speech-to-text transcription, topic generation, automated summaries, and translations. Most reliable option is Modal deployment

- **Real-time Transcription**: Convert speech to text using [Whisper](https://github.com/openai/whisper) (multi-language) or [Parakeet](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) (English) models
- **Speaker Diarization**: Identify and label different speakers using [Pyannote](https://github.com/pyannote/pyannote-audio) 3.1
- **Live Translation**: Translate audio content in real-time to many languages with [Facebook Seamless-M4T](https://github.com/facebookresearch/seamless_communication)
- **Topic Detection & Summarization**: Extract key topics and generate concise summaries using LLMs
- **Meeting Recording**: Create permanent records of meetings with searchable transcripts
+It also uses authentik for authentication if activated, and Vercel for deployment and configuration of the front-end.

-## Architecture
+## Contribution Guidelines

-The project consists of three primary components:
-
- **Back-End**: Python FastAPI server with async database operations and background processing, found in `server/`.
- **Front-End**: Next.js 14 React application with Chakra UI, located in `www/`.
- **GPU Models**: Specialized ML models for transcription, diarization, translation, and summarization.
-
-Currently, Reflector supports two input methods:
- **Screenshare capture**: Real-time audio capture from your browser via WebRTC
- **Audio file upload**: Upload pre-recorded audio files for processing
-
-## Installation
-
-For full deployment instructions, see the [Self-Hosted Production Guide](docsv2/selfhosted-production.md) and the [Architecture Reference](docsv2/selfhosted-architecture.md).
-
-### Self-Hosted Deployment
-
-The self-hosted setup script configures and launches everything on a single server:
-
-```bash
-# GPU with local Ollama LLM, local S3 storage, and Caddy reverse proxy
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
-
-# With a custom domain (enables Let's Encrypt auto-HTTPS)
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --domain reflector.example.com
-
-# CPU-only mode (slower, no NVIDIA GPU required)
-./scripts/setup-selfhosted.sh --cpu --ollama-cpu --garage --caddy
-
-# With password authentication
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --password mysecretpass
-```
-
-The script is idempotent and safe to re-run. See `./scripts/setup-selfhosted.sh --help` for all options.
-
-### Authentication
-
-Reflector supports three authentication modes:
-
- **Password authentication (recommended for self-hosted / single-user)**: Use the `--password` flag in the setup script. This creates an `admin@localhost` user with the provided password. Users must log in to create, edit, or delete transcripts.
-
-  ```bash
-  ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --password mysecretpass
-  ```
-
- **Authentik OIDC**: For multi-user or enterprise deployments, Reflector supports [Authentik](https://goauthentik.io/) as an OAuth/OIDC provider. This enables SSO, LDAP/AD integration, and centralized user management. Requires configuring `AUTH_BACKEND=jwt` on the backend and `AUTH_PROVIDER=authentik` on the frontend. See the [Self-Hosted Production Guide](docsv2/selfhosted-production.md) for details.
-
- **Public mode (default when no auth is configured)**: If neither password nor Authentik is set up, Reflector runs in public mode. In this mode, no login is required — anyone with access to the URL can use the application. Transcripts are created anonymously (not tied to any user account), which means they **cannot be edited or deleted** through the UI or API. Anonymous transcripts are automatically cleaned up after 7 days. This mode is suitable for demos or testing but not recommended for production use.
-
-### Development Setup
-
-```bash
-# Backend
-cd server
-uv sync
-docker compose up -d redis
-uv run alembic upgrade head
-uv run -m reflector.app --reload
-
-# In a separate terminal — start the worker
-cd server
-uv run celery -A reflector.worker.app worker --loglevel=info
-
-# Frontend
-cd www
-pnpm install
-cp .env_template .env
-pnpm dev
-```
-
-### Modal.com GPU (Optional)
-
-Reflector also supports deploying specialized models (transcription, diarization) to [Modal.com](https://modal.com/) for serverless GPU processing. This is **not integrated into the self-hosted setup script** and must be configured manually.
-
-See [Modal.com Setup Guide](docs/docs/installation/modal-setup.md) for deployment instructions.
-
-## Audio Processing Commands
-
-### Process a local audio file
-
-```bash
-cd server
-uv run python -m reflector.tools.process path/to/audio.wav
-```
-
-### Reprocess an existing transcription
-
-Re-run the processing pipeline on a previously uploaded transcription by its UUID:
-
-```bash
-cd server
-uv run -m reflector.tools.process_transcript <transcript-uuid> --sync
-```
+All new contributions should be made in a separate branch, and goes through a Pull Request.
+[Conventional commits](https://www.conventionalcommits.org/en/v1.0.0/) must be used for the PR title and commits.

 ## Usage

@@ -167,59 +70,82 @@ Note: We currently do not have instructions for Windows users.
 - Then goto `System Preferences -> Sound` and choose the devices created from the Output and Input tabs.
 - The input from your local microphone, the browser run meeting should be aggregated into one virtual stream to listen to and the output should be fed back to your specified output devices if everything is configured properly.

-## Build-time env variables
+## Installation

-Next.js projects are more used to NEXT_PUBLIC_ prefixed buildtime vars. We don't have those for the reason we need to serve a customizable prebuilt docker container.
+### Frontend

-Instead, all the variables are runtime. Variables needed to the frontend are served to the frontend app at initial render.
+Start with `cd www`.

-It also means there's no static prebuild and no static files to serve for js/html.
+**Installation**

-## Feature Flags
-
-Reflector uses environment variable-based feature flags to control application functionality. These flags allow you to enable or disable features without code changes.
-
-### Available Feature Flags
-
-| Feature Flag | Environment Variable |
-|-------------|---------------------|
-| `requireLogin` | `FEATURE_REQUIRE_LOGIN` |
-| `privacy` | `FEATURE_PRIVACY` |
-| `browse` | `FEATURE_BROWSE` |
-| `sendToZulip` | `FEATURE_SEND_TO_ZULIP` |
-| `rooms` | `FEATURE_ROOMS` |
-
-### Setting Feature Flags
-
-Feature flags are controlled via environment variables using the pattern `FEATURE_{FEATURE_NAME}` where `{FEATURE_NAME}` is the SCREAMING_SNAKE_CASE version of the feature name.
-
-**Examples:**
 ```bash
-# Enable user authentication requirement
-FEATURE_REQUIRE_LOGIN=true
-
-# Disable browse functionality
-FEATURE_BROWSE=false
-
-# Enable Zulip integration
-FEATURE_SEND_TO_ZULIP=true
+pnpm install
+cp .env_template .env
+cp config-template.ts config.ts
 ```

-## Contribution Guidelines
+Then, fill in the environment variables in `.env` and the configuration in `config.ts` as needed. If you are unsure on how to proceed, ask in Zulip.

-All new contributions should be made in a separate branch, and goes through a Pull Request.
-[Conventional commits](https://www.conventionalcommits.org/en/v1.0.0/) must be used for the PR title and commits.
+**Run in development mode**

-## Future Plans
+```bash
+pnpm dev
+```

- **Multi-language support enhancement**: Default language selection per room/user, automatic language detection improvements, multi-language diarization, and RTL language UI support
- **Jitsi integration**: Self-hosted video conferencing rooms with no external API keys, full control over video infrastructure, and enhanced privacy
- **Calendar integration**: Google Calendar and Microsoft Outlook synchronization, automatic meeting room creation, and post-meeting transcript delivery
- **Enhanced analytics**: Meeting insights dashboard, speaker participation metrics, topic trends over time, and team collaboration patterns
- **Advanced AI features**: Real-time sentiment analysis, emotion detection, meeting quality scores, and automated coaching suggestions
- **Integration ecosystem**: Slack/Teams notifications, CRM integration (Salesforce, HubSpot), project management tools (Jira, Asana), and knowledge bases (Notion, Confluence)
- **Performance improvements**: WebAssembly for client-side processing, edge computing support, and network optimization
+Then (after completing server setup and starting it) open [http://localhost:3000](http://localhost:3000) to view it in the browser.

-## Legacy Documentation
+**OpenAPI Code Generation**

-The `docs/` folder contains an older Docusaurus-based documentation site. These docs are **no longer actively maintained** and may be outdated. For current installation and deployment instructions, refer to the [`docsv2/`](docsv2/) folder instead.
+To generate the TypeScript files from the openapi.json file, make sure the python server is running, then run:
+
+```bash
+pnpm openapi
+```
+
+### Backend
+
+Start with `cd server`.
+
+**Run in development mode**
+
+```bash
+docker compose up -d redis
+
+# on the first run, or if the schemas changed
+uv run alembic upgrade head
+
+# start the worker
+uv run celery -A reflector.worker.app worker --loglevel=info
+
+# start the app
+uv run -m reflector.app --reload
+```
+
+Then fill `.env` with the omitted values (ask in Zulip).
+
+**Crontab (optional)**
+
+For crontab (only healthcheck for now), start the celery beat (you don't need it on your local dev environment):
+
+```bash
+uv run celery -A reflector.worker.app beat
+```
+
+### GPU models
+
+Currently, reflector heavily use custom local models, deployed on modal. All the micro services are available in server/gpu/
+
+To deploy llm changes to modal, you need:
+- a modal account
+- set up the required secret in your modal account (REFLECTOR_GPU_APIKEY)
+- install the modal cli
+- connect your modal cli to your account if not done previously
+- `modal run path/to/required/llm`
+
+## Using local files
+
+You can manually process an audio file by calling the process tool:
+
+```bash
+uv run python -m reflector.tools.process path/to/audio.wav
+```
--- a/compose.yml
+++ b/compose.yml
@@ -0,0 +1,64 @@
+services:
+  server:
+    build:
+      context: server
+    ports:
+      - 1250:1250
+    volumes:
+      - ./server/:/app/
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: server
+
+  worker:
+    build:
+      context: server
+    volumes:
+      - ./server/:/app/
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: worker
+
+  beat:
+    build:
+      context: server
+    volumes:
+      - ./server/:/app/
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: beat
+
+  redis:
+    image: redis:7.2
+    ports:
+      - 6379:6379
+  web:
+    image: node:18
+    ports:
+      - "3000:3000"
+    command: sh -c "corepack enable && pnpm install && pnpm dev"
+    restart: unless-stopped
+    working_dir: /app
+    volumes:
+      - ./www:/app/
+      - /app/node_modules
+    env_file:
+      - ./www/.env.local
+
+  postgres:
+    image: postgres:17
+    ports:
+      - 5432:5432
+    environment:
+      POSTGRES_USER: reflector
+      POSTGRES_PASSWORD: reflector
+      POSTGRES_DB: reflector
+    volumes:
+      - ./data/postgres:/var/lib/postgresql/data
+
+networks:
+  default:
+    attachable: true
--- a/docker-compose.prod.yml
+++ b/docker-compose.prod.yml
@@ -1,120 +0,0 @@
-# Production Docker Compose configuration
-# Usage: docker compose -f docker-compose.prod.yml up -d
-#
-# Caddy (reverse proxy on ports 80/443) is OPTIONAL and behind the "caddy" profile:
-# - With Caddy (self-hosted, you manage SSL): docker compose -f docker-compose.prod.yml --profile caddy up -d
-# - Without Caddy (Coolify/Traefik/nginx already on 80/443): docker compose -f docker-compose.prod.yml up -d
-#   Then point your proxy at web:3000 (frontend) and server:1250 (API).
-#
-# Prerequisites:
-# 1. Copy .env.example to .env and configure for both server/ and www/
-# 2. If using Caddy: copy Caddyfile.example to Caddyfile and edit your domains
-# 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
-
-services:
-  web:
-    image: monadicalsas/reflector-frontend:latest
-    restart: unless-stopped
-    env_file:
-      - ./www/.env
-    pull_policy: always
-    environment:
-      - KV_URL=redis://redis:6379
-    depends_on:
-      - redis
-
-  server:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: server
-    depends_on:
-      - postgres
-      - redis
-    volumes:
-      - server_data:/app/data
-      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-  worker:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: worker
-    depends_on:
-      - postgres
-      - redis
-    volumes:
-      - server_data:/app/data
-      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-  beat:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: beat
-    depends_on:
-      - postgres
-      - redis
-
-  redis:
-    image: redis:7.2-alpine
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "redis-cli", "ping"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-    volumes:
-      - redis_data:/data
-
-  postgres:
-    image: postgres:17-alpine
-    restart: unless-stopped
-    environment:
-      POSTGRES_USER: reflector
-      POSTGRES_PASSWORD: reflector
-      POSTGRES_DB: reflector
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U reflector"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-
-  caddy:
-    profiles:
-      - caddy
-    image: caddy:2-alpine
-    restart: unless-stopped
-    ports:
-      - "80:80"
-      - "443:443"
-    volumes:
-      - ./Caddyfile:/etc/caddy/Caddyfile:ro
-      - caddy_data:/data
-      - caddy_config:/config
-    depends_on:
-      - web
-      - server
-
-  docs:
-    build: ./docs
-    restart: unless-stopped
-
-volumes:
-  redis_data:
-  postgres_data:
-  server_data:
-  caddy_data:
-  caddy_config:
-
-networks:
-  default:
-    attachable: true
--- a/docker-compose.selfhosted.yml
+++ b/docker-compose.selfhosted.yml
@@ -1,398 +0,0 @@
-# Self-hosted production Docker Compose — single file for everything.
-#
-# Usage: ./scripts/setup-selfhosted.sh <--gpu|--cpu|--hosted> [--ollama-gpu|--ollama-cpu] [--garage] [--caddy]
-#   or:  docker compose -f docker-compose.selfhosted.yml [--profile gpu] [--profile ollama-gpu] [--profile garage] [--profile caddy] up -d
-#
-# ML processing modes (pick ONE — required):
-#   --gpu                  NVIDIA GPU container for transcription/diarization/translation (profile: gpu)
-#   --cpu                  In-process CPU processing on server/worker (no ML container needed)
-#   --hosted               Remote GPU service URL (no ML container needed)
-#
-# Local LLM (optional — for summarization/topics):
-#   --profile ollama-gpu   Local Ollama with NVIDIA GPU
-#   --profile ollama-cpu   Local Ollama on CPU only
-#
-# Daily.co multitrack processing (auto-detected from server/.env):
-#   --profile dailyco      Hatchet workflow engine + CPU/LLM workers
-#
-# Other optional services:
-#   --profile garage       Local S3-compatible storage (Garage)
-#   --profile caddy        Reverse proxy with auto-SSL
-#
-# Prerequisites:
-#   1. Run ./scripts/setup-selfhosted.sh to generate env files and secrets
-#   2. Or manually create server/.env and www/.env from the .selfhosted.example templates
-
-services:
-  # ===========================================================
-  # Always-on core services (no profile required)
-  # ===========================================================
-
-  server:
-    build:
-      context: ./server
-      dockerfile: Dockerfile
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    ports:
-      - "127.0.0.1:1250:1250"
-      - "51000-51100:51000-51100/udp"
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: server
-      # Docker-internal overrides (always correct inside compose network)
-      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-      REDIS_HOST: redis
-      CELERY_BROKER_URL: redis://redis:6379/1
-      CELERY_RESULT_BACKEND: redis://redis:6379/1
-      # ML backend config comes from env_file (server/.env), set per-mode by setup script
-      # HF_TOKEN needed for in-process pyannote diarization (--cpu mode)
-      HF_TOKEN: ${HF_TOKEN:-}
-      # WebRTC: fixed UDP port range for ICE candidates (mapped above)
-      WEBRTC_PORT_RANGE: "51000-51100"
-    depends_on:
-      postgres:
-        condition: service_healthy
-      redis:
-        condition: service_started
-    volumes:
-      - server_data:/app/data
-
-  worker:
-    build:
-      context: ./server
-      dockerfile: Dockerfile
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: worker
-      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-      REDIS_HOST: redis
-      CELERY_BROKER_URL: redis://redis:6379/1
-      CELERY_RESULT_BACKEND: redis://redis:6379/1
-      # ML backend config comes from env_file (server/.env), set per-mode by setup script
-      HF_TOKEN: ${HF_TOKEN:-}
-    depends_on:
-      postgres:
-        condition: service_healthy
-      redis:
-        condition: service_started
-    volumes:
-      - server_data:/app/data
-
-  beat:
-    build:
-      context: ./server
-      dockerfile: Dockerfile
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: beat
-      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-      REDIS_HOST: redis
-      CELERY_BROKER_URL: redis://redis:6379/1
-      CELERY_RESULT_BACKEND: redis://redis:6379/1
-    depends_on:
-      postgres:
-        condition: service_healthy
-      redis:
-        condition: service_started
-
-  web:
-    build:
-      context: ./www
-      dockerfile: Dockerfile
-    image: monadicalsas/reflector-frontend:latest
-    restart: unless-stopped
-    ports:
-      - "127.0.0.1:3000:3000"
-    env_file:
-      - ./www/.env
-    environment:
-      NODE_ENV: production
-      NODE_TLS_REJECT_UNAUTHORIZED: "0"
-      SERVER_API_URL: http://server:1250
-      KV_URL: redis://redis:6379
-      KV_USE_TLS: "false"
-      NEXTAUTH_URL_INTERNAL: http://localhost:3000
-    depends_on:
-      - redis
-
-  redis:
-    image: redis:7.2-alpine
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "redis-cli", "ping"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-    volumes:
-      - redis_data:/data
-
-  postgres:
-    image: postgres:17-alpine
-    restart: unless-stopped
-    command: ["postgres", "-c", "max_connections=200"]
-    environment:
-      POSTGRES_USER: reflector
-      POSTGRES_PASSWORD: reflector
-      POSTGRES_DB: reflector
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-      - ./server/docker/init-hatchet-db.sql:/docker-entrypoint-initdb.d/init-hatchet-db.sql:ro
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U reflector"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-
-  # ===========================================================
-  # Specialized model containers (transcription, diarization, translation)
-  # Only the gpu profile is activated by the setup script (--gpu mode).
-  # The cpu service definition is kept for manual/standalone use but is
-  # NOT activated by --cpu mode (which uses in-process local backends).
-  # Both services get alias "transcription" so server config never changes.
-  # ===========================================================
-
-  gpu:
-    build:
-      context: ./gpu/self_hosted
-      dockerfile: Dockerfile
-    profiles: [gpu]
-    restart: unless-stopped
-    ports:
-      - "127.0.0.1:8000:8000"
-    environment:
-      HF_TOKEN: ${HF_TOKEN:-}
-    volumes:
-      - gpu_cache:/root/.cache
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
-      interval: 15s
-      timeout: 5s
-      retries: 10
-      start_period: 120s
-    networks:
-      default:
-        aliases:
-          - transcription
-
-  cpu:
-    build:
-      context: ./gpu/self_hosted
-      dockerfile: Dockerfile.cpu
-    profiles: [cpu]
-    restart: unless-stopped
-    ports:
-      - "127.0.0.1:8000:8000"
-    environment:
-      HF_TOKEN: ${HF_TOKEN:-}
-    volumes:
-      - gpu_cache:/root/.cache
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
-      interval: 15s
-      timeout: 5s
-      retries: 10
-      start_period: 120s
-    networks:
-      default:
-        aliases:
-          - transcription
-
-  # ===========================================================
-  # Ollama — local LLM for summarization & topic detection
-  # Only started with --ollama-gpu or --ollama-cpu modes.
-  # ===========================================================
-
-  ollama:
-    image: ollama/ollama:latest
-    profiles: [ollama-gpu]
-    restart: unless-stopped
-    ports:
-      - "127.0.0.1:11435:11435"
-    volumes:
-      - ollama_data:/root/.ollama
-    environment:
-      OLLAMA_HOST: "0.0.0.0:11435"
-      OLLAMA_KEEP_ALIVE: "24h"
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:11435/api/tags"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-
-  ollama-cpu:
-    image: ollama/ollama:latest
-    profiles: [ollama-cpu]
-    restart: unless-stopped
-    ports:
-      - "127.0.0.1:11435:11435"
-    volumes:
-      - ollama_data:/root/.ollama
-    environment:
-      OLLAMA_HOST: "0.0.0.0:11435"
-      OLLAMA_KEEP_ALIVE: "24h"         # keep model loaded to avoid reload delays
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:11435/api/tags"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-
-  # ===========================================================
-  # Garage — local S3-compatible object storage (optional)
-  # ===========================================================
-
-  garage:
-    image: dxflrs/garage:v1.1.0
-    profiles: [garage]
-    restart: unless-stopped
-    ports:
-      - "3900:3900"   # S3 API
-      - "3903:3903"   # Admin API
-    volumes:
-      - garage_data:/var/lib/garage/data
-      - garage_meta:/var/lib/garage/meta
-      - ./data/garage.toml:/etc/garage.toml:ro
-    healthcheck:
-      test: ["CMD", "/garage", "stats"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-      start_period: 5s
-
-  # ===========================================================
-  # Caddy — reverse proxy with automatic SSL (optional)
-  # Maps 80:80 and 443:443 — only exposed ports in the stack.
-  # ===========================================================
-
-  caddy:
-    image: caddy:2-alpine
-    profiles: [caddy]
-    restart: unless-stopped
-    ports:
-      - "80:80"
-      - "443:443"
-    volumes:
-      - ./Caddyfile:/etc/caddy/Caddyfile:ro
-      - caddy_data:/data
-      - caddy_config:/config
-    depends_on:
-      - web
-      - server
-
-  # ===========================================================
-  # Hatchet + Daily.co workers (optional — for Daily.co multitrack processing)
-  # Auto-enabled when DAILY_API_KEY is configured in server/r
-  # ===========================================================
-
-  hatchet:
-    image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
-    profiles: [dailyco]
-    restart: on-failure
-    depends_on:
-      postgres:
-        condition: service_healthy
-    ports:
-      - "8888:8888"
-      - "7078:7077"
-    env_file:
-      - ./.env.hatchet
-    environment:
-      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable&connect_timeout=30"
-      SERVER_AUTH_COOKIE_INSECURE: "t"
-      SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
-      SERVER_GRPC_INSECURE: "t"
-      SERVER_GRPC_BROADCAST_ADDRESS: hatchet:7077
-      SERVER_GRPC_PORT: "7077"
-      SERVER_AUTH_SET_EMAIL_VERIFIED: "t"
-      SERVER_INTERNAL_CLIENT_INTERNAL_GRPC_BROADCAST_ADDRESS: hatchet:7077
-    volumes:
-      - hatchet_config:/config
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8888/api/live"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 30s
-
-  hatchet-worker-cpu:
-    build:
-      context: ./server
-      dockerfile: Dockerfile
-    image: monadicalsas/reflector-backend:latest
-    profiles: [dailyco]
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: hatchet-worker-cpu
-      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-      REDIS_HOST: redis
-      CELERY_BROKER_URL: redis://redis:6379/1
-      CELERY_RESULT_BACKEND: redis://redis:6379/1
-      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
-      HATCHET_CLIENT_HOST_PORT: hatchet:7077
-    depends_on:
-      hatchet:
-        condition: service_healthy
-    volumes:
-      - server_data:/app/data
-
-  hatchet-worker-llm:
-    build:
-      context: ./server
-      dockerfile: Dockerfile
-    image: monadicalsas/reflector-backend:latest
-    profiles: [dailyco]
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: hatchet-worker-llm
-      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-      REDIS_HOST: redis
-      CELERY_BROKER_URL: redis://redis:6379/1
-      CELERY_RESULT_BACKEND: redis://redis:6379/1
-      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
-      HATCHET_CLIENT_HOST_PORT: hatchet:7077
-    depends_on:
-      hatchet:
-        condition: service_healthy
-    volumes:
-      - server_data:/app/data
-
-volumes:
-  postgres_data:
-  redis_data:
-  server_data:
-  gpu_cache:
-  garage_data:
-  garage_meta:
-  ollama_data:
-  caddy_data:
-  caddy_config:
-  hatchet_config:
-
-networks:
-  default:
-    attachable: true
--- a/docker-compose.standalone.yml
+++ b/docker-compose.standalone.yml
@@ -1,241 +0,0 @@
-# Self-contained standalone compose for fully local deployment (no external dependencies).
-# Usage: docker compose -f docker-compose.standalone.yml up -d
-#
-# On Linux with NVIDIA GPU, also pass: --profile ollama-gpu
-# On Linux without GPU:                --profile ollama-cpu
-# On Mac: Ollama runs natively (Metal GPU) — no profile needed, services here unused.
-
-services:
-  caddy:
-    image: caddy:2-alpine
-    restart: unless-stopped
-    ports:
-      - "3043:443"
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    volumes:
-      - ./Caddyfile:/etc/caddy/Caddyfile:ro
-      - caddy_data:/data
-      - caddy_config:/config
-    depends_on:
-      - web
-      - server
-
-  server:
-    build:
-      context: server
-    ports:
-      - "1250:1250"
-      - "50000-50100:50000-50100/udp"
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: server
-      # Docker DNS names instead of localhost
-      DATABASE_URL: postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-      REDIS_HOST: redis
-      CELERY_BROKER_URL: redis://redis:6379/1
-      CELERY_RESULT_BACKEND: redis://redis:6379/1
-      # Standalone doesn't run Hatchet
-      HATCHET_CLIENT_SERVER_URL: ""
-      HATCHET_CLIENT_HOST_PORT: ""
-      # Self-hosted transcription/diarization via CPU service
-      TRANSCRIPT_BACKEND: modal
-      TRANSCRIPT_URL: http://cpu:8000
-      TRANSCRIPT_MODAL_API_KEY: local
-      DIARIZATION_BACKEND: modal
-      DIARIZATION_URL: http://cpu:8000
-      # Caddy reverse proxy prefix
-      ROOT_PATH: /server-api
-      # WebRTC: fixed UDP port range for ICE candidates (mapped above).
-      # WEBRTC_HOST is set by setup-standalone.sh in server/.env (LAN IP detection).
-      WEBRTC_PORT_RANGE: "50000-50100"
-    depends_on:
-      postgres:
-        condition: service_healthy
-      redis:
-        condition: service_started
-
-  worker:
-    build:
-      context: server
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: worker
-      HATCHET_CLIENT_SERVER_URL: ""
-      HATCHET_CLIENT_HOST_PORT: ""
-      TRANSCRIPT_BACKEND: modal
-      TRANSCRIPT_URL: http://cpu:8000
-      TRANSCRIPT_MODAL_API_KEY: local
-      DIARIZATION_BACKEND: modal
-      DIARIZATION_URL: http://cpu:8000
-    depends_on:
-      redis:
-        condition: service_started
-
-  beat:
-    build:
-      context: server
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: beat
-    depends_on:
-      redis:
-        condition: service_started
-
-  redis:
-    image: redis:7.2
-    ports:
-      - 6379:6379
-
-  postgres:
-    image: postgres:17
-    command: postgres -c 'max_connections=200'
-    ports:
-      - 5432:5432
-    environment:
-      POSTGRES_USER: reflector
-      POSTGRES_PASSWORD: reflector
-      POSTGRES_DB: reflector
-    volumes:
-      - ./data/postgres:/var/lib/postgresql/data
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -d reflector -U reflector"]
-      interval: 5s
-      timeout: 5s
-      retries: 10
-      start_period: 15s
-
-  web:
-    image: reflector-frontend-standalone
-    build:
-      context: ./www
-    ports:
-      - "3000:3000"
-    command: ["node", "server.js"]
-    env_file:
-      - ./www/.env.local
-    environment:
-      NODE_ENV: production
-      # API_URL, WEBSOCKET_URL, SITE_URL, NEXTAUTH_URL from www/.env.local (allows HTTPS)
-      # Server-side URLs (docker-network internal)
-      SERVER_API_URL: http://server:1250
-      KV_URL: redis://redis:6379
-      KV_USE_TLS: "false"
-      # Standalone: no external auth provider
-      FEATURE_REQUIRE_LOGIN: "false"
-      FEATURE_ROOMS: "false"
-      NEXTAUTH_SECRET: standalone-local-secret
-      # Nullify partial auth vars inherited from base env_file
-      AUTHENTIK_ISSUER: ""
-      AUTHENTIK_REFRESH_TOKEN_URL: ""
-
-  garage:
-    image: dxflrs/garage:v1.1.0
-    ports:
-      - "3900:3900"   # S3 API
-      - "3903:3903"   # Admin API
-    volumes:
-      - garage_data:/var/lib/garage/data
-      - garage_meta:/var/lib/garage/meta
-      - ./data/garage.toml:/etc/garage.toml:ro
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "/garage", "stats"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-      start_period: 5s
-
-  cpu:
-    build:
-      context: ./gpu/self_hosted
-      dockerfile: Dockerfile.cpu
-    ports:
-      - "8100:8000"
-    volumes:
-      - gpu_cache:/root/.cache
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
-      interval: 15s
-      timeout: 5s
-      retries: 10
-      start_period: 120s
-
-  gpu-nvidia:
-    build:
-      context: ./gpu/self_hosted
-    profiles: ["gpu-nvidia"]
-    volumes:
-      - gpu_cache:/root/.cache
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
-      interval: 15s
-      timeout: 5s
-      retries: 10
-      start_period: 120s
-
-  ollama:
-    image: ollama/ollama:latest
-    profiles: ["ollama-gpu"]
-    ports:
-      - "11434:11434"
-    volumes:
-      - ollama_data:/root/.ollama
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-
-  ollama-cpu:
-    image: ollama/ollama:latest
-    profiles: ["ollama-cpu"]
-    ports:
-      - "11434:11434"
-    volumes:
-      - ollama_data:/root/.ollama
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
-      interval: 10s
-      timeout: 5s
-      retries: 5
-
-volumes:
-  garage_data:
-  garage_meta:
-  ollama_data:
-  gpu_cache:
-  caddy_data:
-  caddy_config:
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,153 +0,0 @@
-services:
-  server:
-    build:
-      context: server
-    network_mode: host
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: server
-      DATABASE_URL: postgresql+asyncpg://reflector:reflector@localhost:5432/reflector
-      REDIS_HOST: localhost
-      CELERY_BROKER_URL: redis://localhost:6379/1
-      CELERY_RESULT_BACKEND: redis://localhost:6379/1
-      HATCHET_CLIENT_SERVER_URL: http://localhost:8889
-      HATCHET_CLIENT_HOST_PORT: localhost:7078
-
-  worker:
-    build:
-      context: server
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: worker
-      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
-      HATCHET_CLIENT_HOST_PORT: hatchet:7077
-    depends_on:
-      redis:
-        condition: service_started
-
-  beat:
-    build:
-      context: server
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: beat
-    depends_on:
-      redis:
-        condition: service_started
-
-  hatchet-worker-cpu:
-    build:
-      context: server
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: hatchet-worker-cpu
-      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
-      HATCHET_CLIENT_HOST_PORT: hatchet:7077
-    depends_on:
-      hatchet:
-        condition: service_healthy
-  hatchet-worker-llm:
-    build:
-      context: server
-    volumes:
-      - ./server/:/app/
-      - /app/.venv
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: hatchet-worker-llm
-      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
-      HATCHET_CLIENT_HOST_PORT: hatchet:7077
-    depends_on:
-      hatchet:
-        condition: service_healthy
-
-  redis:
-    image: redis:7.2
-    ports:
-      - 6379:6379
-  web:
-    build:
-      context: ./www
-      dockerfile: Dockerfile
-    ports:
-      - "3000:3000"
-    env_file:
-      - ./www/.env.local
-    environment:
-      NODE_ENV: development
-      SERVER_API_URL: http://host.docker.internal:1250
-      KV_URL: redis://redis:6379
-    extra_hosts:
-      - "host.docker.internal:host-gateway"
-    depends_on:
-      redis:
-        condition: service_started
-
-  postgres:
-    image: postgres:17
-    command: postgres -c 'max_connections=200'
-    ports:
-      - 5432:5432
-    environment:
-      POSTGRES_USER: reflector
-      POSTGRES_PASSWORD: reflector
-      POSTGRES_DB: reflector
-    volumes:
-      - ./data/postgres:/var/lib/postgresql/data
-      - ./server/docker/init-hatchet-db.sql:/docker-entrypoint-initdb.d/init-hatchet-db.sql:ro
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -d reflector -U reflector"]
-      interval: 5s
-      timeout: 5s
-      retries: 10
-      start_period: 15s
-
-  hatchet:
-    image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
-    restart: on-failure
-    ports:
-      - "8889:8888"
-      - "7078:7077"
-    depends_on:
-      postgres:
-        condition: service_healthy
-    environment:
-      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable&connect_timeout=30"
-      SERVER_AUTH_COOKIE_DOMAIN: localhost
-      SERVER_AUTH_COOKIE_INSECURE: "t"
-      SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
-      SERVER_GRPC_INSECURE: "t"
-      SERVER_GRPC_BROADCAST_ADDRESS: hatchet:7077
-      SERVER_GRPC_PORT: "7077"
-      SERVER_URL: http://localhost:8889
-      SERVER_AUTH_SET_EMAIL_VERIFIED: "t"
-      # SERVER_DEFAULT_ENGINE_VERSION: "V1"  # default
-      SERVER_INTERNAL_CLIENT_INTERNAL_GRPC_BROADCAST_ADDRESS: hatchet:7077
-    volumes:
-      - ./data/hatchet-config:/config
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8888/api/live"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-      start_period: 30s
-
-volumes:
-  next_cache:
--- a/docs/.dockerignore
+++ b/docs/.dockerignore
@@ -1,7 +0,0 @@
-node_modules
-build
-.git
-.gitignore
-*.log
-.DS_Store
-.env*
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -1,20 +0,0 @@
-# Dependencies
-/node_modules
-
-# Production
-/build
-
-# Generated files
-.docusaurus
-.cache-loader
-
-# Misc
-.DS_Store
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
--- a/docs/Dockerfile
+++ b/docs/Dockerfile
@@ -1,42 +0,0 @@
-FROM node:20-alpine AS builder
-WORKDIR /app
-
-# Install curl for fetching OpenAPI spec
-RUN apk add --no-cache curl
-
-# Enable pnpm
-RUN corepack enable && corepack prepare pnpm@latest --activate
-
-# Copy package files and lockfile
-COPY package.json pnpm-lock.yaml* ./
-
-# Install dependencies
-RUN pnpm install --frozen-lockfile
-
-# Copy source
-COPY . .
-
-# Fetch OpenAPI spec from production API
-ARG OPENAPI_URL=https://api-reflector.monadical.com/openapi.json
-RUN mkdir -p ./static && curl -sf "${OPENAPI_URL}" -o ./static/openapi.json || echo '{}' > ./static/openapi.json
-
-# Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
-RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
-
-# Build static site (skip prebuild hook by calling docusaurus directly)
-RUN pnpm exec docusaurus build
-
-# Production image
-FROM nginx:alpine
-
-# Copy built static files
-COPY --from=builder /app/build /usr/share/nginx/html
-
-# Healthcheck for container orchestration
-HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
-  CMD wget --no-verbose --tries=1 --spider http://localhost/ || exit 1
-
-# Expose port
-EXPOSE 80
-
-CMD ["nginx", "-g", "daemon off;"]
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,41 +0,0 @@
-# Website
-
-This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
-
-### Installation
-
-```
-$ pnpm install
-```
-
-### Local Development
-
-```
-$ pnpm start
-```
-
-This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
-
-### Build
-
-```
-$ pnpm build
-```
-
-This command generates static content into the `build` directory and can be served using any static contents hosting service.
-
-### Deployment
-
-Using SSH:
-
-```
-$ USE_SSH=true pnpm deploy
-```
-
-Not using SSH:
-
-```
-$ GIT_USER=<Your GitHub username> pnpm deploy
-```
-
-If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
--- a/docs/TODO.md
+++ b/docs/TODO.md
@@ -1,48 +0,0 @@
-# Documentation TODO - PR #778 Review Comments
-
-Remaining items from Tito's review. See CHANGES.md for completed items.
-
---
-
-## Remaining Items
-
-| File | Issue | Priority | Notes |
-|------|-------|----------|-------|
-| ~~`intro.md:10`~~ | ~~Add screenshots~~ | ~~Low~~ | ✅ **DONE** - Added transcript view screenshot |
-| `file-pipeline.md:47` | chunk_size example shows 30s | Low | Unclear what example config should show (~16s actual) |
-| ~~`self-hosted-gpu-setup.md:235`~~ | ~~systemd template in repo~~ | ~~Medium~~ | ✅ **REMOVED** - Systemd support removed entirely |
-| ~~`installation/overview.md:85`~~ | ~~uv tool install~~ | ~~Low~~ | ✅ **DONE** - Changed to `uv tool install modal` |
-| ~~`installation/overview.md:101`~~ | ~~"Why systemd?"~~ | ~~Low~~ | ✅ **REMOVED** - Systemd support removed entirely |
-| `installation/overview.md:271` | Caddyfile copy removal | Low | Keeping for clarity |
-
---
-
-## Skipped (Decided Not To Fix)
-
-| File | Issue | Reason |
-|------|-------|--------|
-| `installation/overview.md:40` | Model size requirements | Uncertain about exact requirements |
-| `installation/overview.md:136` | WebRTC ports | Handled by Daily/Whereby, not us |
-| `installation/overview.md:136` | Security section | Risk of incomplete/misleading docs |
-| `installation/overview.md:179` | AWS setup order | Low priority, works as-is |
-| `installation/overview.md:410` | Redundant next steps | Issue doesn't exist (file ends at 401) |
-
---
-
-## Completed
-
-See CHANGES.md for full list. Summary:
-
-### Removals (9)
- Encrypted data storage, session management, analytics claims
- "coming soon" GPU, 30-second segments, CPU optimization
- Encryption at rest, manual migrations, modprobe commands
-
-### Fixes (9)
- WebRTC + Daily/Whereby, 4 API endpoints, Docker docs link
- NVIDIA steps merged, compose.yml referenced, cross-reference duplicate
- tee→nano, MOV format, troubleshooting link
-
-### Previously Fixed (7)
- Blog removal, Daily.co added, rate limiting removed (x2)
- PII claim removed, python→yaml, LUFS removed
--- a/docs/create-docs.sh
+++ b/docs/create-docs.sh
@@ -1,777 +0,0 @@
-#!/bin/bash
-
-# Create directory structure
-mkdir -p docs/concepts
-mkdir -p docs/installation
-mkdir -p docs/pipelines
-mkdir -p docs/reference/architecture
-mkdir -p docs/reference/processors
-mkdir -p docs/reference/api
-
-# Create all documentation files with content
-echo "Creating documentation files..."
-
-# Concepts - Modes
-cat > docs/concepts/modes.md << 'EOF'
---
-sidebar_position: 2
-title: Operating Modes
---
-
-# Operating Modes
-
-Reflector operates in two distinct modes to accommodate different use cases and security requirements.
-
-## Public Mode
-
-Public mode provides immediate access to core transcription features without requiring authentication.
-
-### Features Available
- **File Upload**: Process audio files up to 2GB
- **Live Transcription**: Stream audio from microphone
- **Basic Processing**: Transcription and diarization
- **Temporary Storage**: Results available for 24 hours
-
-### Limitations
- No persistent storage
- No meeting rooms
- Limited to single-user sessions
- No team collaboration features
-
-### Use Cases
- Quick transcription needs
- Testing and evaluation
- Individual users
- Public demonstrations
-
-## Private Mode
-
-Private mode unlocks the full potential of Reflector with authentication and persistent storage.
-
-### Additional Features
- **Virtual Meeting Rooms**: Whereby integration
- **Team Collaboration**: Share transcripts with team
- **Persistent Storage**: Long-term transcript archive
- **Advanced Analytics**: Meeting insights and trends
- **Custom Integration**: Webhooks and API access
- **User Management**: Role-based access control
-
-### Authentication Options
-
-#### Authentik Integration
-Enterprise-grade SSO with support for:
- SAML 2.0
- OAuth 2.0 / OIDC
- LDAP / Active Directory
- Multi-factor authentication
-
-#### JWT Authentication
-Stateless token-based auth for:
- API access
- Service-to-service communication
- Mobile applications
-
-### Room Management
-
-Virtual rooms provide dedicated spaces for meetings:
- **Persistent URLs**: Same link for recurring meetings
- **Access Control**: Invite-only or open rooms
- **Recording Consent**: Automatic consent management
- **Custom Settings**: Per-room configuration
-
-## Mode Selection
-
-The mode is determined by your deployment configuration:
-
-```yaml
-# Public Mode (no authentication)
-REFLECTOR_AUTH_BACKEND=none
-
-# Private Mode (with authentication)
-REFLECTOR_AUTH_BACKEND=jwt
-# or
-REFLECTOR_AUTH_BACKEND=authentik
-```
-
-## Feature Comparison
-
-| Feature | Public Mode | Private Mode |
-|---------|------------|--------------|
-| File Upload | ✅ | ✅ |
-| Live Transcription | ✅ | ✅ |
-| Speaker Diarization | ✅ | ✅ |
-| Translation | ✅ | ✅ |
-| Summarization | ✅ | ✅ |
-| Meeting Rooms | ❌ | ✅ |
-| Persistent Storage | ❌ | ✅ |
-| Team Collaboration | ❌ | ✅ |
-| API Access | Limited | Full |
-| User Management | ❌ | ✅ |
-| Custom Branding | ❌ | ✅ |
-| Analytics | ❌ | ✅ |
-| Webhooks | ❌ | ✅ |
-
-## Security Considerations
-
-### Public Mode Security
- Rate limiting to prevent abuse
- File size restrictions
- Automatic cleanup of old data
- No PII storage
-
-### Private Mode Security
- Encrypted data storage
- Audit logging
- Session management
- Access control lists
- Data retention policies
-
-## Choosing the Right Mode
-
-### Choose Public Mode if:
- You need quick, one-time transcriptions
- You're evaluating Reflector
- You don't need persistent storage
- You're processing non-sensitive content
-
-### Choose Private Mode if:
- You need team collaboration
- You require persistent storage
- You're processing sensitive content
- You need meeting room functionality
- You want advanced analytics
-EOF
-
-# Concepts - Independence
-cat > docs/concepts/independence.md << 'EOF'
---
-sidebar_position: 3
-title: Data Independence
---
-
-# Data Independence & Privacy
-
-Reflector is designed with privacy and data independence as core principles, giving you complete control over your data and processing.
-
-## Privacy by Design
-
-### No Third-Party Data Sharing
-
-Your audio and transcripts are never shared with third parties:
- **Local Processing**: All ML models can run on your infrastructure
- **No Training on User Data**: Your content is never used to improve models
- **Isolated Processing**: Each transcript is processed in isolation
- **No Analytics Tracking**: No usage analytics sent to external services
-
-### Data Ownership
-
-You maintain complete ownership of all data:
- **Export Anytime**: Download all your transcripts and audio
- **Delete on Demand**: Permanent deletion with no recovery
- **API Access**: Full programmatic access to your data
- **No Vendor Lock-in**: Standard formats for easy migration
-
-## Processing Transparency
-
-### What Happens to Your Audio
-
-1. **Upload/Stream**: Audio received by your server
-2. **Temporary Storage**: Stored only for processing duration
-3. **Processing**: ML models process audio locally or on Modal
-4. **Results Storage**: Transcripts stored in your database
-5. **Cleanup**: Original audio deleted (unless configured otherwise)
-
-### Local vs Cloud Processing
-
-#### Local Processing
-When configured for local processing:
- All models run on your hardware
- No data leaves your infrastructure
- Complete air-gap capability
- Higher hardware requirements
-
-#### Modal.com Processing
-When using Modal for GPU acceleration:
- Audio chunks sent to Modal for processing
- Processed immediately and deleted
- No long-term storage on Modal
- Modal's security: SOC 2 Type II compliant
-
-### Data Retention
-
-Default retention policies:
- **Public Mode**: 24 hours then automatic deletion
- **Private Mode**: Configurable (default: indefinite)
- **Audio Files**: Deleted after processing (configurable)
- **Transcripts**: Retained based on policy
-
-## Compliance Features
-
-### GDPR Compliance
-
- **Right to Access**: Export all user data
- **Right to Deletion**: Permanent data removal
- **Data Portability**: Standard export formats
- **Privacy by Default**: Minimal data collection
-
-### HIPAA Considerations
-
-For healthcare deployments:
- **Self-hosted Option**: Complete infrastructure control
- **Encryption**: At rest and in transit
- **Audit Logging**: Complete access trail
- **Access Controls**: Role-based permissions
-
-### Industry Standards
-
- **TLS 1.3**: Modern encryption for data in transit
- **AES-256**: Encryption for data at rest
- **JWT Tokens**: Secure, stateless authentication
- **OWASP Guidelines**: Security best practices
-
-## Self-Hosted Deployment
-
-### Complete Independence
-
-Self-hosting provides maximum control:
- **Your Infrastructure**: Run on your servers
- **Your Network**: No external connections required
- **Your Policies**: Implement custom retention
- **Your Compliance**: Meet specific requirements
-
-### Air-Gap Capability
-
-Reflector can run completely offline:
-1. Download all models during setup
-2. Configure for local processing only
-3. Disable all external integrations
-4. Run in isolated network environment
-
-## Data Flow Control
-
-### Configurable Processing
-
-Control where each step happens:
-
-```yaml
-# All in-process processing
-TRANSCRIPT_BACKEND=whisper
-DIARIZATION_BACKEND=pyannote
-TRANSLATION_BACKEND=marian
-
-# Hybrid approach
-TRANSCRIPT_BACKEND=modal    # Fast GPU processing
-DIARIZATION_BACKEND=pyannote # Sensitive speaker data
-TRANSLATION_BACKEND=modal    # Non-sensitive translation
-```
-
-### Storage Options
-
-Choose where data is stored:
- **Local Filesystem**: Complete control
- **PostgreSQL**: Self-hosted database
- **S3-Compatible**: MinIO or AWS with encryption
- **Hybrid**: Different storage for different data types
-
-## Security Architecture
-
-### Defense in Depth
-
-Multiple layers of security:
-1. **Network Security**: Firewalls and VPNs
-2. **Application Security**: Input validation and sanitization
-3. **Data Security**: Encryption and access controls
-4. **Operational Security**: Logging and monitoring
-
-### Zero Trust Principles
-
- **Verify Everything**: All requests authenticated
- **Least Privilege**: Minimal permissions granted
- **Assume Breach**: Design for compromise containment
- **Encrypt Everything**: No plaintext transmission
-
-## Audit and Compliance
-
-### Audit Logging
-
-Comprehensive logging of:
- **Access Events**: Who accessed what and when
- **Processing Events**: What was processed and how
- **Configuration Changes**: System modifications
- **Security Events**: Failed authentication attempts
-
-### Compliance Reporting
-
-Generate reports for:
- **Data Processing**: What data was processed
- **Data Access**: Who accessed the data
- **Data Retention**: What was retained or deleted
- **Security Events**: Security-related incidents
-
-## Best Practices
-
-### For Maximum Privacy
-
-1. **Self-host** all components
-2. **Use local processing** for all models
-3. **Implement short retention** periods
-4. **Encrypt all storage** at rest
-5. **Use VPN** for all connections
-6. **Regular audits** of access logs
-
-### For Balanced Approach
-
-1. **Self-host core services** (database, API)
-2. **Use Modal for processing** (faster, cost-effective)
-3. **Implement encryption** everywhere
-4. **Regular backups** with encryption
-5. **Monitor access** patterns
-EOF
-
-# Concepts - Pipeline
-cat > docs/concepts/pipeline.md << 'EOF'
---
-sidebar_position: 4
-title: Processing Pipeline
---
-
-# Processing Pipeline
-
-Reflector uses a sophisticated pipeline architecture to process audio efficiently and accurately.
-
-## Pipeline Overview
-
-The processing pipeline consists of modular components that can be combined and configured based on your needs:
-
-```mermaid
-graph LR
-    A[Audio Input] --> B[Pre-processing]
-    B --> C[Chunking]
-    C --> D[Transcription]
-    D --> E[Diarization]
-    E --> F[Alignment]
-    F --> G[Post-processing]
-    G --> H[Output]
-```
-
-## Pipeline Components
-
-### Audio Input
-
-Accepts various input sources:
- **File Upload**: MP3, WAV, M4A, WebM, MP4
- **WebRTC Stream**: Live browser audio
- **Recording Integration**: Whereby recordings
- **API Upload**: Direct API submission
-
-### Pre-processing
-
-Prepares audio for optimal processing:
- **Format Conversion**: Convert to 16kHz mono WAV
- **Normalization**: Adjust volume to -23 LUFS
- **Noise Reduction**: Optional background noise removal
- **Validation**: Check duration and quality
-
-### Chunking
-
-Splits audio for parallel processing:
- **Fixed Size**: 30-second chunks by default
- **Overlap**: 1-second overlap for continuity
- **Smart Boundaries**: Attempt to split at silence
- **Metadata**: Track chunk positions
-
-### Transcription
-
-Converts speech to text:
- **Model Selection**: Whisper or Parakeet
- **Language Detection**: Automatic or specified
- **Timestamp Generation**: Word-level timing
- **Confidence Scores**: Quality indicators
-
-### Diarization
-
-Identifies different speakers:
- **Voice Activity Detection**: Find speech segments
- **Speaker Embedding**: Extract voice characteristics
- **Clustering**: Group similar voices
- **Label Assignment**: Assign speaker IDs
-
-### Alignment
-
-Merges all processing results:
- **Chunk Assembly**: Combine transcription chunks
- **Speaker Mapping**: Align speakers with text
- **Overlap Resolution**: Handle chunk boundaries
- **Timeline Creation**: Build unified timeline
-
-### Post-processing
-
-Enhances the final output:
- **Formatting**: Apply punctuation and capitalization
- **Translation**: Convert to target languages
- **Summarization**: Generate concise summaries
- **Topic Extraction**: Identify key themes
- **Action Items**: Extract tasks and decisions
-
-## Processing Modes
-
-### Batch Processing
-
-For uploaded files:
- Optimized for throughput
- Parallel chunk processing
- Higher accuracy models
- Complete file analysis
-
-### Stream Processing
-
-For live audio:
- Optimized for latency
- Sequential processing
- Real-time feedback
- Progressive results
-
-### Hybrid Processing
-
-For meetings:
- Stream during meeting
- Batch after completion
- Best of both modes
- Maximum accuracy
-
-## Pipeline Configuration
-
-### Model Selection
-
-Choose models based on requirements:
-
-```python
-# High accuracy (slower)
-config = {
-    "transcription_model": "whisper-large-v3",
-    "diarization_model": "pyannote-3.1",
-    "translation_model": "seamless-m4t-large"
-}
-
-# Balanced (default)
-config = {
-    "transcription_model": "whisper-base",
-    "diarization_model": "pyannote-3.1",
-    "translation_model": "seamless-m4t-medium"
-}
-
-# Fast processing
-config = {
-    "transcription_model": "whisper-tiny",
-    "diarization_model": "pyannote-3.1-fast",
-    "translation_model": "seamless-m4t-small"
-}
-```
-
-### Processing Options
-
-Customize pipeline behavior:
-
-```yaml
-# Parallel processing
-max_parallel_chunks: 10
-chunk_size_seconds: 30
-chunk_overlap_seconds: 1
-
-# Quality settings
-enable_noise_reduction: true
-enable_normalization: true
-min_speech_confidence: 0.5
-
-# Post-processing
-enable_translation: true
-target_languages: ["es", "fr", "de"]
-enable_summarization: true
-summary_length: "medium"
-```
-
-## Performance Characteristics
-
-### Processing Times
-
-For 1 hour of audio:
-
-| Pipeline Config | Processing Time | Accuracy |
-|----------------|-----------------|----------|
-| Fast | 2-3 minutes | 85-90% |
-| Balanced | 5-8 minutes | 92-95% |
-| High Accuracy | 15-20 minutes | 95-98% |
-
-### Resource Usage
-
-| Component | CPU Usage | Memory | GPU |
-|-----------|-----------|---------|-----|
-| Transcription | Medium | 2-4 GB | Required |
-| Diarization | High | 4-8 GB | Required |
-| Translation | Low | 2-3 GB | Optional |
-| Post-processing | Low | 1-2 GB | Not needed |
-
-## Pipeline Orchestration
-
-### Celery Task Chain
-
-The pipeline is orchestrated using Celery:
-
-```python
-chain = (
-    chunk_audio.s(audio_id) |
-    group(transcribe_chunk.s(chunk) for chunk in chunks) |
-    merge_transcriptions.s() |
-    diarize_audio.s() |
-    align_speakers.s() |
-    post_process.s()
-)
-```
-
-### Error Handling
-
-Robust error recovery:
- **Automatic Retry**: Failed tasks retry up to 3 times
- **Partial Recovery**: Continue with successful chunks
- **Fallback Models**: Use alternative models on failure
- **Error Reporting**: Detailed error messages
-
-### Progress Tracking
-
-Real-time progress updates:
- **Chunk Progress**: Track individual chunk processing
- **Overall Progress**: Percentage completion
- **ETA Calculation**: Estimated completion time
- **WebSocket Updates**: Live progress to clients
-
-## Optimization Strategies
-
-### GPU Utilization
-
-Maximize GPU efficiency:
- **Batch Processing**: Process multiple chunks together
- **Model Caching**: Keep models loaded in memory
- **Dynamic Batching**: Adjust batch size based on GPU memory
- **Multi-GPU Support**: Distribute across available GPUs
-
-### Memory Management
-
-Efficient memory usage:
- **Streaming Processing**: Process large files in chunks
- **Garbage Collection**: Clean up after each chunk
- **Memory Limits**: Prevent out-of-memory errors
- **Disk Caching**: Use disk for large intermediate results
-
-### Network Optimization
-
-Minimize network overhead:
- **Compression**: Compress audio before transfer
- **CDN Integration**: Use CDN for static assets
- **Connection Pooling**: Reuse network connections
- **Parallel Uploads**: Multiple concurrent uploads
-
-## Quality Assurance
-
-### Accuracy Metrics
-
-Monitor processing quality:
- **Word Error Rate (WER)**: Transcription accuracy
- **Diarization Error Rate (DER)**: Speaker identification accuracy
- **Translation BLEU Score**: Translation quality
- **Summary Coherence**: Summary quality metrics
-
-### Validation Steps
-
-Ensure output quality:
- **Confidence Thresholds**: Filter low-confidence segments
- **Consistency Checks**: Verify timeline consistency
- **Language Validation**: Ensure correct language detection
- **Format Validation**: Check output format compliance
-
-## Advanced Features
-
-### Custom Models
-
-Use your own models:
- **Fine-tuned Whisper**: Domain-specific models
- **Custom Diarization**: Trained on your speakers
- **Specialized Post-processing**: Industry-specific formatting
-
-### Pipeline Extensions
-
-Add custom processing steps:
- **Sentiment Analysis**: Analyze emotional tone
- **Entity Extraction**: Identify people, places, organizations
- **Custom Metrics**: Calculate domain-specific metrics
- **Integration Hooks**: Call external services
-EOF
-
-# Create installation documentation
-cat > docs/installation/overview.md << 'EOF'
---
-sidebar_position: 1
-title: Installation Overview
---
-
-# Installation Overview
-
-Reflector is designed for self-hosted deployment, giving you complete control over your infrastructure and data.
-
-## Deployment Options
-
-### Docker Deployment (Recommended)
-
-The easiest way to deploy Reflector:
- Pre-configured containers
- Automated dependency management
- Consistent environment
- Easy updates
-
-### Manual Installation
-
-For custom deployments:
- Greater control over configuration
- Integration with existing infrastructure
- Custom optimization options
- Development environments
-
-## Requirements
-
-### System Requirements
-
-**Minimum Requirements:**
- CPU: 4 cores
- RAM: 8 GB
- Storage: 50 GB
- OS: Ubuntu 20.04+ or similar Linux
-
-**Recommended Requirements:**
- CPU: 8+ cores
- RAM: 16 GB
- Storage: 100 GB SSD
- GPU: NVIDIA GPU with 8GB+ VRAM (for local processing)
-
-### Network Requirements
-
- Public IP address (for WebRTC)
- Ports: 80, 443, 8000, 3000
- Domain name (for SSL)
- SSL certificate (Let's Encrypt supported)
-
-## Required Services
-
-### Core Services
-
-These services are required for basic operation:
-
-1. **PostgreSQL** - Primary database
-2. **Redis** - Message broker and cache
-3. **Docker** - Container runtime
-
-### GPU Processing
-
-Choose one:
- **Modal.com** - Serverless GPU (recommended)
- **Local GPU** - Self-hosted GPU processing
-
-### Optional Services
-
-Enhance functionality with:
- **AWS S3** - Long-term storage
- **Whereby** - Video conferencing rooms
- **Authentik** - Enterprise authentication
- **Zulip** - Chat integration
-
-## Quick Start
-
-### Using Docker Compose
-
-1. Clone the repository:
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector
-```
-
-2. Navigate to docker directory:
-```bash
-cd docker
-```
-
-3. Copy and configure environment:
-```bash
-cp .env.example .env
-# Edit .env with your settings
-```
-
-4. Start services:
-```bash
-docker compose up -d
-```
-
-5. Access Reflector:
- Frontend: https://your-domain.com
- API: https://your-domain.com/api
-
-## Configuration Overview
-
-### Essential Configuration
-
-```env
-# Database
-DATABASE_URL=postgresql://user:pass@localhost/reflector
-
-# Redis
-REDIS_URL=redis://localhost:6379
-
-# Modal.com (for GPU processing)
-TRANSCRIPT_MODAL_API_KEY=your-key
-DIARIZATION_MODAL_API_KEY=your-key
-
-# Domain
-DOMAIN=your-domain.com
-```
-
-### Security Configuration
-
-```env
-# Authentication
-REFLECTOR_AUTH_BACKEND=jwt
-NEXTAUTH_SECRET=generate-strong-secret
-
-# SSL (handled by Caddy)
-# Automatic with Let's Encrypt
-```
-
-## Service Architecture
-
-```mermaid
-graph TD
-    A[Caddy Reverse Proxy] --> B[Frontend - Next.js]
-    A --> C[Backend - FastAPI]
-    C --> D[PostgreSQL]
-    C --> E[Redis]
-    C --> F[Celery Workers]
-    F --> G[Modal.com GPU]
-```
-
-## Next Steps
-
-1. **Review Requirements**: [System Requirements](./requirements)
-2. **Docker Setup**: [Docker Deployment Guide](./docker-setup)
-3. **Configure Services**:
-   - [Modal.com Setup](./modal-setup)
-   - [Whereby Setup](./whereby-setup)
-   - [AWS S3 Setup](./aws-setup)
-4. **Optional Services**:
-   - [Authentik Setup](./authentik-setup)
-   - [Zulip Setup](./zulip-setup)
-
-## Getting Help
-
- [Troubleshooting Guide](../reference/troubleshooting)
- [GitHub Issues](https://github.com/monadical-sas/reflector/issues)
- [Community Discord](#)
-EOF
-
-chmod +x create-docs.sh
-echo "Documentation creation script ready. Run ./create-docs.sh to generate all docs."
--- a/docs/docs/concepts/modes.md
+++ b/docs/docs/concepts/modes.md
@@ -1,115 +0,0 @@
---
-sidebar_position: 2
-title: Operating Modes
---
-
-# Operating Modes
-
-Reflector operates in two distinct modes to accommodate different use cases and security requirements.
-
-## Public Mode
-
-Public mode provides immediate access to core transcription features without requiring authentication.
-
-### Features Available
- **File Upload**: Process audio files
- **Live Transcription**: Stream audio from microphone
- **Basic Processing**: Transcription and diarization
- **Temporary Storage**: Temporary data retention (configurable)
-
-### Limitations
- No persistent storage
- No meeting rooms
- Limited to single-user sessions
- No team collaboration features
-
-### Use Cases
- Quick transcription needs
- Testing and evaluation
- Individual users
- Public demonstrations
-
-## Private Mode
-
-Private mode unlocks the full potential of Reflector with authentication and persistent storage.
-
-### Additional Features
- **Virtual Meeting Rooms**: Whereby and Daily.co integration
- **Team Collaboration**: Share transcripts with team
- **Persistent Storage**: Long-term transcript archive
- **Meeting History**: Search and browse past transcripts
- **Custom Integration**: Webhooks and API access
- **User Management**: Role-based access control
-
-### Authentication Options
-
-#### Authentik Integration
-Enterprise-grade SSO with support for:
- SAML 2.0
- OAuth 2.0 / OIDC
- LDAP / Active Directory
- Multi-factor authentication
-
-### Room Management
-
-Virtual rooms provide dedicated spaces for meetings:
- **Persistent URLs**: Same link for recurring meetings
- **Access Control**: Invite-only or open rooms
- **Recording Consent**: Automatic consent management
- **Custom Settings**: Per-room configuration
-
-## Mode Selection
-
-The mode is determined by your deployment configuration:
-
-```yaml
-# Public Mode (no authentication)
-AUTH_BACKEND=none
-
-# Private Mode (with authentication)
-AUTH_BACKEND=jwt
-```
-
-See [Authentication Setup](../installation/auth-setup) for configuring JWT authentication.
-
-## Feature Comparison
-
-| Feature | Public Mode | Private Mode |
-|---------|------------|--------------|
-| File Upload | ✅ | ✅ |
-| Live Transcription | ✅ | ✅ |
-| Speaker Diarization | ✅ | ✅ |
-| Summarization | ✅ | ✅ |
-| Meeting Rooms | ❌ | ✅ |
-| Persistent Storage | ❌ | ✅ |
-| Team Collaboration | ❌ | ✅ |
-| API Access | Limited | Full |
-| User Management | ❌ | ✅ |
-| Custom Branding | ❌ | ✅ |
-| Meeting History | ❌ | ✅ |
-| Webhooks | ❌ | ✅ |
-
-## Security Considerations
-
-### Public Mode Security
- File size restrictions
- Automatic cleanup of old data
-
-### Private Mode Security
- Access control lists
- Data retention policies
-
-## Choosing the Right Mode
-
-### Choose Public Mode if:
- You need quick, one-time transcriptions
- You're evaluating Reflector
- You don't need persistent storage
- You're processing non-sensitive content
-
-### Choose Private Mode if:
- You need team collaboration
- You require persistent storage
- You're processing sensitive content
- You need meeting room functionality
- You want searchable meeting history
--- a/docs/docs/concepts/overview.md
+++ b/docs/docs/concepts/overview.md
@@ -1,201 +0,0 @@
---
-sidebar_position: 1
-title: Architecture Overview
---
-
-# Architecture Overview
-
-Reflector is built as a modern, scalable, microservices-based application designed to handle audio processing workloads efficiently while maintaining data privacy and control.
-
-## System Components
-
-### Frontend Application
-
-The user interface is built with **Next.js 16** using the App Router pattern, providing:
-
- Server-side rendering for optimal performance
- Real-time WebSocket connections for live transcription
- WebRTC support for audio streaming and live meetings (via Daily.co or Whereby)
- Responsive design with Chakra UI components
-
-### Backend API Server
-
-The core API is powered by **FastAPI**, a modern Python framework that provides:
-
- High-performance async request handling
- Automatic OpenAPI documentation generation
- Type safety with Pydantic models
- WebSocket support for real-time updates
-
-### Processing Pipeline
-
-Audio processing is handled through a modular pipeline architecture:
-
-```
-Audio Input → Chunking → Transcription → Diarization → Post-Processing → Storage
-```
-
-Each step can run independently and in parallel, allowing for:
- Scalable processing of large files
- Real-time streaming capabilities
- Fault tolerance and retry mechanisms
-
-### Worker Architecture
-
-Background tasks are managed by **Celery** workers with **Redis** as the message broker:
-
- Distributed task processing
- Priority queues for time-sensitive operations
- Automatic retry on failure
- Progress tracking and notifications
-
-### GPU Acceleration
-
-ML models run on GPU-accelerated infrastructure:
-
- **Modal.com** for serverless GPU processing
- **Self-hosted GPU** with Docker deployment
- Automatic scaling based on demand
- Cost-effective pay-per-use model
-
-## Data Flow
-
-### Daily.co Meeting Recording Flow
-
-1. **Recording**: Daily.co captures separate audio tracks per participant
-2. **Webhook**: Daily.co notifies Reflector when recording is ready
-3. **Track Download**: Individual participant tracks fetched from S3
-4. **Padding**: Tracks padded with silence based on join time for synchronization
-5. **Transcription**: Each track transcribed independently (speaker = track index)
-6. **Merge**: Transcriptions sorted by timestamp and combined
-7. **Mixdown**: Tracks mixed to single MP3 for playback
-8. **Post-Processing**: Topics, title, and summaries generated via LLM
-9. **Delivery**: Results stored and user notified via WebSocket
-
-### File Upload Flow
-
-1. **Upload**: User uploads audio file through web interface
-2. **Storage**: File stored temporarily
-3. **Transcription**: Full file transcribed via Whisper
-4. **Diarization**: ML-based speaker identification (Pyannote)
-5. **Post-Processing**: Topics, title, summaries
-6. **Delivery**: Results stored and user notified
-
-### Live Streaming Flow
-
-1. **WebRTC Connection**: Browser establishes peer connection via Daily.co or Whereby
-2. **Audio Capture**: Microphone audio streamed to server
-3. **Buffering**: Audio buffered for processing
-4. **Real-time Processing**: Segments transcribed as they arrive
-5. **WebSocket Updates**: Results streamed back to client
-6. **Continuous Assembly**: Full transcript built progressively
-
-## Deployment Architecture
-
-### Container-Based Deployment
-
-All components are containerized for consistent deployment:
-
-```yaml
-services:
-  web:         # Next.js application
-  server:      # FastAPI server
-  worker:      # Celery workers
-  redis:       # Message broker
-  postgres:    # Database
-  caddy:       # Reverse proxy
-```
-
-### Networking
-
- **Host Network Mode**: Required for WebRTC/ICE compatibility
- **Caddy Reverse Proxy**: Handles SSL termination and routing
- **WebSocket Upgrade**: Supports real-time connections
-
-## Scalability Considerations
-
-### Horizontal Scaling
-
- **Stateless Backend**: Multiple API server instances
- **Worker Pools**: Add workers based on queue depth
- **Database Pooling**: Connection management for concurrent access
-
-### Vertical Scaling
-
- **GPU Workers**: Scale up for faster model inference
- **Memory Optimization**: Efficient audio buffering
-
-## Security Architecture
-
-### Authentication & Authorization
-
- **JWT Tokens**: Stateless authentication
- **Authentik Integration**: Enterprise SSO support
- **Role-Based Access**: Granular permissions
-
-### Data Protection
-
- **Encryption in Transit**: TLS for all connections
- **Temporary Storage**: Automatic cleanup of processed files
-
-### Privacy by Design
-
- **Local Processing**: Option to process entirely on-premises
- **No Training on User Data**: Models are pre-trained
- **Data Isolation**: Multi-tenant data separation
-
-## Integration Points
-
-### External Services
-
- **Modal.com**: GPU processing
- **AWS S3**: Long-term storage
- **Whereby**: Video conferencing rooms
- **Zulip**: Chat integration (optional)
-
-### APIs and Webhooks
-
- **RESTful API**: Standard CRUD operations
- **WebSocket API**: Real-time updates
- **Webhook Notifications**: Processing completion events
- **OpenAPI Specification**: Machine-readable API definition
-
-## Performance Optimization
-
-### Caching Strategy
-
- **Redis Cache**: Frequently accessed data
- **CDN**: Static asset delivery
- **Browser Cache**: Client-side optimization
-
-### Database Optimization
-
- **Indexed Queries**: Fast search and retrieval
- **Connection Pooling**: Efficient resource usage
- **Query Optimization**: N+1 query prevention
-
-### Processing Optimization
-
- **Batch Processing**: Efficient GPU utilization
- **Parallel Execution**: Multi-core CPU usage
- **Stream Processing**: Reduced memory footprint
-
-## Monitoring and Observability
-
-### Metrics Collection
-
- **Application Metrics**: Request rates, response times
- **System Metrics**: CPU, memory, disk usage
- **Business Metrics**: Transcription accuracy, processing times
-
-### Logging
-
- **Structured Logging**: JSON format for analysis
- **Log Aggregation**: Centralized log management
- **Error Tracking**: Sentry integration
-
-### Health Checks
-
- **Liveness Probes**: Component availability
- **Readiness Probes**: Service readiness
- **Dependency Checks**: External service status
--- a/docs/docs/concepts/pipeline.md
+++ b/docs/docs/concepts/pipeline.md
@@ -1,183 +0,0 @@
---
-sidebar_position: 4
-title: Processing Pipeline
---
-
-# Processing Pipeline
-
-Reflector uses a modular pipeline architecture to process audio efficiently and accurately.
-
-## Pipeline Overview
-
-The processing pipeline consists of modular components that can be combined and configured based on your needs:
-
-```mermaid
-graph LR
-    A[Audio Input] --> B[Pre-processing]
-    B --> C[Chunking]
-    C --> D[Transcription]
-    D --> E[Diarization]
-    E --> F[Alignment]
-    F --> G[Post-processing]
-    G --> H[Output]
-```
-
-## Pipeline Components
-
-### Audio Input
-
-Accepts various input sources:
- **File Upload**: MP3, WAV, M4A, WebM, MP4
- **WebRTC Stream**: Live browser audio
- **Recording Integration**: Daily.co and Whereby recordings
- **API Upload**: Direct API submission
-
-### Pre-processing
-
-Prepares audio for optimal processing:
- **Format Conversion**: Convert to 16kHz mono WAV
- **Noise Reduction**: Optional background noise removal
- **Validation**: Check duration and quality
-
-### Chunking
-
-Splits audio for parallel processing:
- **Configurable Size**: Audio split into processable segments
- **Silence Detection**: Optional splitting at natural pauses
- **Metadata**: Track chunk positions
-
-### Transcription
-
-Converts speech to text:
- **Model Selection**: Whisper or Parakeet
- **Language Detection**: Automatic or specified
- **Timestamp Generation**: Word-level timing
- **Confidence Scores**: Quality indicators
-
-### Diarization
-
-Identifies different speakers:
- **Voice Activity Detection**: Find speech segments
- **Speaker Embedding**: Extract voice characteristics
- **Clustering**: Group similar voices
- **Label Assignment**: Assign speaker IDs
-
-### Alignment
-
-Merges all processing results:
- **Chunk Assembly**: Combine transcription chunks
- **Speaker Mapping**: Align speakers with text
- **Overlap Resolution**: Handle chunk boundaries
- **Timeline Creation**: Build unified timeline
-
-### Post-processing
-
-Enhances the final output:
- **Formatting**: Apply punctuation and capitalization
- **Summarization**: Generate concise summaries
- **Topic Extraction**: Identify key themes
- **Action Items**: Extract tasks and decisions
-
-## Processing Modes
-
-### Batch Processing
-
-For uploaded files:
- Optimized for throughput
- Parallel chunk processing
- Higher accuracy models
- Complete file analysis
-
-### Stream Processing
-
-For live audio:
- Optimized for latency
- Sequential processing
- Real-time feedback
- Progressive results
-
-### Hybrid Processing
-
-For meetings:
- Stream during meeting
- Batch after completion
- Best of both modes
- Maximum accuracy
-
-## Pipeline Orchestration
-
-### Error Handling
-
-Error recovery:
- **Automatic Retry**: Failed tasks retry up to 3 times
- **Partial Recovery**: Continue with successful chunks
- **Fallback Models**: Use alternative models on failure
- **Error Reporting**: Detailed error messages
-
-### Progress Tracking
-
-Real-time progress updates:
- **Chunk Progress**: Track individual chunk processing
- **Overall Progress**: Percentage completion
- **ETA Calculation**: Estimated completion time
- **WebSocket Updates**: Live progress to clients
-
-## Optimization Strategies
-
-### GPU Utilization
-
-Maximize GPU efficiency:
- **Batch Processing**: Process multiple chunks together
- **Model Caching**: Keep models loaded in memory
- **Dynamic Batching**: Adjust batch size based on GPU memory
- **Multi-GPU Support**: Distribute across available GPUs
-
-### Memory Management
-
-Efficient memory usage:
- **Streaming Processing**: Process large files in chunks
- **Garbage Collection**: Clean up after each chunk
- **Memory Limits**: Prevent out-of-memory errors
- **Disk Caching**: Use disk for large intermediate results
-
-### Network Optimization
-
-Minimize network overhead:
- **Compression**: Compress audio before transfer
- **CDN Integration**: Use CDN for static assets
- **Connection Pooling**: Reuse network connections
- **Parallel Uploads**: Multiple concurrent uploads
-
-## Quality Assurance
-
-### Accuracy Metrics
-
-Monitor processing quality:
- **Word Error Rate (WER)**: Transcription accuracy
- **Diarization Error Rate (DER)**: Speaker identification accuracy
- **Summary Coherence**: Summary quality metrics
-
-### Validation Steps
-
-Ensure output quality:
- **Confidence Thresholds**: Filter low-confidence segments
- **Consistency Checks**: Verify timeline consistency
- **Language Validation**: Ensure correct language detection
- **Format Validation**: Check output format compliance
-
-## Advanced Features
-
-### Custom Models
-
-Use your own models:
- **Fine-tuned Whisper**: Domain-specific models
- **Custom Diarization**: Trained on your speakers
- **Specialized Post-processing**: Industry-specific formatting
-
-### Pipeline Extensions
-
-Add custom processing steps:
- **Sentiment Analysis**: Analyze emotional tone
- **Entity Extraction**: Identify people, places, organizations
- **Custom Metrics**: Calculate domain-specific metrics
- **Integration Hooks**: Call external services
--- a/docs/docs/installation/auth-setup.md
+++ b/docs/docs/installation/auth-setup.md
@@ -1,285 +0,0 @@
---
-sidebar_position: 5
-title: Authentication Setup
---
-
-# Authentication Setup
-
-This page covers authentication setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
-
-Reflector uses [Authentik](https://goauthentik.io/) for OAuth/OIDC authentication. This guide walks you through setting up Authentik and connecting it to Reflector.
-
-The guide simplistically sets Authentic on the same server as Reflector. You can use your own Authentic instance instead.
-
-## Overview
-
-Reflector's authentication flow:
-1. User clicks "Sign In" on frontend
-2. Frontend redirects to Authentik login page
-3. User authenticates with Authentik
-4. Authentik redirects back with OAuth tokens
-5. Frontend stores tokens, backends verify JWT signature
-
-## Option 1: Self-Hosted Authentik (Same Server)
-
-This setup runs Authentik on the same server as Reflector, with Caddy proxying to both.
-
-### Deploy Authentik
-
-```bash
-# Create directory for Authentik
-mkdir -p ~/authentik && cd ~/authentik
-
-# Download docker-compose file
-curl -O https://goauthentik.io/docker-compose.yml
-
-# Generate secrets and bootstrap credentials
-cat > .env << 'EOF'
-PG_PASS=$(openssl rand -base64 36 | tr -d '\n')
-AUTHENTIK_SECRET_KEY=$(openssl rand -base64 60 | tr -d '\n')
-# Privacy-focused choice for self-hosted deployments
-AUTHENTIK_ERROR_REPORTING__ENABLED=false
-AUTHENTIK_BOOTSTRAP_PASSWORD=YourSecurePassword123
-AUTHENTIK_BOOTSTRAP_EMAIL=admin@example.com
-EOF
-
-# Start Authentik
-sudo docker compose up -d
-```
-
-Authentik takes ~2 minutes to run migrations and apply blueprints on first start.
-
-### Connect Authentik to Reflector's Network
-
-If Authentik runs in a separate Docker Compose project, connect it to Reflector's network so Caddy can proxy to it:
-
-```bash
-# Wait for Authentik to be healthy
-# Connect Authentik server to Reflector's network
-sudo docker network connect reflector_default authentik-server-1
-```
-
-**Important:** This step must be repeated if you restart Authentik with `docker compose down`. Add it to your deployment scripts or use `docker compose up -d` (which preserves containers) instead of down/up.
-
-### Add Authentik to Caddy
-
-Uncomment the Authentik section in your `Caddyfile` and set your domain:
-
-```bash
-nano Caddyfile
-```
-
-Uncomment and edit:
-```
-{$AUTHENTIK_DOMAIN:authentik.example.com} {
-    reverse_proxy authentik-server-1:9000
-}
-```
-
-Reload Caddy:
-```bash
-docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
-```
-
-### Create OAuth2 Provider in Authentik
-
-**Option A: Automated Setup (Recommended)**
-
-**Location: Reflector server**
-
-Run the setup script from the Reflector repository:
-
-```bash
-ssh user@your-server-ip
-cd ~/reflector
-./scripts/setup-authentik-oauth.sh https://authentik.example.com YourSecurePassword123 https://app.example.com
-```
-
-**Important:** The script must be run from the `~/reflector` directory on your server, as it creates files using relative paths.
-
-The script will output the configuration values to add to your `.env` files. Skip to "Update docker-compose.prod.yml".
-
-**Option B: Manual Setup**
-
-1. **Login to Authentik Admin** at `https://authentik.example.com/`
-   - Username: `akadmin`
-   - Password: The `AUTHENTIK_BOOTSTRAP_PASSWORD` you set in .env
-
-2. **Create OAuth2 Provider:**
-   - Go to **Applications > Providers > Create**
-   - Select **OAuth2/OpenID Provider**
-   - Configure:
-     - **Name**: `Reflector`
-     - **Authorization flow**: `default-provider-authorization-implicit-consent`
-     - **Client type**: `Confidential`
-     - **Client ID**: Note this value (auto-generated)
-     - **Client Secret**: Note this value (auto-generated)
-     - **Redirect URIs**: Add entry with:
-       ```
-       https://app.example.com/api/auth/callback/authentik
-       ```
-   - Scroll down to **Advanced protocol settings**
-   - In **Scopes**, add these three mappings:
-     - `authentik default OAuth Mapping: OpenID 'email'`
-     - `authentik default OAuth Mapping: OpenID 'openid'`
-     - `authentik default OAuth Mapping: OpenID 'profile'`
-   - Click **Finish**
-
-3. **Create Application:**
-   - Go to **Applications > Applications > Create**
-   - Configure:
-     - **Name**: `Reflector`
-     - **Slug**: `reflector` (auto-filled)
-     - **Provider**: Select the `Reflector` provider you just created
-   - Click **Create**
-
-### Get Public Key for JWT Verification
-
-**Location: Reflector server**
-
-Extract the public key from Authentik's JWKS endpoint:
-
-```bash
-mkdir -p ~/reflector/server/reflector/auth/jwt/keys
-curl -s https://authentik.example.com/application/o/reflector/jwks/ | \
-  jq -r '.keys[0].x5c[0]' | base64 -d | openssl x509 -pubkey -noout \
-  > ~/reflector/server/reflector/auth/jwt/keys/authentik_public.pem
-```
-
-### Update docker-compose.prod.yml
-
-**Location: Reflector server**
-
-**Note:** This step is already done in the current `docker-compose.prod.yml`. Verify the volume mounts exist:
-
-```yaml
-server:
-  image: monadicalsas/reflector-backend:latest
-  # ... other config ...
-  volumes:
-    - server_data:/app/data
-    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-worker:
-  image: monadicalsas/reflector-backend:latest
-  # ... other config ...
-  volumes:
-    - server_data:/app/data
-    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-```
-
-### Configure Reflector Backend
-
-**Location: Reflector server**
-
-Update `server/.env`:
-```env
-# Authentication
-AUTH_BACKEND=jwt
-AUTH_JWT_PUBLIC_KEY=authentik_public.pem
-AUTH_JWT_AUDIENCE=<your-client-id>
-CORS_ALLOW_CREDENTIALS=true
-```
-
-Replace `<your-client-id>` with the Client ID from previous steps.
-
-### Configure Reflector Frontend
-
-**Location: Reflector server**
-
-Update `www/.env`:
-```env
-# Authentication
-FEATURE_REQUIRE_LOGIN=true
-
-# Authentik OAuth
-AUTHENTIK_ISSUER=https://authentik.example.com/application/o/reflector
-AUTHENTIK_REFRESH_TOKEN_URL=https://authentik.example.com/application/o/token/
-AUTHENTIK_CLIENT_ID=<your-client-id>
-AUTHENTIK_CLIENT_SECRET=<your-client-secret>
-
-# NextAuth
-NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
-```
-
-### Restart Services
-
-**Location: Reflector server**
-
-```bash
-cd ~/reflector
-sudo docker compose -f docker-compose.prod.yml up -d --force-recreate server worker web
-```
-
-### Verify Authentication
-
-1. Visit `https://app.example.com`
-2. Click "Log in" or navigate to `/api/auth/signin`
-3. Click "Sign in with Authentik"
-4. Login with your Authentik credentials
-5. You should be redirected back and see "Log out" in the header
-
-## Option 2: Disable Authentication
-
-For testing or internal deployments where authentication isn't needed:
-
-**Backend `server/.env`:**
-```env
-AUTH_BACKEND=none
-```
-
-**Frontend `www/.env`:**
-```env
-FEATURE_REQUIRE_LOGIN=false
-```
-
-**Note:** The pre-built Docker images have `FEATURE_REQUIRE_LOGIN=true` baked in. To disable auth, you'll need to rebuild the frontend image with the env var set at build time, or set up Authentik.
-
-## Troubleshooting
-
-### "Invalid redirect URI" error
- Verify the redirect URI in Authentik matches exactly:
-  ```
-  https://app.example.com/api/auth/callback/authentik
-  ```
- Check for trailing slashes - they must match exactly
-
-### "Invalid audience" JWT error
- Ensure `AUTH_JWT_AUDIENCE` in `server/.env` matches the Client ID from Authentik
- The audience value is the OAuth Client ID, not the issuer URL
-
-### "JWT verification failed" error
- Verify the public key file is mounted in the container
- Check `AUTH_JWT_PUBLIC_KEY` points to the correct filename
- Ensure the key was extracted from the correct provider's JWKS endpoint
-
-### Caddy returns 503 for Authentik
- Verify Authentik container is connected to Reflector's network:
-  ```bash
-  sudo docker network connect reflector_default authentik-server-1
-  ```
- Check Authentik is healthy: `cd ~/authentik && sudo docker compose ps`
-
-### Users can't access protected pages
- Verify `FEATURE_REQUIRE_LOGIN=true` in frontend
- Check `AUTH_BACKEND=jwt` in backend
- Verify CORS settings allow credentials
-
-### Token refresh errors
- Ensure Redis is running (frontend uses Redis for token caching)
- Verify `KV_URL` is set correctly in frontend env
- Check `AUTHENTIK_REFRESH_TOKEN_URL` is correct
-
-## API Key Authentication
-
-For programmatic access (scripts, integrations), users can generate API keys:
-
-1. Login to Reflector
-2. Go to Settings > API Keys
-3. Click "Generate New Key"
-4. Use the key in requests:
-   ```bash
-   curl -H "X-API-Key: your-api-key" https://api.example.com/v1/transcripts
-   ```
-
-API keys are stored hashed and can be revoked at any time.
--- a/docs/docs/installation/daily-setup.md
+++ b/docs/docs/installation/daily-setup.md
@@ -1,165 +0,0 @@
---
-sidebar_position: 6
-title: Daily.co Setup
---
-
-# Daily.co Setup
-
-This page covers Daily.co video platform setup for live meeting rooms. For the complete deployment guide, see [Deployment Guide](./overview).
-
-Daily.co enables live video meetings with automatic recording and transcription.
-
-## What You'll Set Up
-
-```
-User joins meeting → Daily.co video room → Recording to S3 → [Webhook] → Reflector transcribes
-```
-
-## Prerequisites
-
- [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
- [ ] **AWS account** - For S3 storage
- [ ] **Reflector deployed** - Complete steps from [Deployment Guide](./overview)
-
---
-
-## Create Daily.co Account
-
-1. Visit https://dashboard.daily.co and sign up
-2. Verify your email
-3. Note your subdomain (e.g., `yourname.daily.co` → subdomain is `yourname`)
-
---
-
-## Get Daily.co API Key
-
-1. In Daily.co dashboard, go to **Developers**
-2. Click **API Keys**
-3. Click **Create API Key**
-4. Copy the key (starts with a long string)
-
-Save this for later.
-
---
-
-## Create AWS S3 Bucket
-
-Daily.co needs somewhere to store recordings before Reflector processes them.
-
-```bash
-# Choose a unique bucket name
-BUCKET_NAME="reflector-dailyco-yourname" # -yourname is not a requirement, you can name the bucket as you wish
-AWS_REGION="us-east-1"
-
-# Create bucket
-aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
-
-# Enable versioning (required)
-aws s3api put-bucket-versioning \
-  --bucket $BUCKET_NAME \
-  --versioning-configuration Status=Enabled
-```
-
---
-
-## Create IAM Role for Daily.co
-
-Daily.co needs permission to write recordings to your S3 bucket.
-
-Follow the guide https://docs.daily.co/guides/products/live-streaming-recording/storing-recordings-in-a-custom-s3-bucket
-
-Save the role ARN - you'll need it soon.
-
-It looks like: `arn:aws:iam::123456789012:role/DailyCo`
-
-Shortly, you'll need to set up a role and give this role your s3 bucket access
-
-No additional setup is required from Daily.co settings website side: the app code takes care of letting Daily know where to save the recordings.
-
---
-
-## Configure Reflector
-
-**Location: Reflector server**
-
-Add to `server/.env`:
-
-```env
-# Daily.co Configuration
-DEFAULT_VIDEO_PLATFORM=daily
-DAILY_API_KEY=<your-api-key-from-daily-setup>
-DAILY_SUBDOMAIN=<your-subdomain-from-daily-setup>
-
-# S3 Storage for Daily.co recordings
-DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket-from-daily-setup>
-DAILYCO_STORAGE_AWS_REGION=us-east-1
-DAILYCO_STORAGE_AWS_ROLE_ARN=<your-role-arn-from-daily-setup>
-
-# Transcript storage (should already be configured from main setup)
-# TRANSCRIPT_STORAGE_BACKEND=aws
-# TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=<your-key>
-# TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=<your-secret>
-# TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=<your-bucket-name>
-# TRANSCRIPT_STORAGE_AWS_REGION=<your-bucket-region>
-```
-
---
-
-## Restart Services
-
-After changing `.env` files, reload with `up -d`:
-
-```bash
-sudo docker compose -f docker-compose.prod.yml up -d server worker
-```
-
-**Note**: `docker compose up -d` detects env changes and recreates containers automatically.
-
---
-
-## Test Live Room
-
-1. Visit your Reflector frontend: `https://app.example.com`
-2. Go to **Rooms**
-3. Click **Create Room**
-4. Select **Daily** as the platform
-5. Allow camera/microphone access
-6. You should see Daily.co video interface
-7. Speak for 10-20 seconds
-8. Leave the meeting
-9. Recording should appear in **Transcripts** within 5 minutes (if webhooks aren't set up yet, see [Webhook Configuration](#webhook-configuration-optional) below)
-
---
-
-## Troubleshooting
-
-### Recording doesn't appear in S3
-
-1. Check Daily.co dashboard → **Logs** for errors
-2. Verify IAM role trust policy has correct Daily.co account ID and your Daily.co subdomain
-3. Verify that the bucket has
-
-### Recording in S3 but not transcribed
-
-1. Check webhook is configured (Reflector should auto-create it)
-2. Check worker logs:
-   ```bash
-   docker compose -f docker-compose.prod.yml logs worker --tail 50
-   ```
-3. Verify `DAILYCO_STORAGE_AWS_*` vars in `server/.env`
-
-### "Access Denied" when Daily.co tries to write to S3
-
-1. Double-check IAM role ARN in Daily.co settings
-2. Verify bucket name matches exactly
-3. Check IAM policy has `s3:PutObject` permission
-
---
-
-## Webhook Configuration [optional]
-
-`manage_daily_webhook.py` script guides you through creating a webhook for Daily recordings.
-
-The webhook isn't required - polling mechanism is the default and performed automatically.
-
-This guide won't go deep into webhook setup.
--- a/docs/docs/installation/docker-setup.md
+++ b/docs/docs/installation/docker-setup.md
@@ -1,217 +0,0 @@
---
-sidebar_position: 3
-title: Docker Reference
---
-
-# Docker Reference
-
-This page documents the Docker Compose configuration for Reflector. For the complete deployment guide, see [Deployment Guide](./overview).
-
-## Services
-
-The `docker-compose.prod.yml` includes these services:
-
-| Service    | Image                             | Purpose                                                                     |
-| ---------- | --------------------------------- | --------------------------------------------------------------------------- |
-| `web`      | `monadicalsas/reflector-frontend` | Next.js frontend                                                            |
-| `server`   | `monadicalsas/reflector-backend`  | FastAPI backend                                                             |
-| `worker`   | `monadicalsas/reflector-backend`  | Celery worker for background tasks                                          |
-| `beat`     | `monadicalsas/reflector-backend`  | Celery beat scheduler                                                       |
-| `redis`    | `redis:7.2-alpine`                | Message broker and cache                                                    |
-| `postgres` | `postgres:17-alpine`              | Primary database                                                            |
-| `caddy`    | `caddy:2-alpine`                  | Reverse proxy with auto-SSL (optional; see [Caddy profile](#caddy-profile)) |
-
-## Environment Files
-
-Reflector uses two separate environment files:
-
-### Backend (`server/.env`)
-
-Used by: `server`, `worker`, `beat`
-
-Key variables:
-
-```env
-# Database connection
-DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-
-# Redis
-REDIS_HOST=redis
-CELERY_BROKER_URL=redis://redis:6379/1
-CELERY_RESULT_BACKEND=redis://redis:6379/1
-
-# API domain and CORS
-BASE_URL=https://api.example.com
-CORS_ORIGIN=https://app.example.com
-
-# Modal GPU processing
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://...
-TRANSCRIPT_MODAL_API_KEY=...
-```
-
-### Frontend (`www/.env`)
-
-Used by: `web`
-
-Key variables:
-
-```env
-# Domain configuration
-SITE_URL=https://app.example.com
-API_URL=https://api.example.com
-WEBSOCKET_URL=wss://api.example.com
-SERVER_API_URL=http://server:1250
-
-# Authentication
-NEXTAUTH_URL=https://app.example.com
-NEXTAUTH_SECRET=...
-```
-
-Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-side (SSR).
-
-## Volumes
-
-| Volume          | Purpose                       |
-| --------------- | ----------------------------- |
-| `redis_data`    | Redis persistence             |
-| `postgres_data` | PostgreSQL data               |
-| `server_data`   | Uploaded files, local storage |
-| `caddy_data`    | SSL certificates              |
-| `caddy_config`  | Caddy configuration           |
-
-## Network
-
-All services share the default network. The network is marked `attachable: true` to allow external containers (like Authentik) to join.
-
-## Caddy profile
-
-Caddy (ports 80 and 443) is **optional** and behind the `caddy` profile so it does not conflict with an existing reverse proxy (e.g. Coolify, Traefik, nginx).
-
- **With Caddy** (you want Reflector to handle SSL):
-  `docker compose -f docker-compose.prod.yml --profile caddy up -d`
- **Without Caddy** (Coolify or another proxy already on 80/443):
-  `docker compose -f docker-compose.prod.yml up -d`
-  Then configure your proxy to send traffic to `web:3000` (frontend) and `server:1250` (API).
-
-## Common Commands
-
-### Start all services
-
-```bash
-# Without Caddy (e.g. when using Coolify)
-docker compose -f docker-compose.prod.yml up -d
-
-# With Caddy as reverse proxy
-docker compose -f docker-compose.prod.yml --profile caddy up -d
-```
-
-### View logs
-
-```bash
-# All services
-docker compose -f docker-compose.prod.yml logs -f
-
-# Specific service
-docker compose -f docker-compose.prod.yml logs server --tail 50
-```
-
-### Restart a service
-
-```bash
-# Quick restart (doesn't reload .env changes)
-docker compose -f docker-compose.prod.yml restart server
-
-# Reload .env and restart
-docker compose -f docker-compose.prod.yml up -d server
-```
-
-### Run database migrations
-
-```bash
-docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
-```
-
-### Access database
-
-```bash
-docker compose -f docker-compose.prod.yml exec postgres psql -U reflector
-```
-
-### Pull latest images
-
-```bash
-docker compose -f docker-compose.prod.yml pull
-docker compose -f docker-compose.prod.yml up -d
-```
-
-### Stop all services
-
-```bash
-docker compose -f docker-compose.prod.yml down
-```
-
-### Full reset (WARNING: deletes data)
-
-```bash
-docker compose -f docker-compose.prod.yml down -v
-```
-
-## Customization
-
-### Using a different database
-
-To use an external PostgreSQL:
-
-1. Remove `postgres` service from compose file
-2. Update `DATABASE_URL` in `server/.env`:
-   ```env
-   DATABASE_URL=postgresql+asyncpg://user:pass@external-host:5432/reflector
-   ```
-
-### Using external Redis
-
-1. Remove `redis` service from compose file
-2. Update Redis settings in `server/.env`:
-   ```env
-   REDIS_HOST=external-redis-host
-   CELERY_BROKER_URL=redis://external-redis-host:6379/1
-   ```
-
-### Adding Authentik
-
-To add Authentik for authentication, see [Authentication Setup](./auth-setup). Quick steps:
-
-1. Deploy Authentik separately
-2. Connect to Reflector's network:
-   ```bash
-   docker network connect reflector_default authentik-server-1
-   ```
-3. Add to Caddyfile:
-   ```
-   authentik.example.com {
-       reverse_proxy authentik-server-1:9000
-   }
-   ```
-
-## Caddyfile Reference
-
-The Caddyfile supports environment variable substitution:
-
-```
-{$FRONTEND_DOMAIN:app.example.com} {
-    reverse_proxy web:3000
-}
-
-{$API_DOMAIN:api.example.com} {
-    reverse_proxy server:1250
-}
-```
-
-Set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables, or edit the file directly.
-
-### Reload Caddy after changes
-
-```bash
-docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
-```
--- a/docs/docs/installation/docs-deployment.md
+++ b/docs/docs/installation/docs-deployment.md
@@ -1,140 +0,0 @@
---
-sidebar_position: 10
-title: Docs Website Deployment
---
-
-# Docs Website Deployment
-
-This guide covers deploying the Reflector documentation website. **This is optional and intended for internal/experimental use only.**
-
-## Overview
-
-The documentation is built using Docusaurus and deployed as a static nginx-served site.
-
-## Prerequisites
-
- Reflector already deployed (Steps 1-7 from [Deployment Guide](./overview))
- DNS A record for docs subdomain (e.g., `docs.example.com`)
-
-## Deployment Steps
-
-### Step 1: Pre-fetch OpenAPI Spec
-
-The docs site includes API reference from your running backend. Fetch it before building:
-
-```bash
-cd ~/reflector
-docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
-```
-
-This creates `docs/static/openapi.json` (should be ~70KB) which will be copied during Docker build.
-
-**Why not fetch during build?** Docker build containers are network-isolated and can't access the running backend services.
-
-### Step 2: Verify Dockerfile
-
-The Dockerfile is already in `docs/Dockerfile`:
-
-```dockerfile
-FROM node:20-alpine AS builder
-WORKDIR /app
-
-# Enable pnpm and copy package files + lockfile
-RUN corepack enable && corepack prepare pnpm@latest --activate
-COPY package.json pnpm-lock.yaml* ./
-
-# Install dependencies
-RUN pnpm install --frozen-lockfile
-
-# Copy source (includes static/openapi.json if pre-fetched)
-COPY . .
-
-# Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
-RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
-
-# Build static site
-RUN pnpm exec docusaurus build
-
-FROM nginx:alpine
-COPY --from=builder /app/build /usr/share/nginx/html
-EXPOSE 80
-CMD ["nginx", "-g", "daemon off;"]
-```
-
-### Step 3: Add Docs Service to docker-compose.prod.yml
-
-Add this service to `docker-compose.prod.yml`:
-
-```yaml
-docs:
-  build: ./docs
-  restart: unless-stopped
-  networks:
-    - default
-```
-
-### Step 4: Add Caddy Route
-
-Add to `Caddyfile`:
-
-```
-{$DOCS_DOMAIN:docs.example.com} {
-    reverse_proxy docs:80
-}
-```
-
-### Step 5: Build and Deploy
-
-```bash
-cd ~/reflector
-docker compose -f docker-compose.prod.yml up -d --build docs
-docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
-```
-
-### Step 6: Verify
-
-```bash
-# Check container status
-docker compose -f docker-compose.prod.yml ps docs
-# Should show "Up"
-
-# Test URL
-curl -I https://docs.example.com
-# Should return HTTP/2 200
-```
-
-Visit `https://docs.example.com` in your browser
-
-## Updating Documentation
-
-When docs are updated:
-
-```bash
-cd ~/reflector
-git pull
-
-# Refresh OpenAPI spec from backend
-docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
-
-# Rebuild docs
-docker compose -f docker-compose.prod.yml up -d --build docs
-```
-
-## Troubleshooting
-
-### Missing openapi.json during build
- Make sure you ran the pre-fetch step first (Step 1)
- Verify `docs/static/openapi.json` exists and is ~70KB
- Re-run: `docker compose exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json`
-
-### Build fails with "Docusaurus found broken links"
- This happens if `onBrokenLinks: 'throw'` is set in docusaurus.config.ts
- Solution is already in Dockerfile: uses `sed` to change to `'warn'` during build
-
-### 404 on all pages
- Docusaurus baseUrl might be wrong - should be `/` for custom domain
- Check `docs/docusaurus.config.ts`: `baseUrl: '/'`
-
-### Docs not updating after rebuild
- Force rebuild: `docker compose -f docker-compose.prod.yml build --no-cache docs`
- Then: `docker compose -f docker-compose.prod.yml up -d docs`
--- a/docs/docs/installation/modal-setup.md
+++ b/docs/docs/installation/modal-setup.md
@@ -1,171 +0,0 @@
---
-sidebar_position: 4
-title: Modal.com Setup
---
-
-# Modal.com Setup
-
-This page covers Modal.com GPU setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
-
-Reflector uses [Modal.com](https://modal.com) for GPU-accelerated audio processing. This guide walks you through deploying the required GPU functions.
-
-## What is Modal.com?
-
-Modal is a serverless GPU platform. You deploy Python code that runs on their GPUs, and pay only for actual compute time. Reflector uses Modal for:
-
- **Transcription**: Whisper model for speech-to-text
- **Diarization**: Pyannote model for speaker identification
-
-## Prerequisites
-
-1. **Modal.com account** - Sign up at https://modal.com (free tier available)
-2. **HuggingFace account** - Required for Pyannote diarization models:
-   - Create account at https://huggingface.co
-   - Accept **both** Pyannote licenses:
-     - https://huggingface.co/pyannote/speaker-diarization-3.1
-     - https://huggingface.co/pyannote/segmentation-3.0
-   - Generate access token at https://huggingface.co/settings/tokens
-
-## Deployment
-
-**Location: YOUR LOCAL COMPUTER (laptop/desktop)**
-
-Modal CLI requires browser authentication, so this must run on a machine with a browser - not on a headless server.
-
-### Install Modal CLI
-
-```bash
-uv tool install modal
-```
-
-### Authenticate with Modal
-
-```bash
-modal setup
-```
-
-This opens your browser for authentication. Complete the login flow.
-
-### Clone Repository and Deploy
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/modal_deployments
-./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
-```
-
-Or run interactively (script will prompt for token):
-```bash
-./deploy-all.sh
-```
-
-### What the Script Does
-
-1. **Prompts for HuggingFace token** - Needed to download the Pyannote diarization model
-2. **Generates API key** - Creates a secure random key for authenticating requests to GPU functions
-3. **Creates Modal secrets**:
-   - `hf_token` - Your HuggingFace token
-   - `reflector-gpu` - The generated API key
-4. **Deploys GPU functions** - Transcriber (Whisper) and Diarizer (Pyannote)
-5. **Outputs configuration** - Prints URLs and API key to console
-
-### Example Output
-
-```
-==========================================
-Reflector GPU Functions Deployment
-==========================================
-
-Generating API key for GPU services...
-Creating Modal secrets...
-  -> Creating secret: hf_token
-  -> Creating secret: reflector-gpu
-
-Deploying transcriber (Whisper)...
-  -> https://yourname--reflector-transcriber-web.modal.run
-
-Deploying diarizer (Pyannote)...
-  -> https://yourname--reflector-diarizer-web.modal.run
-
-==========================================
-Deployment complete!
-==========================================
-
-Copy these values to your server's server/.env file:
-
-# --- Modal GPU Configuration ---
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=abc123...
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=abc123...
-# --- End Modal Configuration ---
-```
-
-Copy the output and paste it into your `server/.env` file on your server.
-
-## Costs
-
-Modal charges based on GPU compute time:
- Functions scale to zero when not in use (no cost when idle)
- You only pay for actual processing time
- Free tier includes $30/month of credits
-
-Typical costs for audio processing:
- Transcription: ~$0.01-0.05 per minute of audio
- Diarization: ~$0.02-0.10 per minute of audio
-
-## Troubleshooting
-
-### "Modal CLI not installed"
-```bash
-uv tool install modal
-```
-
-### "Not authenticated with Modal"
-```bash
-modal setup
-# Complete browser authentication
-```
-
-### "Failed to create secret hf_token"
- Verify your HuggingFace token is valid
- Ensure you've accepted the Pyannote license
- Token needs `read` permission
-
-### Deployment fails
-Check the Modal dashboard for detailed error logs:
- Visit https://modal.com/apps
- Click on the failed function
- View build and runtime logs
-
-### Re-running deployment
-The script is safe to re-run. It will:
- Update existing secrets if they exist
- Redeploy functions with latest code
- Output new configuration (API key stays the same if secret exists)
-
-## Manual Deployment (Advanced)
-
-If you prefer to deploy functions individually:
-
-```bash
-cd gpu/modal_deployments
-
-# Create secrets manually
-modal secret create hf_token HF_TOKEN=your-hf-token
-modal secret create reflector-gpu REFLECTOR_GPU_APIKEY=$(openssl rand -hex 32)
-
-# Deploy each function
-modal deploy reflector_transcriber.py
-modal deploy reflector_diarizer.py
-```
-
-## Monitoring
-
-View your deployed functions and their usage:
- **Modal Dashboard**: https://modal.com/apps
- **Function logs**: Click on any function to view logs
- **Usage**: View compute time and costs in the dashboard
--- a/docs/docs/installation/overview.md
+++ b/docs/docs/installation/overview.md
@@ -1,439 +0,0 @@
---
-sidebar_position: 1
-title: Deployment Guide
---
-
-# Deployment Guide
-
-This guide walks you through deploying Reflector from scratch. Follow these steps in order.
-
-## What You'll Set Up
-
-```mermaid
-flowchart LR
-    User --> Caddy["Caddy (auto-SSL)"]
-    Caddy --> Frontend["Frontend (Next.js)"]
-    Caddy --> Backend["Backend (FastAPI)"]
-    Backend --> PostgreSQL
-    Backend --> Redis
-    Backend --> Workers["Celery Workers"]
-    Workers --> PostgreSQL
-    Workers --> Redis
-    Workers --> GPU["GPU Processing<br/>(Modal.com OR Self-hosted)"]
-```
-
-## Prerequisites
-
-Before starting, you need:
-
- **Production server** - 4+ cores, 8GB+ RAM, public IP
- **Two domain names** - e.g., `app.example.com` (frontend) and `api.example.com` (backend)
- **GPU processing** - Choose one:
-  - Modal.com account, OR
-  - GPU server with NVIDIA GPU (8GB+ VRAM)
- **HuggingFace account** - Free at https://huggingface.co
-  - Accept both Pyannote licenses (required for speaker diarization):
-    - https://huggingface.co/pyannote/speaker-diarization-3.1
-    - https://huggingface.co/pyannote/segmentation-3.0
- **LLM API** - For summaries and topic detection. Choose one:
-  - OpenAI API key at https://platform.openai.com/account/api-keys, OR
-  - Any OpenAI-compatible endpoint (vLLM, LiteLLM, Ollama, etc.)
- **AWS S3 bucket** - For storing audio files and transcripts (see [S3 Setup](#create-s3-bucket-for-transcript-storage) below)
-
-### Optional (for live meeting rooms)
-
- [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
- [ ] **AWS S3 bucket + IAM Role** - For Daily.co recording storage (separate from transcript storage)
-
---
-
-## Configure DNS
-
-```
-Type: A    Name: app    Value: <your-server-ip>
-Type: A    Name: api    Value: <your-server-ip>
-```
-
---
-
-## Deploy GPU Processing
-
-Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
-
-|              | **Modal.com (Cloud)**             | **Self-Hosted GPU**          |
-| ------------ | --------------------------------- | ---------------------------- |
-| **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
-| **Pricing**  | Pay-per-use                       | Fixed infrastructure cost    |
-
-### Option A: Modal.com (Serverless Cloud GPU)
-
-#### Accept HuggingFace Licenses
-
-Visit both pages and click "Accept":
-
- https://huggingface.co/pyannote/speaker-diarization-3.1
- https://huggingface.co/pyannote/segmentation-3.0
-
-Generate a token at https://huggingface.co/settings/tokens
-
-#### Deploy to Modal
-
-There's an install script to help with this setup. It's using modal API to set all necessary moving parts.
-
-As an alternative, all those operations that script does could be performed in modal settings in modal UI.
-
-```bash
-uv tool install modal
-modal setup  # opens browser for authentication
-
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/modal_deployments
-./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
-```
-
-**Save the output** - copy the configuration block, you'll need it soon.
-
-See [Modal Setup](./modal-setup) for troubleshooting and details.
-
-### Option B: Self-Hosted GPU
-
-**Location: YOUR GPU SERVER**
-
-Requires: NVIDIA GPU with 8GB+ VRAM, Ubuntu 22.04+, 40-50GB disk.
-
-See [Self-Hosted GPU Setup](./self-hosted-gpu-setup) for complete instructions. Quick summary:
-
-1. Install NVIDIA drivers and Docker
-2. Clone repository: `git clone https://github.com/monadical-sas/reflector.git`
-3. Configure `.env` with HuggingFace token
-4. Start service with Docker compose
-5. Set up Caddy reverse proxy for HTTPS
-
-**Save your API key and HTTPS URL** - you'll need them soon.
-
---
-
-## Prepare Server
-
-**Location: dedicated reflector server**
-
-### Install Docker
-
-```bash
-ssh user@your-server-ip
-
-curl -fsSL https://get.docker.com | sh
-sudo usermod -aG docker $USER
-
-# Log out and back in for group changes
-exit
-ssh user@your-server-ip
-
-docker --version  # verify
-```
-
-### Firewall
-
-Ensure ports 80 (HTTP) and 443 (HTTPS) are open for inbound traffic. The method varies by cloud provider and OS configuration.
-
-**For live transcription without Daily/Whereby rooms**: WebRTC requires UDP port range 49152-65535 for media traffic.
-
-### Clone Repository
-
-The Docker images contain all application code. You clone the repository for configuration files and the compose definition:
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector
-```
-
---
-
-## Create S3 Bucket for Transcript Storage
-
-Reflector requires AWS S3 to store audio files during processing.
-
-### Create Bucket
-
-```bash
-# Choose a unique bucket name
-BUCKET_NAME="reflector-transcripts-yourname"
-AWS_REGION="us-east-1"
-
-# Create bucket
-aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
-```
-
-### Create IAM User
-
-Create an IAM user with S3 access for Reflector:
-
-1. Go to AWS IAM Console → Users → Create User
-2. Name: `reflector-transcripts`
-3. Attach policy: `AmazonS3FullAccess` (or create a custom policy for just your bucket)
-4. Create access key (Access key ID + Secret access key)
-
-Save these credentials - you'll need them in the next step.
-
---
-
-## Configure Environment
-
-Reflector has two env files:
-
- `server/.env` - Backend configuration
- `www/.env` - Frontend configuration
-
-### Backend Configuration
-
-```bash
-cp server/.env.example server/.env
-nano server/.env
-```
-
-**Required settings:**
-
-```env
-# Database (defaults work with docker-compose.prod.yml)
-DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-
-# Redis
-REDIS_HOST=redis
-CELERY_BROKER_URL=redis://redis:6379/1
-CELERY_RESULT_BACKEND=redis://redis:6379/1
-
-# Your domains
-BASE_URL=https://api.example.com
-CORS_ORIGIN=https://app.example.com
-CORS_ALLOW_CREDENTIALS=true
-
-# Secret key - generate with: openssl rand -hex 32
-SECRET_KEY=<your-generated-secret>
-
-# GPU Processing - choose ONE option:
-
-# Option A: Modal.com (paste from deploy-all.sh output)
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=<from-deploy-all.sh-output>
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=<from-deploy-all.sh-output>
-
-# Option B: Self-hosted GPU (use your GPU server URL and API key)
-# TRANSCRIPT_BACKEND=modal
-# TRANSCRIPT_URL=https://gpu.example.com
-# TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
-# DIARIZATION_BACKEND=modal
-# DIARIZATION_URL=https://gpu.example.com
-# DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
-
-# Storage - where to store audio files and transcripts (requires AWS S3)
-TRANSCRIPT_STORAGE_BACKEND=aws
-TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
-TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
-TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=reflector-media
-TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
-
-# LLM - for generating titles, summaries, and topics
-LLM_API_KEY=sk-your-openai-api-key
-LLM_MODEL=gpt-4o-mini
-# LLM_URL=https://api.openai.com/v1  # Optional: custom endpoint (vLLM, LiteLLM, Ollama, etc.)
-
-# Auth - disable for initial setup (see a dedicated step for authentication)
-AUTH_BACKEND=none
-```
-
-### Frontend Configuration
-
-```bash
-cp www/.env.example www/.env
-nano www/.env
-```
-
-**Required settings:**
-
-```env
-# Your domains
-SITE_URL=https://app.example.com
-API_URL=https://api.example.com
-WEBSOCKET_URL=wss://api.example.com
-SERVER_API_URL=http://server:1250
-
-# NextAuth
-NEXTAUTH_URL=https://app.example.com
-NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
-
-# Disable login requirement for initial setup
-FEATURE_REQUIRE_LOGIN=false
-```
-
---
-
-## Reverse proxy (Caddy or existing)
-
-**If Coolify, Traefik, or nginx already use ports 80/443** (e.g. Coolify on your host): skip Caddy. Start the stack without the Caddy profile (see [Start Services](#start-services) below), then point your proxy at `web:3000` (frontend) and `server:1250` (API).
-
-**If you want Reflector to provide the reverse proxy and SSL:**
-
-```bash
-cp Caddyfile.example Caddyfile
-nano Caddyfile
-```
-
-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.
-
-```
-{$FRONTEND_DOMAIN:app.example.com} {
-    reverse_proxy web:3000
-}
-
-{$API_DOMAIN:api.example.com} {
-    reverse_proxy server:1250
-}
-```
-
---
-
-## Start Services
-
-**Without Caddy** (e.g. Coolify already on 80/443):
-
-```bash
-docker compose -f docker-compose.prod.yml up -d
-```
-
-**With Caddy** (Reflector handles SSL):
-
-```bash
-docker compose -f docker-compose.prod.yml --profile caddy up -d
-```
-
-Wait for containers to start (first run may take 1-2 minutes to pull images and initialize).
-
---
-
-## Verify Deployment
-
-### Check services
-
-```bash
-docker compose -f docker-compose.prod.yml ps
-# All should show "Up"
-```
-
-### Test API
-
-```bash
-curl https://api.example.com/health
-# Should return: {"status":"healthy"}
-```
-
-### Test Frontend
-
- Visit https://app.example.com
- You should see the Reflector interface
- Try uploading an audio file to test transcription
-
-If any verification fails, see [Troubleshooting](#troubleshooting) below.
-
---
-
-## Enable Authentication (Required for Live Rooms)
-
-By default, Reflector is open (no login required). **Authentication is required if you want to use Live Meeting Rooms.**
-
-See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
-
-Quick summary:
-
-1. Deploy Authentik on your server
-2. Create OAuth provider in Authentik
-3. Extract public key for JWT verification
-4. Update `server/.env`: `AUTH_BACKEND=jwt` + `AUTH_JWT_AUDIENCE`
-5. Update `www/.env`: `FEATURE_REQUIRE_LOGIN=true` + Authentik credentials
-6. Mount JWT keys volume and restart services
-
---
-
-## Enable Live Meeting Rooms
-
-**Requires: Authentication Step**
-
-Live rooms require Daily.co and AWS S3. See [Daily.co Setup](./daily-setup) for complete S3/IAM configuration instructions.
-
-Note that Reflector also supports Whereby as a call provider - this doc doesn't cover its setup yet.
-
-Quick config - Add to `server/.env`:
-
-```env
-DEFAULT_VIDEO_PLATFORM=daily
-DAILY_API_KEY=<from-daily.co-dashboard>
-DAILY_SUBDOMAIN=<your-daily-subdomain>
-
-# S3 for recording storage
-DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket>
-DAILYCO_STORAGE_AWS_REGION=us-east-1
-DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
-```
-
-Reload env and restart:
-
-```bash
-docker compose -f docker-compose.prod.yml up -d server worker
-```
-
---
-
-## Troubleshooting
-
-### Check logs for errors
-
-```bash
-docker compose -f docker-compose.prod.yml logs server --tail 20
-docker compose -f docker-compose.prod.yml logs worker --tail 20
-```
-
-### Services won't start
-
-```bash
-docker compose -f docker-compose.prod.yml logs
-```
-
-### CORS errors in browser
-
- Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
- Reload env: `docker compose -f docker-compose.prod.yml up -d server`
-
-### SSL certificate errors (when using Caddy)
-
- Caddy auto-provisions Let's Encrypt certificates
- Ensure ports 80 and 443 are open and not used by another proxy
- Check: `docker compose -f docker-compose.prod.yml logs caddy`
- If port 80 is already in use (e.g. by Coolify), run without Caddy: `docker compose -f docker-compose.prod.yml up -d` and use your existing proxy
-
-### Transcription not working
-
- Check Modal dashboard: https://modal.com/apps
- Verify URLs in `server/.env` match deployed functions
- Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
-
-### "Login required" but auth not configured
-
- Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
- Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
-
-### Database migrations or connectivity issues
-
-Migrations run automatically on server startup. To check database connectivity or debug migration failures:
-
-```bash
-# Check server logs for migration errors
-docker compose -f docker-compose.prod.yml logs server | grep -i -E "(alembic|migration|database|postgres)"
-
-# Verify database connectivity
-docker compose -f docker-compose.prod.yml exec server uv run python -c "from reflector.db import engine; print('DB connected')"
-
-# Manually run migrations (if needed)
-docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
-```
--- a/docs/docs/installation/requirements.md
+++ b/docs/docs/installation/requirements.md
@@ -1,63 +0,0 @@
---
-sidebar_position: 2
-title: System Requirements
---
-
-# System Requirements
-
-This page lists hardware and software requirements. For the complete deployment guide, see [Deployment Guide](./overview).
-
-## Server Requirements
-
-### Minimum Requirements
-
- **CPU**: 4 cores
- **RAM**: 8 GB
- **Storage**: 50 GB SSD
- **OS**: Ubuntu 22.04+ or compatible Linux
- **Network**: Public IP address
-
-### Recommended Requirements
-
- **CPU**: 8+ cores
- **RAM**: 16 GB
- **Storage**: 100 GB SSD
- **Network**: 1 Gbps connection
-
-## Software Requirements
-
- Docker Engine 20.10+
- Docker Compose 2.0+
-
-## External Services
-
-### Required
-
- **Two domain names** - One for frontend (e.g., `app.example.com`), one for API (e.g., `api.example.com`)
- **Modal.com account** - For GPU-accelerated transcription and diarization (free tier available)
- **HuggingFace account** - For Pyannote diarization model access
- **LLM API** - For generating summaries and topic detection. Options:
-  - OpenAI API (https://platform.openai.com/account/api-keys)
-  - Any OpenAI-compatible endpoint (vLLM, LiteLLM, Ollama)
-  - Self-hosted: Phi-4 14B 4-bit recommended (~8GB VRAM)
-
-### Required for Live Meeting Rooms
-
- **Daily.co account** - For video conferencing (free tier available at https://dashboard.daily.co)
- **AWS S3 bucket + IAM Role** - For Daily.co to store recordings
- **Another AWS S3 bucket (optional, can reuse the one above)** - For Reflector to store "compiled" mp3 files and transient diarization process temporary files
-
-### Optional
-
- **AWS S3** - For cloud storage of recordings and transcripts
- **Authentik** - For SSO/OIDC authentication
- **Sentry** - For error tracking
-
-## Development Requirements
-
-For local development only (not required for production deployment):
-
- Node.js 22+ (for frontend development)
- Python 3.12+ (for backend development)
- pnpm (for frontend package management)
- uv (for Python package management)
--- a/docs/docs/installation/self-hosted-gpu-setup.md
+++ b/docs/docs/installation/self-hosted-gpu-setup.md
@@ -1,307 +0,0 @@
---
-sidebar_position: 5
-title: Self-Hosted GPU Setup
---
-
-# Self-Hosted GPU Setup
-
-This guide covers deploying Reflector's GPU processing on your own server instead of Modal.com. For the complete deployment guide, see [Deployment Guide](./overview).
-
-## When to Use Self-Hosted GPU
-
-**Choose self-hosted GPU if you:**
- Have GPU hardware available (NVIDIA required)
- Want full control over processing
- Prefer fixed infrastructure costs over pay-per-use
- Have privacy or data locality requirements
- Need to process audio without external API calls
-
-**Choose Modal.com instead if you:**
- Don't have GPU hardware
- Want zero infrastructure management
- Prefer pay-per-use pricing
- Need instant scaling for variable workloads
-
-See [Modal.com Setup](./modal-setup) for cloud GPU deployment.
-
-## What Gets Deployed
-
-The self-hosted GPU service provides the same API endpoints as Modal:
- `POST /v1/audio/transcriptions` - Whisper transcription
- `POST /v1/audio/transcriptions-from-url` - Transcribe from URL
- `POST /diarize` - Pyannote speaker diarization
- `POST /translate` - Audio translation
-
-Your main Reflector server connects to this service exactly like it connects to Modal - only the URL changes.
-
-## Prerequisites
-
-### Hardware
- **GPU**: NVIDIA GPU with 8GB+ VRAM (tested on Tesla T4 with 15GB)
- **CPU**: 4+ cores recommended
- **RAM**: 8GB minimum, 16GB recommended
- **Disk**: 40-50GB minimum
-
-### Software
- Public IP address
- Domain name with DNS A record pointing to server
-
-### Accounts
- **HuggingFace account** with accepted Pyannote licenses:
-  - https://huggingface.co/pyannote/speaker-diarization-3.1
-  - https://huggingface.co/pyannote/segmentation-3.0
- **HuggingFace access token** from https://huggingface.co/settings/tokens
-
-## Docker Deployment
-
-### Step 1: Install NVIDIA Driver
-
-```bash
-sudo apt update
-sudo apt install -y nvidia-driver-535
-sudo reboot
-
-# After reboot, verify installation
-nvidia-smi
-```
-
-Expected output: GPU details with driver version and CUDA version.
-
-### Step 2: Install Docker
-
-Follow the [official Docker installation guide](https://docs.docker.com/engine/install/ubuntu/) for your distribution.
-
-After installation, add your user to the docker group:
-
-```bash
-sudo usermod -aG docker $USER
-
-# Log out and back in for group changes
-exit
-# SSH back in
-```
-
-### Step 3: Install NVIDIA Container Toolkit
-
-```bash
-# Add NVIDIA repository and install toolkit
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
-  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-
-curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-
-sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
-sudo nvidia-ctk runtime configure --runtime=docker
-sudo systemctl restart docker
-```
-
-### Step 4: Clone Repository and Configure
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/self_hosted
-
-# Create environment file
-cat > .env << EOF
-REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
-HF_TOKEN=your_huggingface_token_here
-EOF
-
-# Note the generated API key - you'll need it for main server config
-cat .env
-```
-
-### Step 5: Build and Start
-
-The repository includes a `compose.yml` file. Build and start:
-
-
-```bash
-# Build image (takes ~5 minutes, downloads ~10GB)
-sudo docker compose build
-
-# Start service
-sudo docker compose up -d
-
-# Wait for startup and verify
-sleep 30
-sudo docker compose logs
-```
-
-Look for: `INFO: Application startup complete. Uvicorn running on http://0.0.0.0:8000`
-
-### Step 7: Verify GPU Access
-
-```bash
-# Check GPU is accessible from container
-sudo docker exec $(sudo docker ps -q) nvidia-smi
-```
-
-Should show GPU with ~3GB VRAM used (models loaded).
-
---
-
-## Configure HTTPS with Caddy
-
-Caddy handles SSL automatically.
-
-### Install Caddy
-
-```bash
-sudo apt install -y debian-keyring debian-archive-keyring apt-transport-https curl
-
-curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | \
-  sudo gpg --dearmor -o /usr/share/keyrings/caddy-stable-archive-keyring.gpg
-
-curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | \
-  sudo tee /etc/apt/sources.list.d/caddy-stable.list
-
-sudo apt update
-sudo apt install -y caddy
-```
-
-### Configure Reverse Proxy
-
-Edit the Caddyfile with your domain:
-
-```bash
-sudo nano /etc/caddy/Caddyfile
-```
-
-Add (replace `gpu.example.com` with your domain):
-
-```
-gpu.example.com {
-    reverse_proxy localhost:8000
-}
-```
-
-Reload Caddy (auto-provisions SSL certificate):
-
-```bash
-sudo systemctl reload caddy
-```
-
-### Verify HTTPS
-
-```bash
-curl -I https://gpu.example.com/docs
-# Should return HTTP/2 200
-```
-
---
-
-## Configure Main Reflector Server
-
-On your main Reflector server, update `server/.env`:
-
-```env
-# GPU Processing - Self-hosted
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://gpu.example.com
-TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://gpu.example.com
-DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
-```
-
-**Note:** The backend type is `modal` because the self-hosted GPU service implements the same API contract as Modal.com. This allows you to switch between cloud and self-hosted GPU processing by only changing the URL and API key.
-
-Restart services to apply:
-
-```bash
-docker compose -f docker-compose.prod.yml restart server worker
-```
-
---
-
-## Service Management
-
-All commands in this section assume you're in `~/reflector/gpu/self_hosted/`.
-
-```bash
-# View logs
-sudo docker compose logs -f
-
-# Restart service
-sudo docker compose restart
-
-# Stop service
-sudo docker compose down
-
-# Check status
-sudo docker compose ps
-```
-
-### Monitor GPU
-
-```bash
-# Check GPU usage
-nvidia-smi
-
-# Watch in real-time
-watch -n 1 nvidia-smi
-```
-
-**Typical GPU memory usage:**
- Idle (models loaded): ~3GB VRAM
- During transcription: ~4-5GB VRAM
-
---
-
-## Troubleshooting
-
-### nvidia-smi fails after driver install
-
-```bash
-# Manually load kernel modules
-sudo modprobe nvidia
-nvidia-smi
-```
-
-### Service fails with "Could not download pyannote pipeline"
-
-1. Verify HF_TOKEN is valid: `echo $HF_TOKEN`
-2. Check model access at https://huggingface.co/pyannote/speaker-diarization-3.1
-3. Update .env with correct token
-4. Restart service: `sudo docker compose restart`
-
-### Cannot connect to HTTPS endpoint
-
-1. Verify DNS resolves: `dig +short gpu.example.com`
-2. Check firewall: `sudo ufw status` (ports 80, 443 must be open)
-3. Check Caddy: `sudo systemctl status caddy`
-4. View Caddy logs: `sudo journalctl -u caddy -n 50`
-
-### SSL certificate not provisioning
-
-Requirements for Let's Encrypt:
- Ports 80 and 443 publicly accessible
- DNS resolves to server's public IP
- Valid domain (not localhost or private IP)
-
-### Docker container won't start
-
-```bash
-# Check logs
-sudo docker compose logs
-
-# Common issues:
-# - Port 8000 already in use
-# - GPU not accessible (nvidia-ctk not configured)
-# - Missing .env file
-```
-
---
-
-## Updating
-
-```bash
-cd ~/reflector/gpu/self_hosted
-git pull
-sudo docker compose build
-sudo docker compose up -d
-```
--- a/docs/docs/installation/setup-standalone.md
+++ b/docs/docs/installation/setup-standalone.md
@@ -1,310 +0,0 @@
---
-sidebar_position: 2
-title: Standalone Local Setup
---
-
-# Standalone Local Setup
-
-**The goal**: a clueless user clones the repo, runs one script, and has a working Reflector instance locally. No cloud accounts, no API keys, no manual env file editing.
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector
-./scripts/setup-standalone.sh
-```
-
-On Ubuntu, the setup script installs Docker automatically if missing.
-
-The script is idempotent — safe to re-run at any time. It detects what's already set up and skips completed steps.
-
-## Prerequisites
-
- Docker with Compose V2 plugin (Docker Desktop, OrbStack, or Docker Engine + compose plugin)
- Mac (Apple Silicon) or Linux
- 16GB+ RAM (32GB recommended for 14B LLM models)
- **Mac only**: [Ollama](https://ollama.com/download) installed (`brew install ollama`)
-
-### Installing Docker (if not already installed)
-
-**Ubuntu**: The setup script runs `install-docker-ubuntu.sh` automatically when Docker is missing. Or run it manually:
-
-```bash
-./scripts/install-docker-ubuntu.sh
-```
-
-**Mac**: Install [Docker Desktop](https://www.docker.com/products/docker-desktop/) or [OrbStack](https://orbstack.dev/).
-
-## What the script does
-
-### 1. LLM inference via Ollama
-
-**Mac**: starts Ollama natively (Metal GPU acceleration). Pulls the LLM model. Docker containers reach it via `host.docker.internal:11435`.
-
-**Linux**: starts containerized Ollama via `docker-compose.standalone.yml` profile (`ollama-gpu` with NVIDIA, `ollama-cpu` without). Pulls model inside the container.
-
-### 2. Environment files
-
-Generates `server/.env` and `www/.env.local` with standalone defaults:
-
-**`server/.env`** — key settings:
-
-| Variable              | Value                                              | Why                                 |
-| --------------------- | -------------------------------------------------- | ----------------------------------- |
-| `DATABASE_URL`        | `postgresql+asyncpg://...@postgres:5432/reflector` | Docker-internal hostname            |
-| `REDIS_HOST`          | `redis`                                            | Docker-internal hostname            |
-| `CELERY_BROKER_URL`   | `redis://redis:6379/1`                             | Docker-internal hostname            |
-| `AUTH_BACKEND`        | `none`                                             | No Authentik in standalone          |
-| `TRANSCRIPT_BACKEND`  | `modal`                                            | HTTP API to self-hosted CPU service |
-| `TRANSCRIPT_URL`      | `http://cpu:8000`                                  | Docker-internal CPU service         |
-| `DIARIZATION_BACKEND` | `modal`                                            | HTTP API to self-hosted CPU service |
-| `DIARIZATION_URL`     | `http://cpu:8000`                                  | Docker-internal CPU service         |
-| `TRANSLATION_BACKEND` | `passthrough`                                      | No Modal                            |
-| `LLM_URL`             | `http://host.docker.internal:11435/v1` (Mac)       | Ollama endpoint                     |
-
-**`www/.env.local`** — key settings:
-
-| Variable                | Value                                      |
-| ----------------------- | ------------------------------------------ |
-| `API_URL`               | `https://localhost:3043` or `https://YOUR_IP:3043` (Linux) |
-| `SERVER_API_URL`        | `http://server:1250`                       |
-| `WEBSOCKET_URL`         | `auto`                                    |
-| `FEATURE_REQUIRE_LOGIN` | `false`                                    |
-| `NEXTAUTH_SECRET`       | `standalone-dev-secret-not-for-production` |
-
-If env files already exist (including symlinks from worktree setup), the script resolves symlinks and ensures all standalone-critical vars are set. Existing vars not related to standalone are preserved.
-
-### 3. Object storage (Garage)
-
-Standalone uses [Garage](https://garagehq.deuxfleurs.fr/) — a lightweight S3-compatible object store running in Docker. The setup script starts Garage, initializes the layout, creates a bucket and access key, and writes the credentials to `server/.env`.
-
-**`server/.env`** — storage settings added by the script:
-
-| Variable                                   | Value                | Why                                   |
-| ------------------------------------------ | -------------------- | ------------------------------------- |
-| `TRANSCRIPT_STORAGE_BACKEND`               | `aws`                | Uses the S3-compatible storage driver |
-| `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL`      | `http://garage:3900` | Docker-internal Garage S3 API         |
-| `TRANSCRIPT_STORAGE_AWS_BUCKET_NAME`       | `reflector-media`    | Created by the script                 |
-| `TRANSCRIPT_STORAGE_AWS_REGION`            | `garage`             | Must match Garage config              |
-| `TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID`     | _(auto-generated)_   | Created by `garage key create`        |
-| `TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY` | _(auto-generated)_   | Created by `garage key create`        |
-
-The `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL` setting enables S3-compatible backends. When set, the storage driver uses path-style addressing and routes all requests to the custom endpoint. When unset (production AWS), behavior is unchanged.
-
-Garage config template lives at `scripts/garage.toml`. The setup script generates `data/garage.toml` (gitignored) with a random RPC secret and mounts it read-only into the container. Single-node, `replication_factor=1`.
-
-> **Note**: Presigned URLs embed the Garage Docker hostname (`http://garage:3900`). This is fine — the server proxies S3 responses to the browser. Modal GPU workers cannot reach internal Garage, but standalone doesn't use Modal.
-
-### 4. Transcription and diarization
-
-Standalone runs the self-hosted ML service (`gpu/self_hosted/`) in a CPU-only Docker container named `cpu`. This is the same FastAPI service used for Modal.com GPU deployments, but built with `Dockerfile.cpu` (no NVIDIA CUDA dependencies). The compose service is named `cpu` (not `gpu`) to make clear it runs without GPU acceleration; the source code lives in `gpu/self_hosted/` because it's shared with the GPU deployment.
-
-The `modal` backend name is reused — it just means "HTTP API client". Setting `TRANSCRIPT_URL` / `DIARIZATION_URL` to `http://cpu:8000` routes requests to the local container instead of Modal.com.
-
-On first start, the service downloads pyannote speaker diarization models (~1GB) from a public S3 bundle. Models are cached in a Docker volume (`gpu_cache`) so subsequent starts are fast. No HuggingFace token or API key needed.
-
-> **Performance**: CPU-only transcription and diarization work but are slow (~15 min for a 3 min file). For faster processing on Linux with NVIDIA GPU, use `--profile gpu-nvidia` instead (see `docker-compose.standalone.yml`).
-
-### 5. Docker services
-
-```bash
-docker compose up -d postgres redis garage cpu server worker beat web
-```
-
-All services start in a single command. Garage and `cpu` are already started by earlier steps but included for idempotency. No Hatchet in standalone mode — LLM processing (summaries, topics, titles) runs via Celery tasks.
-
-### 6. Database migrations
-
-Run automatically by the `server` container on startup (`runserver.sh` calls `alembic upgrade head`). No manual step needed.
-
-### 7. Health check
-
-Verifies:
-
- CPU service responds (transcription + diarization ready)
- Server responds at `http://localhost:1250/health`
- Frontend serves at `http://localhost:3000` (or via Caddy at `https://localhost:3043`)
- LLM endpoint reachable from inside containers
-
-## Services
-
-| Service    | Port       | Purpose                                            |
-| ---------- | ---------- | -------------------------------------------------- |
-| `caddy`    | 3043       | Reverse proxy (HTTPS, self-signed cert)            |
-| `server`   | 1250       | FastAPI backend (runs migrations on start)         |
-| `web`      | 3000       | Next.js frontend                                   |
-| `postgres` | 5432       | PostgreSQL database                                |
-| `redis`    | 6379       | Cache + Celery broker                              |
-| `garage`   | 3900, 3903 | S3-compatible object storage (S3 API + admin API)  |
-| `cpu`      | —          | Self-hosted transcription + diarization (CPU-only) |
-| `worker`   | —          | Celery worker (live pipeline post-processing)      |
-| `beat`     | —          | Celery beat (scheduled tasks)                      |
-
-## Testing programmatically
-
-After the setup script completes, verify the full pipeline (upload, transcription, diarization, LLM summary) via the API:
-
-```bash
-# 1. Create a transcript
-TRANSCRIPT_ID=$(curl -s -X POST 'http://localhost:1250/v1/transcripts' \
-  -H 'Content-Type: application/json' \
-  -d '{"name":"test-upload"}' | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])")
-echo "Created: $TRANSCRIPT_ID"
-
-# 2. Upload an audio file (single-chunk upload)
-curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}/record/upload?chunk_number=0&total_chunks=1" \
-  -X POST -F "chunk=@/path/to/audio.mp3"
-
-# 3. Poll until processing completes (status: ended or error)
-while true; do
-  STATUS=$(curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" \
-    | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
-  echo "Status: $STATUS"
-  case "$STATUS" in ended|error) break;; esac
-  sleep 10
-done
-
-# 4. Check the result
-curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" | python3 -m json.tool
-```
-
-Expected result: status `ended`, auto-generated `title`, `short_summary`, `long_summary`, and `transcript` text with `Speaker 0` / `Speaker 1` labels.
-
-CPU-only processing is slow (~15 min for a 3 min audio file). Diarization finishes in ~3 min, transcription takes the rest.
-
-## Enabling HTTPS (droplet via IP)
-
-To serve Reflector over HTTPS on a droplet accessed by IP (self-signed certificate):
-
-1. **Copy the Caddyfile** (no edits needed — `:443` catches all HTTPS inside container, mapped to host port 3043):
-   ```bash
-   cp Caddyfile.standalone.example Caddyfile
-   ```
-
-2. **Update `www/.env.local`** with HTTPS URLs (port 3043):
-   ```env
-   API_URL=https://YOUR_IP:3043
-   WEBSOCKET_URL=wss://YOUR_IP:3043
-   SITE_URL=https://YOUR_IP:3043
-   NEXTAUTH_URL=https://YOUR_IP:3043
-   ```
-
-3. **Restart services**:
-   ```bash
-   docker compose -f docker-compose.standalone.yml --profile ollama-cpu up -d
-   ```
-   (Use `ollama-gpu` instead of `ollama-cpu` if you have an NVIDIA GPU.)
-
-4. **Access** at `https://YOUR_IP:3043`. The browser will warn about the self-signed cert — click **Advanced** → **Proceed to YOUR_IP (unsafe)**. All traffic (page, API, WebSocket) uses the same origin, so accepting once is enough.
-
-## Troubleshooting
-
-### ERR_SSL_PROTOCOL_ERROR when accessing https://YOUR_IP
-
-You do **not** need a domain — the setup works with an IP address. This error usually means Caddy isn't serving TLS on port 3043. Check in order:
-
-1. **Caddyfile** — must use the `:443` catch-all (container-internal; Docker maps host 3043 → container 443):
-   ```bash
-   cp Caddyfile.standalone.example Caddyfile
-   ```
-
-2. **Firewall** — allow port 3043 (common on DigitalOcean):
-   ```bash
-   sudo ufw allow 3043
-   sudo ufw status
-   ```
-
-3. **Caddy running** — verify and restart:
-   ```bash
-   docker compose -f docker-compose.standalone.yml ps
-   docker compose -f docker-compose.standalone.yml logs caddy --tail 20
-   docker compose -f docker-compose.standalone.yml --profile ollama-cpu up -d
-   ```
-
-4. **Test from the droplet** — if this works, the issue is external (firewall, network):
-   ```bash
-   curl -vk https://localhost:3043
-   ```
-
-5. **localhost works but external IP fails** — Re-run the setup script; it generates a Caddyfile with your droplet IP explicitly, so Caddy provisions the cert at startup:
-   ```bash
-   ./scripts/setup-standalone.sh
-   ```
-   Or manually create `Caddyfile` with your IP (replace 138.197.162.116):
-   ```
-   https://138.197.162.116, localhost {
-       tls internal
-       handle /v1/* { reverse_proxy server:1250 }
-       handle /health { reverse_proxy server:1250 }
-       handle { reverse_proxy web:3000 }
-   }
-   ```
-   Then restart: `docker compose -f docker-compose.standalone.yml --profile ollama-cpu up -d`
-
-6. **Still failing?** Try HTTP (no TLS) — create `Caddyfile`:
-   ```
-   :80 {
-       handle /v1/* { reverse_proxy server:1250 }
-       handle /health { reverse_proxy server:1250 }
-       handle { reverse_proxy web:3000 }
-   }
-   ```
-   Update `www/.env.local`: `API_URL=http://YOUR_IP:3043`, `WEBSOCKET_URL=ws://YOUR_IP:3043`, `SITE_URL=http://YOUR_IP:3043`, `NEXTAUTH_URL=http://YOUR_IP:3043`. Restart, then access `http://YOUR_IP:3043`.
-
-### Docker not ready
-
-If setup fails with "Docker not ready", on Ubuntu run `./scripts/install-docker-ubuntu.sh`. If Docker is installed but you're not root, run `newgrp docker` then run the setup script again.
-
-### Port conflicts (most common issue)
-
-If the frontend or backend behaves unexpectedly (e.g., env vars seem ignored, changes don't take effect), **check for port conflicts first**:
-
-```bash
-# Check what's listening on key ports
-lsof -i :3000   # frontend
-lsof -i :1250   # backend
-lsof -i :5432   # postgres
-lsof -i :3900   # Garage S3 API
-lsof -i :6379   # Redis
-
-# Kill stale processes on a port
-lsof -ti :3000 | xargs kill
-```
-
-Common causes:
-
- A stale `next dev` or `pnpm dev` process from another terminal/worktree
- Another Docker Compose project (different worktree) with containers on the same ports — the setup script only manages its own project; containers from other projects must be stopped manually (`docker ps` to find them, `docker stop` to kill them)
-
-The setup script checks ports 3000, 1250, 5432, 6379, 3900, 3903 for conflicts before starting services. It ignores OrbStack/Docker Desktop port forwarding processes (which always bind these ports but are not real conflicts).
-
-### OrbStack false port-conflict warnings (Mac)
-
-If you use OrbStack as your Docker runtime, `lsof` will show OrbStack binding ports like 3000, 1250, etc. even when no containers are running. This is OrbStack's port forwarding mechanism — not a real conflict. The setup script filters these out automatically.
-
-### Re-enabling authentication
-
-Standalone runs without authentication (`FEATURE_REQUIRE_LOGIN=false`, `AUTH_BACKEND=none`). To re-enable:
-
-1. In `www/.env.local`: set `FEATURE_REQUIRE_LOGIN=true`, uncomment `AUTHENTIK_ISSUER` and `AUTHENTIK_REFRESH_TOKEN_URL`
-2. In `server/.env`: set `AUTH_BACKEND=authentik` (or your backend), configure `AUTH_JWT_AUDIENCE`
-3. Restart: `docker compose -f docker-compose.standalone.yml up -d --force-recreate web server`
-
-## What's NOT covered
-
-These require external accounts and infrastructure that can't be scripted:
-
- **Live meeting rooms** — requires Daily.co account, S3 bucket, IAM roles
- **Authentication** — requires Authentik deployment and OAuth configuration
- **Hatchet workflows** — requires separate Hatchet setup for multitrack processing
- **Production deployment** — see [Deployment Guide](./overview)
-
-## Current status
-
-All steps implemented. The setup script handles everything end-to-end:
-
- Step 1 (Ollama/LLM) — implemented
- Step 2 (environment files) — implemented
- Step 3 (object storage / Garage) — implemented
- Step 4 (transcription/diarization) — implemented (self-hosted GPU service)
- Steps 5-7 (Docker, migrations, health) — implemented
- **Unified script**: `scripts/setup-standalone.sh`
--- a/docs/docs/intro.md
+++ b/docs/docs/intro.md
@@ -1,61 +0,0 @@
---
-sidebar_position: 1
-title: Introduction
---
-
-# Welcome to Reflector
-
-Reflector is a privacy-focused, self-hosted AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, and summarization for audio content and live meetings. With complete control over your data and infrastructure, you can run models on your own hardware (roadmap - currently supports Modal.com for GPU processing).
-
-## What is Reflector?
-
-Reflector is a web application that utilizes AI to process audio content, providing:
-
- **Real-time Transcription**: Convert speech to text using [Whisper](https://github.com/openai/whisper) (multi-language) or [Parakeet](https://github.com/NVIDIA/NeMo) (English) models
- **Speaker Diarization**: Identify and label different speakers using [Pyannote](https://github.com/pyannote/pyannote-audio) 3.1
- **Topic Detection & Summarization**: Extract key topics and generate concise summaries using LLMs
- **Meeting Recording**: Create permanent records of meetings with searchable transcripts
-
-![Reflector Transcript View](/img/reflector-transcript-view.png)
-
-## Features
-
-| Feature                                    | Public Mode | Private Mode |
-|--------------------------------------------|------------|--------------|
-| **Authentication**                         | None required | Required |
-| **Audio Upload**                           | ✅ | ✅ |
-| **Live Microphone Streaming**              | ✅ | ✅ |
-| **Transcription**                          | ✅ | ✅ |
-| **Speaker Diarization**                    | ✅ | ✅ |
-| **Topic Detection**                        | ✅ | ✅ |
-| **Summarization**                          | ✅ | ✅ |
-| **Virtual Meeting Rooms (Whereby, Daily)** | ❌ | ✅ |
-| **Browse Transcripts Page**                | ❌ | ✅ |
-| **Search Functionality**                   | ❌ | ✅ |
-| **Persistent Storage**                     | ❌ | ✅ |
-
-## Architecture Overview
-
-Reflector consists of three main components:
-
- **Frontend**: React application built with Next.js
- **Backend**: Python server using FastAPI
- **Processing**: Scalable GPU workers for ML inference (Modal.com or local)
-
-## Getting Started
-
-Ready to deploy Reflector? Head over to our [Installation Guide](./installation/overview) to set up your own instance.
-
-For a quick overview of how Reflector processes audio, check out our [Pipeline Documentation](./concepts/pipeline).
-
-## Open Source
-
-Reflector is open source software developed by [Monadical](https://monadical.com) and licensed under the **MIT License**. We welcome contributions from the community!
-
- [GitHub Repository](https://github.com/monadical-sas/reflector)
- [Issue Tracker](https://github.com/monadical-sas/reflector/issues)
- [Pull Requests](https://github.com/monadical-sas/reflector/pulls)
-
-## Support
-
-Need help? Reach out to the community through GitHub Discussions.
--- a/docs/docs/pipelines/file-pipeline.md
+++ b/docs/docs/pipelines/file-pipeline.md
@@ -1,83 +0,0 @@
---
-sidebar_position: 2
-title: File Processing Pipeline
---
-
-# File Processing Pipeline
-
-The file processing pipeline handles uploaded audio files, optimizing for accuracy and throughput.
-
-## Pipeline Stages
-
-### 1. Input Stage
-
-**Accepted Formats:**
- MP3 (most common)
- WAV (uncompressed)
- M4A (Apple format)
- WebM (browser recordings)
- MP4 (video with audio track)
-
-**File Validation:**
- Sample rate: Any (will be resampled to 16kHz)
-
-### 2. Pre-processing
-
-**Audio Normalization:**
-```yaml
-# Convert to standard format
- Sample rate: 16kHz (Whisper requirement)
- Channels: Mono
- Bit depth: 16-bit
- Format: WAV
-```
-
-**Noise Reduction (Optional):**
- Background noise removal
- Echo cancellation
- High-pass filter for rumble
-
-### 3. Chunking Strategy
-
-Audio is split into segments for processing:
- Configurable chunk sizes
- Optional silence detection for natural breaks
- Parallel processing of chunks
-
-### 4. Transcription Processing
-
-Transcription uses OpenAI Whisper models via Modal.com or self-hosted GPU:
- Automatic language detection
- Word-level timestamps
-
-### 5. Diarization (Speaker Identification)
-
-Speaker diarization uses Pyannote 3.1:
-
-1. **Voice Activity Detection (VAD)** - Identifies speech segments
-2. **Speaker Embedding** - Extracts voice characteristics
-3. **Clustering** - Groups similar voices
-4. **Segmentation** - Assigns speaker labels to time segments
-
-### 6. Alignment & Merging
-
- Combines transcription with speaker diarization
- Maps speaker labels to transcript segments
- Resolves timing overlaps
- Validates timeline consistency
-
-### 7. Post-processing Chain
-
- **Text Formatting**: Punctuation, capitalization
- **Topic Detection**: LLM-based topic extraction
- **Summarization**: AI-generated summaries and action items
-
-### 8. Storage & Delivery
-
-**File Storage:**
- Original audio: S3 (optional)
- Transcript exports: JSON, VTT, TXT
-
-**Notifications:**
- WebSocket updates during processing
- Webhook notifications on completion (optional)
--- a/docs/docs/reference/api.md
+++ b/docs/docs/reference/api.md
@@ -1,28 +0,0 @@
---
-title: API Reference
---
-
-# API Reference
-
-The complete API documentation is auto-generated from the OpenAPI specification.
-
-## Interactive Documentation
-
-When running Reflector, interactive API docs are available at:
-
- **Swagger UI**: `https://your-api-domain/docs`
- **ReDoc**: `https://your-api-domain/redoc`
-
-## OpenAPI Specification
-
-The raw OpenAPI 3.0 specification can be downloaded from:
-
-```
-https://your-api-domain/openapi.json
-```
-
-A static copy is also available: [openapi.json](/openapi.json)
-
-## Authentication
-
-See [Authentication Setup](../installation/auth-setup) for configuring API authentication.
--- a/docs/docs/roadmap.md
+++ b/docs/docs/roadmap.md
@@ -1,112 +0,0 @@
---
-sidebar_position: 100
-title: Roadmap
---
-
-# Product Roadmap
-
-Our development roadmap for Reflector, focusing on expanding capabilities while maintaining privacy and performance.
-
-## Planned Features
-
-### 🌍 Multi-Language Support Enhancement
-
-**Current State:**
- Whisper supports multi-language transcription
- Parakeet supports English only with high accuracy
-
-**Planned Improvements:**
- Default language selection per room/user
- Automatic language detection improvements
- Multi-language diarization support
- RTL (Right-to-Left) language UI support
- Language-specific post-processing rules
-
-### 🏠 Self-Hosted Room Providers
-
-**Jitsi Integration**
-
-Moving beyond Whereby to support self-hosted video conferencing:
-
- No API keys required
- Complete control over video infrastructure
- Custom branding and configuration
- Lower operational costs
- Enhanced privacy with self-hosted video
-
-**Implementation Plan:**
- WebRTC bridge for Jitsi Meet
- Room management API integration
- Recording synchronization
- Participant tracking
-
-### 📅 Calendar Integration
-
-**Planned Capabilities:**
- Google Calendar synchronization
- Microsoft Outlook integration
- Automatic meeting room creation
- Pre-meeting document preparation
- Post-meeting transcript delivery
- Recurring meeting support
-
-**Features:**
- Auto-join scheduled meetings
- Calendar-based access control
- Meeting agenda import
- Action item export to calendar
-
-## Future Considerations
-
-### Enhanced Analytics
- Meeting insights dashboard
- Speaker participation metrics
- Topic trends over time
- Team collaboration patterns
-
-### Advanced AI Features
- Real-time sentiment analysis
- Emotion detection
- Meeting quality scores
- Automated coaching suggestions
-
-### Integration Ecosystem
- Slack/Teams notifications
- CRM integration (Salesforce, HubSpot)
- Project management tools (Jira, Asana)
- Knowledge bases (Notion, Confluence)
-
-### Performance Improvements
- WebAssembly for client-side processing
- Edge computing support
- 5G network optimization
- Blockchain for transcript verification
-
-## Contributing
-
-We welcome community contributions! Areas where you can help:
-
-1. **Language Support**: Add support for your language
-2. **Integrations**: Connect with your favorite tools
-3. **Models**: Fine-tune models for specific domains
-4. **Documentation**: Improve guides and examples
-
-See our [Contributing Guide](https://github.com/monadical-sas/reflector/blob/main/CONTRIBUTING.md) for details.
-
-## Timeline
-
-We don't provide specific dates as development depends on community contributions and priorities. Features are generally released when they're ready and properly tested.
-
-## Feature Requests
-
-Have an idea for Reflector? We'd love to hear it!
-
- [Open a GitHub Issue](https://github.com/monadical-sas/reflector/issues/new)
- [Join our Discord](#)
- [Email us](mailto:reflector@monadical.com)
-
-## Stay Updated
-
- Watch our [GitHub repository](https://github.com/monadical-sas/reflector)
- Follow our [blog](#)
- Subscribe to our [newsletter](#)
--- a/docs/docusaurus.config.ts
+++ b/docs/docusaurus.config.ts
@@ -1,163 +0,0 @@
-import {themes as prismThemes} from 'prism-react-renderer';
-import type {Config} from '@docusaurus/types';
-import type * as Preset from '@docusaurus/preset-classic';
-import type * as OpenApiPlugin from 'docusaurus-plugin-openapi-docs';
-
-const config: Config = {
-  title: 'Reflector',
-  tagline: 'AI-powered audio transcription and meeting analysis platform',
-  favicon: 'img/favicon.ico',
-
-  url: 'https://monadical-sas.github.io',
-  baseUrl: '/',
-
-  organizationName: 'monadical-sas',
-  projectName: 'reflector',
-
-  onBrokenLinks: 'throw',
-  onBrokenMarkdownLinks: 'warn',
-
-  markdown: {
-    mermaid: true,
-  },
-
-  i18n: {
-    defaultLocale: 'en',
-    locales: ['en'],
-  },
-
-  presets: [
-    [
-      'classic',
-      {
-        docs: {
-          sidebarPath: './sidebars.ts',
-          editUrl: 'https://github.com/monadical-sas/reflector/tree/main/docs/',
-        },
-        blog: false,
-        theme: {
-          customCss: './src/css/custom.css',
-        },
-      } satisfies Preset.Options,
-    ],
-  ],
-
-  plugins: [
-    [
-      'docusaurus-plugin-openapi-docs',
-      {
-        id: 'openapi',
-        docsPluginId: 'classic',
-        config: {
-          reflectorapi: {
-            specPath: 'static/openapi.json', // Use local file fetched by script
-            outputDir: 'docs/reference/api-generated',
-            sidebarOptions: {
-              groupPathsBy: 'tag',
-              categoryLinkSource: 'tag',
-            },
-            downloadUrl: '/openapi.json',
-            hideSendButton: false,
-            showExtensions: true,
-          },
-        } satisfies OpenApiPlugin.Options,
-      },
-    ],
-  ],
-
-  themes: ['docusaurus-theme-openapi-docs', '@docusaurus/theme-mermaid'],
-
-  themeConfig: {
-    image: 'img/reflector-social-card.jpg',
-    colorMode: {
-      defaultMode: 'light',
-      disableSwitch: false,
-      respectPrefersColorScheme: true,
-    },
-    navbar: {
-      title: 'Reflector',
-      logo: {
-        alt: 'Reflector Logo',
-        src: 'img/reflector-logo.svg',
-      },
-      items: [
-        {
-          type: 'docSidebar',
-          sidebarId: 'tutorialSidebar',
-          position: 'left',
-          label: 'Documentation',
-        },
-        {
-          to: '/docs/reference/api',
-          label: 'API',
-          position: 'left',
-        },
-        {
-          href: 'https://github.com/monadical-sas/reflector',
-          label: 'GitHub',
-          position: 'right',
-        },
-      ],
-    },
-    footer: {
-      style: 'dark',
-      links: [
-        {
-          title: 'Documentation',
-          items: [
-            {
-              label: 'Introduction',
-              to: '/docs/intro',
-            },
-            {
-              label: 'Installation',
-              to: '/docs/installation/overview',
-            },
-            {
-              label: 'API Reference',
-              to: '/docs/reference/api',
-            },
-          ],
-        },
-        {
-          title: 'Resources',
-          items: [
-            {
-              label: 'Architecture',
-              to: '/docs/concepts/overview',
-            },
-            {
-              label: 'Pipelines',
-              to: '/docs/concepts/pipeline',
-            },
-            {
-              label: 'Roadmap',
-              to: '/docs/roadmap',
-            },
-          ],
-        },
-        {
-          title: 'More',
-          items: [
-            {
-              label: 'GitHub',
-              href: 'https://github.com/monadical-sas/reflector',
-            },
-            {
-              label: 'Docker Hub',
-              href: 'https://hub.docker.com/r/reflector/backend',
-            },
-          ],
-        },
-      ],
-      copyright: `Copyright © ${new Date().getFullYear()} <a href="https://monadical.com" target="_blank" rel="noopener noreferrer">Monadical</a>. Licensed under MIT. Built with Docusaurus.`,
-    },
-    prism: {
-      theme: prismThemes.github,
-      darkTheme: prismThemes.dracula,
-      additionalLanguages: ['python', 'bash', 'docker', 'yaml'],
-    },
-  } satisfies Preset.ThemeConfig,
-};
-
-export default config;
--- a/docs/package.json
+++ b/docs/package.json
@@ -1,64 +0,0 @@
-{
-  "name": "docs",
-  "version": "0.0.0",
-  "private": true,
-  "scripts": {
-    "docusaurus": "docusaurus",
-    "start": "docusaurus start",
-    "build": "docusaurus build",
-    "swizzle": "docusaurus swizzle",
-    "deploy": "docusaurus deploy",
-    "clear": "docusaurus clear",
-    "serve": "docusaurus serve",
-    "write-translations": "docusaurus write-translations",
-    "write-heading-ids": "docusaurus write-heading-ids",
-    "typecheck": "tsc",
-    "fetch-openapi": "./scripts/fetch-openapi.sh",
-    "gen-api-docs": "pnpm run fetch-openapi && docusaurus gen-api-docs reflector",
-    "prebuild": "pnpm run fetch-openapi"
-  },
-  "dependencies": {
-    "@docusaurus/core": "3.9.2",
-    "@docusaurus/preset-classic": "3.9.2",
-    "@docusaurus/theme-mermaid": "3.9.2",
-    "@mdx-js/react": "^3.1.1",
-    "clsx": "^2.1.1",
-    "docusaurus-plugin-openapi-docs": "^4.7.1",
-    "docusaurus-theme-openapi-docs": "^4.7.1",
-    "prism-react-renderer": "^2.4.1",
-    "react": "^19.2.4",
-    "react-dom": "^19.2.4"
-  },
-  "devDependencies": {
-    "@docusaurus/module-type-aliases": "3.9.2",
-    "@docusaurus/tsconfig": "3.9.2",
-    "@docusaurus/types": "3.9.2",
-    "typescript": "~5.9.3"
-  },
-  "browserslist": {
-    "production": [
-      ">0.5%",
-      "not dead",
-      "not op_mini all"
-    ],
-    "development": [
-      "last 3 chrome version",
-      "last 3 firefox version",
-      "last 5 safari version"
-    ]
-  },
-  "engines": {
-    "node": ">=18.0"
-  },
-  "pnpm": {
-    "overrides": {
-      "minimatch@<3.1.4": "3.1.5",
-      "minimatch@>=5.0.0 <5.1.8": "5.1.8",
-      "minimatch@>=9.0.0 <9.0.7": "9.0.7",
-      "lodash@<4.17.23": "4.17.23",
-      "js-yaml@<4.1.1": "4.1.1",
-      "gray-matter": "github:jonschlinkert/gray-matter#234163e",
-      "serialize-javascript": "7.0.4"
-    }
-  }
-}
--- a/docs/pnpm-lock.yaml
+++ b/docs/pnpm-lock.yaml
--- a/docs/scripts/fetch-openapi.sh
+++ b/docs/scripts/fetch-openapi.sh
@@ -1,115 +0,0 @@
-#!/bin/bash
-
-# Script to fetch OpenAPI specification from FastAPI backend
-# Used during documentation build process
-
-set -e
-
-echo "📡 Fetching OpenAPI specification from FastAPI backend..."
-
-# Colors for output
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-NC='\033[0m' # No Color
-
-# Configuration
-BACKEND_DIR="../server"
-OPENAPI_OUTPUT="./static/openapi.json"
-SERVER_PORT=1250  # Reflector uses port 1250 by default
-MAX_WAIT=30
-
-# Check if backend directory exists
-if [ ! -d "$BACKEND_DIR" ]; then
-    echo -e "${RED}Error: Backend directory not found at $BACKEND_DIR${NC}"
-    exit 1
-fi
-
-# Function to check if server is running
-check_server() {
-    curl -s -o /dev/null -w "%{http_code}" "http://localhost:${SERVER_PORT}/openapi.json" 2>/dev/null
-}
-
-# Function to cleanup on exit
-cleanup() {
-    if [ ! -z "$SERVER_PID" ]; then
-        echo -e "\n${YELLOW}Stopping FastAPI server (PID: $SERVER_PID)...${NC}"
-        kill $SERVER_PID 2>/dev/null || true
-        wait $SERVER_PID 2>/dev/null || true
-    fi
-}
-
-# Set trap to cleanup on exit
-trap cleanup EXIT INT TERM
-
-# Change to backend directory
-cd "$BACKEND_DIR"
-
-# Check if uv is installed
-if ! command -v uv &> /dev/null; then
-    echo -e "${YELLOW}uv not found, checking for python...${NC}"
-    if command -v python3 &> /dev/null; then
-        PYTHON_CMD="python3"
-    elif command -v python &> /dev/null; then
-        PYTHON_CMD="python"
-    else
-        echo -e "${RED}Error: Neither uv nor python found${NC}"
-        exit 1
-    fi
-    RUN_CMD="$PYTHON_CMD -m"
-else
-    RUN_CMD="uv run -m"
-fi
-
-# Start the FastAPI server in the background (let it use default port 1250)
-echo -e "${YELLOW}Starting FastAPI server...${NC}"
-$RUN_CMD reflector.app > /dev/null 2>&1 &
-SERVER_PID=$!
-
-# Wait for server to be ready
-echo -n "Waiting for server to be ready"
-WAITED=0
-while [ $WAITED -lt $MAX_WAIT ]; do
-    if [ "$(check_server)" = "200" ]; then
-        echo -e " ${GREEN}✓${NC}"
-        break
-    fi
-    echo -n "."
-    sleep 1
-    WAITED=$((WAITED + 1))
-done
-
-if [ $WAITED -ge $MAX_WAIT ]; then
-    echo -e " ${RED}✗${NC}"
-    echo -e "${RED}Error: Server failed to start within ${MAX_WAIT} seconds${NC}"
-    exit 1
-fi
-
-# Change back to docs directory
-cd - > /dev/null
-
-# Create static directory if it doesn't exist
-mkdir -p "$(dirname "$OPENAPI_OUTPUT")"
-
-# Fetch the OpenAPI specification
-echo -e "${YELLOW}Fetching OpenAPI specification...${NC}"
-if curl -s "http://localhost:${SERVER_PORT}/openapi.json" -o "$OPENAPI_OUTPUT"; then
-    echo -e "${GREEN}✓ OpenAPI specification saved to $OPENAPI_OUTPUT${NC}"
-
-    # Validate JSON
-    if command -v jq &> /dev/null; then
-        if jq empty "$OPENAPI_OUTPUT" 2>/dev/null; then
-            echo -e "${GREEN}✓ OpenAPI specification is valid JSON${NC}"
-            # Pretty print the JSON
-            jq . "$OPENAPI_OUTPUT" > "${OPENAPI_OUTPUT}.tmp" && mv "${OPENAPI_OUTPUT}.tmp" "$OPENAPI_OUTPUT"
-        else
-            echo -e "${RED}Error: Invalid JSON in OpenAPI specification${NC}"
-            exit 1
-        fi
-    fi
-else
-    echo -e "${RED}Error: Failed to fetch OpenAPI specification${NC}"
-    exit 1
-fi
-
-echo -e "${GREEN}✅ OpenAPI specification successfully fetched!${NC}"
--- a/docs/sidebars.ts
+++ b/docs/sidebars.ts
@@ -1,58 +0,0 @@
-import type {SidebarsConfig} from '@docusaurus/plugin-content-docs';
-
-const sidebars: SidebarsConfig = {
-  tutorialSidebar: [
-    'intro',
-    {
-      type: 'category',
-      label: 'Concepts',
-      collapsed: false,
-      items: [
-        'concepts/overview',
-        'concepts/modes',
-        'concepts/pipeline',
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Installation',
-      collapsed: false,
-      items: [
-        'installation/overview',
-        'installation/requirements',
-        'installation/docker-setup',
-        'installation/modal-setup',
-        'installation/self-hosted-gpu-setup',
-        'installation/auth-setup',
-        'installation/daily-setup',
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Pipelines',
-      items: [
-        'pipelines/file-pipeline',
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Reference',
-      items: [
-        {
-          type: 'category',
-          label: 'API',
-          items: [
-            {
-              type: 'link',
-              label: 'OpenAPI Reference',
-              href: '/docs/reference/api',
-            },
-          ],
-        },
-      ],
-    },
-    'roadmap',
-  ],
-};
-
-export default sidebars;
--- a/docs/src/components/HomepageFeatures/index.tsx
+++ b/docs/src/components/HomepageFeatures/index.tsx
@@ -1,70 +0,0 @@
-import clsx from 'clsx';
-import Heading from '@theme/Heading';
-import styles from './styles.module.css';
-
-type FeatureItem = {
-  title: string;
-  Svg: React.ComponentType<React.ComponentProps<'svg'>>;
-  description: JSX.Element;
-};
-
-const FeatureList: FeatureItem[] = [
-  {
-    title: 'Easy to Use',
-    Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
-    description: (
-      <>
-        Docusaurus was designed from the ground up to be easily installed and
-        used to get your website up and running quickly.
-      </>
-    ),
-  },
-  {
-    title: 'Focus on What Matters',
-    Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
-    description: (
-      <>
-        Docusaurus lets you focus on your docs, and we&apos;ll do the chores. Go
-        ahead and move your docs into the <code>docs</code> directory.
-      </>
-    ),
-  },
-  {
-    title: 'Powered by React',
-    Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
-    description: (
-      <>
-        Extend or customize your website layout by reusing React. Docusaurus can
-        be extended while reusing the same header and footer.
-      </>
-    ),
-  },
-];
-
-function Feature({title, Svg, description}: FeatureItem) {
-  return (
-    <div className={clsx('col col--4')}>
-      <div className="text--center">
-        <Svg className={styles.featureSvg} role="img" />
-      </div>
-      <div className="text--center padding-horiz--md">
-        <Heading as="h3">{title}</Heading>
-        <p>{description}</p>
-      </div>
-    </div>
-  );
-}
-
-export default function HomepageFeatures(): JSX.Element {
-  return (
-    <section className={styles.features}>
-      <div className="container">
-        <div className="row">
-          {FeatureList.map((props, idx) => (
-            <Feature key={idx} {...props} />
-          ))}
-        </div>
-      </div>
-    </section>
-  );
-}
--- a/docs/src/components/HomepageFeatures/styles.module.css
+++ b/docs/src/components/HomepageFeatures/styles.module.css
@@ -1,11 +0,0 @@
-.features {
-  display: flex;
-  align-items: center;
-  padding: 2rem 0;
-  width: 100%;
-}
-
-.featureSvg {
-  height: 200px;
-  width: 200px;
-}
--- a/docs/src/css/custom.css
+++ b/docs/src/css/custom.css
@@ -1,46 +0,0 @@
-/**
- * Reflector Documentation Theme
- * Based on frontend colors from www/app/styles/theme.ts
- */
-
-@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700&display=swap');
-
-:root {
-  --ifm-color-primary: #3158E2;
-  --ifm-color-primary-dark: #2847C9;
-  --ifm-color-primary-darker: #2442BF;
-  --ifm-color-primary-darkest: #1D369C;
-  --ifm-color-primary-light: #4A6FE5;
-  --ifm-color-primary-lighter: #5F81E8;
-  --ifm-color-primary-lightest: #8DA6F0;
-
-  --ifm-background-color: #FFFFFF;
-  --ifm-background-surface-color: #F4F4F4;
-  --ifm-font-color-base: #1A202C;
-  --ifm-font-color-secondary: #838383;
-
-  --ifm-code-font-size: 95%;
-  --docusaurus-highlighted-code-line-bg: rgba(49, 88, 226, 0.1);
-
-  --ifm-font-family-base: 'Poppins', system-ui, -apple-system, sans-serif;
-  --ifm-font-family-monospace: 'Fira Code', 'Monaco', 'Consolas', monospace;
-  --ifm-navbar-background-color: #FFFFFF;
-  --ifm-heading-font-weight: 600;
-}
-
-[data-theme='dark'] {
-  --ifm-color-primary: #B1CBFF;
-  --ifm-color-primary-dark: #91B3FF;
-  --ifm-color-primary-darker: #81A7FF;
-  --ifm-color-primary-darkest: #5189FF;
-  --ifm-color-primary-light: #D1DFFF;
-  --ifm-color-primary-lighter: #E1EBFF;
-  --ifm-color-primary-lightest: #F0F5FF;
-
-  --ifm-background-color: #0C0D0E;
-  --ifm-background-surface-color: #1A202C;
-  --ifm-font-color-base: #E2E8F0;
-  --ifm-font-color-secondary: #A0AEC0;
-  --docusaurus-highlighted-code-line-bg: rgba(177, 203, 255, 0.1);
-  --ifm-navbar-background-color: #1A202C;
-}
--- a/docs/src/pages/index.module.css
+++ b/docs/src/pages/index.module.css
@@ -1,23 +0,0 @@
-/**
- * CSS files with the .module.css suffix will be treated as CSS modules
- * and scoped locally.
- */
-
-.heroBanner {
-  padding: 4rem 0;
-  text-align: center;
-  position: relative;
-  overflow: hidden;
-}
-
-@media screen and (max-width: 996px) {
-  .heroBanner {
-    padding: 2rem;
-  }
-}
-
-.buttons {
-  display: flex;
-  align-items: center;
-  justify-content: center;
-}
--- a/docs/src/pages/index.tsx
+++ b/docs/src/pages/index.tsx
@@ -1,7 +0,0 @@
-import React from 'react';
-import { Redirect } from '@docusaurus/router';
-import useBaseUrl from '@docusaurus/useBaseUrl';
-
-export default function Home(): JSX.Element {
-  return <Redirect to={useBaseUrl('/docs/intro')} />;
-}
--- a/docs/src/pages/markdown-page.md
+++ b/docs/src/pages/markdown-page.md
@@ -1,7 +0,0 @@
---
-title: Markdown page example
---
-
-# Markdown page example
-
-You don't need React to write simple standalone pages.
--- a/docs/static/.nojekyll
+++ b/docs/static/.nojekyll
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-  <path d="M344.38682,555.65836h53.08286V529.11693H344.38682Z" transform="translate(-35.5 -118.5)" fill="#3ecc5f" fill-rule="evenodd" />
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-</svg>
--- a/docs/static/openapi.json
+++ b/docs/static/openapi.json
--- a/docs/transcript.md
+++ b/docs/transcript.md
@@ -1,241 +0,0 @@
-# Transcript Formats
-
-The Reflector API provides multiple output formats for transcript data through the `transcript_format` query parameter on the GET `/v1/transcripts/{id}` endpoint.
-
-## Overview
-
-When retrieving a transcript, you can specify the desired format using the `transcript_format` query parameter. The API supports four formats optimized for different use cases:
-
- **text** - Plain text with speaker names (default)
- **text-timestamped** - Timestamped text with speaker names
- **webvtt-named** - WebVTT subtitle format with participant names
- **json** - Structured JSON segments with full metadata
-
-All formats include participant information when available, resolving speaker IDs to actual names.
-
-## Query Parameter Usage
-
-```
-GET /v1/transcripts/{id}?transcript_format={format}
-```
-
-### Parameters
-
- `transcript_format` (optional): The desired output format
-  - Type: `"text" | "text-timestamped" | "webvtt-named" | "json"`
-  - Default: `"text"`
-
-## Format Descriptions
-
-### Text Format (`text`)
-
-**Use case:** Simple, human-readable transcript for display or export.
-
-**Format:** Speaker names followed by their dialogue, one line per segment.
-
-**Example:**
-```
-John Smith: Hello everyone
-Jane Doe: Hi there
-John Smith: How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=text
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "text",
-  "transcript": "John Smith: Hello everyone\nJane Doe: Hi there\nJohn Smith: How are you today?",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### Text Timestamped Format (`text-timestamped`)
-
-**Use case:** Transcript with timing information for navigation or reference.
-
-**Format:** `[MM:SS]` timestamp prefix before each speaker and dialogue.
-
-**Example:**
-```
-[00:00] John Smith: Hello everyone
-[00:05] Jane Doe: Hi there
-[00:12] John Smith: How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=text-timestamped
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "text-timestamped",
-  "transcript": "[00:00] John Smith: Hello everyone\n[00:05] Jane Doe: Hi there\n[00:12] John Smith: How are you today?",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### WebVTT Named Format (`webvtt-named`)
-
-**Use case:** Subtitle files for video players, accessibility tools, or video editing.
-
-**Format:** Standard WebVTT subtitle format with voice tags using participant names.
-
-**Example:**
-```
-WEBVTT
-
-00:00:00.000 --> 00:00:05.000
-<v John Smith>Hello everyone
-
-00:00:05.000 --> 00:00:12.000
-<v Jane Doe>Hi there
-
-00:00:12.000 --> 00:00:18.000
-<v John Smith>How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=webvtt-named
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "webvtt-named",
-  "transcript": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v John Smith>Hello everyone\n\n...",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### JSON Format (`json`)
-
-**Use case:** Programmatic access with full timing and speaker metadata.
-
-**Format:** Array of segment objects with speaker information, text content, and precise timing.
-
-**Example:**
-```json
-[
-  {
-    "speaker": 0,
-    "speaker_name": "John Smith",
-    "text": "Hello everyone",
-    "start": 0.0,
-    "end": 5.0
-  },
-  {
-    "speaker": 1,
-    "speaker_name": "Jane Doe",
-    "text": "Hi there",
-    "start": 5.0,
-    "end": 12.0
-  },
-  {
-    "speaker": 0,
-    "speaker_name": "John Smith",
-    "text": "How are you today?",
-    "start": 12.0,
-    "end": 18.0
-  }
-]
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=json
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "json",
-  "transcript": [
-    {
-      "speaker": 0,
-      "speaker_name": "John Smith",
-      "text": "Hello everyone",
-      "start": 0.0,
-      "end": 5.0
-    },
-    {
-      "speaker": 1,
-      "speaker_name": "Jane Doe",
-      "text": "Hi there",
-      "start": 5.0,
-      "end": 12.0
-    }
-  ],
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-## Response Structure
-
-All formats return the same base transcript metadata with an additional `transcript_format` field and format-specific `transcript` field:
-
-### Common Fields
-
- `id`: Transcript identifier
- `user_id`: Owner user ID (if authenticated)
- `name`: Transcript name
- `status`: Processing status
- `locked`: Whether transcript is locked for editing
- `duration`: Total duration in seconds
- `title`: Auto-generated or custom title
- `short_summary`: Brief summary
- `long_summary`: Detailed summary
- `created_at`: Creation timestamp
- `share_mode`: Access control setting
- `source_language`: Original audio language
- `target_language`: Translation target language
- `reviewed`: Whether transcript has been reviewed
- `meeting_id`: Associated meeting ID (if applicable)
- `source_kind`: Source type (live, file, room)
- `room_id`: Associated room ID (if applicable)
- `audio_deleted`: Whether audio has been deleted
- `participants`: Array of participant objects with speaker mappings
-
-### Format-Specific Fields
-
- `transcript_format`: The format identifier (discriminator field)
- `transcript`: The formatted transcript content (string for text/webvtt formats, array for json format)
-
-## Speaker Name Resolution
-
-All formats resolve speaker IDs to participant names when available:
-
- If a participant exists for the speaker ID, their name is used
- If no participant exists, a default name like "Speaker 0" is generated
- Speaker IDs are integers (0, 1, 2, etc.) assigned during diarization
--- a/docs/tsconfig.json
+++ b/docs/tsconfig.json
@@ -1,8 +0,0 @@
-{
-  // This file is not used in compilation. It is here just for a nice editor experience.
-  "extends": "@docusaurus/tsconfig",
-  "compilerOptions": {
-    "baseUrl": "."
-  },
-  "exclude": [".docusaurus", "build"]
-}
--- a/docsv2/selfhosted-architecture.md
+++ b/docsv2/selfhosted-architecture.md
@@ -1,472 +0,0 @@
-# How the Self-Hosted Setup Works
-
-This document explains the internals of the self-hosted deployment: how the setup script orchestrates everything, how the Docker Compose profiles work, how services communicate, and how configuration flows from flags to running containers.
-
-> For quick-start instructions and flag reference, see [Self-Hosted Production Deployment](selfhosted-production.md).
-
-## Table of Contents
-
- [Overview](#overview)
- [The Setup Script Step by Step](#the-setup-script-step-by-step)
- [Docker Compose Profile System](#docker-compose-profile-system)
- [Service Architecture](#service-architecture)
- [Configuration Flow](#configuration-flow)
- [Storage Architecture](#storage-architecture)
- [SSL/TLS and Reverse Proxy](#ssltls-and-reverse-proxy)
- [Build vs Pull Workflow](#build-vs-pull-workflow)
- [Background Task Processing](#background-task-processing)
- [Network and Port Layout](#network-and-port-layout)
-
---
-
-## Overview
-
-The self-hosted deployment runs the entire Reflector platform on a single server using Docker Compose. A single bash script (`scripts/setup-selfhosted.sh`) handles all configuration and orchestration. The key design principles are:
-
- **One command to deploy** — flags select which features to enable
- **Idempotent** — safe to re-run without losing existing configuration
- **Profile-based composition** — Docker Compose profiles activate optional services
- **No external dependencies required** — with `--garage` and `--ollama-*`, everything runs locally
-
-## The Setup Script Step by Step
-
-The script (`scripts/setup-selfhosted.sh`) runs 7 sequential steps. Here's what each one does and why.
-
-### Step 0: Prerequisites
-
-Validates the environment before doing anything:
-
- **Docker Compose V2** — checks `docker compose version` output (not the legacy `docker-compose`)
- **Docker daemon** — verifies `docker info` succeeds
- **NVIDIA GPU** — only checked when `--gpu` or `--ollama-gpu` is used; runs `nvidia-smi` to confirm drivers are installed
- **Compose file** — verifies `docker-compose.selfhosted.yml` exists at the expected path
-
-If any check fails, the script exits with a clear error message and remediation steps.
-
-### Step 1: Generate Secrets
-
-Creates cryptographic secrets needed by the backend and frontend:
-
- **`SECRET_KEY`** — used by the FastAPI server for session signing (64 hex chars via `openssl rand -hex 32`)
- **`NEXTAUTH_SECRET`** — used by Next.js NextAuth for JWT signing
-
-Secrets are only generated if they don't already exist or are still set to the placeholder value `changeme`. This is what makes the script idempotent for secrets.
-
-If `--password` is passed, this step also generates a PBKDF2-SHA256 password hash from the provided password. The hash is computed using Python's stdlib (`hashlib.pbkdf2_hmac`) with 100,000 iterations and a random 16-byte salt, producing a hash in the format `pbkdf2:sha256:100000$<salt_hex>$<hash_hex>`.
-
-### Step 2: Generate `server/.env`
-
-Creates or updates the backend environment file from `server/.env.selfhosted.example`. Sets:
-
- **Infrastructure** — PostgreSQL URL, Redis host, Celery broker (all pointing to Docker-internal hostnames)
- **Public URLs** — `BASE_URL` and `CORS_ORIGIN` computed from the domain (if `--domain`), IP (if detected on Linux), or `localhost`
- **WebRTC** — `WEBRTC_HOST` set to the server's LAN IP so browsers can reach UDP ICE candidates
- **Specialized models** — always points to `http://transcription:8000` (the Docker network alias shared by GPU and CPU containers)
- **HuggingFace token** — prompts interactively for pyannote model access; writes to root `.env` so Docker Compose can inject it into GPU/CPU containers
- **LLM** — if `--ollama-*` is used, configures `LLM_URL` pointing to the Ollama container. Otherwise, warns that the user needs to configure an external LLM
- **Public mode** — sets `PUBLIC_MODE=true` so the app is accessible without authentication by default
- **Password auth** — if `--password` is passed, sets `AUTH_BACKEND=password`, `PUBLIC_MODE=false`, `ADMIN_EMAIL=admin@localhost`, and `ADMIN_PASSWORD_HASH` (the hash generated in Step 1). The admin user is provisioned in the database on container startup via `runserver.sh`
-
-The script uses `env_set` for each variable, which either updates an existing line or appends a new one. This means re-running the script updates values in-place without duplicating keys.
-
-### Step 3: Generate `www/.env`
-
-Creates or updates the frontend environment file from `www/.env.selfhosted.example`. Sets:
-
- **`SITE_URL` / `NEXTAUTH_URL` / `API_URL`** — all set to the same public-facing URL (with `https://` if Caddy is enabled)
- **`WEBSOCKET_URL`** — set to `auto`, which tells the frontend to derive the WebSocket URL from the page URL automatically
- **`SERVER_API_URL`** — always `http://server:1250` (Docker-internal, used for server-side rendering)
- **`KV_URL`** — Redis URL for Next.js caching
- **`FEATURE_REQUIRE_LOGIN`** — `false` by default (matches `PUBLIC_MODE=true` on the backend)
- **Password auth** — if `--password` is passed, sets `FEATURE_REQUIRE_LOGIN=true` and `AUTH_PROVIDER=credentials`, which tells the frontend to use a local email/password login form instead of Authentik OAuth
-
-### Step 4: Storage Setup
-
-Branches based on whether `--garage` was passed:
-
-**With `--garage` (local S3):**
-
-1. Generates `data/garage.toml` from a template, injecting a random RPC secret
-2. Starts only the Garage container (`docker compose --profile garage up -d garage`)
-3. Waits for the Garage admin API to respond on port 3903
-4. Assigns the node to a storage layout (1GB capacity, zone `dc1`)
-5. Creates the `reflector-media` bucket
-6. Creates an access key named `reflector` and grants it read/write on the bucket
-7. Writes all S3 credentials (`ENDPOINT_URL`, `BUCKET_NAME`, `REGION`, `ACCESS_KEY_ID`, `SECRET_ACCESS_KEY`) to `server/.env`
-
-The Garage endpoint is `http://garage:3900` (Docker-internal), and the region is set to `garage` (arbitrary, Garage ignores it). The boto3 client uses path-style addressing when an endpoint URL is configured, which is required for S3-compatible services like Garage.
-
-**Without `--garage` (external S3):**
-
-1. Checks `server/.env` for the four required S3 variables
-2. If any are missing, prompts interactively for each one
-3. Optionally prompts for an endpoint URL (for MinIO, Backblaze B2, etc.)
-
-### Step 5: Caddyfile
-
-Only runs when `--caddy` or `--domain` is used. Generates a Caddy configuration file:
-
-**With `--domain`:** Creates a named site block (`reflector.example.com { ... }`). Caddy automatically provisions a Let's Encrypt certificate for this domain. Requires DNS pointing to the server and ports 80/443 open.
-
-**Without `--domain` (IP access):** Creates a catch-all `:443 { tls internal ... }` block. Caddy generates a self-signed certificate. Browsers will show a security warning.
-
-Both configurations route:
- `/v1/*` and `/health` to the backend (`server:1250`)
- Everything else to the frontend (`web:3000`)
-
-### Step 6: Start Services
-
-1. **Always builds the GPU/CPU model image** — these are never prebuilt because they contain ML model download logic specific to the host's hardware
-2. **With `--build`:** Also builds backend (server, worker, beat) and frontend (web) images from source
-3. **Without `--build`:** Pulls prebuilt images from the Docker registry (`monadicalsas/reflector-backend:latest`, `monadicalsas/reflector-frontend:latest`)
-4. **Starts all services** — `docker compose up -d` with the active profiles
-5. **Quick sanity check** — after 3 seconds, checks for any containers that exited immediately
-
-### Step 7: Health Checks
-
-Waits for each service in order, with generous timeouts:
-
-| Service | Check | Timeout | Notes |
-|---------|-------|---------|-------|
-| GPU/CPU models | `curl http://localhost:8000/docs` | 10 min (120 x 5s) | First start downloads ~1GB of models |
-| Ollama | `curl http://localhost:11435/api/tags` | 3 min (60 x 3s) | Then pulls the selected model |
-| Server API | `curl http://localhost:1250/health` | 7.5 min (90 x 5s) | First start runs database migrations |
-| Frontend | `curl http://localhost:3000` | 1.5 min (30 x 3s) | Next.js build on first start |
-| Caddy | `curl -k https://localhost` | Quick check | After other services are up |
-
-If the server container exits during the health check, the script dumps diagnostics (container statuses + logs) before exiting.
-
-After the Ollama health check passes, the script checks if the selected model is already pulled. If not, it runs `ollama pull <model>` inside the container.
-
---
-
-## Docker Compose Profile System
-
-The compose file (`docker-compose.selfhosted.yml`) uses Docker Compose profiles to make services optional. Only services whose profiles match the active `--profile` flags are started.
-
-### Always-on Services (no profile)
-
-These start regardless of which flags you pass:
-
-| Service | Role | Image |
-|---------|------|-------|
-| `server` | FastAPI backend, API endpoints, WebRTC | `monadicalsas/reflector-backend:latest` |
-| `worker` | Celery worker for background processing | Same image, `ENTRYPOINT=worker` |
-| `beat` | Celery beat scheduler for periodic tasks | Same image, `ENTRYPOINT=beat` |
-| `web` | Next.js frontend | `monadicalsas/reflector-frontend:latest` |
-| `redis` | Message broker + caching | `redis:7.2-alpine` |
-| `postgres` | Primary database | `postgres:17-alpine` |
-
-### Profile-Based Services
-
-| Profile | Service | Role |
-|---------|---------|------|
-| `gpu` | `gpu` | NVIDIA GPU-accelerated transcription/diarization/translation |
-| `cpu` | `cpu` | CPU-only transcription/diarization/translation |
-| `ollama-gpu` | `ollama` | Local Ollama LLM with GPU |
-| `ollama-cpu` | `ollama-cpu` | Local Ollama LLM on CPU |
-| `garage` | `garage` | Local S3-compatible object storage |
-| `caddy` | `caddy` | Reverse proxy with SSL |
-
-### The "transcription" Alias
-
-Both the `gpu` and `cpu` services define a Docker network alias of `transcription`. This means the backend always connects to `http://transcription:8000` regardless of which profile is active. The alias is defined in the compose file's `networks.default.aliases` section.
-
---
-
-## Service Architecture
-
-```
-                    ┌─────────────┐
-  Internet ────────>│    Caddy     │ :80/:443   (profile: caddy)
-                    └──────┬──────┘
-                           │
-              ┌────────────┼────────────┐
-              │            │            │
-              v            v            │
-         ┌─────────┐  ┌─────────┐      │
-         │   web   │  │ server  │      │
-         │ :3000   │  │ :1250   │      │
-         └─────────┘  └────┬────┘      │
-                           │            │
-                      ┌────┴────┐       │
-                      │ worker  │       │
-                      │  beat   │       │
-                      └────┬────┘       │
-                           │            │
-            ┌──────────────┼────────────┤
-            │              │            │
-            v              v            v
-      ┌───────────┐  ┌─────────┐  ┌─────────┐
-      │transcription│ │postgres │  │  redis  │
-      │ (gpu/cpu) │  │ :5432   │  │ :6379   │
-      │ :8000     │  └─────────┘  └─────────┘
-      └───────────┘
-            │
-      ┌─────┴─────┐     ┌─────────┐
-      │  ollama   │     │ garage  │
-      │(optional) │     │(optional│
-      │ :11435    │     │  S3)    │
-      └───────────┘     └─────────┘
-```
-
-### How Services Interact
-
-1. **User request** hits Caddy (if enabled), which routes to `web` (pages) or `server` (API)
-2. **`web`** renders pages server-side using `SERVER_API_URL=http://server:1250` and client-side using the public `API_URL`
-3. **`server`** handles API requests, file uploads, WebRTC streaming. Dispatches background work to Celery via Redis
-4. **`worker`** picks up Celery tasks (transcription pipelines, audio processing). Calls `transcription:8000` for ML inference and uploads results to S3 storage
-5. **`beat`** schedules periodic tasks (cleanup, webhook retries) by pushing them onto the Celery queue
-6. **`transcription` (gpu/cpu)** runs Whisper/Parakeet (transcription), Pyannote (diarization), and translation models. Stateless HTTP API
-7. **`ollama`** provides an OpenAI-compatible API for summarization and topic detection. Called by the worker during post-processing
-8. **`garage`** provides S3-compatible storage for audio files and processed results. Accessed by the worker via boto3
-
---
-
-## Configuration Flow
-
-Environment variables flow through multiple layers. Understanding this prevents confusion when debugging:
-
-```
-Flags (--gpu, --garage, etc.)
-  │
-  ├── setup-selfhosted.sh interprets flags
-  │     │
-  │     ├── Writes server/.env (backend config)
-  │     ├── Writes www/.env (frontend config)
-  │     ├── Writes .env (HF_TOKEN for compose interpolation)
-  │     └── Writes Caddyfile (proxy routes)
-  │
-  └── docker-compose.selfhosted.yml reads:
-        ├── env_file: ./server/.env   (loaded into server, worker, beat)
-        ├── env_file: ./www/.env      (loaded into web)
-        ├── .env                      (compose variable interpolation, e.g. ${HF_TOKEN})
-        └── environment: {...}        (hardcoded overrides, always win over env_file)
-```
-
-### Precedence Rules
-
-Docker Compose `environment:` keys **always override** `env_file:` values. This is by design — the compose file hardcodes infrastructure values that must be correct inside the Docker network (like `DATABASE_URL=postgresql+asyncpg://...@postgres:5432/...`) regardless of what's in `server/.env`.
-
-The `server/.env` file is still useful for:
- Values not overridden in the compose file (LLM config, storage credentials, auth settings)
- Running the server outside Docker during development
-
-### The Three `.env` Files
-
-| File | Used By | Contains |
-|------|---------|----------|
-| `server/.env` | server, worker, beat | Backend config: database, Redis, S3, LLM, auth, public URLs |
-| `www/.env` | web | Frontend config: site URL, auth, feature flags |
-| `.env` (root) | Docker Compose interpolation | Only `HF_TOKEN` — injected into GPU/CPU container env |
-
---
-
-## Storage Architecture
-
-All audio files and processing results are stored in S3-compatible object storage. The backend uses boto3 (via aioboto3) with automatic path-style addressing when a custom endpoint URL is configured.
-
-### How Garage Works
-
-Garage is a lightweight, self-hosted S3-compatible storage engine. In this deployment:
-
- Runs as a single-node cluster with 1GB capacity allocation
- Listens on port 3900 (S3 API) and 3903 (admin API)
- Data persists in Docker volumes (`garage_data`, `garage_meta`)
- Accessed by the worker at `http://garage:3900` (Docker-internal)
-
-The setup script creates:
- A bucket called `reflector-media`
- An access key called `reflector` with read/write permissions on that bucket
-
-### Path-Style vs Virtual-Hosted Addressing
-
-AWS S3 uses virtual-hosted addressing by default (`bucket.s3.amazonaws.com`). S3-compatible services like Garage require path-style addressing (`endpoint/bucket`). The `AwsStorage` class detects this automatically: when `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL` is set, it configures boto3 with `addressing_style: "path"`.
-
---
-
-## SSL/TLS and Reverse Proxy
-
-### With `--domain` (Production)
-
-Caddy automatically obtains and renews a Let's Encrypt certificate. Requirements:
- DNS A record pointing to the server
- Ports 80 (HTTP challenge) and 443 (HTTPS) open to the internet
-
-The generated Caddyfile uses the domain as the site address, which triggers Caddy's automatic HTTPS.
-
-### Without `--domain` (Development/LAN)
-
-Caddy generates a self-signed certificate and listens on `:443` as a catch-all. Browsers will show a security warning that must be accepted manually.
-
-### Without `--caddy` (BYO Proxy)
-
-No ports are exposed to the internet. The services listen on `127.0.0.1` only:
- Frontend: `localhost:3000`
- Backend API: `localhost:1250`
-
-You can point your own reverse proxy (nginx, Traefik, etc.) at these ports.
-
-### WebRTC and UDP
-
-The server exposes UDP ports 50000-50100 for WebRTC ICE candidates. The `WEBRTC_HOST` variable tells the server which IP to advertise in ICE candidates — this must be the server's actual IP address (not a domain), because WebRTC uses UDP which doesn't go through the HTTP reverse proxy.
-
---
-
-## Build vs Pull Workflow
-
-### Default (no `--build` flag)
-
-```
-GPU/CPU model image: Always built from source (./gpu/self_hosted/)
-Backend image:       Pulled from monadicalsas/reflector-backend:latest
-Frontend image:      Pulled from monadicalsas/reflector-frontend:latest
-```
-
-The GPU/CPU image is always built because it contains hardware-specific build steps and ML model download logic.
-
-### With `--build`
-
-```
-GPU/CPU model image: Built from source (./gpu/self_hosted/)
-Backend image:       Built from source (./server/)
-Frontend image:      Built from source (./www/)
-```
-
-Use `--build` when:
- You've made local code changes
- The prebuilt registry images are outdated
- You want to verify the build works on your hardware
-
-### Rebuilding Individual Services
-
-```bash
-# Rebuild just the backend
-docker compose -f docker-compose.selfhosted.yml build server worker beat
-
-# Rebuild just the frontend
-docker compose -f docker-compose.selfhosted.yml build web
-
-# Rebuild the GPU model container
-docker compose -f docker-compose.selfhosted.yml build gpu
-
-# Force a clean rebuild (no cache)
-docker compose -f docker-compose.selfhosted.yml build --no-cache server
-```
-
---
-
-## Background Task Processing
-
-### Celery Architecture
-
-The backend uses Celery for all background work, with Redis as the message broker:
-
- **`worker`** — picks up tasks from the Redis queue and executes them
- **`beat`** — schedules periodic tasks (cron-like) by pushing them onto the queue
- **`Redis`** — acts as both message broker and result backend
-
-### The Audio Processing Pipeline
-
-When a file is uploaded, the worker runs a multi-step pipeline:
-
-```
-Upload → Extract Audio → Upload to S3
-                           │
-                    ┌──────┼──────┐
-                    │      │      │
-                    v      v      v
-              Transcribe  Diarize  Waveform
-                    │      │      │
-                    └──────┼──────┘
-                           │
-                       Assemble
-                           │
-                    ┌──────┼──────┐
-                    v      v      v
-                Topics  Title  Summaries
-                           │
-                         Done
-```
-
-Transcription, diarization, and waveform generation run in parallel. After assembly, topic detection, title generation, and summarization also run in parallel. Each step calls the appropriate service (transcription container for ML, Ollama/external LLM for text generation, S3 for storage).
-
-### Event Loop Management
-
-Each Celery task runs in its own `asyncio.run()` call, which creates a fresh event loop. The `asynctask` decorator in `server/reflector/asynctask.py` handles:
-
-1. **Database connections** — resets the connection pool before each task (connections from a previous event loop would cause "Future attached to a different loop" errors)
-2. **Redis connections** — resets the WebSocket manager singleton so Redis pub/sub reconnects on the current loop
-3. **Cleanup** — disconnects the database and clears the context variable in the `finally` block
-
---
-
-## Network and Port Layout
-
-All services communicate over Docker's default bridge network. Only specific ports are exposed to the host:
-
-| Port | Service | Binding | Purpose |
-|------|---------|---------|---------|
-| 80 | Caddy | `0.0.0.0:80` | HTTP (redirect to HTTPS / Let's Encrypt challenge) |
-| 443 | Caddy | `0.0.0.0:443` | HTTPS (main entry point) |
-| 1250 | Server | `127.0.0.1:1250` | Backend API (localhost only) |
-| 3000 | Web | `127.0.0.1:3000` | Frontend (localhost only) |
-| 3900 | Garage | `0.0.0.0:3900` | S3 API (for admin/debug access) |
-| 3903 | Garage | `0.0.0.0:3903` | Garage admin API |
-| 8000 | GPU/CPU | `127.0.0.1:8000` | ML model API (localhost only) |
-| 11435 | Ollama | `127.0.0.1:11435` | Ollama API (localhost only) |
-| 50000-50100/udp | Server | `0.0.0.0:50000-50100` | WebRTC ICE candidates |
-
-Services bound to `127.0.0.1` are only accessible from the host itself (not from the network). Caddy is the only service exposed to the internet on standard HTTP/HTTPS ports.
-
-### Docker-Internal Hostnames
-
-Inside the Docker network, services reach each other by their compose service name:
-
-| Hostname | Resolves To |
-|----------|-------------|
-| `server` | Backend API container |
-| `web` | Frontend container |
-| `postgres` | PostgreSQL container |
-| `redis` | Redis container |
-| `transcription` | GPU or CPU container (network alias) |
-| `ollama` / `ollama-cpu` | Ollama container |
-| `garage` | Garage S3 container |
-
---
-
-## Diagnostics and Error Handling
-
-The setup script includes an `ERR` trap that automatically dumps diagnostics when any command fails:
-
-1. Lists all container statuses
-2. Shows the last 30 lines of logs for any stopped/exited containers
-3. Shows the last 40 lines of the specific failing service
-
-This means if something goes wrong during setup, you'll see the relevant logs immediately without having to run manual debug commands.
-
-### Common Debug Commands
-
-```bash
-# Overall status
-docker compose -f docker-compose.selfhosted.yml ps
-
-# Logs for a specific service
-docker compose -f docker-compose.selfhosted.yml logs server --tail 50
-docker compose -f docker-compose.selfhosted.yml logs worker --tail 50
-
-# Check environment inside a container
-docker compose -f docker-compose.selfhosted.yml exec server env | grep TRANSCRIPT
-
-# Health check from inside the network
-docker compose -f docker-compose.selfhosted.yml exec server curl http://localhost:1250/health
-
-# Check S3 storage connectivity
-docker compose -f docker-compose.selfhosted.yml exec server curl http://garage:3900
-
-# Database access
-docker compose -f docker-compose.selfhosted.yml exec postgres psql -U reflector -c "SELECT id, status FROM transcript ORDER BY created_at DESC LIMIT 5;"
-
-# List files in server data directory
-docker compose -f docker-compose.selfhosted.yml exec server ls -la /app/data/
-```
--- a/docsv2/selfhosted-production.md
+++ b/docsv2/selfhosted-production.md
@@ -1,638 +0,0 @@
-# Self-Hosted Production Deployment
-
-Deploy Reflector on a single server with everything running in Docker. Transcription, diarization, and translation use specialized ML models (Whisper/Parakeet, Pyannote); only summarization and topic detection require an LLM.
-
-> For a detailed walkthrough of how the setup script and infrastructure work under the hood, see [How the Self-Hosted Setup Works](selfhosted-architecture.md).
-
-## Prerequisites
-
-### Hardware
- **With GPU**: Linux server with NVIDIA GPU (8GB+ VRAM recommended), 16GB+ RAM, 50GB+ disk
- **CPU-only**: 8+ cores, 32GB+ RAM (transcription is slower but works)
- Disk space for ML models (~2GB on first run) + audio storage
-
-### Software
- Docker Engine 24+ with Compose V2
- NVIDIA drivers + `nvidia-container-toolkit` (GPU modes only)
- `curl`, `openssl` (usually pre-installed)
-
-### Accounts & Credentials (depending on options)
-
-**Always recommended:**
- **HuggingFace token** — For downloading pyannote speaker diarization models. Get one at https://huggingface.co/settings/tokens and accept the model licenses:
-  - https://huggingface.co/pyannote/speaker-diarization-3.1
-  - https://huggingface.co/pyannote/segmentation-3.0
-  - The setup script will prompt for this. If skipped, diarization falls back to a public model bundle (may be less reliable).
-
-**LLM for summarization & topic detection (pick one):**
- **With `--ollama-gpu` or `--ollama-cpu`**: Nothing extra — Ollama runs locally and pulls the model automatically
- **Without `--ollama-*`**: An OpenAI-compatible LLM API key and endpoint. Examples:
-  - OpenAI: `LLM_URL=https://api.openai.com/v1`, `LLM_API_KEY=sk-...`, `LLM_MODEL=gpt-4o-mini`
-  - Anthropic, Together, Groq, or any OpenAI-compatible API
-  - A self-managed vLLM or Ollama instance elsewhere on the network
-
-**Object storage (pick one):**
- **With `--garage`**: Nothing extra — Garage (local S3-compatible storage) is auto-configured by the script
- **Without `--garage`**: S3-compatible storage credentials. The script will prompt for these, or you can pre-fill `server/.env`. Options include:
-  - **AWS S3**: Access Key ID, Secret Access Key, bucket name, region
-  - **MinIO**: Same credentials + `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL=http://your-minio:9000`
-  - **Any S3-compatible provider** (Backblaze B2, Cloudflare R2, DigitalOcean Spaces, etc.): same fields + custom endpoint URL
-
-**Optional add-ons (configure after initial setup):**
- **Authentik** (user authentication): Requires an Authentik instance with an OAuth2/OIDC application configured for Reflector. See [Enabling Authentication](#enabling-authentication-authentik) below.
-
-## Quick Start
-
-```bash
-git clone https://github.com/Monadical-SAS/reflector.git
-cd reflector
-
-# GPU + local Ollama LLM + local Garage storage + Caddy SSL (with domain):
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --domain reflector.example.com
-
-# Same but without a domain (self-signed cert, access via IP):
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
-
-# CPU-only (in-process ML, no GPU container):
-./scripts/setup-selfhosted.sh --cpu --ollama-cpu --garage --caddy
-
-# Remote GPU service (your own hosted GPU, no local ML container):
-./scripts/setup-selfhosted.sh --hosted --garage --caddy
-
-# With password authentication (single admin user):
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --password mysecretpass
-
-# Build from source instead of pulling prebuilt images:
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --build
-```
-
-That's it. The script generates env files, secrets, starts all containers, waits for health checks, and prints the URL.
-
-## ML Processing Modes (Required)
-
-Pick `--gpu`, `--cpu`, or `--hosted`. This determines how **transcription, diarization, translation, and audio padding** run:
-
-| Flag | What it does | Requires |
-|------|-------------|----------|
-| `--gpu` | NVIDIA GPU container for ML models | NVIDIA GPU + drivers + `nvidia-container-toolkit` |
-| `--cpu` | In-process CPU processing on server/worker (no ML container) | 8+ cores, 16GB+ RAM (32GB recommended for large files) |
-| `--hosted` | Remote GPU service URL (no local ML container) | A running GPU service instance (e.g. `gpu/self_hosted/`) |
-
-## Local LLM (Optional)
-
-Optionally add `--ollama-gpu` or `--ollama-cpu` for a **local Ollama instance** that handles summarization and topic detection. If omitted, configure an external OpenAI-compatible LLM in `server/.env`.
-
-| Flag | What it does | Requires |
-|------|-------------|----------|
-| `--ollama-gpu` | Local Ollama with NVIDIA GPU acceleration | NVIDIA GPU |
-| `--ollama-cpu` | Local Ollama on CPU only | Nothing extra |
-| `--llm-model MODEL` | Choose which Ollama model to download (default: `qwen2.5:14b`) | `--ollama-gpu` or `--ollama-cpu` |
-| *(omitted)* | User configures external LLM (OpenAI, Anthropic, etc.) | LLM API key |
-
-### macOS / Apple Silicon
-
-`--ollama-gpu` requires an NVIDIA GPU and **does not work on macOS**. Docker on macOS cannot access Apple GPU acceleration, so the containerized Ollama will run on CPU only regardless of the flag used.
-
-For the best performance on Mac, we recommend running Ollama **natively outside Docker** (install from https://ollama.com) — this gives Ollama direct access to Apple Metal GPU acceleration. Then omit `--ollama-gpu`/`--ollama-cpu` from the setup script and point the backend to your local Ollama instance:
-
-```env
-# In server/.env
-LLM_URL=http://host.docker.internal:11434/v1
-LLM_MODEL=qwen2.5:14b
-LLM_API_KEY=not-needed
-```
-
-`--ollama-cpu` does work on macOS but will be significantly slower than a native Ollama install with Metal acceleration.
-
-### Choosing an Ollama model
-
-The default model is `qwen2.5:14b` (~9GB download, good multilingual support and summary quality). Override with `--llm-model`:
-
-```bash
-# Default (qwen2.5:14b)
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
-
-# Mistral — good balance of speed and quality (~4.1GB)
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --llm-model mistral --garage --caddy
-
-# Phi-4 — smaller and faster (~9.1GB)
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --llm-model phi4 --garage --caddy
-
-# Llama 3.3 70B — best quality, needs 48GB+ RAM or GPU VRAM (~43GB)
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --llm-model llama3.3:70b --garage --caddy
-
-# Gemma 2 9B (~5.4GB)
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --llm-model gemma2 --garage --caddy
-
-# DeepSeek R1 8B — reasoning model, verbose but thorough summaries (~4.9GB)
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --llm-model deepseek-r1:8b --garage --caddy
-```
-
-Browse all available models at https://ollama.com/library.
-
-### Recommended combinations
-
- **`--gpu --ollama-gpu`**: Best for servers with NVIDIA GPU. Fully self-contained, no external API keys needed.
- **`--cpu --ollama-cpu`**: No GPU available but want everything self-contained. Slower but works.
- **`--hosted --ollama-cpu`**: Remote GPU for ML, local CPU for LLM. Great when you have a separate GPU server.
- **`--gpu --ollama-cpu`**: GPU for transcription, CPU for LLM. Saves GPU VRAM for ML models.
- **`--gpu`**: Have NVIDIA GPU but prefer a cloud LLM (faster/better summaries with GPT-4, Claude, etc.).
- **`--cpu`**: No GPU, prefer cloud LLM. Slowest transcription but best summary quality.
- **`--hosted`**: Remote GPU, cloud LLM. No local ML at all.
-
-## Other Optional Flags
-
-| Flag | What it does |
-|------|-------------|
-| `--garage` | Starts Garage (local S3-compatible storage). Auto-configures bucket, keys, and env vars. |
-| `--caddy` | Starts Caddy reverse proxy on ports 80/443 with self-signed cert. |
-| `--domain DOMAIN` | Use a real domain with Let's Encrypt auto-HTTPS (implies `--caddy`). Requires DNS A record pointing to this server and ports 80/443 open. |
-| `--password PASS` | Enable password authentication with an `admin@localhost` user. Sets `AUTH_BACKEND=password`, `PUBLIC_MODE=false`. See [Enabling Password Authentication](#enabling-password-authentication). |
-| `--build` | Build backend (server, worker, beat) and frontend (web) Docker images from source instead of pulling prebuilt images from the registry. Useful for development or when running a version with local changes. |
-
-Without `--garage`, you **must** provide S3-compatible credentials (the script will prompt interactively or you can pre-fill `server/.env`).
-
-Without `--caddy` or `--domain`, no ports are exposed. Point your own reverse proxy at `web:3000` (frontend) and `server:1250` (API).
-
-**Using a domain (recommended for production):** Point a DNS A record at your server's IP, then pass `--domain your.domain.com`. Caddy will automatically obtain and renew a Let's Encrypt certificate. Ports 80 and 443 must be open.
-
-**Without a domain:** `--caddy` alone uses a self-signed certificate. Browsers will show a security warning that must be accepted.
-
-## What the Script Does
-
-1. **Prerequisites check** — Docker, NVIDIA GPU (if needed), compose file exists
-2. **Generate secrets** — `SECRET_KEY`, `NEXTAUTH_SECRET` via `openssl rand`
-3. **Generate `server/.env`** — From template, sets infrastructure defaults, configures LLM based on mode, enables `PUBLIC_MODE`
-4. **Generate `www/.env`** — Auto-detects server IP, sets URLs
-5. **Storage setup** — Either initializes Garage (bucket, keys, permissions) or prompts for external S3 credentials
-6. **Caddyfile** — Generates domain-specific (Let's Encrypt) or IP-specific (self-signed) configuration
-7. **Build & start** — For `--gpu`, builds the GPU model image from source. For `--cpu` and `--hosted`, no ML container is built. With `--build`, also builds backend and frontend from source; otherwise pulls prebuilt images from the registry
-8. **Auto-detects video platforms** — If `DAILY_API_KEY` is found in `server/.env`, generates `.env.hatchet` (dashboard URL/cookie config), starts Hatchet workflow engine, and generates an API token. If any video platform is configured, enables the Rooms feature
-9. **Health checks** — Waits for each service, pulls Ollama model if needed, warns about missing LLM config
-
-> For a deeper dive into each step, see [How the Self-Hosted Setup Works](selfhosted-architecture.md).
-
-## Configuration Reference
-
-### Server Environment (`server/.env`)
-
-| Variable | Description | Default |
-|----------|-------------|---------|
-| `DATABASE_URL` | PostgreSQL connection | Auto-set (Docker internal) |
-| `REDIS_HOST` | Redis hostname | Auto-set (`redis`) |
-| `SECRET_KEY` | App secret | Auto-generated |
-| `AUTH_BACKEND` | Authentication method (`none`, `password`, `jwt`) | `none` |
-| `PUBLIC_MODE` | Allow unauthenticated access | `true` |
-| `ADMIN_EMAIL` | Admin email for password auth | *(unset)* |
-| `ADMIN_PASSWORD_HASH` | PBKDF2 hash for password auth | *(unset)* |
-| `WEBRTC_HOST` | IP advertised in WebRTC ICE candidates | Auto-detected (server IP) |
-| `TRANSCRIPT_URL` | Specialized model endpoint | `http://transcription:8000` |
-| `PADDING_BACKEND` | Audio padding backend (`pyav` or `modal`) | `modal` (selfhosted), `pyav` (default) |
-| `PADDING_URL` | Audio padding endpoint (when `PADDING_BACKEND=modal`) | `http://transcription:8000` |
-| `LLM_URL` | OpenAI-compatible LLM endpoint | Auto-set for Ollama modes |
-| `LLM_API_KEY` | LLM API key | `not-needed` for Ollama |
-| `LLM_MODEL` | LLM model name | `qwen2.5:14b` for Ollama (override with `--llm-model`) |
-| `CELERY_BEAT_POLL_INTERVAL` | Override all worker polling intervals (seconds). `0` = use individual defaults | `300` (selfhosted), `0` (other) |
-| `TRANSCRIPT_STORAGE_BACKEND` | Storage backend | `aws` |
-| `TRANSCRIPT_STORAGE_AWS_*` | S3 credentials | Auto-set for Garage |
-| `DAILY_API_KEY` | Daily.co API key (enables live rooms) | *(unset)* |
-| `DAILY_SUBDOMAIN` | Daily.co subdomain | *(unset)* |
-| `DAILYCO_STORAGE_AWS_ACCESS_KEY_ID` | AWS access key for reading Daily's recording bucket | *(unset)* |
-| `DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY` | AWS secret key for reading Daily's recording bucket | *(unset)* |
-| `HATCHET_CLIENT_TOKEN` | Hatchet API token (auto-generated) | *(unset)* |
-| `HATCHET_CLIENT_SERVER_URL` | Hatchet server URL | Auto-set when Daily.co configured |
-| `HATCHET_CLIENT_HOST_PORT` | Hatchet gRPC address | Auto-set when Daily.co configured |
-| `TRANSCRIPT_FILE_TIMEOUT` | HTTP timeout (seconds) for file transcription requests | `600` (`3600` in CPU mode) |
-| `DIARIZATION_FILE_TIMEOUT` | HTTP timeout (seconds) for file diarization requests | `600` (`3600` in CPU mode) |
-
-### Frontend Environment (`www/.env`)
-
-| Variable | Description | Default |
-|----------|-------------|---------|
-| `SITE_URL` | Public-facing URL | Auto-detected |
-| `API_URL` | API URL (browser-side) | Same as SITE_URL |
-| `SERVER_API_URL` | API URL (server-side) | `http://server:1250` |
-| `NEXTAUTH_SECRET` | Auth secret | Auto-generated |
-| `FEATURE_REQUIRE_LOGIN` | Require authentication | `false` |
-| `AUTH_PROVIDER` | Auth provider (`authentik` or `credentials`) | *(unset)* |
-| `FEATURE_ROOMS` | Enable meeting rooms UI | Auto-set when video platform configured |
-
-## Storage Options
-
-### Garage (Recommended for Self-Hosted)
-
-Use `--garage` flag. The script automatically:
- Generates `data/garage.toml` with a random RPC secret
- Starts the Garage container
- Creates the `reflector-media` bucket
- Creates an access key with read/write permissions
- Writes all S3 credentials to `server/.env`
-
-### External S3 (AWS, MinIO, etc.)
-
-Don't use `--garage`. The script will prompt for:
- Access Key ID
- Secret Access Key
- Bucket Name
- Region
- Endpoint URL (for non-AWS like MinIO)
-
-Or pre-fill in `server/.env`:
-```env
-TRANSCRIPT_STORAGE_BACKEND=aws
-TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=your-key
-TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=your-secret
-TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=reflector-media
-TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
-# For non-AWS S3 (MinIO, etc.):
-TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL=http://minio:9000
-```
-
-## What Authentication Enables
-
-By default, Reflector runs in **public mode** (`AUTH_BACKEND=none`, `PUBLIC_MODE=true`) — anyone can create and view transcripts without logging in. Transcripts are anonymous (not linked to any user) and cannot be edited or deleted after creation.
-
-Enabling authentication (either password or Authentik) unlocks:
-
-| Feature | Public mode (no auth) | With authentication |
-|---------|----------------------|---------------------|
-| Create transcripts (record/upload) | Yes (anonymous, unowned) | Yes (owned by user) |
-| View transcripts | All transcripts visible | Own transcripts + shared rooms |
-| Edit/delete transcripts | No | Yes (owner only) |
-| Privacy controls (private/semi-private/public) | No (everything public) | Yes (owner can set share mode) |
-| Speaker reassignment and merging | No | Yes (owner only) |
-| Participant management (add/edit/delete) | Read-only | Full CRUD (owner only) |
-| Create rooms | No | Yes |
-| Edit/delete rooms | No | Yes (owner only) |
-| Room calendar (ICS) sync | No | Yes (owner only) |
-| API key management | No | Yes |
-| Post to Zulip | No | Yes (owner only) |
-| Real-time WebSocket notifications | No (connection closed) | Yes (transcript create/delete events) |
-| Meeting host access (Daily.co token) | No | Yes (room owner) |
-
-In short: public mode is "demo-friendly" — great for trying Reflector out. Authentication adds **ownership, privacy, and management** of your data.
-
-## Authentication Options
-
-Reflector supports three authentication backends:
-
-| Backend | `AUTH_BACKEND` | Use case |
-|---------|---------------|----------|
-| `none` | `none` | Public/demo mode, no login required |
-| `password` | `password` | Single-user self-hosted, simple email/password login |
-| `jwt` | `jwt` | Multi-user via Authentik (OAuth2/OIDC) |
-
-## Enabling Password Authentication
-
-The simplest way to add authentication. Creates a single admin user with email/password login — no external identity provider needed.
-
-### Quick setup (recommended)
-
-Pass `--password` to the setup script:
-
-```bash
-./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy --password mysecretpass
-```
-
-This automatically:
- Sets `AUTH_BACKEND=password` and `PUBLIC_MODE=false` in `server/.env`
- Creates an `admin@localhost` user with the given password
- Sets `FEATURE_REQUIRE_LOGIN=true` and `AUTH_PROVIDER=credentials` in `www/.env`
- Provisions the admin user in the database on container startup
-
-### Manual setup
-
-If you prefer to configure manually or want to change the admin email:
-
-1. Generate a password hash:
-   ```bash
-   cd server
-   uv run python -m reflector.tools.create_admin --hash-only --password yourpassword
-   ```
-
-2. Update `server/.env`:
-   ```env
-   AUTH_BACKEND=password
-   PUBLIC_MODE=false
-   ADMIN_EMAIL=admin@yourdomain.com
-   ADMIN_PASSWORD_HASH=pbkdf2:sha256:100000$<salt>$<hash>
-   ```
-
-3. Update `www/.env`:
-   ```env
-   FEATURE_REQUIRE_LOGIN=true
-   AUTH_PROVIDER=credentials
-   ```
-
-4. Restart:
-   ```bash
-   docker compose -f docker-compose.selfhosted.yml down
-   ./scripts/setup-selfhosted.sh <same-flags>
-   ```
-
-### How it works
-
- The backend issues HS256 JWTs (signed with `SECRET_KEY`) on successful login via `POST /v1/auth/login`
- Tokens expire after 24 hours; the user must log in again after expiry
- The frontend shows a login page at `/login` with email and password fields
- A rate limiter blocks IPs after 10 failed login attempts within 5 minutes
- The admin user is provisioned automatically on container startup from `ADMIN_EMAIL` and `ADMIN_PASSWORD_HASH` environment variables
- Passwords are hashed with PBKDF2-SHA256 (100,000 iterations) — no additional dependencies required
-
-### Changing the admin password
-
-```bash
-cd server
-uv run python -m reflector.tools.create_admin --email admin@localhost --password newpassword
-```
-
-Or update `ADMIN_PASSWORD_HASH` in `server/.env` and restart the containers.
-
-## Enabling Authentication (Authentik)
-
-For multi-user deployments with SSO. Requires an external Authentik instance.
-
-By default, authentication is disabled (`AUTH_BACKEND=none`, `FEATURE_REQUIRE_LOGIN=false`). To enable:
-
-1. Deploy an Authentik instance (see [Authentik docs](https://goauthentik.io/docs/installation))
-2. Create an OAuth2/OIDC application for Reflector
-3. Update `server/.env`:
-   ```env
-   AUTH_BACKEND=jwt
-   AUTH_JWT_AUDIENCE=your-client-id
-   ```
-4. Update `www/.env`:
-   ```env
-   FEATURE_REQUIRE_LOGIN=true
-   AUTH_PROVIDER=authentik
-   AUTHENTIK_ISSUER=https://authentik.example.com/application/o/reflector
-   AUTHENTIK_REFRESH_TOKEN_URL=https://authentik.example.com/application/o/token/
-   AUTHENTIK_CLIENT_ID=your-client-id
-   AUTHENTIK_CLIENT_SECRET=your-client-secret
-   ```
-5. Restart: `docker compose -f docker-compose.selfhosted.yml down && ./scripts/setup-selfhosted.sh <same-flags>`
-
-## Enabling Daily.co Live Rooms
-
-Daily.co enables real-time meeting rooms with automatic recording and per-participant
-audio tracks for improved diarization. When configured, the setup script automatically
-starts the Hatchet workflow engine for multitrack recording processing.
-
-### Prerequisites
-
- **Daily.co account** — Sign up at https://www.daily.co/
- **API key** — From Daily.co Dashboard → Developers → API Keys
- **Subdomain** — The `yourname` part of `yourname.daily.co`
- **AWS S3 bucket** — For Daily.co to store recordings. See [Daily.co recording storage docs](https://docs.daily.co/guides/products/live-streaming-recording/storing-recordings-in-a-custom-s3-bucket)
- **IAM role ARN** — An AWS IAM role that Daily.co assumes to write recordings to your bucket
-
-### Setup
-
-1. Configure Daily.co env vars in `server/.env` **before** running the setup script:
-
-   ```env
-   DAILY_API_KEY=your-daily-api-key
-   DAILY_SUBDOMAIN=your-subdomain
-   DEFAULT_VIDEO_PLATFORM=daily
-   DAILYCO_STORAGE_AWS_BUCKET_NAME=your-recordings-bucket
-   DAILYCO_STORAGE_AWS_REGION=us-east-1
-   DAILYCO_STORAGE_AWS_ROLE_ARN=arn:aws:iam::123456789:role/DailyCoAccess
-   # Worker credentials for reading/deleting recordings from Daily's S3 bucket.
-   # Required when transcript storage is separate from Daily's bucket
-   # (e.g., selfhosted with Garage or a different S3 account).
-   DAILYCO_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
-   DAILYCO_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
-   ```
-
-   > **Important:** The `DAILYCO_STORAGE_AWS_ACCESS_KEY_ID` and `SECRET_ACCESS_KEY` are AWS IAM
-   > credentials that allow the Hatchet workers to **read and delete** recording files from Daily's
-   > S3 bucket. These are separate from the `ROLE_ARN` (which Daily's API uses to *write* recordings).
-   > Without these keys, multitrack processing will fail with 404 errors when transcript storage
-   > (e.g., Garage) uses different credentials than the Daily recording bucket.
-
-2. Run the setup script as normal:
-
-   ```bash
-   ./scripts/setup-selfhosted.sh --gpu --ollama-gpu --garage --caddy
-   ```
-
-   The script detects `DAILY_API_KEY` and automatically:
-   - Starts the Hatchet workflow engine (`hatchet` container)
-   - Starts Hatchet CPU and LLM workers (`hatchet-worker-cpu`, `hatchet-worker-llm`)
-   - Generates a `HATCHET_CLIENT_TOKEN` and saves it to `server/.env`
-   - Sets `HATCHET_CLIENT_SERVER_URL` and `HATCHET_CLIENT_HOST_PORT`
-   - Enables `FEATURE_ROOMS=true` in `www/.env`
-   - Registers Daily.co beat tasks (recording polling, presence reconciliation)
-
-3. (Optional) For faster recording discovery, configure a Daily.co webhook:
-   - In the Daily.co dashboard, add a webhook pointing to `https://your-domain/v1/daily/webhook`
-   - Set `DAILY_WEBHOOK_SECRET` in `server/.env` (the signing secret from Daily.co)
-   - Without webhooks, the system polls the Daily.co API every 15 seconds
-
-### What Gets Started
-
-| Service | Purpose |
-|---------|---------|
-| `hatchet` | Workflow orchestration engine (manages multitrack processing pipelines) |
-| `hatchet-worker-cpu` | CPU-heavy audio tasks (track mixdown, waveform generation) |
-| `hatchet-worker-llm` | Transcription, LLM inference (summaries, topics, titles), orchestration |
-
-### Hatchet Dashboard
-
-The Hatchet workflow engine includes a web dashboard for monitoring workflow runs and debugging. The setup script auto-generates `.env.hatchet` at the project root with the dashboard URL and cookie domain configuration. This file is git-ignored.
-
- **With Caddy**: Accessible at `https://your-domain:8888` (TLS via Caddy)
- **Without Caddy**: Accessible at `http://your-ip:8888` (direct port mapping)
-
-### Conditional Beat Tasks
-
-Beat tasks are registered based on which services are configured:
-
- **Whereby tasks** (only if `WHEREBY_API_KEY` or `AWS_PROCESS_RECORDING_QUEUE_URL`): `process_messages`, `reprocess_failed_recordings`
- **Daily.co tasks** (only if `DAILY_API_KEY`): `poll_daily_recordings`, `trigger_daily_reconciliation`, `reprocess_failed_daily_recordings`
- **Platform tasks** (if any video platform configured): `process_meetings`, `sync_all_ics_calendars`, `create_upcoming_meetings`
- **Always registered**: `cleanup_old_public_data` (if `PUBLIC_MODE`), `healthcheck_ping` (if `HEALTHCHECK_URL`)
-
-## Enabling Real Domain with Let's Encrypt
-
-By default, Caddy uses self-signed certificates. For a real domain:
-
-1. Point your domain's DNS to your server's IP
-2. Ensure ports 80 and 443 are open
-3. Edit `Caddyfile`:
-   ```
-   reflector.example.com {
-       handle /v1/* {
-           reverse_proxy server:1250
-       }
-       handle /health {
-           reverse_proxy server:1250
-       }
-       handle {
-           reverse_proxy web:3000
-       }
-   }
-   ```
-4. Update `www/.env`:
-   ```env
-   SITE_URL=https://reflector.example.com
-   NEXTAUTH_URL=https://reflector.example.com
-   API_URL=https://reflector.example.com
-   ```
-5. Restart Caddy: `docker compose -f docker-compose.selfhosted.yml restart caddy web`
-
-## Worker Polling Frequency
-
-The selfhosted setup defaults all background worker polling intervals to **300 seconds (5 minutes)** to reduce CPU and memory usage. This controls how often the beat scheduler triggers tasks like recording discovery, meeting reconciliation, and calendar sync.
-
-To change the interval, edit `server/.env`:
-
-```env
-# Poll every 60 seconds (more responsive, uses more resources)
-CELERY_BEAT_POLL_INTERVAL=60
-
-# Poll every 5 minutes (default for selfhosted)
-CELERY_BEAT_POLL_INTERVAL=300
-
-# Use individual per-task defaults (production SaaS behavior)
-CELERY_BEAT_POLL_INTERVAL=0
-```
-
-After changing, restart the beat and worker containers:
-
-```bash
-docker compose -f docker-compose.selfhosted.yml restart beat worker
-```
-
-**Affected tasks when `CELERY_BEAT_POLL_INTERVAL` is set:**
-
-| Task | Default (no override) | With override |
-|------|-----------------------|---------------|
-| SQS message polling | 60s | Override value |
-| Daily.co recording discovery | 15s (no webhook) / 180s (webhook) | Override value |
-| Meeting reconciliation | 30s | Override value |
-| ICS calendar sync | 60s | Override value |
-| Upcoming meeting creation | 30s | Override value |
-
-> **Note:** Daily crontab tasks (failed recording reprocessing at 05:00 UTC, public data cleanup at 03:00 UTC) and healthcheck pings (10 min) are **not** affected by this setting.
-
-## Troubleshooting
-
-### Check service status
-```bash
-docker compose -f docker-compose.selfhosted.yml ps
-```
-
-### View logs for a specific service
-```bash
-docker compose -f docker-compose.selfhosted.yml logs server --tail 50
-docker compose -f docker-compose.selfhosted.yml logs gpu --tail 50
-docker compose -f docker-compose.selfhosted.yml logs web --tail 50
-```
-
-### GPU service taking too long
-First start downloads ~1-2GB of ML models. Check progress:
-```bash
-docker compose -f docker-compose.selfhosted.yml logs gpu -f
-```
-
-### Server exits immediately
-Usually a database migration issue. Check:
-```bash
-docker compose -f docker-compose.selfhosted.yml logs server --tail 50
-```
-
-### Caddy certificate issues
-For self-signed certs, your browser will warn. Click Advanced > Proceed.
-For Let's Encrypt, ensure ports 80/443 are open and DNS is pointed correctly.
-
-### File processing timeout on CPU
-CPU transcription and diarization are significantly slower than GPU. A 20-minute audio file can take 20-40 minutes to process on CPU. The setup script automatically sets `TRANSCRIPT_FILE_TIMEOUT=3600` and `DIARIZATION_FILE_TIMEOUT=3600` (1 hour) for `--cpu` mode. If you still hit timeouts with very long files, increase these values in `server/.env`:
-```bash
-# Increase to 2 hours for files over 1 hour
-TRANSCRIPT_FILE_TIMEOUT=7200
-DIARIZATION_FILE_TIMEOUT=7200
-```
-Then restart the worker: `docker compose -f docker-compose.selfhosted.yml restart worker`
-
-### Summaries/topics not generating
-Check LLM configuration:
-```bash
-grep LLM_ server/.env
-```
-If you didn't use `--ollama-gpu` or `--ollama-cpu`, you must set `LLM_URL`, `LLM_API_KEY`, and `LLM_MODEL`.
-
-### Health check from inside containers
-```bash
-docker compose -f docker-compose.selfhosted.yml exec server curl http://localhost:1250/health
-docker compose -f docker-compose.selfhosted.yml exec gpu curl http://localhost:8000/docs
-```
-
-## Updating
-
-```bash
-# Option A: Pull latest prebuilt images and restart
-docker compose -f docker-compose.selfhosted.yml down
-./scripts/setup-selfhosted.sh <same-flags-as-before>
-
-# Option B: Build from source (after git pull) and restart
-git pull
-docker compose -f docker-compose.selfhosted.yml down
-./scripts/setup-selfhosted.sh <same-flags-as-before> --build
-
-# Rebuild only the GPU/CPU model image (picks up model updates)
-docker compose -f docker-compose.selfhosted.yml build gpu  # or cpu
-```
-
-The setup script is idempotent — it won't overwrite existing secrets or env vars that are already set.
-
-## Architecture Overview
-
-```
-                    ┌─────────┐
-  Internet ────────>│  Caddy  │ :80/:443
-                    └────┬────┘
-                         │
-            ┌────────────┼────────────┐
-            │            │            │
-            v            v            │
-       ┌─────────┐  ┌─────────┐      │
-       │   web   │  │ server  │      │
-       │ :3000   │  │ :1250   │      │
-       └─────────┘  └────┬────┘      │
-                         │            │
-                    ┌────┴────┐       │
-                    │ worker  │       │
-                    │  beat   │       │
-                    └────┬────┘       │
-                         │            │
-          ┌──────────────┼────────────┤
-          │              │            │
-          v              v            v
-    ┌───────────┐  ┌─────────┐  ┌─────────┐
-    │ ML models │  │postgres │  │  redis  │
-    │ (varies)  │  │ :5432   │  │ :6379   │
-    └───────────┘  └─────────┘  └─────────┘
-          │
-    ┌─────┴─────┐     ┌─────────┐
-    │  ollama   │     │ garage  │
-    │ (optional)│     │(optional│
-    │ :11435    │     │ S3)     │
-    └───────────┘     └─────────┘
-
-    ┌───────────────────────────────────┐
-    │  Hatchet (optional — Daily.co)   │
-    │  ┌─────────┐  ┌───────────────┐  │
-    │  │ hatchet │  │ hatchet-worker│  │
-    │  │ :8888   │──│  -cpu / -llm  │  │
-    │  └─────────┘  └───────────────┘  │
-    └───────────────────────────────────┘
-
-ML models box varies by mode:
-  --gpu:    Local GPU container (transcription:8000)
-  --cpu:    In-process on server/worker (no container)
-  --hosted: Remote GPU service (user URL)
-```
-
-All services communicate over Docker's internal network. Only Caddy (if enabled) exposes ports to the internet. Hatchet services are only started when `DAILY_API_KEY` is configured.
-
--- a/gpu/modal_deployments/.gitignore
+++ b/gpu/modal_deployments/.gitignore
@@ -1,33 +0,0 @@
-# OS / Editor
-.DS_Store
-.vscode/
-.idea/
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-
-# Logs
-*.log
-
-# Env and secrets
-.env
-.env.*
-*.env
-*.secret
-
-# Build / dist
-build/
-dist/
-.eggs/
-*.egg-info/
-
-# Coverage / test
-.pytest_cache/
-.coverage*
-htmlcov/
-
-# Modal local state (if any)
-modal_mounts/
-.modal_cache/
--- a/gpu/modal_deployments/deploy-all.sh
+++ b/gpu/modal_deployments/deploy-all.sh
@@ -1,161 +0,0 @@
-#!/bin/bash
-set -e
-
-# --- Usage ---
-usage() {
-    echo "Usage: $0 [OPTIONS]"
-    echo ""
-    echo "Options:"
-    echo "  --hf-token TOKEN    HuggingFace token"
-    echo "  --help              Show this help message"
-    echo ""
-    echo "Examples:"
-    echo "  $0                              # Interactive mode"
-    echo "  $0 --hf-token hf_xxxxx          # Non-interactive mode"
-    echo ""
-    exit 0
-}
-
-# --- Parse Arguments ---
-HF_TOKEN=""
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        --hf-token)
-            HF_TOKEN="$2"
-            shift 2
-            ;;
-        --help)
-            usage
-            ;;
-        *)
-            echo "Unknown option: $1"
-            usage
-            ;;
-    esac
-done
-
-echo "=========================================="
-echo "Reflector GPU Functions Deployment"
-echo "=========================================="
-echo ""
-
-# --- Check Dependencies ---
-if ! command -v modal &> /dev/null; then
-    echo "Error: Modal CLI not installed."
-    echo "  Install with: pip install modal"
-    exit 1
-fi
-
-if ! command -v openssl &> /dev/null; then
-    echo "Error: openssl not found."
-    echo "  Mac: brew install openssl"
-    echo "  Ubuntu: sudo apt-get install openssl"
-    exit 1
-fi
-
-# Check Modal authentication
-if ! modal profile current &> /dev/null; then
-    echo "Error: Not authenticated with Modal."
-    echo "  Run: modal setup"
-    exit 1
-fi
-
-# --- HuggingFace Token Setup ---
-if [ -z "$HF_TOKEN" ]; then
-    echo "HuggingFace token required for Pyannote diarization model."
-    echo "1. Create account at https://huggingface.co"
-    echo "2. Accept license at https://huggingface.co/pyannote/speaker-diarization-3.1"
-    echo "3. Generate token at https://huggingface.co/settings/tokens"
-    echo ""
-    read -p "Enter your HuggingFace token: " HF_TOKEN
-fi
-
-if [ -z "$HF_TOKEN" ]; then
-    echo "Error: HuggingFace token is required for diarization"
-    exit 1
-fi
-
-# Basic token format validation
-if [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
-    echo "Warning: HuggingFace tokens usually start with 'hf_'"
-    if [ -t 0 ]; then
-        read -p "Continue anyway? (y/n): " confirm
-        if [ "$confirm" != "y" ]; then
-            exit 1
-        fi
-    else
-        echo "Non-interactive mode: proceeding anyway"
-    fi
-fi
-
-# --- Auto-generate reflector<->GPU API Key ---
-echo ""
-echo "Generating API key for GPU services..."
-API_KEY=$(openssl rand -hex 32)
-
-# --- Create Modal Secrets ---
-echo "Creating Modal secrets..."
-
-# Create or update hf_token secret (delete first if exists)
-if modal secret list 2>/dev/null | grep -q "hf_token"; then
-    echo "  -> Recreating secret: hf_token"
-    modal secret delete hf_token --yes 2>/dev/null || true
-fi
-echo "  -> Creating secret: hf_token"
-modal secret create hf_token HF_TOKEN="$HF_TOKEN"
-
-# Create or update reflector-gpu secret (delete first if exists)
-if modal secret list 2>/dev/null | grep -q "reflector-gpu"; then
-    echo "  -> Recreating secret: reflector-gpu"
-    modal secret delete reflector-gpu --yes 2>/dev/null || true
-fi
-echo "  -> Creating secret: reflector-gpu"
-modal secret create reflector-gpu REFLECTOR_GPU_APIKEY="$API_KEY"
-
-# --- Deploy Functions ---
-echo ""
-echo "Deploying transcriber (Whisper)..."
-TRANSCRIBER_URL=$(modal deploy reflector_transcriber.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
-if [ -z "$TRANSCRIBER_URL" ]; then
-    echo "Error: Failed to deploy transcriber. Check Modal dashboard for details."
-    exit 1
-fi
-echo "  -> $TRANSCRIBER_URL"
-
-echo ""
-echo "Deploying diarizer (Pyannote)..."
-DIARIZER_URL=$(modal deploy reflector_diarizer.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
-if [ -z "$DIARIZER_URL" ]; then
-    echo "Error: Failed to deploy diarizer. Check Modal dashboard for details."
-    exit 1
-fi
-echo "  -> $DIARIZER_URL"
-
-echo ""
-echo "Deploying padding (CPU audio processing via Modal SDK)..."
-modal deploy reflector_padding.py
-if [ $? -ne 0 ]; then
-    echo "Error: Failed to deploy padding. Check Modal dashboard for details."
-    exit 1
-fi
-echo "  -> reflector-padding.pad_track (Modal SDK function)"
-
-# --- Output Configuration ---
-echo ""
-echo "=========================================="
-echo "Deployment complete!"
-echo "=========================================="
-echo ""
-echo "Copy these values to your server's server/.env file:"
-echo ""
-echo "# --- Modal GPU Configuration ---"
-echo "TRANSCRIPT_BACKEND=modal"
-echo "TRANSCRIPT_URL=$TRANSCRIBER_URL"
-echo "TRANSCRIPT_MODAL_API_KEY=$API_KEY"
-echo ""
-echo "DIARIZATION_BACKEND=modal"
-echo "DIARIZATION_URL=$DIARIZER_URL"
-echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
-echo ""
-echo "# Padding uses Modal SDK (requires MODAL_TOKEN_ID/SECRET in worker containers)"
-echo "# --- End Modal Configuration ---"
--- a/gpu/modal_deployments/reflector_padding.py
+++ b/gpu/modal_deployments/reflector_padding.py
@@ -1,277 +0,0 @@
-"""
-Reflector GPU backend - audio padding
-======================================
-
-CPU-intensive audio padding service for adding silence to audio tracks.
-Uses PyAV filter graph (adelay) for precise track synchronization.
-
-IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
-for Modal deployment isolation (Modal can't import from server/reflector/). If you modify
-the PyAV filter graph or padding algorithm, you MUST update both:
-  - gpu/modal_deployments/reflector_padding.py (this file)
-  - server/reflector/utils/audio_padding.py
-
-Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
-"""
-
-import os
-import tempfile
-from fractions import Fraction
-import math
-import asyncio
-
-import modal
-
-S3_TIMEOUT = 60 # happens 2 times
-PADDING_TIMEOUT = 600 + (S3_TIMEOUT * 2)
-SCALEDOWN_WINDOW = 60  # The maximum duration (in seconds) that individual containers can remain idle when scaling down.
-DISCONNECT_CHECK_INTERVAL = 2  # Check for client disconnect
-
-
-app = modal.App("reflector-padding")
-
-# CPU-based image
-image = (
-    modal.Image.debian_slim(python_version="3.12")
-    .apt_install("ffmpeg")  # Required by PyAV
-    .pip_install(
-        "av==13.1.0",  # PyAV for audio processing
-        "requests==2.32.3",  # HTTP for presigned URL downloads/uploads
-        "fastapi==0.115.12",  # API framework
-    )
-)
-
-# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
-OPUS_STANDARD_SAMPLE_RATE = 48000
-# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
-OPUS_DEFAULT_BIT_RATE = 128000
-
-
-@app.function(
-    cpu=2.0,
-    timeout=PADDING_TIMEOUT,
-    scaledown_window=SCALEDOWN_WINDOW,
-    image=image,
-)
-@modal.asgi_app()
-def web():
-    from fastapi import FastAPI, Request, HTTPException
-    from pydantic import BaseModel
-
-    class PaddingRequest(BaseModel):
-        track_url: str
-        output_url: str
-        start_time_seconds: float
-        track_index: int
-
-    class PaddingResponse(BaseModel):
-        size: int
-        cancelled: bool = False
-
-    web_app = FastAPI()
-
-    @web_app.post("/pad")
-    async def pad_track_endpoint(request: Request, req: PaddingRequest) -> PaddingResponse:
-        """Modal web endpoint for padding audio tracks with disconnect detection.
-        """
-        import logging
-
-        logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-        logger = logging.getLogger(__name__)
-
-        if not req.track_url:
-            raise HTTPException(status_code=400, detail="track_url cannot be empty")
-        if not req.output_url:
-            raise HTTPException(status_code=400, detail="output_url cannot be empty")
-        if req.start_time_seconds <= 0:
-            raise HTTPException(status_code=400, detail=f"start_time_seconds must be positive, got {req.start_time_seconds}")
-        if req.start_time_seconds > 18000:
-            raise HTTPException(status_code=400, detail=f"start_time_seconds exceeds maximum 18000s (5 hours)")
-
-        logger.info(f"Padding request: track {req.track_index}, delay={req.start_time_seconds}s")
-
-        # Thread-safe cancellation flag shared between async disconnect checker and blocking thread
-        import threading
-        cancelled = threading.Event()
-
-        async def check_disconnect():
-            """Background task to check for client disconnect every 2 seconds."""
-            while not cancelled.is_set():
-                await asyncio.sleep(DISCONNECT_CHECK_INTERVAL)
-                if await request.is_disconnected():
-                    logger.warning("Client disconnected, setting cancellation flag")
-                    cancelled.set()
-                    break
-
-        # Start disconnect checker in background
-        disconnect_task = asyncio.create_task(check_disconnect())
-
-        try:
-            result = await asyncio.get_event_loop().run_in_executor(
-                None, _pad_track_blocking, req, cancelled, logger
-            )
-            return PaddingResponse(**result)
-        finally:
-            cancelled.set()
-            disconnect_task.cancel()
-            try:
-                await disconnect_task
-            except asyncio.CancelledError:
-                pass
-
-    def _pad_track_blocking(req, cancelled, logger) -> dict:
-        """Blocking CPU-bound padding work with periodic cancellation checks.
-
-        Args:
-            cancelled: threading.Event for thread-safe cancellation signaling
-        """
-        import av
-        import requests
-        from av.audio.resampler import AudioResampler
-        import time
-
-        temp_dir = tempfile.mkdtemp()
-        input_path = None
-        output_path = None
-        last_check = time.time()
-
-        try:
-            logger.info("Downloading track for padding")
-            response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
-            response.raise_for_status()
-
-            input_path = os.path.join(temp_dir, "track.webm")
-            total_bytes = 0
-            chunk_count = 0
-            with open(input_path, "wb") as f:
-                for chunk in response.iter_content(chunk_size=8192):
-                    if chunk:
-                        f.write(chunk)
-                        total_bytes += len(chunk)
-                        chunk_count += 1
-
-                        # Check for cancellation every arbitrary amount of chunks
-                        if chunk_count % 12 == 0:
-                            now = time.time()
-                            if now - last_check >= DISCONNECT_CHECK_INTERVAL:
-                                if cancelled.is_set():
-                                    logger.info("Cancelled during download, exiting early")
-                                    return {"size": 0, "cancelled": True}
-                                last_check = now
-            logger.info(f"Track downloaded: {total_bytes} bytes")
-
-            if cancelled.is_set():
-                logger.info("Cancelled after download, exiting early")
-                return {"size": 0, "cancelled": True}
-
-            # Apply padding using PyAV
-            output_path = os.path.join(temp_dir, "padded.webm")
-            delay_ms = math.floor(req.start_time_seconds * 1000)
-            logger.info(f"Padding track {req.track_index} with {delay_ms}ms delay using PyAV")
-
-            in_container = av.open(input_path)
-            in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
-            if in_stream is None:
-                raise ValueError("No audio stream in input")
-
-            with av.open(output_path, "w", format="webm") as out_container:
-                out_stream = out_container.add_stream("libopus", rate=OPUS_STANDARD_SAMPLE_RATE)
-                out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
-                graph = av.filter.Graph()
-
-                abuf_args = (
-                    f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
-                    f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
-                    f"sample_fmt=s16:"
-                    f"channel_layout=stereo"
-                )
-                src = graph.add("abuffer", args=abuf_args, name="src")
-                aresample_f = graph.add("aresample", args="async=1", name="ares")
-                delays_arg = f"{delay_ms}|{delay_ms}"
-                adelay_f = graph.add("adelay", args=f"delays={delays_arg}:all=1", name="delay")
-                sink = graph.add("abuffersink", name="sink")
-
-                src.link_to(aresample_f)
-                aresample_f.link_to(adelay_f)
-                adelay_f.link_to(sink)
-                graph.configure()
-
-                resampler = AudioResampler(
-                    format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
-                )
-
-                for frame in in_container.decode(in_stream):
-                    # Check for cancellation periodically
-                    now = time.time()
-                    if now - last_check >= DISCONNECT_CHECK_INTERVAL:
-                        if cancelled.is_set():
-                            logger.info("Cancelled during processing, exiting early")
-                            in_container.close()
-                            return {"size": 0, "cancelled": True}
-                        last_check = now
-
-                    out_frames = resampler.resample(frame) or []
-                    for rframe in out_frames:
-                        rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                        rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                        src.push(rframe)
-
-                        while True:
-                            try:
-                                f_out = sink.pull()
-                            except Exception:
-                                break
-                            f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                            f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                            for packet in out_stream.encode(f_out):
-                                out_container.mux(packet)
-
-                # Flush filter graph
-                src.push(None)
-                while True:
-                    try:
-                        f_out = sink.pull()
-                    except Exception:
-                        break
-                    f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                    f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                    for packet in out_stream.encode(f_out):
-                        out_container.mux(packet)
-
-                # Flush encoder
-                for packet in out_stream.encode(None):
-                    out_container.mux(packet)
-
-            in_container.close()
-
-            file_size = os.path.getsize(output_path)
-            logger.info(f"Padding complete: {file_size} bytes")
-
-            logger.info("Uploading padded track to S3")
-
-            with open(output_path, "rb") as f:
-                upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
-
-            upload_response.raise_for_status()
-            logger.info(f"Upload complete: {file_size} bytes")
-
-            return {"size": file_size}
-
-        finally:
-            if input_path and os.path.exists(input_path):
-                try:
-                    os.unlink(input_path)
-                except Exception as e:
-                    logger.warning(f"Failed to cleanup input file: {e}")
-            if output_path and os.path.exists(output_path):
-                try:
-                    os.unlink(output_path)
-                except Exception as e:
-                    logger.warning(f"Failed to cleanup output file: {e}")
-            try:
-                os.rmdir(temp_dir)
-            except Exception as e:
-                logger.warning(f"Failed to cleanup temp directory: {e}")
-
-    return web_app
-
--- a/gpu/modal_deployments/reflector_transcriber.py
+++ b/gpu/modal_deployments/reflector_transcriber.py
@@ -1,634 +0,0 @@
-import os
-import sys
-import threading
-import uuid
-from typing import Generator, Mapping, NamedTuple, NewType, TypedDict
-from urllib.parse import urlparse
-
-import modal
-
-MODEL_NAME = "large-v2"
-MODEL_COMPUTE_TYPE: str = "float16"
-MODEL_NUM_WORKERS: int = 1
-MINUTES = 60  # seconds
-SAMPLERATE = 16000
-UPLOADS_PATH = "/uploads"
-CACHE_PATH = "/models"
-SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-VAD_CONFIG = {
-    "batch_max_duration": 30.0,
-    "silence_padding": 0.5,
-    "window_size": 512,
-}
-
-
-WhisperUniqFilename = NewType("WhisperUniqFilename", str)
-AudioFileExtension = NewType("AudioFileExtension", str)
-
-app = modal.App("reflector-transcriber")
-
-model_cache = modal.Volume.from_name("models", create_if_missing=True)
-upload_volume = modal.Volume.from_name("whisper-uploads", create_if_missing=True)
-
-
-class TimeSegment(NamedTuple):
-    """Represents a time segment with start and end times."""
-
-    start: float
-    end: float
-
-
-class AudioSegment(NamedTuple):
-    """Represents an audio segment with timing and audio data."""
-
-    start: float
-    end: float
-    audio: any
-
-
-class TranscriptResult(NamedTuple):
-    """Represents a transcription result with text and word timings."""
-
-    text: str
-    words: list["WordTiming"]
-
-
-class WordTiming(TypedDict):
-    """Represents a word with its timing information."""
-
-    word: str
-    start: float
-    end: float
-
-
-def download_model():
-    from faster_whisper import download_model
-
-    model_cache.reload()
-
-    download_model(MODEL_NAME, cache_dir=CACHE_PATH)
-
-    model_cache.commit()
-
-
-image = (
-    modal.Image.debian_slim(python_version="3.12")
-    .env(
-        {
-            "HF_HUB_ENABLE_HF_TRANSFER": "1",
-            "LD_LIBRARY_PATH": (
-                "/usr/local/lib/python3.12/site-packages/nvidia/cudnn/lib/:"
-                "/opt/conda/lib/python3.12/site-packages/nvidia/cublas/lib/"
-            ),
-        }
-    )
-    .apt_install("ffmpeg")
-    .pip_install(
-        "huggingface_hub==0.27.1",
-        "hf-transfer==0.1.9",
-        "torch==2.5.1",
-        "faster-whisper==1.1.1",
-        "fastapi==0.115.12",
-        "python-multipart",
-        "requests",
-        "librosa==0.10.1",
-        "numpy<2",
-        "silero-vad==5.1.0",
-    )
-    .run_function(download_model, volumes={CACHE_PATH: model_cache})
-)
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py
-#   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
-    parsed_url = urlparse(url)
-    url_path = parsed_url.path
-
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return AudioFileExtension(ext)
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return AudioFileExtension("mp3")
-    if "audio/wav" in content_type:
-        return AudioFileExtension("wav")
-    if "audio/mp4" in content_type:
-        return AudioFileExtension("mp4")
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return AudioFileExtension("webm")
-
-    raise ValueError(
-        f"Unsupported audio format for URL: {url}. "
-        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-    )
-
-
-def download_audio_to_volume(
-    audio_file_url: str,
-) -> tuple[WhisperUniqFilename, AudioFileExtension]:
-    import requests
-    from fastapi import HTTPException
-
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = WhisperUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
-    file_path = f"{UPLOADS_PATH}/{unique_filename}"
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    upload_volume.commit()
-    return unique_filename, audio_suffix
-
-
-def pad_audio(audio_array, sample_rate: int = SAMPLERATE):
-    """Add 0.5s of silence if audio is shorter than the silence_padding window.
-
-    Whisper does not require this strictly, but aligning behavior with Parakeet
-    avoids edge-case crashes on extremely short inputs and makes comparisons easier.
-    """
-    import numpy as np
-
-    audio_duration = len(audio_array) / sample_rate
-    if audio_duration < VAD_CONFIG["silence_padding"]:
-        silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
-        silence = np.zeros(silence_samples, dtype=np.float32)
-        return np.concatenate([audio_array, silence])
-    return audio_array
-
-
-@app.cls(
-    gpu="A10G",
-    timeout=5 * MINUTES,
-    scaledown_window=5 * MINUTES,
-    image=image,
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-)
-@modal.concurrent(max_inputs=10)
-class TranscriberWhisperLive:
-    """Live transcriber class for small audio segments (A10G).
-
-    Mirrors the Parakeet live class API but uses Faster-Whisper under the hood.
-    """
-
-    @modal.enter()
-    def enter(self):
-        import faster_whisper
-        import torch
-
-        self.lock = threading.Lock()
-        self.use_gpu = torch.cuda.is_available()
-        self.device = "cuda" if self.use_gpu else "cpu"
-        self.model = faster_whisper.WhisperModel(
-            MODEL_NAME,
-            device=self.device,
-            compute_type=MODEL_COMPUTE_TYPE,
-            num_workers=MODEL_NUM_WORKERS,
-            download_root=CACHE_PATH,
-            local_files_only=True,
-        )
-        print(f"Model is on device: {self.device}")
-
-    @modal.method()
-    def transcribe_segment(
-        self,
-        filename: str,
-        language: str = "en",
-    ):
-        """Transcribe a single uploaded audio file by filename."""
-        upload_volume.reload()
-
-        file_path = f"{UPLOADS_PATH}/{filename}"
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        with self.lock:
-            with NoStdStreams():
-                segments, _ = self.model.transcribe(
-                    file_path,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-
-        segments = list(segments)
-        text = "".join(segment.text for segment in segments).strip()
-        words = [
-            {
-                "word": word.word,
-                "start": round(float(word.start), 2),
-                "end": round(float(word.end), 2),
-            }
-            for segment in segments
-            for word in segment.words
-        ]
-
-        return {"text": text, "words": words}
-
-    @modal.method()
-    def transcribe_batch(
-        self,
-        filenames: list[str],
-        language: str = "en",
-    ):
-        """Transcribe multiple uploaded audio files and return per-file results."""
-        upload_volume.reload()
-
-        results = []
-        for filename in filenames:
-            file_path = f"{UPLOADS_PATH}/{filename}"
-            if not os.path.exists(file_path):
-                raise FileNotFoundError(f"Batch file not found: {file_path}")
-
-            with self.lock:
-                with NoStdStreams():
-                    segments, _ = self.model.transcribe(
-                        file_path,
-                        language=language,
-                        beam_size=5,
-                        word_timestamps=True,
-                        vad_filter=True,
-                        vad_parameters={"min_silence_duration_ms": 500},
-                    )
-
-            segments = list(segments)
-            text = "".join(seg.text for seg in segments).strip()
-            words = [
-                {
-                    "word": w.word,
-                    "start": round(float(w.start), 2),
-                    "end": round(float(w.end), 2),
-                }
-                for seg in segments
-                for w in seg.words
-            ]
-
-            results.append(
-                {
-                    "filename": filename,
-                    "text": text,
-                    "words": words,
-                }
-            )
-
-        return results
-
-
-@app.cls(
-    gpu="L40S",
-    timeout=15 * MINUTES,
-    image=image,
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-)
-class TranscriberWhisperFile:
-    """File transcriber for larger/longer audio, using VAD-driven batching (L40S)."""
-
-    @modal.enter()
-    def enter(self):
-        import faster_whisper
-        import torch
-        from silero_vad import load_silero_vad
-
-        self.lock = threading.Lock()
-        self.use_gpu = torch.cuda.is_available()
-        self.device = "cuda" if self.use_gpu else "cpu"
-        self.model = faster_whisper.WhisperModel(
-            MODEL_NAME,
-            device=self.device,
-            compute_type=MODEL_COMPUTE_TYPE,
-            num_workers=MODEL_NUM_WORKERS,
-            download_root=CACHE_PATH,
-            local_files_only=True,
-        )
-        self.vad_model = load_silero_vad(onnx=False)
-
-    @modal.method()
-    def transcribe_segment(
-        self, filename: str, timestamp_offset: float = 0.0, language: str = "en"
-    ):
-        import librosa
-        import numpy as np
-        from silero_vad import VADIterator
-
-        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
-        # If you modify this function, you MUST update all copies:
-        #   - gpu/modal_deployments/reflector_transcriber.py (this file)
-        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-        #   - gpu/self_hosted/app/services/transcriber.py
-        def vad_segments(
-            audio_array,
-            sample_rate: int = SAMPLERATE,
-            window_size: int = VAD_CONFIG["window_size"],
-        ) -> Generator[TimeSegment, None, None]:
-            """Generate speech segments as TimeSegment using Silero VAD."""
-            iterator = VADIterator(self.vad_model, sampling_rate=sample_rate)
-            audio_duration = len(audio_array) / float(SAMPLERATE)
-            start = None
-            for i in range(0, len(audio_array), window_size):
-                chunk = audio_array[i : i + window_size]
-                if len(chunk) < window_size:
-                    chunk = np.pad(
-                        chunk, (0, window_size - len(chunk)), mode="constant"
-                    )
-                speech = iterator(chunk)
-                if not speech:
-                    continue
-                if "start" in speech:
-                    start = speech["start"]
-                    continue
-                if "end" in speech and start is not None:
-                    end = speech["end"]
-                    yield TimeSegment(
-                        start / float(SAMPLERATE), end / float(SAMPLERATE)
-                    )
-                    start = None
-            # Handle case where audio ends while speech is still active
-            if start is not None:
-                yield TimeSegment(start / float(SAMPLERATE), audio_duration)
-            iterator.reset_states()
-
-        upload_volume.reload()
-        file_path = f"{UPLOADS_PATH}/{filename}"
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        audio_array, _sr = librosa.load(file_path, sr=SAMPLERATE, mono=True)
-
-        # Batch segments up to ~30s windows by merging contiguous VAD segments
-        merged_batches: list[TimeSegment] = []
-        batch_start = None
-        batch_end = None
-        max_duration = VAD_CONFIG["batch_max_duration"]
-        for segment in vad_segments(audio_array):
-            seg_start, seg_end = segment.start, segment.end
-            if batch_start is None:
-                batch_start, batch_end = seg_start, seg_end
-                continue
-            if seg_end - batch_start <= max_duration:
-                batch_end = seg_end
-            else:
-                merged_batches.append(TimeSegment(batch_start, batch_end))
-                batch_start, batch_end = seg_start, seg_end
-        if batch_start is not None and batch_end is not None:
-            merged_batches.append(TimeSegment(batch_start, batch_end))
-
-        all_text = []
-        all_words = []
-
-        for segment in merged_batches:
-            start_time, end_time = segment.start, segment.end
-            s_idx = int(start_time * SAMPLERATE)
-            e_idx = int(end_time * SAMPLERATE)
-            segment = audio_array[s_idx:e_idx]
-            segment = pad_audio(segment, SAMPLERATE)
-
-            with self.lock:
-                segments, _ = self.model.transcribe(
-                    segment,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-
-            segments = list(segments)
-            text = "".join(seg.text for seg in segments).strip()
-            words = [
-                {
-                    "word": w.word,
-                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
-                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
-                }
-                for seg in segments
-                for w in seg.words
-            ]
-            if text:
-                all_text.append(text)
-            all_words.extend(words)
-
-        return {"text": " ".join(all_text), "words": all_words}
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py
-#   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: dict) -> str:
-    from urllib.parse import urlparse
-
-    from fastapi import HTTPException
-
-    url_path = urlparse(url).path
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return ext
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return "mp3"
-    if "audio/wav" in content_type:
-        return "wav"
-    if "audio/mp4" in content_type:
-        return "mp4"
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return "webm"
-
-    raise HTTPException(
-        status_code=400,
-        detail=(
-            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-        ),
-    )
-
-
-def download_audio_to_volume(audio_file_url: str) -> tuple[str, str]:
-    import requests
-    from fastapi import HTTPException
-
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-    file_path = f"{UPLOADS_PATH}/{unique_filename}"
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    upload_volume.commit()
-    return unique_filename, audio_suffix
-
-
-@app.function(
-    scaledown_window=60,
-    timeout=600,
-    secrets=[
-        modal.Secret.from_name("reflector-gpu"),
-    ],
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-    image=image,
-)
-@modal.concurrent(max_inputs=40)
-@modal.asgi_app()
-def web():
-    from fastapi import (
-        Body,
-        Depends,
-        FastAPI,
-        Form,
-        HTTPException,
-        UploadFile,
-        status,
-    )
-    from fastapi.security import OAuth2PasswordBearer
-
-    transcriber_live = TranscriberWhisperLive()
-    transcriber_file = TranscriberWhisperFile()
-
-    app = FastAPI()
-
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey == os.environ["REFLECTOR_GPU_APIKEY"]:
-            return
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail="Invalid API key",
-            headers={"WWW-Authenticate": "Bearer"},
-        )
-
-    class TranscriptResponse(dict):
-        pass
-
-    @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
-    def transcribe(
-        file: UploadFile = None,
-        files: list[UploadFile] | None = None,
-        model: str = Form(MODEL_NAME),
-        language: str = Form("en"),
-        batch: bool = Form(False),
-    ):
-        if not file and not files:
-            raise HTTPException(
-                status_code=400, detail="Either 'file' or 'files' parameter is required"
-            )
-        if batch and not files:
-            raise HTTPException(
-                status_code=400, detail="Batch transcription requires 'files'"
-            )
-
-        upload_files = [file] if file else files
-
-        uploaded_filenames: list[str] = []
-        for upload_file in upload_files:
-            audio_suffix = upload_file.filename.split(".")[-1]
-            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-                    ),
-                )
-
-            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-            file_path = f"{UPLOADS_PATH}/{unique_filename}"
-            with open(file_path, "wb") as f:
-                content = upload_file.file.read()
-                f.write(content)
-            uploaded_filenames.append(unique_filename)
-
-        upload_volume.commit()
-
-        try:
-            if batch and len(upload_files) > 1:
-                func = transcriber_live.transcribe_batch.spawn(
-                    filenames=uploaded_filenames,
-                    language=language,
-                )
-                results = func.get()
-                return {"results": results}
-
-            results = []
-            for filename in uploaded_filenames:
-                func = transcriber_live.transcribe_segment.spawn(
-                    filename=filename,
-                    language=language,
-                )
-                result = func.get()
-                result["filename"] = filename
-                results.append(result)
-
-            return {"results": results} if len(results) > 1 else results[0]
-        finally:
-            for filename in uploaded_filenames:
-                try:
-                    file_path = f"{UPLOADS_PATH}/{filename}"
-                    os.remove(file_path)
-                except Exception:
-                    pass
-            upload_volume.commit()
-
-    @app.post("/v1/audio/transcriptions-from-url", dependencies=[Depends(apikey_auth)])
-    def transcribe_from_url(
-        audio_file_url: str = Body(
-            ..., description="URL of the audio file to transcribe"
-        ),
-        model: str = Body(MODEL_NAME),
-        language: str = Body("en"),
-        timestamp_offset: float = Body(0.0),
-    ):
-        unique_filename, _audio_suffix = download_audio_to_volume(audio_file_url)
-        try:
-            func = transcriber_file.transcribe_segment.spawn(
-                filename=unique_filename,
-                timestamp_offset=timestamp_offset,
-                language=language,
-            )
-            result = func.get()
-            return result
-        finally:
-            try:
-                file_path = f"{UPLOADS_PATH}/{unique_filename}"
-                os.remove(file_path)
-                upload_volume.commit()
-            except Exception:
-                pass
-
-    return app
-
-
-class NoStdStreams:
-    def __init__(self):
-        self.devnull = open(os.devnull, "w")
-
-    def __enter__(self):
-        self._stdout, self._stderr = sys.stdout, sys.stderr
-        self._stdout.flush()
-        self._stderr.flush()
-        sys.stdout, sys.stderr = self.devnull, self.devnull
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.stdout, sys.stderr = self._stdout, self._stderr
-        self.devnull.close()
--- a/gpu/self_hosted/.env.example
+++ b/gpu/self_hosted/.env.example
@@ -1,2 +0,0 @@
-REFLECTOR_GPU_APIKEY=
-HF_TOKEN=
--- a/gpu/self_hosted/.gitignore
+++ b/gpu/self_hosted/.gitignore
@@ -1,38 +0,0 @@
-cache/
-
-# OS / Editor
-.DS_Store
-.vscode/
-.idea/
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-
-# Env and secrets
-.env
-*.env
-*.secret
-HF_TOKEN
-REFLECTOR_GPU_APIKEY
-
-# Virtual env / uv
-.venv/
-venv/
-ENV/
-uv/
-
-# Build / dist
-build/
-dist/
-.eggs/
-*.egg-info/
-
-# Coverage / test
-.pytest_cache/
-.coverage*
-htmlcov/
-
-# Logs
-*.log
--- a/gpu/self_hosted/DEV_SETUP.md
+++ b/gpu/self_hosted/DEV_SETUP.md
@@ -1,137 +0,0 @@
-# Local Development GPU Setup
-
-Run transcription and diarization locally for development/testing.
-
-> **For production deployment**, see the [Self-Hosted GPU Setup Guide](../../docs/docs/installation/self-hosted-gpu-setup.md).
-
-## Prerequisites
-
-1. **Python 3.12+** and **uv** package manager
-2. **FFmpeg** installed and on PATH
-3. **HuggingFace account** with access to pyannote models
-
-### Accept Pyannote Licenses (Required)
-
-Before first run, accept licenses for these gated models (logged into HuggingFace):
- https://hf.co/pyannote/speaker-diarization-3.1
- https://hf.co/pyannote/segmentation-3.0
-
-## Quick Start
-
-### 1. Install dependencies
-
-```bash
-cd gpu/self_hosted
-uv sync
-```
-
-### 2. Start the GPU service
-
-```bash
-cd gpu/self_hosted
-HF_TOKEN=<your-huggingface-token> \
-REFLECTOR_GPU_APIKEY=dev-key-12345 \
-.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-Note: The `.env` file is NOT auto-loaded. Pass env vars explicitly or use:
-```bash
-export HF_TOKEN=<your-token>
-export REFLECTOR_GPU_APIKEY=dev-key-12345
-.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-### 3. Configure Reflector to use local GPU
-
-Edit `server/.env`:
-
-```bash
-# Transcription - local GPU service
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=http://host.docker.internal:8000
-TRANSCRIPT_MODAL_API_KEY=dev-key-12345
-
-# Diarization - local GPU service
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=http://host.docker.internal:8000
-DIARIZATION_MODAL_API_KEY=dev-key-12345
-```
-
-Note: Use `host.docker.internal` because Reflector server runs in Docker.
-
-### 4. Restart Reflector server
-
-```bash
-cd server
-docker compose restart server worker
-```
-
-## Testing
-
-### Test transcription
-
-```bash
-curl -s -X POST http://localhost:8000/v1/audio/transcriptions \
-  -H "Authorization: Bearer dev-key-12345" \
-  -F "file=@/path/to/audio.wav" \
-  -F "language=en"
-```
-
-### Test diarization
-
-```bash
-curl -s -X POST "http://localhost:8000/diarize?audio_file_url=<audio-url>" \
-  -H "Authorization: Bearer dev-key-12345"
-```
-
-## Platform Notes
-
-### macOS (ARM)
-
-Docker build fails - CUDA packages are x86_64 only. Use local Python instead:
-```bash
-uv sync
-HF_TOKEN=xxx REFLECTOR_GPU_APIKEY=xxx .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-### Linux with NVIDIA GPU
-
-Docker works with CUDA acceleration:
-```bash
-docker compose up -d
-```
-
-### CPU-only
-
-Works on any platform, just slower. PyTorch auto-detects and falls back to CPU.
-
-## Switching Back to Modal.com
-
-Edit `server/.env`:
-
-```bash
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=<modal-api-key>
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=<modal-api-key>
-```
-
-## Troubleshooting
-
-### "Could not download pyannote pipeline"
- Accept model licenses at HuggingFace (see Prerequisites)
- Verify HF_TOKEN is set and valid
-
-### Service won't start
- Check port 8000 is free: `lsof -i :8000`
- Kill orphan processes if needed
-
-### Transcription returns empty text
- Ensure audio contains speech (not just tones/silence)
- Check audio format is supported (wav, mp3, etc.)
-
-### Deprecation warnings from torchaudio/pyannote
- Safe to ignore - doesn't affect functionality
--- a/gpu/self_hosted/Dockerfile
+++ b/gpu/self_hosted/Dockerfile
@@ -1,57 +0,0 @@
-FROM python:3.12-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy \
-    UV_NO_CACHE=1
-
-# patch until nvidia updates the sha1 repo
-ADD sequoia.config /etc/crypto-policies/back-ends/sequoia.config
-
-WORKDIR /tmp
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
- --mount=type=cache,target=/var/lib/apt,sharing=locked \
- apt-get update \
- && apt-get install -y \
-    ffmpeg \
-    curl \
-    ca-certificates \
-    gnupg \
-    wget
-# Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
-ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
- --mount=type=cache,target=/var/lib/apt,sharing=locked \
- dpkg -i /cuda-keyring.deb \
- && rm /cuda-keyring.deb \
- && apt-get update \
- && apt-get install -y --no-install-recommends \
-    cuda-cudart-12-6 \
-    libcublas-12-6 \
-    libcudnn9-cuda-12 \
-    libcudnn9-dev-cuda-12
-ADD https://astral.sh/uv/install.sh /uv-installer.sh
-RUN sh /uv-installer.sh && rm /uv-installer.sh
-ENV PATH="/root/.local/bin/:$PATH"
-ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH"
-
-RUN mkdir -p /app
-WORKDIR /app
-COPY pyproject.toml uv.lock /app/
-
-
-COPY ./app /app/app
-COPY ./main.py /app/
-COPY ./runserver.sh /app/
-
-# prevent uv failing with too many open files on big cpus
-ENV UV_CONCURRENT_INSTALLS=16
-
-# first install
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv sync --compile-bytecode --locked
-
-EXPOSE 8000
-
-CMD ["sh", "/app/runserver.sh"]
-
-
--- a/gpu/self_hosted/Dockerfile.cpu
+++ b/gpu/self_hosted/Dockerfile.cpu
@@ -1,39 +0,0 @@
-FROM python:3.12-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy \
-    UV_NO_CACHE=1
-
-WORKDIR /tmp
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
- --mount=type=cache,target=/var/lib/apt,sharing=locked \
- apt-get update \
- && apt-get install -y \
-    ffmpeg \
-    curl \
-    ca-certificates \
-    gnupg \
-    wget
-ADD https://astral.sh/uv/install.sh /uv-installer.sh
-RUN sh /uv-installer.sh && rm /uv-installer.sh
-ENV PATH="/root/.local/bin/:$PATH"
-
-RUN mkdir -p /app
-WORKDIR /app
-COPY pyproject.toml uv.lock /app/
-
-
-COPY ./app /app/app
-COPY ./main.py /app/
-COPY ./runserver.sh /app/
-
-# prevent uv failing with too many open files on big cpus
-ENV UV_CONCURRENT_INSTALLS=16
-
-# first install
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv sync --compile-bytecode --locked
-
-EXPOSE 8000
-
-CMD ["sh", "/app/runserver.sh"]
--- a/gpu/self_hosted/README.md
+++ b/gpu/self_hosted/README.md
@@ -1,77 +0,0 @@
-# Self-hosted Model API
-
-Run transcription, translation, and diarization services compatible with Reflector's GPU Model API. Works on CPU or GPU.
-
-Environment variables
-
- REFLECTOR_GPU_APIKEY: Optional Bearer token. If unset, auth is disabled.
- HF_TOKEN: Optional. Required for diarization to download pyannote pipelines
-
-Requirements
-
- FFmpeg must be installed and on PATH (used for URL-based and segmented transcription)
- Python 3.12+
- NVIDIA GPU optional. If available, it will be used automatically
-
-Local run
-Set env vars in self_hosted/.env file
-uv sync
-
-uv run uvicorn main:app --host 0.0.0.0 --port 8000
-
-Authentication
-
- If REFLECTOR_GPU_APIKEY is set, include header: Authorization: Bearer <key>
-
-Endpoints
-
- POST /v1/audio/transcriptions
-
-  - multipart/form-data
-  - fields: file (single file) OR files[] (multiple files), language, batch (true/false)
-  - response: single { text, words, filename } or { results: [ ... ] }
-
- POST /v1/audio/transcriptions-from-url
-
-  - application/json
-  - body: { audio_file_url, language, timestamp_offset }
-  - response: { text, words }
-
- POST /translate
-
-  - text: query parameter
-  - body (application/json): { source_language, target_language }
-  - response: { text: { <src>: original, <tgt>: translated } }
-
- POST /diarize
-  - query parameters: audio_file_url, timestamp (optional)
-  - requires HF_TOKEN to be set (for pyannote)
-  - response: { diarization: [ { start, end, speaker } ] }
-
-OpenAPI docs
-
- Visit /docs when the server is running
-
-Docker
-
- Not yet provided in this directory. A Dockerfile will be added later. For now, use Local run above
-
-# Setup
-
-[SETUP.md](SETUP.md)
-
-# Conformance tests
-
-## From this directory
-
-TRANSCRIPT_URL=http://localhost:8000 \
-TRANSCRIPT_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_transcript.py
-
-TRANSLATION_URL=http://localhost:8000 \
-TRANSLATION_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_translation.py
-
-DIARIZATION_URL=http://localhost:8000 \
-DIARIZATION_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_diarization.py
--- a/gpu/self_hosted/app/auth.py
+++ b/gpu/self_hosted/app/auth.py
@@ -1,19 +0,0 @@
-import os
-
-from fastapi import Depends, HTTPException, status
-from fastapi.security import OAuth2PasswordBearer
-
-oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
-
-
-def apikey_auth(apikey: str | None = Depends(oauth2_scheme)):
-    required_key = os.environ.get("REFLECTOR_GPU_APIKEY")
-    if not required_key:
-        return
-    if apikey and apikey == required_key:
-        return
-    raise HTTPException(
-        status_code=status.HTTP_401_UNAUTHORIZED,
-        detail="Invalid API key",
-        headers={"WWW-Authenticate": "Bearer"},
-    )
--- a/gpu/self_hosted/app/config.py
+++ b/gpu/self_hosted/app/config.py
@@ -1,12 +0,0 @@
-from pathlib import Path
-
-SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-SAMPLE_RATE = 16000
-VAD_CONFIG = {
-    "batch_max_duration": 30.0,
-    "silence_padding": 0.5,
-    "window_size": 512,
-}
-
-# App-level paths
-UPLOADS_PATH = Path("/tmp/whisper-uploads")
--- a/gpu/self_hosted/app/factory.py
+++ b/gpu/self_hosted/app/factory.py
@@ -1,32 +0,0 @@
-from contextlib import asynccontextmanager
-
-from fastapi import FastAPI
-
-from .routers.diarization import router as diarization_router
-from .routers.padding import router as padding_router
-from .routers.transcription import router as transcription_router
-from .routers.translation import router as translation_router
-from .services.transcriber import WhisperService
-from .services.diarizer import PyannoteDiarizationService
-from .utils import ensure_dirs
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    ensure_dirs()
-    whisper_service = WhisperService()
-    whisper_service.load()
-    app.state.whisper = whisper_service
-    diarization_service = PyannoteDiarizationService()
-    diarization_service.load()
-    app.state.diarizer = diarization_service
-    yield
-
-
-def create_app() -> FastAPI:
-    app = FastAPI(lifespan=lifespan)
-    app.include_router(transcription_router)
-    app.include_router(translation_router)
-    app.include_router(diarization_router)
-    app.include_router(padding_router)
-    return app
--- a/gpu/self_hosted/app/routers/diarization.py
+++ b/gpu/self_hosted/app/routers/diarization.py
@@ -1,30 +0,0 @@
-from typing import List
-
-from fastapi import APIRouter, Depends, Request
-from pydantic import BaseModel
-
-from ..auth import apikey_auth
-from ..services.diarizer import PyannoteDiarizationService
-from ..utils import download_audio_file
-
-router = APIRouter(tags=["diarization"])
-
-
-class DiarizationSegment(BaseModel):
-    start: float
-    end: float
-    speaker: int
-
-
-class DiarizationResponse(BaseModel):
-    diarization: List[DiarizationSegment]
-
-
-@router.post(
-    "/diarize", dependencies=[Depends(apikey_auth)], response_model=DiarizationResponse
-)
-def diarize(request: Request, audio_file_url: str, timestamp: float = 0.0):
-    with download_audio_file(audio_file_url) as (file_path, _ext):
-        file_path = str(file_path)
-        diarizer: PyannoteDiarizationService = request.app.state.diarizer
-        return diarizer.diarize_file(file_path, timestamp=timestamp)
--- a/gpu/self_hosted/app/routers/padding.py
+++ b/gpu/self_hosted/app/routers/padding.py
@@ -1,199 +0,0 @@
-"""
-Audio padding endpoint for selfhosted GPU service.
-
-CPU-intensive audio padding service for adding silence to audio tracks.
-Uses PyAV filter graph (adelay) for precise track synchronization.
-
-IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
-for deployment isolation (self_hosted can't import from server/reflector/). If you modify
-the PyAV filter graph or padding algorithm, you MUST update both:
-  - gpu/self_hosted/app/routers/padding.py (this file)
-  - server/reflector/utils/audio_padding.py
-
-Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
-"""
-
-import logging
-import math
-import os
-import tempfile
-from fractions import Fraction
-
-import av
-import requests
-from av.audio.resampler import AudioResampler
-from fastapi import APIRouter, Depends, HTTPException
-from pydantic import BaseModel
-
-from ..auth import apikey_auth
-
-logger = logging.getLogger(__name__)
-
-router = APIRouter(tags=["padding"])
-
-# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
-OPUS_STANDARD_SAMPLE_RATE = 48000
-OPUS_DEFAULT_BIT_RATE = 128000
-
-S3_TIMEOUT = 60
-
-
-class PaddingRequest(BaseModel):
-    track_url: str
-    output_url: str
-    start_time_seconds: float
-    track_index: int
-
-
-class PaddingResponse(BaseModel):
-    size: int
-    cancelled: bool = False
-
-
-@router.post("/pad", dependencies=[Depends(apikey_auth)], response_model=PaddingResponse)
-def pad_track(req: PaddingRequest):
-    """Pad audio track with silence using PyAV adelay filter graph."""
-    if not req.track_url:
-        raise HTTPException(status_code=400, detail="track_url cannot be empty")
-    if not req.output_url:
-        raise HTTPException(status_code=400, detail="output_url cannot be empty")
-    if req.start_time_seconds <= 0:
-        raise HTTPException(
-            status_code=400,
-            detail=f"start_time_seconds must be positive, got {req.start_time_seconds}",
-        )
-    if req.start_time_seconds > 18000:
-        raise HTTPException(
-            status_code=400,
-            detail="start_time_seconds exceeds maximum 18000s (5 hours)",
-        )
-
-    logger.info(
-        "Padding request: track %d, delay=%.3fs", req.track_index, req.start_time_seconds
-    )
-
-    temp_dir = tempfile.mkdtemp()
-    input_path = None
-    output_path = None
-
-    try:
-        # Download source audio
-        logger.info("Downloading track for padding")
-        response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
-        response.raise_for_status()
-
-        input_path = os.path.join(temp_dir, "track.webm")
-        total_bytes = 0
-        with open(input_path, "wb") as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                if chunk:
-                    f.write(chunk)
-                    total_bytes += len(chunk)
-        logger.info("Track downloaded: %d bytes", total_bytes)
-
-        # Apply padding using PyAV
-        output_path = os.path.join(temp_dir, "padded.webm")
-        delay_ms = math.floor(req.start_time_seconds * 1000)
-        logger.info("Padding track %d with %dms delay using PyAV", req.track_index, delay_ms)
-
-        in_container = av.open(input_path)
-        in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
-        if in_stream is None:
-            in_container.close()
-            raise HTTPException(status_code=400, detail="No audio stream in input")
-
-        with av.open(output_path, "w", format="webm") as out_container:
-            out_stream = out_container.add_stream("libopus", rate=OPUS_STANDARD_SAMPLE_RATE)
-            out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
-            graph = av.filter.Graph()
-
-            abuf_args = (
-                f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
-                f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
-                f"sample_fmt=s16:"
-                f"channel_layout=stereo"
-            )
-            src = graph.add("abuffer", args=abuf_args, name="src")
-            aresample_f = graph.add("aresample", args="async=1", name="ares")
-            delays_arg = f"{delay_ms}|{delay_ms}"
-            adelay_f = graph.add(
-                "adelay", args=f"delays={delays_arg}:all=1", name="delay"
-            )
-            sink = graph.add("abuffersink", name="sink")
-
-            src.link_to(aresample_f)
-            aresample_f.link_to(adelay_f)
-            adelay_f.link_to(sink)
-            graph.configure()
-
-            resampler = AudioResampler(
-                format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
-            )
-
-            for frame in in_container.decode(in_stream):
-                out_frames = resampler.resample(frame) or []
-                for rframe in out_frames:
-                    rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                    rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                    src.push(rframe)
-
-                    while True:
-                        try:
-                            f_out = sink.pull()
-                        except Exception:
-                            break
-                        f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                        f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                        for packet in out_stream.encode(f_out):
-                            out_container.mux(packet)
-
-            # Flush filter graph
-            src.push(None)
-            while True:
-                try:
-                    f_out = sink.pull()
-                except Exception:
-                    break
-                f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                for packet in out_stream.encode(f_out):
-                    out_container.mux(packet)
-
-            # Flush encoder
-            for packet in out_stream.encode(None):
-                out_container.mux(packet)
-
-        in_container.close()
-
-        file_size = os.path.getsize(output_path)
-        logger.info("Padding complete: %d bytes", file_size)
-
-        # Upload padded track
-        logger.info("Uploading padded track to S3")
-        with open(output_path, "rb") as f:
-            upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
-        upload_response.raise_for_status()
-        logger.info("Upload complete: %d bytes", file_size)
-
-        return PaddingResponse(size=file_size)
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error("Padding failed for track %d: %s", req.track_index, e, exc_info=True)
-        raise HTTPException(status_code=500, detail=f"Padding failed: {e}") from e
-    finally:
-        if input_path and os.path.exists(input_path):
-            try:
-                os.unlink(input_path)
-            except Exception as e:
-                logger.warning("Failed to cleanup input file: %s", e)
-        if output_path and os.path.exists(output_path):
-            try:
-                os.unlink(output_path)
-            except Exception as e:
-                logger.warning("Failed to cleanup output file: %s", e)
-        try:
-            os.rmdir(temp_dir)
-        except Exception as e:
-            logger.warning("Failed to cleanup temp directory: %s", e)
--- a/gpu/self_hosted/app/routers/transcription.py
+++ b/gpu/self_hosted/app/routers/transcription.py
@@ -1,109 +0,0 @@
-import uuid
-from typing import Optional, Union
-
-from fastapi import APIRouter, Body, Depends, Form, HTTPException, Request, UploadFile
-from pydantic import BaseModel
-from pathlib import Path
-from ..auth import apikey_auth
-from ..config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
-from ..services.transcriber import MODEL_NAME
-from ..utils import cleanup_uploaded_files, download_audio_file
-
-router = APIRouter(prefix="/v1/audio", tags=["transcription"])
-
-
-class WordTiming(BaseModel):
-    word: str
-    start: float
-    end: float
-
-
-class TranscriptResult(BaseModel):
-    text: str
-    words: list[WordTiming]
-    filename: Optional[str] = None
-
-
-class TranscriptBatchResponse(BaseModel):
-    results: list[TranscriptResult]
-
-
-@router.post(
-    "/transcriptions",
-    dependencies=[Depends(apikey_auth)],
-    response_model=Union[TranscriptResult, TranscriptBatchResponse],
-)
-def transcribe(
-    request: Request,
-    file: UploadFile = None,
-    files: list[UploadFile] | None = None,
-    model: str = Form(MODEL_NAME),
-    language: str = Form("en"),
-    batch: bool = Form(False),
-):
-    service = request.app.state.whisper
-    if not file and not files:
-        raise HTTPException(
-            status_code=400, detail="Either 'file' or 'files' parameter is required"
-        )
-    if batch and not files:
-        raise HTTPException(
-            status_code=400, detail="Batch transcription requires 'files'"
-        )
-
-    upload_files = [file] if file else files
-
-    uploaded_paths: list[Path] = []
-    with cleanup_uploaded_files(uploaded_paths):
-        for upload_file in upload_files:
-            audio_suffix = upload_file.filename.split(".")[-1].lower()
-            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-                    ),
-                )
-            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-            file_path = UPLOADS_PATH / unique_filename
-            with open(file_path, "wb") as f:
-                content = upload_file.file.read()
-                f.write(content)
-            uploaded_paths.append(file_path)
-
-        if batch and len(upload_files) > 1:
-            results = []
-            for path in uploaded_paths:
-                result = service.transcribe_file(str(path), language=language)
-                result["filename"] = path.name
-                results.append(result)
-            return {"results": results}
-
-        results = []
-        for path in uploaded_paths:
-            result = service.transcribe_file(str(path), language=language)
-            result["filename"] = path.name
-            results.append(result)
-
-        return {"results": results} if len(results) > 1 else results[0]
-
-
-@router.post(
-    "/transcriptions-from-url",
-    dependencies=[Depends(apikey_auth)],
-    response_model=TranscriptResult,
-)
-def transcribe_from_url(
-    request: Request,
-    audio_file_url: str = Body(..., description="URL of the audio file to transcribe"),
-    model: str = Body(MODEL_NAME),
-    language: str = Body("en"),
-    timestamp_offset: float = Body(0.0),
-):
-    service = request.app.state.whisper
-    with download_audio_file(audio_file_url) as (file_path, _ext):
-        file_path = str(file_path)
-        result = service.transcribe_vad_url_segment(
-            file_path=file_path, timestamp_offset=timestamp_offset, language=language
-        )
-        return result
--- a/gpu/self_hosted/app/routers/translation.py
+++ b/gpu/self_hosted/app/routers/translation.py
@@ -1,28 +0,0 @@
-from typing import Dict
-
-from fastapi import APIRouter, Body, Depends
-from pydantic import BaseModel
-
-from ..auth import apikey_auth
-from ..services.translator import TextTranslatorService
-
-router = APIRouter(tags=["translation"])
-
-translator = TextTranslatorService()
-
-
-class TranslationResponse(BaseModel):
-    text: Dict[str, str]
-
-
-@router.post(
-    "/translate",
-    dependencies=[Depends(apikey_auth)],
-    response_model=TranslationResponse,
-)
-def translate(
-    text: str,
-    source_language: str = Body("en"),
-    target_language: str = Body("fr"),
-):
-    return translator.translate(text, source_language, target_language)
--- a/gpu/self_hosted/app/services/diarizer.py
+++ b/gpu/self_hosted/app/services/diarizer.py
@@ -1,107 +0,0 @@
-import logging
-import os
-import tarfile
-import threading
-from pathlib import Path
-from urllib.request import urlopen
-
-import torch
-import torchaudio
-import yaml
-from pyannote.audio import Pipeline
-
-logger = logging.getLogger(__name__)
-
-S3_BUNDLE_URL = "https://reflector-public.s3.us-east-1.amazonaws.com/pyannote-speaker-diarization-3.1.tar.gz"
-BUNDLE_CACHE_DIR = Path("/root/.cache/pyannote-bundle")
-
-
-def _ensure_model(cache_dir: Path) -> str:
-    """Download and extract S3 model bundle if not cached."""
-    model_dir = cache_dir / "pyannote-speaker-diarization-3.1"
-    config_path = model_dir / "config.yaml"
-
-    if config_path.exists():
-        logger.info("Using cached model bundle at %s", model_dir)
-        return str(model_dir)
-
-    cache_dir.mkdir(parents=True, exist_ok=True)
-    tarball_path = cache_dir / "model.tar.gz"
-
-    logger.info("Downloading model bundle from %s", S3_BUNDLE_URL)
-    with urlopen(S3_BUNDLE_URL) as response, open(tarball_path, "wb") as f:
-        while chunk := response.read(8192):
-            f.write(chunk)
-
-    logger.info("Extracting model bundle")
-    with tarfile.open(tarball_path, "r:gz") as tar:
-        tar.extractall(path=cache_dir, filter="data")
-    tarball_path.unlink()
-
-    _patch_config(model_dir, cache_dir)
-    return str(model_dir)
-
-
-def _patch_config(model_dir: Path, cache_dir: Path) -> None:
-    """Rewrite config.yaml to reference local pytorch_model.bin paths."""
-    config_path = model_dir / "config.yaml"
-    with open(config_path) as f:
-        config = yaml.safe_load(f)
-
-    config["pipeline"]["params"]["segmentation"] = str(
-        cache_dir / "pyannote-segmentation-3.0" / "pytorch_model.bin"
-    )
-    config["pipeline"]["params"]["embedding"] = str(
-        cache_dir / "pyannote-wespeaker-voxceleb-resnet34-LM" / "pytorch_model.bin"
-    )
-
-    with open(config_path, "w") as f:
-        yaml.dump(config, f)
-
-    logger.info("Patched config.yaml with local model paths")
-
-
-class PyannoteDiarizationService:
-    def __init__(self):
-        self._pipeline = None
-        self._device = "cpu"
-        self._lock = threading.Lock()
-
-    def load(self):
-        self._device = "cuda" if torch.cuda.is_available() else "cpu"
-        hf_token = os.environ.get("HF_TOKEN")
-
-        if hf_token:
-            logger.info("Loading pyannote model from HuggingFace (HF_TOKEN set)")
-            self._pipeline = Pipeline.from_pretrained(
-                "pyannote/speaker-diarization-3.1",
-                use_auth_token=hf_token,
-            )
-        else:
-            logger.info("HF_TOKEN not set — loading model from S3 bundle")
-            model_path = _ensure_model(BUNDLE_CACHE_DIR)
-            config_path = Path(model_path) / "config.yaml"
-            self._pipeline = Pipeline.from_pretrained(str(config_path))
-
-        self._pipeline.to(torch.device(self._device))
-
-    def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:
-        if self._pipeline is None:
-            self.load()
-        waveform, sample_rate = torchaudio.load(file_path)
-        with self._lock:
-            diarization = self._pipeline(
-                {"waveform": waveform, "sample_rate": sample_rate}
-            )
-        words = []
-        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
-            words.append(
-                {
-                    "start": round(timestamp + diarization_segment.start, 3),
-                    "end": round(timestamp + diarization_segment.end, 3),
-                    "speaker": int(speaker[-2:])
-                    if speaker and speaker[-2:].isdigit()
-                    else 0,
-                }
-            )
-        return {"diarization": words}
--- a/gpu/self_hosted/app/services/transcriber.py
+++ b/gpu/self_hosted/app/services/transcriber.py
@@ -1,217 +0,0 @@
-import os
-import shutil
-import subprocess
-import threading
-from typing import Generator
-
-import faster_whisper
-import librosa
-import numpy as np
-import torch
-from fastapi import HTTPException
-from silero_vad import VADIterator, load_silero_vad
-
-from ..config import SAMPLE_RATE, VAD_CONFIG
-
-# Whisper configuration (service-local defaults)
-MODEL_NAME = "large-v2"
-# None delegates compute type to runtime: float16 on CUDA, int8 on CPU
-MODEL_COMPUTE_TYPE = None
-MODEL_NUM_WORKERS = 1
-CACHE_PATH = os.path.join(os.path.expanduser("~"), ".cache", "reflector-whisper")
-from ..utils import NoStdStreams
-
-
-class WhisperService:
-    def __init__(self):
-        self.model = None
-        self.device = "cpu"
-        self.lock = threading.Lock()
-
-    def load(self):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        compute_type = MODEL_COMPUTE_TYPE or (
-            "float16" if self.device == "cuda" else "int8"
-        )
-        self.model = faster_whisper.WhisperModel(
-            MODEL_NAME,
-            device=self.device,
-            compute_type=compute_type,
-            num_workers=MODEL_NUM_WORKERS,
-            download_root=CACHE_PATH,
-        )
-
-    def pad_audio(self, audio_array, sample_rate: int = SAMPLE_RATE):
-        audio_duration = len(audio_array) / sample_rate
-        if audio_duration < VAD_CONFIG["silence_padding"]:
-            silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
-            silence = np.zeros(silence_samples, dtype=np.float32)
-            return np.concatenate([audio_array, silence])
-        return audio_array
-
-    def enforce_word_timing_constraints(self, words: list[dict]) -> list[dict]:
-        if len(words) <= 1:
-            return words
-        enforced: list[dict] = []
-        for i, word in enumerate(words):
-            current = dict(word)
-            if i < len(words) - 1:
-                next_start = words[i + 1]["start"]
-                if current["end"] > next_start:
-                    current["end"] = next_start
-            enforced.append(current)
-        return enforced
-
-    def transcribe_file(self, file_path: str, language: str = "en") -> dict:
-        input_for_model: str | "object" = file_path
-        try:
-            audio_array, _sample_rate = librosa.load(
-                file_path, sr=SAMPLE_RATE, mono=True
-            )
-            if len(audio_array) / float(SAMPLE_RATE) < VAD_CONFIG["silence_padding"]:
-                input_for_model = self.pad_audio(audio_array, SAMPLE_RATE)
-        except Exception:
-            pass
-
-        with self.lock:
-            with NoStdStreams():
-                segments, _ = self.model.transcribe(
-                    input_for_model,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-
-        segments = list(segments)
-        text = "".join(segment.text for segment in segments).strip()
-        words = [
-            {
-                "word": word.word,
-                "start": round(float(word.start), 2),
-                "end": round(float(word.end), 2),
-            }
-            for segment in segments
-            for word in segment.words
-        ]
-        words = self.enforce_word_timing_constraints(words)
-        return {"text": text, "words": words}
-
-    def transcribe_vad_url_segment(
-        self, file_path: str, timestamp_offset: float = 0.0, language: str = "en"
-    ) -> dict:
-        def load_audio_via_ffmpeg(input_path: str, sample_rate: int) -> np.ndarray:
-            ffmpeg_bin = shutil.which("ffmpeg") or "ffmpeg"
-            cmd = [
-                ffmpeg_bin,
-                "-nostdin",
-                "-threads",
-                "1",
-                "-i",
-                input_path,
-                "-f",
-                "f32le",
-                "-acodec",
-                "pcm_f32le",
-                "-ac",
-                "1",
-                "-ar",
-                str(sample_rate),
-                "pipe:1",
-            ]
-            try:
-                proc = subprocess.run(
-                    cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True
-                )
-            except Exception as e:
-                raise HTTPException(status_code=400, detail=f"ffmpeg failed: {e}")
-            audio = np.frombuffer(proc.stdout, dtype=np.float32)
-            return audio
-
-        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
-        # If you modify this function, you MUST update all copies:
-        #   - gpu/modal_deployments/reflector_transcriber.py
-        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-        #   - gpu/self_hosted/app/services/transcriber.py (this file)
-        def vad_segments(
-            audio_array,
-            sample_rate: int = SAMPLE_RATE,
-            window_size: int = VAD_CONFIG["window_size"],
-        ) -> Generator[tuple[float, float], None, None]:
-            vad_model = load_silero_vad(onnx=False)
-            iterator = VADIterator(vad_model, sampling_rate=sample_rate)
-            start = None
-            for i in range(0, len(audio_array), window_size):
-                chunk = audio_array[i : i + window_size]
-                if len(chunk) < window_size:
-                    chunk = np.pad(
-                        chunk, (0, window_size - len(chunk)), mode="constant"
-                    )
-                speech = iterator(chunk)
-                if not speech:
-                    continue
-                if "start" in speech:
-                    start = speech["start"]
-                    continue
-                if "end" in speech and start is not None:
-                    end = speech["end"]
-                    yield (start / float(SAMPLE_RATE), end / float(SAMPLE_RATE))
-                    start = None
-            # Handle case where audio ends while speech is still active
-            if start is not None:
-                audio_duration = len(audio_array) / float(sample_rate)
-                yield (start / float(SAMPLE_RATE), audio_duration)
-            iterator.reset_states()
-
-        audio_array = load_audio_via_ffmpeg(file_path, SAMPLE_RATE)
-
-        merged_batches: list[tuple[float, float]] = []
-        batch_start = None
-        batch_end = None
-        max_duration = VAD_CONFIG["batch_max_duration"]
-        for seg_start, seg_end in vad_segments(audio_array):
-            if batch_start is None:
-                batch_start, batch_end = seg_start, seg_end
-                continue
-            if seg_end - batch_start <= max_duration:
-                batch_end = seg_end
-            else:
-                merged_batches.append((batch_start, batch_end))
-                batch_start, batch_end = seg_start, seg_end
-        if batch_start is not None and batch_end is not None:
-            merged_batches.append((batch_start, batch_end))
-
-        all_text = []
-        all_words = []
-        for start_time, end_time in merged_batches:
-            s_idx = int(start_time * SAMPLE_RATE)
-            e_idx = int(end_time * SAMPLE_RATE)
-            segment = audio_array[s_idx:e_idx]
-            segment = self.pad_audio(segment, SAMPLE_RATE)
-            with self.lock:
-                segments, _ = self.model.transcribe(
-                    segment,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-            segments = list(segments)
-            text = "".join(seg.text for seg in segments).strip()
-            words = [
-                {
-                    "word": w.word,
-                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
-                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
-                }
-                for seg in segments
-                for w in seg.words
-            ]
-            if text:
-                all_text.append(text)
-            all_words.extend(words)
-
-        all_words = self.enforce_word_timing_constraints(all_words)
-        return {"text": " ".join(all_text), "words": all_words}
--- a/gpu/self_hosted/app/services/translator.py
+++ b/gpu/self_hosted/app/services/translator.py
@@ -1,44 +0,0 @@
-import threading
-
-from transformers import MarianMTModel, MarianTokenizer, pipeline
-
-
-class TextTranslatorService:
-    """Simple text-to-text translator using HuggingFace MarianMT models.
-
-    This mirrors the modal translator API shape but uses text translation only.
-    """
-
-    def __init__(self):
-        self._pipeline = None
-        self._lock = threading.Lock()
-
-    def load(self, source_language: str = "en", target_language: str = "fr"):
-        # Pick a default MarianMT model pair if available; fall back to Helsinki-NLP en->fr
-        model_name = self._resolve_model_name(source_language, target_language)
-        tokenizer = MarianTokenizer.from_pretrained(model_name)
-        model = MarianMTModel.from_pretrained(model_name)
-        self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
-
-    def _resolve_model_name(self, src: str, tgt: str) -> str:
-        # Minimal mapping; extend as needed
-        pair = (src.lower(), tgt.lower())
-        mapping = {
-            ("en", "fr"): "Helsinki-NLP/opus-mt-en-fr",
-            ("fr", "en"): "Helsinki-NLP/opus-mt-fr-en",
-            ("en", "es"): "Helsinki-NLP/opus-mt-en-es",
-            ("es", "en"): "Helsinki-NLP/opus-mt-es-en",
-            ("en", "de"): "Helsinki-NLP/opus-mt-en-de",
-            ("de", "en"): "Helsinki-NLP/opus-mt-de-en",
-        }
-        return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr")
-
-    def translate(self, text: str, source_language: str, target_language: str) -> dict:
-        if self._pipeline is None:
-            self.load(source_language, target_language)
-        with self._lock:
-            results = self._pipeline(
-                text, src_lang=source_language, tgt_lang=target_language
-            )
-        translated = results[0]["translation_text"] if results else ""
-        return {"text": {source_language: text, target_language: translated}}
--- a/gpu/self_hosted/app/utils.py
+++ b/gpu/self_hosted/app/utils.py
@@ -1,115 +0,0 @@
-import logging
-import os
-import sys
-import uuid
-from contextlib import contextmanager
-from typing import Mapping
-from urllib.parse import urlparse
-from pathlib import Path
-
-import requests
-from fastapi import HTTPException
-
-from .config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
-
-logger = logging.getLogger(__name__)
-
-
-class NoStdStreams:
-    def __init__(self):
-        self.devnull = open(os.devnull, "w")
-
-    def __enter__(self):
-        self._stdout, self._stderr = sys.stdout, sys.stderr
-        self._stdout.flush()
-        self._stderr.flush()
-        sys.stdout, sys.stderr = self.devnull, self.devnull
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.stdout, sys.stderr = self._stdout, self._stderr
-        self.devnull.close()
-
-
-def ensure_dirs():
-    UPLOADS_PATH.mkdir(parents=True, exist_ok=True)
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py (this file)
-#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: Mapping[str, str]) -> str:
-    url_path = urlparse(url).path
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return ext
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return "mp3"
-    if "audio/wav" in content_type:
-        return "wav"
-    if "audio/mp4" in content_type:
-        return "mp4"
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return "webm"
-
-    raise HTTPException(
-        status_code=400,
-        detail=(
-            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-        ),
-    )
-
-
-def download_audio_to_uploads(audio_file_url: str) -> tuple[Path, str]:
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-    file_path: Path = UPLOADS_PATH / unique_filename
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    return file_path, audio_suffix
-
-
-@contextmanager
-def download_audio_file(audio_file_url: str):
-    """Download an audio file to UPLOADS_PATH and remove it after use.
-
-    Yields (file_path: Path, audio_suffix: str).
-    """
-    file_path, audio_suffix = download_audio_to_uploads(audio_file_url)
-    try:
-        yield file_path, audio_suffix
-    finally:
-        try:
-            file_path.unlink(missing_ok=True)
-        except Exception as e:
-            logger.error("Error deleting temporary file %s: %s", file_path, e)
-
-
-@contextmanager
-def cleanup_uploaded_files(file_paths: list[Path]):
-    """Ensure provided file paths are removed after use.
-
-    The provided list can be populated inside the context; all present entries
-    at exit will be deleted.
-    """
-    try:
-        yield file_paths
-    finally:
-        for path in list(file_paths):
-            try:
-                path.unlink(missing_ok=True)
-            except Exception as e:
-                logger.error("Error deleting temporary file %s: %s", path, e)
--- a/gpu/self_hosted/compose.yml
+++ b/gpu/self_hosted/compose.yml
@@ -1,18 +0,0 @@
-services:
-  reflector_gpu:
-    build:
-      context: .
-    ports:
-      - "8000:8000"
-    env_file:
-      - .env
-    volumes:
-      - ./cache:/root/.cache
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    restart: unless-stopped
--- a/gpu/self_hosted/main.py
+++ b/gpu/self_hosted/main.py
@@ -1,3 +0,0 @@
-from app.factory import create_app
-
-app = create_app()
--- a/gpu/self_hosted/pyproject.toml
+++ b/gpu/self_hosted/pyproject.toml
@@ -1,21 +0,0 @@
-[project]
-name = "reflector-gpu"
-version = "0.1.0"
-description = "Self-hosted GPU service for speech transcription, diarization, and translation via FastAPI."
-readme = "README.md"
-requires-python = ">=3.12"
-dependencies = [
-    "fastapi[standard]>=0.116.1",
-    "uvicorn[standard]>=0.30.0",
-    "torch>=2.3.0",
-    "faster-whisper>=1.1.0",
-    "librosa==0.10.1",
-    "numpy<2",
-    "silero-vad==5.1.2",
-    "transformers>=4.35.0",
-    "sentencepiece",
-    "pyannote.audio==3.4.0",
-    "pytorch-lightning<2.6",
-    "torchaudio>=2.3.0",
-    "av>=13.1.0",
-]
--- a/gpu/self_hosted/runserver.sh
+++ b/gpu/self_hosted/runserver.sh
@@ -1,17 +0,0 @@
-#!/bin/sh
-set -e
-
-export PATH="/root/.local/bin:$PATH"
-cd /app
-
-# Install Python dependencies at runtime (first run or when FORCE_SYNC=1)
-if [ ! -d "/app/.venv" ] || [ "$FORCE_SYNC" = "1" ]; then
-  echo "[startup] Installing Python dependencies with uv..."
-  uv sync --compile-bytecode --locked
-else
-  echo "[startup] Using existing virtual environment at /app/.venv"
-fi
-
-exec uv run uvicorn main:app --host 0.0.0.0 --port 8000
-
-
--- a/gpu/self_hosted/sequoia.config
+++ b/gpu/self_hosted/sequoia.config
@@ -1,2 +0,0 @@
-[hash_algorithms]
-sha1 = "always"
--- a/gpu/self_hosted/uv.lock
+++ b/gpu/self_hosted/uv.lock
--- a/node_modules/.yarn-integrity
+++ b/node_modules/.yarn-integrity
@@ -1,10 +0,0 @@
-{
-  "systemParams": "darwin-x64-83",
-  "modulesFolders": [],
-  "flags": [],
-  "linkedModules": [],
-  "topLevelPatterns": [],
-  "lockfileEntries": {},
-  "files": [],
-  "artifacts": {}
-}
--- a/scripts/garage.toml
+++ b/scripts/garage.toml
@@ -1,14 +0,0 @@
-metadata_dir = "/var/lib/garage/meta"
-data_dir = "/var/lib/garage/data"
-replication_factor = 1
-
-rpc_secret = "__GARAGE_RPC_SECRET__"
-rpc_bind_addr = "[::]:3901"
-
-[s3_api]
-api_bind_addr = "[::]:3900"
-s3_region = "garage"
-root_domain = ".s3.garage.localhost"
-
-[admin]
-api_bind_addr = "[::]:3903"
--- a/scripts/install-docker-ubuntu.sh
+++ b/scripts/install-docker-ubuntu.sh
@@ -1,87 +0,0 @@
-#!/usr/bin/env bash
-#
-# Install Docker Engine + Compose plugin on Ubuntu.
-# Ubuntu's default repos don't include docker-compose-plugin, so we add Docker's official repo.
-#
-# Usage:
-#   ./scripts/install-docker-ubuntu.sh
-#
-# Requires: root or sudo
-#
-set -euo pipefail
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-
-# --- Colors ---
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-CYAN='\033[0;36m'
-NC='\033[0m'
-
-info()  { echo -e "${CYAN}==>${NC} $*"; }
-ok()    { echo -e "${GREEN}  ✓${NC} $*"; }
-warn()  { echo -e "${YELLOW}  !${NC} $*"; }
-err()   { echo -e "${RED}  ✗${NC} $*" >&2; }
-
-# Use sudo if available and not root; otherwise run directly
-if [[ $(id -u) -eq 0 ]]; then
-    MAYBE_SUDO=""
-elif command -v sudo &>/dev/null; then
-    MAYBE_SUDO="sudo "
-else
-    err "Need root. Run as root or install sudo: apt install sudo"
-    exit 1
-fi
-
-# Check Ubuntu
-if [[ ! -f /etc/os-release ]]; then
-    err "Cannot detect OS. This script is for Ubuntu."
-    exit 1
-fi
-source /etc/os-release
-if [[ "${ID:-}" != "ubuntu" ]] && [[ "${ID_LIKE:-}" != *"ubuntu"* ]]; then
-    err "This script is for Ubuntu. Detected: ${ID:-unknown}"
-    exit 1
-fi
-
-info "Adding Docker's official repository..."
-${MAYBE_SUDO}apt update
-${MAYBE_SUDO}apt install -y ca-certificates curl
-${MAYBE_SUDO}install -m 0755 -d /etc/apt/keyrings
-${MAYBE_SUDO}rm -f /etc/apt/sources.list.d/docker.list /etc/apt/sources.list.d/docker.sources
-curl -fsSL https://download.docker.com/linux/ubuntu/gpg | ${MAYBE_SUDO}tee /etc/apt/keyrings/docker.asc > /dev/null
-${MAYBE_SUDO}chmod a+r /etc/apt/keyrings/docker.asc
-CODENAME="$(. /etc/os-release && echo "${UBUNTU_CODENAME:-${VERSION_CODENAME:-}}")"
-[[ -z "$CODENAME" ]] && { err "Could not detect Ubuntu version codename."; exit 1; }
-${MAYBE_SUDO}tee /etc/apt/sources.list.d/docker.sources > /dev/null <<EOF
-Types: deb
-URIs: https://download.docker.com/linux/ubuntu
-Suites: ${CODENAME}
-Components: stable
-Signed-By: /etc/apt/keyrings/docker.asc
-EOF
-
-info "Installing Docker Engine and Compose plugin..."
-${MAYBE_SUDO}apt update
-${MAYBE_SUDO}apt install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
-
-if [[ -d /run/systemd/system ]]; then
-    info "Enabling and starting Docker..."
-    ${MAYBE_SUDO}systemctl enable --now docker
-else
-    err "No systemd. This script requires Ubuntu with systemd (e.g. DigitalOcean droplet)."
-    exit 1
-fi
-
-DOCKER_USER="${SUDO_USER:-${USER:-root}}"
-if [[ "$DOCKER_USER" != "root" ]]; then
-    info "Adding $DOCKER_USER to docker group..."
-    ${MAYBE_SUDO}usermod -aG docker "$DOCKER_USER"
-fi
-
-ok "Docker installed successfully."
-echo ""
-echo "  Log out and back in (or run: newgrp docker) so the group change takes effect."
-echo "  Then verify with: docker compose version"
-echo ""
--- a/Show More
+++ b/Show More