sync with parent

Emit multriack pipeline events
dailico track merge vibe
2026-02-04 18:06:48 +00:00 · 2025-10-21 11:59:26 -04:00 · 2025-10-21 16:31:31 +02:00 · 2025-10-21 10:30:19 -04:00 · 2025-10-21 13:33:31 +02:00 · 2025-10-17 10:00:40 -04:00
262 changed files with 10758 additions and 53432 deletions
--- 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
@@ -1,31 +1,35 @@
-name: Build and Push Backend Docker Image (Docker Hub)
+name: Build and Push Frontend Docker Image
 on:
  push:
-    tags:
+    branches:
-      - "v*"
+      - main
    paths:
      - 'www/**'
      - '.github/workflows/docker-frontend.yml'
  workflow_dispatch:
 env:
-  REGISTRY: docker.io
+  REGISTRY: ghcr.io
-  IMAGE_NAME: monadicalsas/reflector-backend
+  IMAGE_NAME: ${{ github.repository }}-frontend
 jobs:
  build-and-push:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      packages: write
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
-      - name: Log in to Docker Hub
+      - name: Log in to GitHub Container Registry
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
-          username: monadicalsas
+          username: ${{ github.actor }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
+          password: ${{ secrets.GITHUB_TOKEN }}
      - name: Extract metadata
        id: meta
@@ -34,7 +38,7 @@ jobs:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
          tags: |
            type=ref,event=branch
-            type=ref,event=tag
+            type=sha,prefix={{branch}}-
            type=raw,value=latest,enable={{is_default_branch}}
      - name: Set up Docker Buildx
@@ -43,11 +47,11 @@ jobs:
      - name: Build and push Docker image
        uses: docker/build-push-action@v5
        with:
-          context: ./server
+          context: ./www
-          file: ./server/Dockerfile
+          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
+          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/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 .DS_Store
 server/.env
 .env
 Caddyfile
 server/exportdanswer
 .vercel
 .env*.local
@@ -19,6 +18,3 @@ CLAUDE.local.md
 www/.env.development
 www/.env.production
 .playwright-mcp
 docs/pnpm-lock.yaml
 .secrets
 opencode.json
--- a/.gitleaksignore
+++ b/.gitleaksignore
@@ -1,5 +1 @@
 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
--- 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
@@ -1,185 +1,5 @@
 # Changelog
 ## [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)
--- a/CODER_BRIEFING.md
+++ b/CODER_BRIEFING.md
@@ -0,0 +1,345 @@
 # Multi-Provider Video Platform Implementation - Coder Briefing
 ## Your Mission
 Implement multi-provider video platform support in Reflector, allowing the system to work with both Whereby and Daily.co video conferencing providers. The goal is to abstract the current Whereby-only implementation and add Daily.co as a second provider, with the ability to switch between them via environment variables.
 **Branch:** `igor/dailico-2` (you're already on it)
 **Estimated Time:** 12-16 hours (senior engineer)
 **Complexity:** Medium-High (requires careful integration with existing codebase)
 ---
 ## What You Have
 ### 1. **PLAN.md** - Your Technical Specification (2,452 lines)
   - Complete step-by-step implementation guide
   - All code examples you need
   - Architecture diagrams and design rationale
   - Testing strategy and success metrics
   - **Read this first** to understand the overall approach
 ### 2. **IMPLEMENTATION_GUIDE.md** - Your Practical Guide
   - What to copy vs. adapt vs. rewrite
   - Common pitfalls and how to avoid them
   - Verification checklists for each phase
   - Decision trees for implementation choices
   - **Use this as your day-to-day reference**
 ### 3. **Reference Implementation** - `./reflector-dailyco-reference/`
   - Working implementation from 2.5 months ago
   - Good architecture and patterns
   - **BUT:** 91 commits behind current main, DO NOT merge directly
   - Use for inspiration and code patterns only
 ---
 ## Critical Context: Why Not Just Merge?
 The reference branch (`origin/igor/feat-dailyco`) was started on August 1, 2025 and is now severely diverged from main:
 - **91 commits behind main**
 - Main has 12x more changes (45,840 insertions vs 3,689)
 - Main added: calendar integration, webhooks, full-text search, React Query migration, security fixes
 - Reference removed: features that main still has and needs
 **Merging would be a disaster.** We're implementing fresh on current main, using the reference for validated patterns.
 ---
 ## High-Level Approach
 ### Phase 1: Analysis (2 hours)
 - Study current Whereby integration
 - Define abstraction requirements
 - Create standard data models
 ### Phase 2: Abstraction Layer (4-5 hours)
 - Build platform abstraction (base class, registry, factory)
 - Extract Whereby into the abstraction
 - Update database schema (add `platform` field)
 - Integrate into rooms.py **without breaking calendar/webhooks**
 ### Phase 3: Daily.co Implementation (4-5 hours)
 - Implement Daily.co client
 - Add webhook handler
 - Create frontend components (rewrite API calls for React Query)
 - Add recording processing
 ### Phase 4: Testing (2-3 hours)
 - Unit tests for platform abstraction
 - Integration tests for webhooks
 - Manual testing with both providers
 ---
 ## Key Files You'll Touch
 ### Backend (New)
 ```
 server/reflector/video_platforms/
 ├── __init__.py
 ├── base.py              ← Abstract base class
 ├── models.py            ← Platform, MeetingData, VideoPlatformConfig
 ├── registry.py          ← Platform registration system
 ├── factory.py           ← Client creation and config
 ├── whereby.py           ← Whereby client wrapper
 ├── daily.py             ← Daily.co client
 └── mock.py              ← Mock client for testing
 server/reflector/views/daily.py       ← Daily.co webhooks
 server/tests/test_video_platforms.py  ← Platform tests
 server/tests/test_daily_webhook.py    ← Webhook tests
 ```
 ### Backend (Modified - Careful!)
 ```
 server/reflector/settings.py          ← Add Daily.co settings
 server/reflector/db/rooms.py          ← Add platform field, PRESERVE calendar fields
 server/reflector/db/meetings.py       ← Add platform field
 server/reflector/views/rooms.py       ← Integrate abstraction, PRESERVE calendar/webhooks
 server/reflector/worker/process.py    ← Add process_recording_from_url task
 server/reflector/app.py               ← Register daily router
 server/env.example                    ← Document new env vars
 ```
 ### Frontend (New)
 ```
 www/app/[roomName]/components/
 ├── RoomContainer.tsx    ← Platform router
 ├── DailyRoom.tsx        ← Daily.co component (rewrite API calls!)
 └── WherebyRoom.tsx      ← Extract existing logic
 ```
 ### Frontend (Modified)
 ```
 www/app/[roomName]/page.tsx           ← Use RoomContainer
 www/package.json                      ← Add @daily-co/daily-js
 ```
 ### Database
 ```
 server/migrations/versions/XXXXXX_add_platform_support.py  ← Generate fresh migration
 ```
 ---
 ## Critical Warnings ⚠️
 ### 1. **DO NOT Copy Database Migrations**
 The reference migration has the wrong `down_revision` and is based on old schema.
 ```bash
 # Instead:
 cd server
 uv run alembic revision -m "add_platform_support"
 # Then edit the generated file
 ```
 ### 2. **DO NOT Remove Main's Features**
 Main has calendar integration, webhooks, ICS sync that reference doesn't have.
 When modifying `rooms.py`, only change meeting creation logic, preserve everything else.
 ### 3. **DO NOT Copy Frontend API Calls**
 Reference uses old OpenAPI client. Main uses React Query.
 Check how main currently makes API calls and replicate that pattern.
 ### 4. **DO NOT Copy package.json/migrations**
 These files are severely outdated in reference.
 ### 5. **Preserve Type Safety**
 Use `TYPE_CHECKING` imports to avoid circular dependencies:
 ```python
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
    from reflector.db.rooms import Room
 ```
 ---
 ## How to Start
 ### Day 1 Morning: Setup & Understanding (2-3 hours)
 ```bash
 # 1. Verify you're on the right branch
 git branch
 # Should show: igor/dailico-2
 # 2. Read the docs (in order)
 # - PLAN.md (skim to understand scope, read Phase 1 carefully)
 # - IMPLEMENTATION_GUIDE.md (read fully, bookmark it)
 # 3. Study current Whereby integration
 cat server/reflector/views/rooms.py | grep -A 20 "whereby"
 cat www/app/[roomName]/page.tsx
 # 4. Check reference implementation structure
 ls -la reflector-dailyco-reference/server/reflector/video_platforms/
 ```
 ### Day 1 Afternoon: Phase 1 Execution (2-3 hours)
 ```bash
 # 5. Copy video_platforms directory from reference
 cp -r reflector-dailyco-reference/server/reflector/video_platforms/ \
      server/reflector/
 # 6. Review and fix imports
 cd server
 uv run ruff check reflector/video_platforms/
 # 7. Add settings to settings.py (see PLAN.md Phase 2.7)
 # 8. Test imports work
 uv run python -c "from reflector.video_platforms import create_platform_client; print('OK')"
 ```
 ### Day 2: Phase 2 - Database & Integration (4-5 hours)
 ```bash
 # 9. Generate migration
 uv run alembic revision -m "add_platform_support"
 # Edit the file following PLAN.md Phase 2.8
 # 10. Update Room/Meeting models
 # Add platform field, PRESERVE all existing fields
 # 11. Integrate into rooms.py
 # Carefully modify meeting creation, preserve calendar/webhooks
 # 12. Add Daily.co webhook handler
 cp reflector-dailyco-reference/server/reflector/views/daily.py \
   server/reflector/views/
 # Register in app.py
 ```
 ### Day 3: Phase 3 - Frontend & Testing (4-5 hours)
 ```bash
 # 13. Create frontend components
 mkdir -p www/app/[roomName]/components
 # 14. Add Daily.co dependency
 cd www
 pnpm add @daily-co/daily-js@^0.81.0
 # 15. Create RoomContainer, DailyRoom, WherebyRoom
 # IMPORTANT: Rewrite API calls using React Query patterns
 # 16. Regenerate types
 pnpm openapi
 # 17. Copy and adapt tests
 cp reflector-dailyco-reference/server/tests/test_*.py server/tests/
 # 18. Run tests
 cd server
 REDIS_HOST=localhost \
 CELERY_BROKER_URL=redis://localhost:6379/1 \
 uv run pytest tests/test_video_platforms.py -v
 ```
 ---
 ## Verification Checklist
 After implementation, all of these must pass:
 **Backend:**
 - [ ] `cd server && uv run ruff check .` passes
 - [ ] `uv run alembic upgrade head` works cleanly
 - [ ] `uv run pytest tests/test_video_platforms.py` passes
 - [ ] Can import: `from reflector.video_platforms import create_platform_client`
 - [ ] Settings has all Daily.co variables
 **Frontend:**
 - [ ] `cd www && pnpm lint` passes
 - [ ] No TypeScript errors
 - [ ] `pnpm openapi` generates platform field
 - [ ] No `@ts-ignore` for platform field
 **Integration:**
 - [ ] Whereby meetings still work (existing flow unchanged)
 - [ ] Calendar/webhook features still work in rooms.py
 - [ ] env.example documents all new variables
 ---
 ## When You're Stuck
 ### Check These Resources:
 1. **PLAN.md** - Detailed code examples for your exact scenario
 2. **IMPLEMENTATION_GUIDE.md** - Common pitfalls section
 3. **Reference code** - See how it was solved before
 4. **Git diff** - Compare reference to your implementation
 ### Compare Files:
 ```bash
 # See what reference did
 diff reflector-dailyco-reference/server/reflector/views/rooms.py \
     server/reflector/views/rooms.py
 # See what changed in main since reference branch
 git log --oneline --since="2025-08-01" -- server/reflector/views/rooms.py
 ```
 ### Common Issues:
 - **Circular imports:** Use `TYPE_CHECKING` pattern
 - **Tests fail with postgres error:** Use `REDIS_HOST=localhost` env vars
 - **Frontend API calls broken:** Check current React Query patterns in main
 - **Migrations fail:** Ensure you generated fresh, not copied
 ---
 ## Success Looks Like
 When you're done:
 - ✅ All tests pass
 - ✅ Linting passes
 - ✅ Can create Whereby meetings (unchanged behavior)
 - ✅ Can create Daily.co meetings (with env vars)
 - ✅ Calendar/webhooks still work
 - ✅ Frontend has no TypeScript errors
 - ✅ Platform selection via environment variables works
 ---
 ## Communication
 If you need clarification on requirements, have questions about architecture decisions, or find issues with the spec, document them clearly with:
 - What you expected
 - What you found
 - Your proposed solution
 The PLAN.md document is comprehensive but you may find edge cases. Use your engineering judgment and document decisions.
 ---
 ## Final Notes
 **This is not a simple copy-paste job.** You're doing careful integration work where you need to:
 - Understand the abstraction pattern (PLAN.md)
 - Preserve all of main's features
 - Adapt reference code to current patterns
 - Think about edge cases and testing
 Take your time with Phase 2 (rooms.py integration) - that's where most bugs will come from if you accidentally break calendar/webhook features.
 **Good luck! You've got comprehensive specs, working reference code, and a clean starting point. You can do this.**
 ---
 ## Quick Reference
 ```bash
 # Your workspace
 ├── PLAN.md                        ← Complete technical spec (read first)
 ├── IMPLEMENTATION_GUIDE.md        ← Practical guide (bookmark this)
 ├── CODER_BRIEFING.md             ← This file
 └── reflector-dailyco-reference/   ← Reference implementation (inspiration only)
 # Key commands
 cd server && uv run ruff check .                    # Lint backend
 cd www && pnpm lint                                  # Lint frontend
 cd server && uv run alembic revision -m "..."       # Create migration
 cd www && pnpm openapi                              # Regenerate types
 cd server && uv run pytest -v                       # Run tests
 ```
--- a/Caddyfile.example
+++ b/Caddyfile.example
@@ -1,22 +0,0 @@
 # Reflector Caddyfile
 # 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/IMPLEMENTATION_GUIDE.md
+++ b/IMPLEMENTATION_GUIDE.md
@@ -0,0 +1,489 @@
 # Daily.co Implementation Guide
 ## Overview
 Implement multi-provider video platform support (Whereby + Daily.co) following PLAN.md.
 ## Reference Code Location
 - **Reference branch:** `origin/igor/feat-dailyco` (on remote)
 - **Worktree location:** `./reflector-dailyco-reference/`
 - **Status:** Reference only - DO NOT merge or copy directly
 ## What Exists in Reference Branch (For Inspiration)
 ### ✅ Can Use As Reference (Well-Implemented)
 ```
 server/reflector/video_platforms/
 ├── base.py              ← Platform abstraction (good design, copy-safe)
 ├── models.py            ← Data models (copy-safe)
 ├── registry.py          ← Registry pattern (copy-safe)
 ├── factory.py           ← Factory pattern (needs settings updates)
 ├── whereby.py           ← Whereby client (needs adaptation)
 ├── daily.py             ← Daily.co client (needs adaptation)
 └── mock.py              ← Mock client (copy-safe for tests)
 server/reflector/views/daily.py       ← Webhook handler (needs adaptation)
 server/tests/test_video_platforms.py  ← Tests (good reference)
 server/tests/test_daily_webhook.py    ← Tests (good reference)
 www/app/[roomName]/components/
 ├── RoomContainer.tsx    ← Platform router (needs React Query)
 ├── DailyRoom.tsx        ← Daily component (needs React Query)
 └── WherebyRoom.tsx      ← Whereby extraction (needs React Query)
 ```
 ### ⚠️ Needs Significant Changes (Use Logic Only)
 - `server/reflector/db/rooms.py` - Reference removed calendar/webhook fields that main has
 - `server/reflector/db/meetings.py` - Same issue (missing user_id handling differences)
 - `server/reflector/views/rooms.py` - Main has calendar integration, webhooks, ICS sync
 - `server/reflector/worker/process.py` - Main has different recording flow
 - Migration files - Must regenerate against current main schema
 ### ❌ Do NOT Use (Outdated/Incompatible)
 - `package.json`/`pnpm-lock.yaml` - Main uses different dependency versions
 - Frontend API client calls - Main uses React Query (reference uses old OpenAPI client)
 - Database migrations - Must create new ones from scratch
 - Any files that delete features present in main (search, calendar, webhooks)
 ## Key Differences: Reference vs Current Main
 | Aspect | Reference Branch | Current Main | Action Required |
 |--------|------------------|--------------|-----------------|
 | **API client** | Old OpenAPI generated | React Query hooks | Rewrite all API calls |
 | **Database schema** | Simplified (removed features) | Has calendar, webhooks, full-text search | Merge carefully, preserve main features |
 | **Settings** | Aug 2025 structure | Current structure | Adapt carefully |
 | **Migrations** | Branched from Aug 1 | Current main (91+ commits ahead) | Regenerate from scratch |
 | **Frontend deps** | `@daily-co/daily-js@0.81.0` | Check current versions | Update to compatible versions |
 | **Package manager** | yarn | pnpm (maybe both?) | Use what main uses |
 ## Branch Divergence Analysis
 **The reference branch is 91 commits behind main and severely diverged:**
 - Reference: 8 commits, 3,689 insertions, 425 deletions
 - Main since divergence: 320 files changed, 45,840 insertions, 16,827 deletions
 - **Main has 12x more changes**
 **Major features in main that reference lacks:**
 1. Calendar integration (ICS sync with rooms)
 2. Self-hosted GPU API infrastructure
 3. Frontend OpenAPI React Query migration
 4. Full-text search (backend + frontend)
 5. Webhook system for room events
 6. Environment variable migration
 7. Security fixes and auth improvements
 8. Docker production frontend
 9. Meeting user ID removal (schema change)
 10. NextJS version upgrades
 **High conflict risk files:**
 - `server/reflector/views/rooms.py` - 12x more changes in main
 - `server/reflector/db/rooms.py` - Main added 7+ fields
 - `www/package.json` - NextJS major version bump
 - Database migrations - 20+ new migrations in main
 ## Implementation Approach
 ### Phase 1: Copy Clean Abstractions (1-2 hours)
 **Files to copy directly from reference:**
 ```bash
 # Core abstraction (review but mostly safe to copy)
 cp -r reflector-dailyco-reference/server/reflector/video_platforms/ \
      server/reflector/
 # BUT review each file for:
 # - Import paths (make sure they match current main)
 # - Settings references (adapt to current settings.py)
 # - Type imports (ensure no circular dependencies)
 ```
 **After copying, immediately:**
 ```bash
 cd server
 # Check for issues
 uv run ruff check reflector/video_platforms/
 # Fix any import errors or type issues
 ```
 ### Phase 2: Adapt to Current Main (2-3 hours)
 **2.1 Settings Integration**
 File: `server/reflector/settings.py`
 Add at the appropriate location (near existing Whereby settings):
 ```python
 # Daily.co API Integration (NEW)
 DAILY_API_KEY: str | None = None
 DAILY_WEBHOOK_SECRET: str | None = None
 DAILY_SUBDOMAIN: str | None = None
 AWS_DAILY_S3_BUCKET: str | None = None
 AWS_DAILY_S3_REGION: str = "us-west-2"
 AWS_DAILY_ROLE_ARN: str | None = None
 # Platform Migration Feature Flags (NEW)
 DAILY_MIGRATION_ENABLED: bool = False  # Conservative default
 DAILY_MIGRATION_ROOM_IDS: list[str] = []
 DEFAULT_VIDEO_PLATFORM: Literal["whereby", "daily"] = "whereby"
 ```
 **2.2 Database Migration**
 ⚠️ **CRITICAL: Do NOT copy migration from reference**
 Generate new migration:
 ```bash
 cd server
 uv run alembic revision -m "add_platform_support"
 ```
 Edit the generated migration file to add `platform` column:
 ```python
 def upgrade():
    with op.batch_alter_table("room", schema=None) as batch_op:
        batch_op.add_column(
            sa.Column("platform", sa.String(), nullable=False, server_default="whereby")
        )
    with op.batch_alter_table("meeting", schema=None) as batch_op:
        batch_op.add_column(
            sa.Column("platform", sa.String(), nullable=False, server_default="whereby")
        )
 ```
 **2.3 Update Database Models**
 File: `server/reflector/db/rooms.py`
 Add platform field (preserve all existing fields from main):
 ```python
 from typing import TYPE_CHECKING
 if TYPE_CHECKING:
    from reflector.video_platforms.models import Platform
 class Room:
    # ... ALL existing fields from main (calendar, webhooks, etc.) ...
    # NEW: Platform field
    platform: "Platform" = sqlalchemy.Column(
        sqlalchemy.String,
        nullable=False,
        server_default="whereby",
    )
 ```
 File: `server/reflector/db/meetings.py`
 Same approach - add platform field, preserve everything from main.
 **2.4 Integrate Platform Abstraction into rooms.py**
 ⚠️ **This is the most delicate part - main has calendar/webhook features**
 File: `server/reflector/views/rooms.py`
 Strategy:
 1. Add imports at top
 2. Modify meeting creation logic only
 3. Preserve all calendar/webhook/ICS logic from main
 ```python
 # Add imports
 from reflector.video_platforms import (
    create_platform_client,
    get_platform_for_room,
 )
 # In create_meeting endpoint:
 # OLD: Direct Whereby API calls
 # NEW: Platform abstraction
 # Find the meeting creation section and replace:
 platform = get_platform_for_room(room.id)
 client = create_platform_client(platform)
 meeting_data = await client.create_meeting(
    room_name_prefix=room.name,
    end_date=meeting_data.end_date,
    room=room,
 )
 # Then create Meeting record with meeting_data.platform, meeting_data.meeting_id, etc.
 ```
 **2.5 Add Daily.co Webhook Handler**
 Copy from reference, minimal changes needed:
 ```bash
 cp reflector-dailyco-reference/server/reflector/views/daily.py \
   server/reflector/views/
 ```
 Register in `server/reflector/app.py`:
 ```python
 from reflector.views import daily
 app.include_router(daily.router, prefix="/v1/daily", tags=["daily"])
 ```
 **2.6 Add Recording Processing Task**
 File: `server/reflector/worker/process.py`
 Add the `process_recording_from_url` task from reference (copy the function).
 ### Phase 3: Frontend Adaptation (3-4 hours)
 **3.1 Determine Current API Client Pattern**
 First, check how main currently makes API calls:
 ```bash
 cd www
 grep -r "api\." app/ | head -20
 # Look for patterns like: api.v1Something()
 ```
 **3.2 Create Components**
 Copy component structure from reference but **rewrite all API calls**:
 ```bash
 mkdir -p www/app/[roomName]/components
 ```
 Files to create:
 - `RoomContainer.tsx` - Platform router (mostly copy-safe, just fix imports)
 - `DailyRoom.tsx` - Needs React Query API calls
 - `WherebyRoom.tsx` - Extract current room page logic
 **Example React Query pattern** (adapt to your actual API):
 ```typescript
 import { api } from '@/app/api/client'
 // In DailyRoom.tsx
 const handleConsent = async () => {
  try {
    await api.v1MeetingAudioConsent({
      path: { meeting_id: meeting.id },
      body: { consent: true },
    })
    // ...
  } catch (error) {
    // ...
  }
 }
 ```
 **3.3 Add Daily.co Dependency**
 Check current package manager:
 ```bash
 cd www
 ls package-lock.json yarn.lock pnpm-lock.yaml
 ```
 Then install:
 ```bash
 # If using pnpm
 pnpm add @daily-co/daily-js@^0.81.0
 # If using yarn
 yarn add @daily-co/daily-js@^0.81.0
 ```
 **3.4 Update TypeScript Types**
 After backend changes, regenerate types:
 ```bash
 cd www
 pnpm openapi  # or yarn openapi
 ```
 This should pick up the new `platform` field on Meeting type.
 ### Phase 4: Testing (2-3 hours)
 **4.1 Copy Test Structure**
 ```bash
 cp reflector-dailyco-reference/server/tests/test_video_platforms.py \
   server/tests/
 cp reflector-dailyco-reference/server/tests/test_daily_webhook.py \
   server/tests/
 ```
 **4.2 Fix Test Imports and Fixtures**
 Update imports to match current test infrastructure:
 - Check `server/tests/conftest.py` for fixture patterns
 - Update database access patterns if changed
 - Fix any import errors
 **4.3 Run Tests**
 ```bash
 cd server
 # Run with environment variables for Mac
 REDIS_HOST=localhost \
 CELERY_BROKER_URL=redis://localhost:6379/1 \
 CELERY_RESULT_BACKEND=redis://localhost:6379/1 \
 uv run pytest tests/test_video_platforms.py -v
 ```
 ### Phase 5: Environment Configuration
 **Update `server/env.example`:**
 Add at the end:
 ```bash
 # Daily.co API Integration
 DAILY_API_KEY=your-daily-api-key
 DAILY_WEBHOOK_SECRET=your-daily-webhook-secret
 DAILY_SUBDOMAIN=your-subdomain
 AWS_DAILY_S3_BUCKET=your-daily-bucket
 AWS_DAILY_S3_REGION=us-west-2
 AWS_DAILY_ROLE_ARN=arn:aws:iam::ACCOUNT:role/DailyRecording
 # Platform Selection
 DAILY_MIGRATION_ENABLED=false           # Master switch
 DAILY_MIGRATION_ROOM_IDS=[]            # Specific room IDs
 DEFAULT_VIDEO_PLATFORM=whereby          # Default platform
 ```
 ## Decision Tree: Copy vs Adapt vs Rewrite
 ```
 ┌─ Is it pure abstraction logic? (base.py, registry.py, models.py)
 │  YES → Copy directly, review imports
 │  NO  → Continue ↓
 │
 ├─ Does it touch database models?
 │  YES → Adapt carefully, preserve main's fields
 │  NO  → Continue ↓
 │
 ├─ Does it make API calls on frontend?
 │  YES → Rewrite using React Query
 │  NO  → Continue ↓
 │
 ├─ Is it a database migration?
 │  YES → Generate fresh from current schema
 │  NO  → Continue ↓
 │
 └─ Does it touch rooms.py or core business logic?
   YES → Merge carefully, preserve calendar/webhooks
   NO  → Safe to adapt from reference
 ```
 ## Verification Checklist
 After each phase, verify:
 **Phase 1 (Abstraction Layer):**
 - [ ] `uv run ruff check server/reflector/video_platforms/` passes
 - [ ] No circular import errors
 - [ ] Can import `from reflector.video_platforms import create_platform_client`
 **Phase 2 (Backend Integration):**
 - [ ] `uv run ruff check server/` passes
 - [ ] Migration file generated (not copied)
 - [ ] Room and Meeting models have platform field
 - [ ] rooms.py still has calendar/webhook features
 **Phase 3 (Frontend):**
 - [ ] `pnpm lint` passes
 - [ ] No TypeScript errors
 - [ ] No `@ts-ignore` for platform field
 - [ ] API calls use React Query patterns
 **Phase 4 (Testing):**
 - [ ] Tests can be collected: `pytest tests/test_video_platforms.py --collect-only`
 - [ ] Database fixtures work
 - [ ] Mock platform works
 **Phase 5 (Config):**
 - [ ] env.example has Daily.co variables
 - [ ] settings.py has all new variables
 - [ ] No duplicate variable definitions
 ## Common Pitfalls
 ### 1. Database Schema Conflicts
 **Problem:** Reference removed fields that main has (calendar, webhooks)
 **Solution:** Always preserve main's fields, only add platform field
 ### 2. Migration Conflicts
 **Problem:** Reference migration has wrong `down_revision`
 **Solution:** Always generate fresh migration from current main
 ### 3. Frontend API Calls
 **Problem:** Reference uses old API client patterns
 **Solution:** Check current main's API usage, replicate that pattern
 ### 4. Import Errors
 **Problem:** Circular imports with TYPE_CHECKING
 **Solution:** Use `if TYPE_CHECKING:` for Room/Meeting imports in video_platforms
 ### 5. Test Database Issues
 **Problem:** Tests fail with "could not translate host name 'postgres'"
 **Solution:** Use environment variables: `REDIS_HOST=localhost DATABASE_URL=...`
 ### 6. Preserved Features Broken
 **Problem:** Calendar/webhook features stop working
 **Solution:** Carefully review rooms.py diff, only change meeting creation, not calendar logic
 ## File Modification Summary
 **New files (can copy):**
 - `server/reflector/video_platforms/*.py` (entire directory)
 - `server/reflector/views/daily.py`
 - `server/tests/test_video_platforms.py`
 - `server/tests/test_daily_webhook.py`
 - `www/app/[roomName]/components/RoomContainer.tsx`
 - `www/app/[roomName]/components/DailyRoom.tsx`
 - `www/app/[roomName]/components/WherebyRoom.tsx`
 **Modified files (careful merging):**
 - `server/reflector/settings.py` - Add Daily.co settings
 - `server/reflector/db/rooms.py` - Add platform field
 - `server/reflector/db/meetings.py` - Add platform field
 - `server/reflector/views/rooms.py` - Integrate platform abstraction
 - `server/reflector/worker/process.py` - Add process_recording_from_url
 - `server/reflector/app.py` - Register daily router
 - `server/env.example` - Add Daily.co variables
 - `www/app/[roomName]/page.tsx` - Use RoomContainer
 - `www/package.json` - Add @daily-co/daily-js
 **Generated files (do not copy):**
 - `server/migrations/versions/XXXXXX_add_platform_support.py` - Generate fresh
 ## Success Metrics
 Implementation is complete when:
 - [ ] All tests pass (including new platform tests)
 - [ ] Linting passes (ruff, pnpm lint)
 - [ ] Migration applies cleanly: `uv run alembic upgrade head`
 - [ ] Can create Whereby meeting (existing flow unchanged)
 - [ ] Can create Daily.co meeting (with env vars set)
 - [ ] Frontend loads without TypeScript errors
 - [ ] No features from main were accidentally removed
 ## Getting Help
 **Reference documentation locations:**
 - Implementation plan: `PLAN.md`
 - Reference implementation: `./reflector-dailyco-reference/`
 - Current main codebase: `./ ` (current directory)
 **Compare implementations:**
 ```bash
 # Compare specific files
 diff reflector-dailyco-reference/server/reflector/video_platforms/base.py \
     server/reflector/video_platforms/base.py
 # See what changed in rooms.py between reference branch point and now
 git log --oneline --since="2025-08-01" -- server/reflector/views/rooms.py
 ```
 **Key insight:** The reference branch validates the approach and provides working code patterns, but you're implementing fresh against current main to avoid merge conflicts and preserve all new features.
--- a/PLAN.md
+++ b/PLAN.md
--- a/README.md
+++ b/README.md
@@ -168,12 +168,6 @@ You can manually process an audio file by calling the process tool:
 uv run python -m reflector.tools.process path/to/audio.wav
 ```
 ## Reprocessing any transcription
 ```bash
 uv run -m reflector.tools.process_transcript 81ec38d1-9dd7-43d2-b3f8-51f4d34a07cd --sync
 ```
 ## Build-time env variables
 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 ccustomizable prebuild docker container.
--- a/docker-compose.prod.yml
+++ b/docker-compose.prod.yml
@@ -1,61 +1,28 @@
-# Production Docker Compose configuration
+# Production Docker Compose configuration for Frontend
 # Usage: docker compose -f docker-compose.prod.yml up -d
 #
 # Prerequisites:
 # 1. Copy .env.example to .env and configure for both server/ and www/
 # 2. Copy Caddyfile.example to Caddyfile and edit with your domains
 # 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
 services:
  web:
-    image: monadicalsas/reflector-frontend:latest
+    build:
-    restart: unless-stopped
+      context: ./www
-    env_file:
+      dockerfile: Dockerfile
-      - ./www/.env
+    image: reflector-frontend:latest
    pull_policy: always
    environment:
-      - KV_URL=redis://redis:6379
+      - KV_URL=${KV_URL:-redis://redis:6379}
      - SITE_URL=${SITE_URL}
      - API_URL=${API_URL}
      - WEBSOCKET_URL=${WEBSOCKET_URL}
      - NEXTAUTH_URL=${NEXTAUTH_URL:-http://localhost:3000}
      - NEXTAUTH_SECRET=${NEXTAUTH_SECRET:-changeme-in-production}
      - AUTHENTIK_ISSUER=${AUTHENTIK_ISSUER}
      - AUTHENTIK_CLIENT_ID=${AUTHENTIK_CLIENT_ID}
      - AUTHENTIK_CLIENT_SECRET=${AUTHENTIK_CLIENT_SECRET}
      - AUTHENTIK_REFRESH_TOKEN_URL=${AUTHENTIK_REFRESH_TOKEN_URL}
      - SENTRY_DSN=${SENTRY_DSN}
      - SENTRY_IGNORE_API_RESOLUTION_ERROR=${SENTRY_IGNORE_API_RESOLUTION_ERROR:-1}
    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
@@ -68,46 +35,5 @@ services:
    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:
    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:
+  redis_data:
  postgres_data:
  server_data:
  caddy_data:
  caddy_config:
 networks:
  default:
    attachable: true
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -34,20 +34,6 @@ services:
    environment:
      ENTRYPOINT: beat
  hatchet-worker:
    build:
      context: server
    volumes:
      - ./server/:/app/
      - /app/.venv
    env_file:
      - ./server/.env
    environment:
      ENTRYPOINT: hatchet-worker
    depends_on:
      hatchet:
        condition: service_healthy
  redis:
    image: redis:7.2
    ports:
@@ -69,7 +55,6 @@ services:
  postgres:
    image: postgres:17
    command: postgres -c 'max_connections=200'
    ports:
      - 5432:5432
    environment:
@@ -78,42 +63,6 @@ services:
      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: 10s
      timeout: 10s
      retries: 5
      start_period: 10s
  hatchet:
    image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
    ports:
      - "8889:8888"
      - "7078:7077"
    depends_on:
      postgres:
        condition: service_healthy
    environment:
      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable"
      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
 networks:
  default:
--- 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
@@ -1,39 +0,0 @@
 FROM node:18-alpine AS builder
 WORKDIR /app
 # Install curl for fetching OpenAPI spec
 RUN apk add --no-cache curl
 # Copy package files
 COPY package*.json ./
 # Install dependencies
 RUN npm ci
 # 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 npx 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
 ```
 $ yarn
 ```
 ### Local Development
 ```
 $ yarn 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
 ```
 $ yarn 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 yarn deploy
 ```
 Not using SSH:
 ```
 $ GIT_USER=<Your GitHub username> yarn 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 local processing
 TRANSCRIPT_BACKEND=local
 DIARIZATION_BACKEND=local
 TRANSLATION_BACKEND=local
 # Hybrid approach
 TRANSCRIPT_BACKEND=modal  # Fast GPU processing
 DIARIZATION_BACKEND=local # 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 15** 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
@@ -1,192 +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 |
 ## 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.
 ## Common Commands
 ### Start all services
 ```bash
 docker compose -f docker-compose.prod.yml 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
@@ -1,139 +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:18-alpine AS builder
 WORKDIR /app
 # Copy package files
 COPY package*.json ./
 # Inshall dependencies
 RUN npm ci
 # 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 npx 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
@@ -1,411 +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
 ```
 ---
 ## Configure Caddy
 ```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
 ```bash
 docker compose -f docker-compose.prod.yml 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
 - Caddy auto-provisions Let's Encrypt certificates
 - Ensure ports 80 and 443 are open
 - Check: `docker compose -f docker-compose.prod.yml logs caddy`
 ### 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/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](./pipelines/overview).
 ## 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/reference/architecture/overview',
            },
            {
              label: 'Pipelines',
              to: '/docs/pipelines/overview',
            },
            {
              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-lock.json
+++ b/docs/package-lock.json
--- a/docs/package.json
+++ b/docs/package.json
@@ -1,53 +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": "npm run fetch-openapi && docusaurus gen-api-docs reflector",
    "prebuild": "npm run fetch-openapi"
  },
  "dependencies": {
    "@docusaurus/core": "3.6.3",
    "@docusaurus/preset-classic": "3.6.3",
    "@mdx-js/react": "^3.0.0",
    "clsx": "^2.0.0",
    "docusaurus-plugin-openapi-docs": "^4.5.1",
    "docusaurus-theme-openapi-docs": "^4.5.1",
    "@docusaurus/theme-mermaid": "3.6.3",
    "prism-react-renderer": "^2.3.0",
    "react": "^18.0.0",
    "react-dom": "^18.0.0"
  },
  "devDependencies": {
    "@docusaurus/module-type-aliases": "3.6.3",
    "@docusaurus/tsconfig": "3.6.3",
    "@docusaurus/types": "3.6.3",
    "typescript": "~5.6.2"
  },
  "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"
  }
 }
--- 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
--- a/docs/static/img/docusaurus-social-card.jpg
+++ b/docs/static/img/docusaurus-social-card.jpg
--- a/docs/static/img/docusaurus.png
+++ b/docs/static/img/docusaurus.png
--- a/docs/static/img/favicon.ico
+++ b/docs/static/img/favicon.ico
--- a/docs/static/img/logo.svg
+++ b/docs/static/img/logo.svg
@@ -1,17 +0,0 @@
 <?xml version="1.0" encoding="utf-8"?>
 <!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
 <svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
 	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
 <style type="text/css">
 	.st0{fill:#B6B6B6;}
 	.st1{fill:#4A4A4A;}
 </style>
 <g>
 	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
 	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
 </g>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
 </image>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
 </image>
 </svg>
--- a/docs/static/img/reflector-logo.svg
+++ b/docs/static/img/reflector-logo.svg
@@ -1,17 +0,0 @@
 <?xml version="1.0" encoding="utf-8"?>
 <!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
 <svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
 	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
 <style type="text/css">
 	.st0{fill:#B6B6B6;}
 	.st1{fill:#4A4A4A;}
 </style>
 <g>
 	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
 	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
 </g>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
 </image>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
 </image>
 </svg>
--- a/docs/static/img/reflector-transcript-view.png
+++ b/docs/static/img/reflector-transcript-view.png
--- a/docs/static/img/undraw_docusaurus_mountain.svg
+++ b/docs/static/img/undraw_docusaurus_mountain.svg
@@ -1,171 +0,0 @@
 <svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
  <title>Easy to Use</title>
  <g id="Group_12" data-name="Group 12" transform="translate(-57 -56)">
    <g id="Group_11" data-name="Group 11" transform="translate(57 56)">
      <path id="Path_83" data-name="Path 83" d="M1017.81,560.461c-5.27,45.15-16.22,81.4-31.25,110.31-20,38.52-54.21,54.04-84.77,70.28a193.275,193.275,0,0,1-27.46,11.94c-55.61,19.3-117.85,14.18-166.74,3.99a657.282,657.282,0,0,0-104.09-13.16q-14.97-.675-29.97-.67c-15.42.02-293.07,5.29-360.67-131.57-16.69-33.76-28.13-75-32.24-125.27-11.63-142.12,52.29-235.46,134.74-296.47,155.97-115.41,369.76-110.57,523.43,7.88C941.15,276.621,1036.99,396.031,1017.81,560.461Z" transform="translate(-56 -106.019)" fill="#3f3d56"/>
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      <path id="Path_85" data-name="Path 85" d="M302,282.962l26-57,36,83-31-60Z" opacity="0.1"/>
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      <circle id="Ellipse_11" data-name="Ellipse 11" cx="3" cy="3" r="3" transform="translate(479 98.962)" fill="#f2f2f2"/>
      <circle id="Ellipse_12" data-name="Ellipse 12" cx="3" cy="3" r="3" transform="translate(396 201.962)" fill="#f2f2f2"/>
      <circle id="Ellipse_13" data-name="Ellipse 13" cx="2" cy="2" r="2" transform="translate(600 220.962)" fill="#f2f2f2"/>
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      <path id="Path_89" data-name="Path 89" d="M306,121.222h-2.76v-2.76h-1.48v2.76H299V122.7h2.76v2.759h1.48V122.7H306Z" fill="#f2f2f2"/>
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      <ellipse id="Ellipse_18" data-name="Ellipse 18" cx="544" cy="30" rx="544" ry="30" transform="translate(0 583.962)" fill="#3f3d56"/>
      <path id="Path_93" data-name="Path 93" d="M624,677.981c0,33.137-14.775,24-33,24s-33,9.137-33-24,33-96,33-96S624,644.844,624,677.981Z" transform="translate(-56 -106.019)" fill="#ff6584"/>
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--- 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/gpu/modal_deployments/deploy-all.sh
+++ b/gpu/modal_deployments/deploy-all.sh
@@ -1,150 +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 "  -> $DIARIZER_URL"
 # --- Output Configuration ---
 echo ""
 echo "=========================================="
 echo "Deployment complete!"
 echo "=========================================="
 echo ""
 echo "Copy these values to your server's server/.env file:"
 echo ""
 echo "# --- Modal GPU Configuration ---"
 echo "TRANSCRIPT_BACKEND=modal"
 echo "TRANSCRIPT_URL=$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 "# --- End Modal Configuration ---"
--- a/gpu/modal_deployments/reflector_diarizer.py
+++ b/gpu/modal_deployments/reflector_diarizer.py
@@ -24,12 +24,6 @@ app = modal.App(name="reflector-diarizer")
 upload_volume = modal.Volume.from_name("diarizer-uploads", create_if_missing=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
 #   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
 #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
 #   - gpu/modal_deployments/reflector_diarizer.py (this file)
 def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
    parsed_url = urlparse(url)
    url_path = parsed_url.path
@@ -45,8 +39,6 @@ def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtens
        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}. "
@@ -113,7 +105,7 @@ def download_pyannote_audio():
 diarizer_image = (
-    modal.Image.debian_slim(python_version="3.10")
+    modal.Image.debian_slim(python_version="3.10.8")
    .pip_install(
        "pyannote.audio==3.1.0",
        "requests",
@@ -124,7 +116,7 @@ diarizer_image = (
        "transformers==4.34.0",
        "sentencepiece",
        "protobuf",
-        "numpy<2",
+        "numpy",
        "huggingface_hub",
        "hf-transfer",
    )
--- a/gpu/modal_deployments/reflector_transcriber.py
+++ b/gpu/modal_deployments/reflector_transcriber.py
@@ -89,7 +89,6 @@ image = (
        "torch==2.5.1",
        "faster-whisper==1.1.1",
        "fastapi==0.115.12",
        "python-multipart",
        "requests",
        "librosa==0.10.1",
        "numpy<2",
@@ -99,12 +98,6 @@ image = (
 )
 # 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
@@ -120,8 +113,6 @@ def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtens
        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}. "
@@ -324,11 +315,6 @@ class TranscriberWhisperFile:
        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,
@@ -336,7 +322,6 @@ class TranscriberWhisperFile:
        ) -> 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]
@@ -356,9 +341,6 @@ class TranscriberWhisperFile:
                        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()
@@ -424,12 +406,6 @@ class TranscriberWhisperFile:
        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
@@ -447,8 +423,6 @@ def detect_audio_format(url: str, headers: dict) -> str:
        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,
--- a/gpu/modal_deployments/reflector_transcriber_parakeet.py
+++ b/gpu/modal_deployments/reflector_transcriber_parakeet.py
@@ -81,21 +81,15 @@ image = (
        "cuda-python==12.8.0",
        "fastapi==0.115.12",
        "numpy<2",
-        "librosa==0.11.0",
+        "librosa==0.10.1",
        "requests",
-        "silero-vad==6.2.0",
+        "silero-vad==5.1.0",
        "torch",
    )
    .entrypoint([])  # silence chatty logs by container on start
 )
 # 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 (2 copies)
 #   - gpu/modal_deployments/reflector_transcriber_parakeet.py (this file)
 #   - 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
@@ -111,8 +105,6 @@ def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtens
        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}. "
@@ -309,17 +301,11 @@ class TranscriberParakeetFile:
            audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
            return audio_array
        # 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 (this file)
        #   - gpu/self_hosted/app/services/transcriber.py
        def vad_segment_generator(
            audio_array,
        ) -> Generator[TimeSegment, None, None]:
            """Generate speech segments using VAD with start/end sample indices"""
            vad_iterator = VADIterator(self.vad_model, sampling_rate=SAMPLERATE)
            audio_duration = len(audio_array) / float(SAMPLERATE)
            window_size = VAD_CONFIG["window_size"]
            start = None
@@ -346,10 +332,6 @@ class TranscriberParakeetFile:
                    yield TimeSegment(start_time, end_time)
                    start = None
            if start is not None:
                start_time = start / float(SAMPLERATE)
                yield TimeSegment(start_time, audio_duration)
            vad_iterator.reset_states()
        def batch_speech_segments(
--- a/gpu/modal_deployments/reflector_translator.py
+++ b/gpu/modal_deployments/reflector_translator.py
@@ -103,7 +103,7 @@ def configure_seamless_m4t():
 transcriber_image = (
-    Image.debian_slim(python_version="3.10")
+    Image.debian_slim(python_version="3.10.8")
    .apt_install("git")
    .apt_install("wget")
    .apt_install("libsndfile-dev")
@@ -119,7 +119,6 @@ transcriber_image = (
        "fairseq2",
        "pyyaml",
        "hf-transfer~=0.1",
        "pydantic",
    )
    .run_function(install_seamless_communication)
    .run_function(download_seamlessm4t_model)
--- 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/README.md
+++ b/gpu/self_hosted/README.md
@@ -56,13 +56,9 @@ Docker
 - Not yet provided in this directory. A Dockerfile will be added later. For now, use Local run above
-# Setup
+Conformance tests
-[SETUP.md](SETUP.md)
+# From this directory
 # Conformance tests
 ## From this directory
 TRANSCRIPT_URL=http://localhost:8000 \
 TRANSCRIPT_API_KEY=dev-key \
--- a/gpu/self_hosted/app/services/transcriber.py
+++ b/gpu/self_hosted/app/services/transcriber.py
@@ -129,11 +129,6 @@ class WhisperService:
            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,
@@ -158,10 +153,6 @@ class WhisperService:
                    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)
--- a/gpu/self_hosted/app/utils.py
+++ b/gpu/self_hosted/app/utils.py
@@ -34,12 +34,6 @@ 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:
@@ -53,8 +47,6 @@ def detect_audio_format(url: str, headers: Mapping[str, str]) -> str:
        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,
--- a/gpu/self_hosted/compose.yml
+++ b/gpu/self_hosted/compose.yml
@@ -8,11 +8,3 @@ services:
      - .env
    volumes:
      - ./cache:/root/.cache
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    restart: unless-stopped
--- a/scripts/setup-authentik-oauth.sh
+++ b/scripts/setup-authentik-oauth.sh
@@ -1,258 +0,0 @@
 #!/bin/bash
 set -e
 # Setup Authentik OAuth provider for Reflector
 #
 # IMPORTANT: Run this script from your Reflector repository directory (cd ~/reflector)
 # The script creates files using relative paths: server/reflector/auth/jwt/keys/
 #
 # Usage: ./setup-authentik-oauth.sh <authentik-url> <admin-password> <frontend-url>
 # Example: ./setup-authentik-oauth.sh https://authentik.example.com MyPassword123 https://app.example.com
 AUTHENTIK_URL="${1:-}"
 ADMIN_PASSWORD="${2:-}"
 FRONTEND_URL="${3:-}"
 if [ -z "$AUTHENTIK_URL" ] || [ -z "$ADMIN_PASSWORD" ] || [ -z "$FRONTEND_URL" ]; then
    echo "Usage: $0 <authentik-url> <admin-password> <frontend-url>"
    echo "Example: $0 https://authentik.example.com MyPassword123 https://app.example.com"
    exit 1
 fi
 # Remove trailing slash from URLs
 AUTHENTIK_URL="${AUTHENTIK_URL%/}"
 FRONTEND_URL="${FRONTEND_URL%/}"
 echo "==========================================="
 echo "Authentik OAuth Setup for Reflector"
 echo "==========================================="
 echo ""
 echo "Authentik URL: $AUTHENTIK_URL"
 echo "Frontend URL: $FRONTEND_URL"
 echo ""
 # Step 1: Create API token via Django shell
 echo "Creating API token..."
 cd ~/authentik || { echo "Error: ~/authentik directory not found"; exit 1; }
 API_TOKEN=$(sudo docker compose exec -T server python -m manage shell 2>&1 << 'PYTHON' | grep "^TOKEN:" | cut -d: -f2
 from authentik.core.models import User, Token, TokenIntents
 user = User.objects.get(username='akadmin')
 token, created = Token.objects.update_or_create(
    identifier='reflector-setup',
    defaults={
        'user': user,
        'intent': TokenIntents.INTENT_API,
        'description': 'Reflector setup token',
        'expiring': False
    }
 )
 print(f"TOKEN:{token.key}")
 PYTHON
 )
 cd - > /dev/null
 if [ -z "$API_TOKEN" ] || [ "$API_TOKEN" = "null" ]; then
    echo "Error: Failed to create API token"
    echo "Make sure Authentik is fully started and akadmin user exists"
    exit 1
 fi
 echo "  -> Got API token"
 # Step 2: Get authorization flow UUID
 echo "Getting authorization flow..."
 FLOW_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/flows/instances/?slug=default-provider-authorization-implicit-consent" \
    -H "Authorization: Bearer $API_TOKEN")
 FLOW_UUID=$(echo "$FLOW_RESPONSE" | jq -r '.results[0].pk')
 if [ -z "$FLOW_UUID" ] || [ "$FLOW_UUID" = "null" ]; then
    echo "Error: Could not find authorization flow"
    echo "Response: $FLOW_RESPONSE"
    exit 1
 fi
 echo "  -> Flow UUID: $FLOW_UUID"
 # Step 3: Get invalidation flow UUID
 echo "Getting invalidation flow..."
 INVALIDATION_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/flows/instances/?slug=default-provider-invalidation-flow" \
    -H "Authorization: Bearer $API_TOKEN")
 INVALIDATION_UUID=$(echo "$INVALIDATION_RESPONSE" | jq -r '.results[0].pk')
 if [ -z "$INVALIDATION_UUID" ] || [ "$INVALIDATION_UUID" = "null" ]; then
    echo "Warning: Could not find invalidation flow, using authorization flow"
    INVALIDATION_UUID="$FLOW_UUID"
 fi
 echo "  -> Invalidation UUID: $INVALIDATION_UUID"
 # Step 4: Get scope mappings (email, openid, profile)
 echo "Getting scope mappings..."
 SCOPE_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/propertymappings/all/" \
    -H "Authorization: Bearer $API_TOKEN")
 EMAIL_SCOPE=$(echo "$SCOPE_RESPONSE" | jq -r '.results[] | select(.name == "authentik default OAuth Mapping: OpenID '\''email'\''") | .pk')
 OPENID_SCOPE=$(echo "$SCOPE_RESPONSE" | jq -r '.results[] | select(.name == "authentik default OAuth Mapping: OpenID '\''openid'\''") | .pk')
 PROFILE_SCOPE=$(echo "$SCOPE_RESPONSE" | jq -r '.results[] | select(.name == "authentik default OAuth Mapping: OpenID '\''profile'\''") | .pk')
 echo "  -> email: $EMAIL_SCOPE"
 echo "  -> openid: $OPENID_SCOPE"
 echo "  -> profile: $PROFILE_SCOPE"
 # Step 5: Get signing key
 echo "Getting signing key..."
 CERT_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/crypto/certificatekeypairs/" \
    -H "Authorization: Bearer $API_TOKEN")
 SIGNING_KEY=$(echo "$CERT_RESPONSE" | jq -r '.results[0].pk')
 echo "  -> Signing key: $SIGNING_KEY"
 # Step 6: Generate client credentials
 CLIENT_ID="reflector"
 CLIENT_SECRET=$(openssl rand -hex 32)
 # Step 7: Create OAuth2 provider
 echo "Creating OAuth2 provider..."
 PROVIDER_RESPONSE=$(curl -s -X POST "$AUTHENTIK_URL/api/v3/providers/oauth2/" \
    -H "Authorization: Bearer $API_TOKEN" \
    -H "Content-Type: application/json" \
    -d "{
        \"name\": \"Reflector\",
        \"authorization_flow\": \"$FLOW_UUID\",
        \"invalidation_flow\": \"$INVALIDATION_UUID\",
        \"client_type\": \"confidential\",
        \"client_id\": \"$CLIENT_ID\",
        \"client_secret\": \"$CLIENT_SECRET\",
        \"redirect_uris\": [{
            \"matching_mode\": \"strict\",
            \"url\": \"$FRONTEND_URL/api/auth/callback/authentik\"
        }],
        \"property_mappings\": [\"$EMAIL_SCOPE\", \"$OPENID_SCOPE\", \"$PROFILE_SCOPE\"],
        \"signing_key\": \"$SIGNING_KEY\",
        \"access_token_validity\": \"hours=1\",
        \"refresh_token_validity\": \"days=30\"
    }")
 PROVIDER_ID=$(echo "$PROVIDER_RESPONSE" | jq -r '.pk')
 if [ -z "$PROVIDER_ID" ] || [ "$PROVIDER_ID" = "null" ]; then
    # Check if provider already exists
    if echo "$PROVIDER_RESPONSE" | grep -q "already exists"; then
        echo "  -> Provider already exists, updating..."
        EXISTING=$(curl -s "$AUTHENTIK_URL/api/v3/providers/oauth2/?name=Reflector" \
            -H "Authorization: Bearer $API_TOKEN")
        PROVIDER_ID=$(echo "$EXISTING" | jq -r '.results[0].pk')
        CLIENT_ID=$(echo "$EXISTING" | jq -r '.results[0].client_id')
        # Update secret and scopes
        curl -s -X PATCH "$AUTHENTIK_URL/api/v3/providers/oauth2/$PROVIDER_ID/" \
            -H "Authorization: Bearer $API_TOKEN" \
            -H "Content-Type: application/json" \
            -d "{
                \"client_secret\": \"$CLIENT_SECRET\",
                \"property_mappings\": [\"$EMAIL_SCOPE\", \"$OPENID_SCOPE\", \"$PROFILE_SCOPE\"],
                \"signing_key\": \"$SIGNING_KEY\"
            }" > /dev/null
    else
        echo "Error: Failed to create provider"
        echo "Response: $PROVIDER_RESPONSE"
        exit 1
    fi
 fi
 echo "  -> Provider ID: $PROVIDER_ID"
 # Step 8: Create application
 echo "Creating application..."
 APP_RESPONSE=$(curl -s -X POST "$AUTHENTIK_URL/api/v3/core/applications/" \
    -H "Authorization: Bearer $API_TOKEN" \
    -H "Content-Type: application/json" \
    -d "{
        \"name\": \"Reflector\",
        \"slug\": \"reflector\",
        \"provider\": $PROVIDER_ID
    }")
 if echo "$APP_RESPONSE" | grep -q "already exists"; then
    echo "  -> Application already exists"
 else
    APP_SLUG=$(echo "$APP_RESPONSE" | jq -r '.slug')
    if [ -z "$APP_SLUG" ] || [ "$APP_SLUG" = "null" ]; then
        echo "Error: Failed to create application"
        echo "Response: $APP_RESPONSE"
        exit 1
    fi
    echo "  -> Application created: $APP_SLUG"
 fi
 # Step 9: Extract public key for JWT verification
 echo "Extracting public key for JWT verification..."
 mkdir -p server/reflector/auth/jwt/keys
 curl -s "$AUTHENTIK_URL/application/o/reflector/jwks/" | \
    jq -r '.keys[0].x5c[0]' | \
    base64 -d | \
    openssl x509 -pubkey -noout > server/reflector/auth/jwt/keys/authentik_public.pem
 if [ ! -s server/reflector/auth/jwt/keys/authentik_public.pem ]; then
    echo "Error: Failed to extract public key"
    exit 1
 fi
 echo "  -> Saved to server/reflector/auth/jwt/keys/authentik_public.pem"
 # Step 10: Update environment files automatically
 echo "Updating environment files..."
 # Update server/.env
 cat >> server/.env << EOF
 # --- Authentik OAuth (added by setup script) ---
 AUTH_BACKEND=jwt
 AUTH_JWT_AUDIENCE=$CLIENT_ID
 AUTH_JWT_PUBLIC_KEY=authentik_public.pem
 # --- End JWT Configuration ---
 EOF
 echo "  -> Updated server/.env"
 # Update www/.env
 cat >> www/.env << EOF
 # --- Authentik OAuth (added by setup script) ---
 FEATURE_REQUIRE_LOGIN=true
 AUTHENTIK_ISSUER=$AUTHENTIK_URL/application/o/reflector
 AUTHENTIK_REFRESH_TOKEN_URL=$AUTHENTIK_URL/application/o/token/
 AUTHENTIK_CLIENT_ID=$CLIENT_ID
 AUTHENTIK_CLIENT_SECRET=$CLIENT_SECRET
 # --- End Authentik Configuration ---
 EOF
 echo "  -> Updated www/.env"
 # Step 11: Restart Reflector services
 echo "Restarting Reflector services..."
 docker compose -f docker-compose.prod.yml up -d server worker web
 echo ""
 echo "==========================================="
 echo "Setup complete!"
 echo "==========================================="
 echo ""
 echo "Authentik admin: $AUTHENTIK_URL"
 echo "  Username: akadmin"
 echo "  Password: (provided as argument)"
 echo ""
 echo "Frontend: $FRONTEND_URL"
 echo "  Authentication is now required"
 echo ""
 echo "Note: Public key saved to server/reflector/auth/jwt/keys/authentik_public.pem"
 echo "      and mounted via docker-compose volume."
 echo ""
 echo "==========================================="
 echo "Configuration values (for reference):"
 echo "==========================================="
 echo ""
 echo "# server/.env"
 echo "AUTH_BACKEND=jwt"
 echo "AUTH_JWT_AUDIENCE=$CLIENT_ID"
 echo "AUTH_JWT_PUBLIC_KEY=authentik_public.pem"
 echo ""
 echo "# www/.env"
 echo "FEATURE_REQUIRE_LOGIN=true"
 echo "AUTHENTIK_ISSUER=$AUTHENTIK_URL/application/o/reflector"
 echo "AUTHENTIK_REFRESH_TOKEN_URL=$AUTHENTIK_URL/application/o/token/"
 echo "AUTHENTIK_CLIENT_ID=$CLIENT_ID"
 echo "AUTHENTIK_CLIENT_SECRET=$CLIENT_SECRET"
 echo ""
--- a/server/DAILYCO_TEST.md
+++ b/server/DAILYCO_TEST.md
@@ -0,0 +1,613 @@
 # Daily.co Integration Test Plan
 ## ✅ IMPLEMENTATION STATUS: Real Transcription Active
 **This test validates Daily.co multitrack recording integration with REAL transcription/diarization.**
 The implementation includes complete audio processing pipeline:
 - **Multitrack recordings** from Daily.co S3 (separate audio stream per participant)
 - **PyAV-based audio mixdown** with PTS-based track alignment
 - **Real transcription** via Modal GPU backend (Whisper)
 - **Real diarization** via Modal GPU backend (speaker identification)
 - **Per-track transcription** with timestamp synchronization
 - **Complete database entities** (recording, transcript, topics, participants, words)
 **Processing pipeline** (`PipelineMainMultitrack`):
 1. Download all audio tracks from Daily.co S3
 2. Align tracks by PTS (presentation timestamp) to handle late joiners
 3. Mix tracks into single audio file for unified playback
 4. Transcribe each track individually with proper offset handling
 5. Perform diarization on mixed audio
 6. Generate topics, summaries, and word-level timestamps
 7. Convert audio to MP3 and generate waveform visualization
 **Note:** A stub processor (`process_daily_recording`) exists for testing webhook flow without GPU costs, but the production code path uses `process_multitrack_recording` with full ML pipeline.
 ---
 ## Prerequisites
 **1. Environment Variables** (check in `.env.development.local`):
 ```bash
 # Daily.co API Configuration
 DAILY_API_KEY=<key>
 DAILY_SUBDOMAIN=monadical
 DAILY_WEBHOOK_SECRET=<base64-encoded-secret>
 AWS_DAILY_S3_BUCKET=reflector-dailyco-local
 AWS_DAILY_S3_REGION=us-east-1
 AWS_DAILY_ROLE_ARN=arn:aws:iam::950402358378:role/DailyCo
 DAILY_MIGRATION_ENABLED=true
 DAILY_MIGRATION_ROOM_IDS=["552640fd-16f2-4162-9526-8cf40cd2357e"]
 # Transcription/Diarization Backend (Required for real processing)
 DIARIZATION_BACKEND=modal
 DIARIZATION_MODAL_API_KEY=<modal-api-key>
 # TRANSCRIPTION_BACKEND is not explicitly set (uses default/modal)
 ```
 **2. Services Running:**
 ```bash
 docker compose ps  # server, postgres, redis, worker, beat should be UP
 ```
 **IMPORTANT:** Worker and beat services MUST be running for transcription processing:
 ```bash
 docker compose up -d worker beat
 ```
 **3. ngrok Tunnel for Webhooks:**
 ```bash
 # Start ngrok (if not already running)
 ngrok http 1250 --log=stdout > /tmp/ngrok.log 2>&1 &
 # Get public URL
 curl -s http://localhost:4040/api/tunnels | python3 -c "import sys, json; data=json.load(sys.stdin); print(data['tunnels'][0]['public_url'])"
 ```
 **Current ngrok URL:** `https://0503947384a3.ngrok-free.app` (as of last registration)
 **4. Webhook Created:**
 ```bash
 cd server
 uv run python scripts/recreate_daily_webhook.py https://0503947384a3.ngrok-free.app/v1/daily/webhook
 # Verify: "Created webhook <uuid> (state: ACTIVE)"
 ```
 **Current webhook status:** ✅ ACTIVE (webhook ID: dad5ad16-ceca-488e-8fc5-dae8650b51d0)
 ---
 ## Test 1: Database Configuration
 **Check room platform:**
 ```bash
 docker-compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT id, name, platform, recording_type FROM room WHERE name = 'test2';"
 ```
 **Expected:**
 ```
 id: 552640fd-16f2-4162-9526-8cf40cd2357e
 name: test2
 platform: whereby  # DB value (overridden by env var DAILY_MIGRATION_ROOM_IDS)
 recording_type: cloud
 ```
 **Clear old meetings:**
 ```bash
 docker-compose exec -T postgres psql -U reflector -d reflector -c \
  "UPDATE meeting SET is_active = false WHERE room_id = '552640fd-16f2-4162-9526-8cf40cd2357e';"
 ```
 ---
 ## Test 2: Meeting Creation with Auto-Recording
 **Create meeting:**
 ```bash
 curl -s -X POST http://localhost:1250/v1/rooms/test2/meeting \
  -H "Content-Type: application/json" \
  -d '{"allow_duplicated":false}' | python3 -m json.tool
 ```
 **Expected Response:**
 ```json
 {
  "room_name": "test2-YYYYMMDDHHMMSS",  // Includes "test2" prefix!
  "room_url": "https://monadical.daily.co/test2-...?t=<JWT_TOKEN>",  // Has token!
  "platform": "daily",
  "recording_type": "cloud"  // DB value (Whereby-specific)
 }
 ```
 **Decode token to verify auto-recording:**
 ```bash
 # Extract token from room_url, decode JWT payload
 echo "<token>" | python3 -c "
 import sys, json, base64
 token = sys.stdin.read().strip()
 payload = token.split('.')[1] + '=' * (4 - len(token.split('.')[1]) % 4)
 print(json.dumps(json.loads(base64.b64decode(payload)), indent=2))
 "
 ```
 **Expected token payload:**
 ```json
 {
  "r": "test2-YYYYMMDDHHMMSS",  // Room name
  "sr": true,  // start_recording: true ✅
  "d": "...",  // Domain ID
  "iat": 1234567890
 }
 ```
 ---
 ## Test 3: Daily.co API Verification
 **Check room configuration:**
 ```bash
 ROOM_NAME="<from previous step>"
 curl -s -X GET "https://api.daily.co/v1/rooms/$ROOM_NAME" \
  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -m json.tool
 ```
 **Expected config:**
 ```json
 {
  "config": {
    "enable_recording": "raw-tracks",  // ✅
    "recordings_bucket": {
      "bucket_name": "reflector-dailyco-local",
      "bucket_region": "us-east-1",
      "assume_role_arn": "arn:aws:iam::950402358378:role/DailyCo"
    }
  }
 }
 ```
 ---
 ## Test 4: Browser UI Test (Playwright MCP)
 **Using Claude Code MCP tools:**
 **Load room:**
 ```
 Use: mcp__playwright__browser_navigate
 Input: {"url": "http://localhost:3000/test2"}
 Then wait 12 seconds for iframe to load
 ```
 **Verify Daily.co iframe loaded:**
 ```
 Use: mcp__playwright__browser_snapshot
 Expected in snapshot:
 - iframe element with src containing "monadical.daily.co"
 - Daily.co pre-call UI visible
 ```
 **Take screenshot:**
 ```
 Use: mcp__playwright__browser_take_screenshot
 Input: {"filename": "test2-before-join.png"}
 Expected: Daily.co pre-call UI with "Join" button visible
 ```
 **Join meeting:**
 ```
 Note: Daily.co iframe interaction requires clicking inside iframe.
 Use: mcp__playwright__browser_click
 Input: {"element": "Join button in Daily.co iframe", "ref": "<ref-from-snapshot>"}
 Then wait 5 seconds for call to connect
 ```
 **Verify in-call:**
 ```
 Use: mcp__playwright__browser_take_screenshot
 Input: {"filename": "test2-in-call.png"}
 Expected: "Waiting for others to join" or participant video visible
 ```
 **Leave meeting:**
 ```
 Use: mcp__playwright__browser_click
 Input: {"element": "Leave button in Daily.co iframe", "ref": "<ref-from-snapshot>"}
 ```
 ---
 **Alternative: JavaScript snippets (for manual testing):**
 ```javascript
 await page.goto('http://localhost:3000/test2');
 await new Promise(f => setTimeout(f, 12000));  // Wait for load
 // Verify iframe
 const iframes = document.querySelectorAll('iframe');
 // Expected: 1 iframe with src containing "monadical.daily.co"
 // Screenshot
 await page.screenshot({ path: 'test2-before-join.png' });
 // Join
 await page.locator('iframe').contentFrame().getByRole('button', { name: 'Join' }).click();
 await new Promise(f => setTimeout(f, 5000));
 // In-call screenshot
 await page.screenshot({ path: 'test2-in-call.png' });
 // Leave
 await page.locator('iframe').contentFrame().getByRole('button', { name: 'Leave' }).click();
 ```
 ---
 ## Test 5: Webhook Verification
 **Check server logs for webhooks:**
 ```bash
 docker-compose logs --since 15m server 2>&1 | grep -i "participant joined\|recording started"
 ```
 **Expected logs:**
 ```
 [info] Participant joined | meeting_id=... | num_clients=1 | recording_type=cloud | recording_trigger=automatic-2nd-participant
 [info] Recording started | meeting_id=... | recording_id=... | platform=daily
 ```
 **Check Daily.co webhook delivery logs:**
 ```bash
 curl -s -X GET "https://api.daily.co/v1/logs/webhooks?limit=20" \
  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -c "
 import sys, json
 logs = json.load(sys.stdin)
 for log in logs[:10]:
    req = json.loads(log['request'])
    room = req.get('payload', {}).get('room') or req.get('payload', {}).get('room_name', 'N/A')
    print(f\"{req['type']:30s} | room: {room:30s} | status: {log['status']}\")
 "
 ```
 **Expected output:**
 ```
 participant.joined             | room: test2-YYYYMMDDHHMMSS       | status: 200
 recording.started              | room: test2-YYYYMMDDHHMMSS       | status: 200
 participant.left               | room: test2-YYYYMMDDHHMMSS       | status: 200
 recording.ready-to-download    | room: test2-YYYYMMDDHHMMSS       | status: 200
 ```
 **Check database updated:**
 ```bash
 docker-compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT room_name, num_clients FROM meeting WHERE room_name LIKE 'test2-%' ORDER BY end_date DESC LIMIT 1;"
 ```
 **Expected:**
 ```
 room_name: test2-YYYYMMDDHHMMSS
 num_clients: 0  // After participant left
 ```
 ---
 ## Test 6: Recording in S3
 **List recent recordings:**
 ```bash
 curl -s -X GET "https://api.daily.co/v1/recordings" \
  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -c "
 import sys, json
 data = json.load(sys.stdin)
 for rec in data.get('data', [])[:5]:
    if 'test2-' in rec.get('room_name', ''):
        print(f\"Room: {rec['room_name']}\")
        print(f\"Status: {rec['status']}\")
        print(f\"Duration: {rec.get('duration', 0)}s\")
        print(f\"S3 key: {rec.get('s3key', 'N/A')}\")
        print(f\"Tracks: {len(rec.get('tracks', []))} files\")
        for track in rec.get('tracks', []):
            print(f\"  - {track['type']}: {track['s3Key'].split('/')[-1]} ({track['size']} bytes)\")
        print()
 "
 ```
 **Expected output:**
 ```
 Room: test2-20251009192341
 Status: finished
 Duration: ~30-120s
 S3 key: monadical/test2-20251009192341/1760037914930
 Tracks: 2 files
  - audio: 1760037914930-<uuid>-cam-audio-1760037915265 (~400 KB)
  - video: 1760037914930-<uuid>-cam-video-1760037915269 (~10-30 MB)
 ```
 **Verify S3 path structure:**
 - `monadical/` - Daily.co subdomain
 - `test2-20251009192341/` - Reflector room name + timestamp
 - `<timestamp>-<participant-uuid>-<media-type>-<track-start>.webm` - Individual track files
 ---
 ## Test 7: Database Check - Recording and Transcript
 **Check recording created:**
 ```bash
 docker-compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT id, bucket_name, object_key, status, meeting_id, recorded_at
   FROM recording
   ORDER BY recorded_at DESC LIMIT 1;"
 ```
 **Expected:**
 ```
 id: <recording-id-from-webhook>
 bucket_name: reflector-dailyco-local
 object_key: monadical/test2-<timestamp>/<recording-timestamp>-<uuid>-cam-audio-<track-start>.webm
 status: completed
 meeting_id: <meeting-id>
 recorded_at: <recent-timestamp>
 ```
 **Check transcript created:**
 ```bash
 docker compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT id, title, status, duration, recording_id, meeting_id, room_id
   FROM transcript
   ORDER BY created_at DESC LIMIT 1;"
 ```
 **Expected (REAL transcription):**
 ```
 id: <transcript-id>
 title: <AI-generated title based on actual conversation content>
 status: uploaded  (audio file processed and available)
 duration: <actual meeting duration in seconds>
 recording_id: <same-as-recording-id-above>
 meeting_id: <meeting-id>
 room_id: 552640fd-16f2-4162-9526-8cf40cd2357e
 ```
 **Note:** Title and content will reflect the ACTUAL conversation, not mock data. Processing time depends on recording length and GPU backend availability (Modal).
 **Verify audio file exists:**
 ```bash
 ls -lh data/<transcript-id>/upload.webm
 ```
 **Expected:**
 ```
 -rw-r--r-- 1 user staff ~100-200K Oct 10 18:48 upload.webm
 ```
 **Check transcript topics (REAL transcription):**
 ```bash
 TRANSCRIPT_ID=$(docker compose exec -T postgres psql -U reflector -d reflector -t -c \
  "SELECT id FROM transcript ORDER BY created_at DESC LIMIT 1;")
 docker compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT
     jsonb_array_length(topics) as num_topics,
     jsonb_array_length(participants) as num_participants,
     short_summary,
     title
   FROM transcript
   WHERE id = '$TRANSCRIPT_ID';"
 ```
 **Expected (REAL data):**
 ```
 num_topics: <varies based on conversation>
 num_participants: <actual number of participants who spoke>
 short_summary: <AI-generated summary of actual conversation>
 title: <AI-generated title based on content>
 ```
 **Check topics contain actual transcription:**
 ```bash
 docker compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT topics->0->'title', topics->0->'summary', topics->0->'transcript'
   FROM transcript
   ORDER BY created_at DESC LIMIT 1;" | head -20
 ```
 **Expected output:** Will contain the ACTUAL transcribed conversation from the Daily.co meeting, not mock data.
 **Check participants:**
 ```bash
 docker compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT participants FROM transcript ORDER BY created_at DESC LIMIT 1;" \
  | python3 -c "import sys, json; data=json.loads(sys.stdin.read()); print(json.dumps(data, indent=2))"
 ```
 **Expected (REAL diarization):**
 ```json
 [
  {
    "id": "<uuid>",
    "speaker": 0,
    "name": "Speaker 1"
  },
  {
    "id": "<uuid>",
    "speaker": 1,
    "name": "Speaker 2"
  }
 ]
 ```
 **Note:** Speaker names will be generic ("Speaker 1", "Speaker 2", etc.) as determined by the diarization backend. Number of participants depends on how many actually spoke during the meeting.
 **Check word-level data:**
 ```bash
 docker compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT jsonb_array_length(topics->0->'words') as num_words_first_topic
   FROM transcript
   ORDER BY created_at DESC LIMIT 1;"
 ```
 **Expected:**
 ```
 num_words_first_topic: <varies based on actual conversation length and topic chunking>
 ```
 **Verify speaker diarization in words:**
 ```bash
 docker compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT
     topics->0->'words'->0->>'text' as first_word,
     topics->0->'words'->0->>'speaker' as speaker,
     topics->0->'words'->0->>'start' as start_time,
     topics->0->'words'->0->>'end' as end_time
   FROM transcript
   ORDER BY created_at DESC LIMIT 1;"
 ```
 **Expected (REAL transcription):**
 ```
 first_word: <actual first word from transcription>
 speaker: 0, 1, 2, ... (actual speaker ID from diarization)
 start_time: <actual timestamp in seconds>
 end_time: <actual end timestamp>
 ```
 **Note:** All timestamps and speaker IDs are from real transcription/diarization, synchronized across tracks.
 ---
 ## Test 8: Recording Type Verification
 **Check what Daily.co received:**
 ```bash
 curl -s -X GET "https://api.daily.co/v1/rooms/test2-<timestamp>" \
  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -m json.tool | grep "enable_recording"
 ```
 **Expected:**
 ```json
 "enable_recording": "raw-tracks"
 ```
 **NOT:** `"enable_recording": "cloud"` (that would be wrong - we want raw tracks)
 ---
 ## Troubleshooting
 ### Issue: No webhooks received
 **Check webhook state:**
 ```bash
 curl -s -X GET "https://api.daily.co/v1/webhooks" \
  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -m json.tool
 ```
 **If state is FAILED:**
 ```bash
 cd server
 uv run python scripts/recreate_daily_webhook.py https://<ngrok-url>/v1/daily/webhook
 ```
 ### Issue: Webhooks return 422
 **Check server logs:**
 ```bash
 docker-compose logs --tail=50 server | grep "Failed to parse webhook event"
 ```
 **Common cause:** Event structure mismatch. Daily.co events use:
 ```json
 {
  "version": "1.0.0",
  "type": "participant.joined",
  "payload": {...},  // NOT "data"
  "event_ts": 123.456  // NOT "ts"
 }
 ```
 ### Issue: Recording not starting
 1. **Check token has `sr: true`:**
   - Decode JWT token from room_url query param
   - Should contain `"sr": true`
 2. **Check Daily.co room config:**
   - `enable_recording` must be set (not false)
   - For raw-tracks: must be exactly `"raw-tracks"`
 3. **Check participant actually joined:**
   - Logs should show "Participant joined"
   - Must click "Join" button, not just pre-call screen
 ### Issue: Recording in S3 but wrong format
 **Daily.co recording types:**
 - `"cloud"` → Single MP4 file (`download_link` in webhook)
 - `"raw-tracks"` → Multiple WebM files (`tracks` array in webhook)
 - `"raw-tracks-audio-only"` → Only audio WebM files
 **Current implementation:** Always uses `"raw-tracks"` (better for transcription)
 ---
 ## Quick Validation Commands
 **One-liner to verify everything:**
 ```bash
 # 1. Check room exists
 docker-compose exec -T postgres psql -U reflector -d reflector -c \
  "SELECT name, platform FROM room WHERE name = 'test2';" && \
 # 2. Create meeting
 MEETING=$(curl -s -X POST http://localhost:1250/v1/rooms/test2/meeting \
  -H "Content-Type: application/json" -d '{"allow_duplicated":false}') && \
 echo "$MEETING" | python3 -c "import sys,json; m=json.load(sys.stdin); print(f'Room: {m[\"room_name\"]}\nURL: {m[\"room_url\"][:80]}...')" && \
 # 3. Check Daily.co config
 ROOM_NAME=$(echo "$MEETING" | python3 -c "import sys,json; print(json.load(sys.stdin)['room_name'])") && \
 curl -s -X GET "https://api.daily.co/v1/rooms/$ROOM_NAME" \
  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -c "import sys,json; print(f'Recording: {json.load(sys.stdin)[\"config\"][\"enable_recording\"]}')"
 ```
 **Expected output:**
 ```
 name: test2, platform: whereby
 Room: test2-20251009192341
 URL: https://monadical.daily.co/test2-20251009192341?t=eyJhbGc...
 Recording: raw-tracks
 ```
 ---
 ## Success Criteria Checklist
 - [x] Room name includes Reflector room prefix (`test2-...`)
 - [x] Meeting URL contains JWT token (`?t=...`)
 - [x] Token has `sr: true` (auto-recording enabled)
 - [x] Daily.co room config: `enable_recording: "raw-tracks"`
 - [x] Browser loads Daily.co interface (not Whereby)
 - [x] Recording auto-starts when participant joins
 - [x] Webhooks received: participant.joined, recording.started, participant.left, recording.ready-to-download
 - [x] Recording status: `finished`
 - [x] S3 contains 2 files: audio (.webm) and video (.webm)
 - [x] S3 path: `monadical/test2-{timestamp}/{recording-start-ts}-{participant-uuid}-cam-{audio|video}-{track-start-ts}`
 - [x] Database `num_clients` increments/decrements correctly
 - [x] **Database recording entry created** with correct S3 path and status `completed`
 - [ ] **Database transcript entry created** with status `uploaded`
 - [ ] **Audio file downloaded** to `data/{transcript_id}/upload.webm`
 - [ ] **Transcript has REAL data**: AI-generated title based on conversation
 - [ ] **Transcript has topics** generated from actual content
 - [ ] **Transcript has participants** with proper speaker diarization
 - [ ] **Topics contain word-level data** with accurate timestamps and speaker IDs
 - [ ] **Total duration** matches actual meeting length
 - [ ] **MP3 and waveform files generated** by file processing pipeline
 - [ ] **Frontend transcript page loads** without "Failed to load audio" error
 - [ ] **Audio player functional** with working playback and waveform visualization
 - [ ] **Multitrack processing completed** without errors in worker logs
 - [ ] **Modal GPU backends accessible** (transcription and diarization)
--- a/server/Dockerfile
+++ b/server/Dockerfile
@@ -6,7 +6,7 @@ ENV PYTHONUNBUFFERED=1 \
 # builder install base dependencies
 WORKDIR /tmp
-RUN apt-get update && apt-get install -y curl && apt-get clean
+RUN apt-get update && apt-get install -y curl ffmpeg && apt-get clean
 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"
--- a/server/README.md
+++ b/server/README.md
@@ -1,29 +1,3 @@
 ## API Key Management
 ### Finding Your User ID
 ```bash
 # Get your OAuth sub (user ID) - requires authentication
 curl -H "Authorization: Bearer <your_jwt>" http://localhost:1250/v1/me
 # Returns: {"sub": "your-oauth-sub-here", "email": "...", ...}
 ```
 ### Creating API Keys
 ```bash
 curl -X POST http://localhost:1250/v1/user/api-keys \
  -H "Authorization: Bearer <your_jwt>" \
  -H "Content-Type: application/json" \
  -d '{"name": "My API Key"}'
 ```
 ### Using API Keys
 ```bash
 # Use X-API-Key header instead of Authorization
 curl -H "X-API-Key: <your_api_key>" http://localhost:1250/v1/transcripts
 ```
 ## AWS S3/SQS usage clarification
 Whereby.com uploads recordings directly to our S3 bucket when meetings end.
@@ -53,36 +27,6 @@ response = sqs.receive_message(QueueUrl=queue_url, ...)
 uv run /app/requeue_uploaded_file.py TRANSCRIPT_ID
 ```
 ## Hatchet Setup (Fresh DB)
 After resetting the Hatchet database:
 ### Option A: Automatic (CLI)
 ```bash
 # Get default tenant ID and create token in one command
 TENANT_ID=$(docker compose exec -T postgres psql -U reflector -d hatchet -t -c \
  "SELECT id FROM \"Tenant\" WHERE slug = 'default';" | tr -d ' \n') && \
 TOKEN=$(docker compose exec -T hatchet /hatchet-admin token create \
  --config /config --tenant-id "$TENANT_ID" 2>/dev/null | tr -d '\n') && \
 echo "HATCHET_CLIENT_TOKEN=$TOKEN"
 ```
 Copy the output to `server/.env`.
 ### Option B: Manual (UI)
 1. Create API token at http://localhost:8889 → Settings → API Tokens
 2. Update `server/.env`: `HATCHET_CLIENT_TOKEN=<new-token>`
 ### Then restart workers
 ```bash
 docker compose restart server hatchet-worker
 ```
 Workflows register automatically when hatchet-worker starts.
 ## Pipeline Management
 ### Continue stuck pipeline from final summaries (identify_participants) step:
--- a/server/docker/init-hatchet-db.sql
+++ b/server/docker/init-hatchet-db.sql
@@ -1,2 +0,0 @@
 -- Create hatchet database for Hatchet workflow engine
 CREATE DATABASE hatchet;
--- a/server/docs/daily_pipeline.md
+++ b/server/docs/daily_pipeline.md
@@ -1,421 +0,0 @@
 # Daily.co pipeline
 This document details every external call, storage operation, and database write that occurs when a new Daily.co recording is discovered.
 It includes a bunch of common logic that other pipelines use, therefore not everything is Daily-oriented.
 **The doc was generated at 12.12.2025 and things may have changed since.**
 ## Trigger
 Two entry points, both converging to the same handler:
 1. **Webhook**: Daily.co sends `POST /v1/daily/webhook` with `recording.ready-to-download`
 2. **Polling**: `GET /recordings` (paginated, max 100/call) → filter new → convert to same payload format
 Both produce `RecordingReadyPayload` and call `handleRecordingReady(payload)`.
 ```
 ┌─────────────────┐     ┌──────────────────────────┐
 │  Daily Webhook  │────▶│  RecordingReadyPayload   │
 │  (push)         │     │  {room_name, recording_id│
 └─────────────────┘     │   tracks[], ...}         │
                        └────────────┬─────────────┘
 ┌─────────────────┐                  │
 │  GET /recordings│                  ▼
 │  (poll)         │────▶ convert ──▶ handleRecordingReady()
 └─────────────────┘                  │
                                     ▼
                        ┌────────────────────────┐
                        │ process_multitrack_    │
                        │ recording pipeline     │
                        └────────────────────────┘
 ```
 **Polling API**: `GET https://api.daily.co/v1/recordings`
 - Pagination: `limit` (max 100), `starting_after`, `ending_before`
 - Rate limit: ~2 req/sec
 - Response: `{total_count, data: Recording[]}`
 ```mermaid
 flowchart TB
    subgraph Trigger["1. Recording Discovery - Daily.co Webhook"]
        DAILY_WEBHOOK["Daily.co sends POST /v1/daily/webhook<br/>type: recording.ready-to-download"]
        VERIFY["Verify X-Webhook-Signature (HMAC)"]
        PARSE["Parse DailyWebhookEvent<br/>Extract tracks[], room_name, recording_id"]
        FILTER["Filter audio tracks only<br/>track_keys = [t.s3Key for t in tracks if t.type == 'audio']"]
        DISPATCH["process_multitrack_recording.delay()"]
        DAILY_WEBHOOK --> VERIFY --> PARSE --> FILTER --> DISPATCH
    end
    subgraph Init["2. Recording Initialization"]
        FETCH_MEETING[DB READ: meetings_controller.get_by_room_name]
        FETCH_ROOM[DB READ: rooms_controller.get_by_name]
        DAILY_API_REC[Daily API: GET /recordings/recording_id]
        DAILY_API_PART[Daily API: GET /meetings/mtgSessionId/participants]
        CREATE_RECORDING[DB WRITE: recordings_controller.create]
        CREATE_TRANSCRIPT[DB WRITE: transcripts_controller.add]
        MAP_PARTICIPANTS[DB WRITE: transcript.participants upsert]
    end
    subgraph Pipeline["3. Processing Pipeline"]
        direction TB
        PAD[Track Padding & Mixdown]
        TRANSCRIBE[GPU: Transcription per track]
        TOPICS[LLM: Topic Detection]
        TITLE[LLM: Title Generation]
        SUMMARY[LLM: Summary Generation]
    end
    subgraph Storage["4. S3 Operations"]
        S3_PRESIGN[S3: generate_presigned_url for tracks]
        S3_UPLOAD_PADDED[S3 UPLOAD: padded tracks temp]
        S3_UPLOAD_MP3[S3 UPLOAD: audio.mp3]
        S3_DELETE_TEMP[S3 DELETE: cleanup temp files]
    end
    subgraph PostProcess["5. Post-Processing"]
        CONSENT[Consent check & cleanup]
        ZULIP[Zulip: send/update message]
        WEBHOOK_OUT[Webhook: POST to room.webhook_url]
    end
    Trigger --> Init --> Pipeline
    Pipeline --> Storage
    Pipeline --> PostProcess
 ```
 ## Detailed Sequence: Daily.co Multitrack Recording
 ```mermaid
 sequenceDiagram
    participant DailyCo as Daily.co
    participant API as FastAPI /v1/daily/webhook
    participant Worker as Celery Worker
    participant DB as PostgreSQL
    participant DailyAPI as Daily.co REST API
    participant S3 as AWS S3
    participant GPU as Modal.com GPU
    participant LLM as LLM Service
    participant WS as WebSocket
    participant Zulip as Zulip
    participant ExtWH as External Webhook
    Note over DailyCo,API: Phase 0: Webhook Receipt
    DailyCo->>API: POST /v1/daily/webhook
    Note right of DailyCo: X-Webhook-Signature, X-Webhook-Timestamp
    API->>API: verify_webhook_signature()
    API->>API: Extract audio track s3Keys from payload.tracks[]
    API->>Worker: process_multitrack_recording.delay()
    API-->>DailyCo: 200 OK
    Note over Worker,DailyAPI: Phase 1: Recording Initialization
    Worker->>DB: SELECT meeting WHERE room_name=?
    Worker->>DB: SELECT room WHERE name=?
    Worker->>DailyAPI: GET /recordings/{recording_id}
    DailyAPI-->>Worker: {mtgSessionId, ...}
    Worker->>DailyAPI: GET /meetings/{mtgSessionId}/participants
    DailyAPI-->>Worker: [{participant_id, user_name}, ...]
    Worker->>DB: INSERT INTO recording
    Worker->>DB: INSERT INTO transcript (status='idle')
    loop For each track_key (parse participant_id from filename)
        Worker->>DB: UPSERT transcript.participants[speaker=idx, name=X]
    end
    Note over Worker,S3: Phase 2: Track Padding
    Worker->>DB: UPDATE transcript SET status='processing'
    Worker->>WS: broadcast STATUS='processing'
    loop For each track in track_keys (N tracks)
        Worker->>S3: generate_presigned_url(track_key, DAILYCO_BUCKET)
        S3-->>Worker: presigned_url (2hr)
        Note over Worker: PyAV: read WebM, extract start_time
        Note over Worker: PyAV: adelay filter (pad silence)
        Worker->>S3: PUT file_pipeline/{id}/tracks/padded_{idx}.webm
        Worker->>S3: generate_presigned_url(padded_{idx}.webm)
    end
    Note over Worker,S3: Phase 3: Audio Mixdown
    Note over Worker: PyAV: amix filter → stereo MP3
    Worker->>DB: UPDATE transcript SET duration=X
    Worker->>WS: broadcast DURATION
    Worker->>S3: PUT {transcript_id}/audio.mp3
    Worker->>DB: UPDATE transcript SET audio_location='storage'
    Note over Worker: Phase 4: Waveform
    Note over Worker: Generate peaks from MP3
    Worker->>DB: UPDATE events+=WAVEFORM
    Worker->>WS: broadcast WAVEFORM
    Note over Worker,GPU: Phase 5: Transcription (N GPU calls)
    loop For each padded track URL (N tracks)
        Worker->>GPU: POST /v1/audio/transcriptions-from-url
        Note right of GPU: {audio_file_url, language, batch:true}
        GPU-->>Worker: {words: [{word, start, end}, ...]}
        Note over Worker: Assign speaker=track_idx to words
    end
    Note over Worker: Merge all words, sort by start time
    Worker->>DB: UPDATE events+=TRANSCRIPT
    Worker->>WS: broadcast TRANSCRIPT
    Note over Worker,S3: Cleanup temp files
    loop For each padded file
        Worker->>S3: DELETE padded_{idx}.webm
    end
    Note over Worker,LLM: Phase 6: Topic Detection (C LLM calls)
    Note over Worker: C = ceil(total_words / 300)
    loop For each 300-word chunk (C chunks)
        Worker->>LLM: TOPIC_PROMPT + words[i:i+300]
        Note right of LLM: "Extract main topic title + 2-sentence summary"
        LLM-->>Worker: TitleSummary{title, summary}
        Worker->>DB: UPSERT topics[]
        Worker->>DB: UPDATE events+=TOPIC
        Worker->>WS: broadcast TOPIC
    end
    Note over Worker,LLM: Phase 7a: Title Generation (1 LLM call)
    Note over Worker: Input: all TitleSummary[].title joined
    Worker->>LLM: TITLE_PROMPT
    Note right of LLM: "Generate concise title from topic titles"
    LLM-->>Worker: "Meeting Title"
    Worker->>DB: UPDATE transcript SET title=X
    Worker->>DB: UPDATE events+=FINAL_TITLE
    Worker->>WS: broadcast FINAL_TITLE
    Note over Worker,LLM: Phase 7b: Summary Generation (2+2M LLM calls)
    Note over Worker: Reconstruct full transcript from TitleSummary[].transcript
    opt If participants unknown
        Worker->>LLM: PARTICIPANTS_PROMPT
        LLM-->>Worker: ParticipantsResponse
    end
    Worker->>LLM: SUBJECTS_PROMPT (call #1)
    Note right of LLM: "Main high-level topics? Max 6"
    LLM-->>Worker: SubjectsResponse{subjects: ["A", "B", ...]}
    loop For each subject (M subjects, max 6)
        Worker->>LLM: DETAILED_SUBJECT_PROMPT (call #2..#1+M)
        Note right of LLM: "Info about 'A': decisions, actions, deadlines"
        LLM-->>Worker: detailed_response (discarded after next call)
        Worker->>LLM: PARAGRAPH_SUMMARY_PROMPT (call #2+M..#1+2M)
        Note right of LLM: "Summarize in 1 paragraph"
        LLM-->>Worker: paragraph → summaries[]
    end
    Worker->>LLM: RECAP_PROMPT (call #2+2M)
    Note right of LLM: "High-level quick recap, 1 paragraph"
    LLM-->>Worker: recap
    Note over Worker: long_summary = "# Quick recap\n{recap}\n# Summary\n**A**\n{para1}..."
    Note over Worker: short_summary = recap only
    Worker->>DB: UPDATE long_summary, short_summary
    Worker->>DB: UPDATE events+=FINAL_LONG_SUMMARY
    Worker->>WS: broadcast FINAL_LONG_SUMMARY
    Worker->>DB: UPDATE events+=FINAL_SHORT_SUMMARY
    Worker->>WS: broadcast FINAL_SHORT_SUMMARY
    Note over Worker,DB: Phase 8: Finalize
    Worker->>DB: UPDATE transcript SET status='ended'
    Worker->>DB: UPDATE events+=STATUS
    Worker->>WS: broadcast STATUS='ended'
    Note over Worker,ExtWH: Phase 9: Post-Processing Chain
    Worker->>DB: SELECT meeting_consent WHERE meeting_id=?
    alt Any consent denied
        Worker->>S3: DELETE tracks from DAILYCO_BUCKET
        Worker->>S3: DELETE audio.mp3 from TRANSCRIPT_BUCKET
        Worker->>DB: UPDATE transcript SET audio_deleted=true
    end
    opt Room has zulip_auto_post=true
        alt Existing zulip_message_id
            Worker->>Zulip: PATCH /api/v1/messages/{id}
        else New
            Worker->>Zulip: POST /api/v1/messages
            Zulip-->>Worker: {id}
            Worker->>DB: UPDATE transcript SET zulip_message_id=X
        end
    end
    opt Room has webhook_url
        Worker->>ExtWH: POST {webhook_url}
        Note right of ExtWH: X-Webhook-Signature: HMAC-SHA256
        Note right of ExtWH: Body: {transcript_id, room_id, ...}
    end
 ```
 ## Title & Summary Generation Data Flow
 ```mermaid
 flowchart TB
    subgraph Input["Input: TitleSummary[] from Topic Detection"]
        TS1["TitleSummary 1<br/>title: 'Q1 Budget'<br/>transcript: words[0:300]"]
        TS2["TitleSummary 2<br/>title: 'Product Launch'<br/>transcript: words[300:600]"]
        TS3["TitleSummary N..."]
    end
    subgraph TitleGen["Title Generation"]
        T1["Extract .title from each TitleSummary"]
        T2["Concatenate: '- Q1 Budget\n- Product Launch\n...'"]
        T3["LLM: TITLE_PROMPT\n'Generate concise title from topic titles'"]
        T4["Output: FinalTitle"]
        T1 --> T2 --> T3 --> T4
    end
    subgraph SummaryGen["Summary Generation"]
        direction TB
        subgraph Reconstruct["1. Reconstruct Full Transcript"]
            S1["For each TitleSummary.transcript.as_segments()"]
            S2["Map speaker ID → name"]
            S3["Build: 'Alice: hello\nBob: hi\n...'"]
            S1 --> S2 --> S3
        end
        subgraph Subjects["2. Extract Subjects - LLM call #1"]
            S4["LLM: SUBJECTS_PROMPT\n'Main high-level topics? Max 6'"]
            S5["subjects[] = ['Budget Review', ...]"]
            S4 --> S5
        end
        subgraph DetailedSum["3. Per-Subject Summary - LLM calls #2 to #(1+2M)"]
            S6["For each subject:"]
            S7["LLM: DETAILED_SUBJECT_PROMPT\n'Info about subject: decisions, actions...'"]
            S8["detailed_response - NOT STORED"]
            S9["LLM: PARAGRAPH_SUMMARY_PROMPT\n'Summarize in 1 paragraph'"]
            S10["paragraph → summaries[]"]
            S6 --> S7 --> S8 --> S9 --> S10
        end
        subgraph Recap["4. Generate Recap - LLM call #(2+2M)"]
            S11["Concatenate paragraph summaries"]
            S12["LLM: RECAP_PROMPT\n'High-level recap, 1 paragraph'"]
            S13["recap"]
            S11 --> S12 --> S13
        end
        subgraph Output["5. Output"]
            S14["long_summary = markdown:\n# Quick recap\n[recap]\n# Summary\n**Subject 1**\n[para1]..."]
            S15["short_summary = recap only"]
            S14 --> S15
        end
        Reconstruct --> Subjects --> DetailedSum --> Recap --> Output
    end
    Input --> TitleGen
    Input --> SummaryGen
 ```
 ### topics[] vs subjects[]
 | | topics[] | subjects[] |
 |-|----------|------------|
 | **Source** | 300-word chunk splitting | LLM extraction from full text |
 | **Count** | Variable (words / 300) | Max 6 |
 | **Purpose** | Timeline segmentation | Summary structure |
 | **Has timestamp?** | Yes | No |
 ## External API Calls Summary
 ### 1. Daily.co REST API (called during initialization)
 | Endpoint | Method | When | Purpose |
 |----------|--------|------|---------|
 | `GET /recordings/{recording_id}` | GET | After webhook | Get mtgSessionId for participant lookup |
 | `GET /meetings/{mtgSessionId}/participants` | GET | After above | Map participant_id → user_name |
 ### 2. GPU Service (Modal.com or Self-Hosted)
 | Endpoint | Method | Count | Request |
 |----------|--------|-------|---------|
 | `{TRANSCRIPT_URL}/v1/audio/transcriptions-from-url` | POST | **N** (N = num tracks) | `{audio_file_url, language, batch: true}` |
 **Note**: Diarization is NOT called for multitrack - speaker identification comes from separate tracks.
 ### 3. LLM Service (OpenAI-compatible via LlamaIndex)
 | Phase | Operation | Input | LLM Calls | Output |
 |-------|-----------|-------|-----------|--------|
 | Topic Detection | TOPIC_PROMPT per 300-word chunk | words[i:i+300] | **C** = ceil(words/300) | TitleSummary{title, summary, timestamp} |
 | Title Generation | TITLE_PROMPT | All topic titles joined | **1** | FinalTitle |
 | Participant ID | PARTICIPANTS_PROMPT | Full transcript | **0-1** (skipped if known) | ParticipantsResponse |
 | Subject Extraction | SUBJECTS_PROMPT | Full transcript | **1** | SubjectsResponse{subjects[]} |
 | Subject Detail | DETAILED_SUBJECT_PROMPT | Full transcript + subject name | **M** (M = subjects, max 6) | detailed text (discarded) |
 | Subject Paragraph | PARAGRAPH_SUMMARY_PROMPT | Detailed text | **M** | paragraph text → summaries[] |
 | Recap | RECAP_PROMPT | All paragraph summaries | **1** | recap text |
 **Total LLM calls**: C + 2M + 3 (+ 1 if participants unknown)
 - Short meeting (1000 words, 3 subjects): ~4 + 6 + 3 = **13 calls**
 - Long meeting (5000 words, 6 subjects): ~17 + 12 + 3 = **32 calls**
 ## S3 Operations Summary
 ### Source Bucket: `DAILYCO_STORAGE_AWS_BUCKET_NAME`
 Daily.co uploads raw-tracks recordings here.
 | Operation | Key Pattern | When |
 |-----------|-------------|------|
 | **READ** (presign) | `{domain}/{room_name}/{ts}/{participant_id}-cam-audio-{ts}.webm` | Track acquisition |
 | **DELETE** | Same as above | Consent denied cleanup |
 ### Transcript Storage Bucket: `TRANSCRIPT_STORAGE_AWS_BUCKET_NAME`
 Reflector's own storage.
 | Operation | Key Pattern | When |
 |-----------|-------------|------|
 | **PUT** | `file_pipeline/{transcript_id}/tracks/padded_{idx}.webm` | After track padding |
 | **READ** (presign) | Same | For GPU transcription |
 | **DELETE** | Same | After transcription complete |
 | **PUT** | `{transcript_id}/audio.mp3` | After mixdown |
 | **DELETE** | Same | Consent denied cleanup |
 ## Database Operations
 ### Tables Written
 | Table | Operation | When |
 |-------|-----------|------|
 | `recording` | INSERT | Initialization |
 | `transcript` | INSERT | Initialization |
 | `transcript` | UPDATE (participants) | After Daily API participant fetch |
 | `transcript` | UPDATE (status, events, duration, topics, title, summaries, etc.) | Throughout pipeline |
 ### Transcript Update Sequence
 ```
 1.  INSERT: id, name, status='idle', source_kind='room', user_id, recording_id, room_id, meeting_id
 2.  UPDATE: participants[] (speaker index → participant name mapping)
 3.  UPDATE: status='processing', events+=[{event:'STATUS', data:{value:'processing'}}]
 4.  UPDATE: duration=X, events+=[{event:'DURATION', data:{duration:X}}]
 5.  UPDATE: audio_location='storage'
 6.  UPDATE: events+=[{event:'WAVEFORM', data:{waveform:[...]}}]
 7.  UPDATE: events+=[{event:'TRANSCRIPT', data:{text, translation}}]
 8.  UPDATE: topics[]+=topic, events+=[{event:'TOPIC'}]  -- repeated per chunk
 9.  UPDATE: title=X, events+=[{event:'FINAL_TITLE'}]
 10. UPDATE: long_summary=X, events+=[{event:'FINAL_LONG_SUMMARY'}]
 11. UPDATE: short_summary=X, events+=[{event:'FINAL_SHORT_SUMMARY'}]
 12. UPDATE: status='ended', events+=[{event:'STATUS', data:{value:'ended'}}]
 13. UPDATE: zulip_message_id=X  -- if Zulip enabled
 14. UPDATE: audio_deleted=true  -- if consent denied
 ```
 ## WebSocket Events
 All broadcast to room `ts:{transcript_id}`:
 | Event | Payload | Trigger |
 |-------|---------|---------|
 | STATUS | `{value: "processing"\|"ended"\|"error"}` | Status transitions |
 | DURATION | `{duration: float}` | After audio processing |
 | WAVEFORM | `{waveform: float[]}` | After waveform generation |
 | TRANSCRIPT | `{text: string, translation: string\|null}` | After transcription merge |
 | TOPIC | `{id, title, summary, timestamp, duration, transcript, words}` | Per topic detected |
 | FINAL_TITLE | `{title: string}` | After LLM title generation |
 | FINAL_LONG_SUMMARY | `{long_summary: string}` | After LLM summary |
 | FINAL_SHORT_SUMMARY | `{short_summary: string}` | After LLM recap |
 User-room broadcasts to `user:{user_id}`:
 - `TRANSCRIPT_STATUS`
 - `TRANSCRIPT_FINAL_TITLE`
 - `TRANSCRIPT_DURATION`
--- a/server/docs/video-platforms/README.md
+++ b/server/docs/video-platforms/README.md
@@ -1,238 +0,0 @@
 # Reflector Architecture: Whereby + Daily.co Recording Storage
 ## System Overview
 ```mermaid
 graph TB
    subgraph "Actors"
        APP[Our App<br/>Reflector]
        WHEREBY[Whereby Service<br/>External]
        DAILY[Daily.co Service<br/>External]
    end
    subgraph "AWS S3 Buckets"
        TRANSCRIPT_BUCKET[Transcript Bucket<br/>reflector-transcripts<br/>Output: Processed MP3s]
        WHEREBY_BUCKET[Whereby Bucket<br/>reflector-whereby-recordings<br/>Input: Raw MP4s]
        DAILY_BUCKET[Daily.co Bucket<br/>reflector-dailyco-recordings<br/>Input: Raw WebM tracks]
    end
    subgraph "AWS Infrastructure"
        SQS[SQS Queue<br/>Whereby notifications]
    end
    subgraph "Database"
        DB[(PostgreSQL<br/>Recordings, Transcripts, Meetings)]
    end
    APP -->|Write processed| TRANSCRIPT_BUCKET
    APP -->|Read/Delete| WHEREBY_BUCKET
    APP -->|Read/Delete| DAILY_BUCKET
    APP -->|Poll| SQS
    APP -->|Store metadata| DB
    WHEREBY -->|Write recordings| WHEREBY_BUCKET
    WHEREBY_BUCKET -->|S3 Event| SQS
    WHEREBY -->|Participant webhooks<br/>room.client.joined/left| APP
    DAILY -->|Write recordings| DAILY_BUCKET
    DAILY -->|Recording webhook<br/>recording.ready-to-download| APP
 ```
 **Note on Webhook vs S3 Event for Recording Processing:**
 - **Whereby**: Uses S3 Events → SQS for recording availability (S3 as source of truth, no race conditions)
 - **Daily.co**: Uses webhooks for recording availability (more immediate, built-in reliability)
 - **Both**: Use webhooks for participant tracking (real-time updates)
 ## Credentials & Permissions
 ```mermaid
 graph LR
    subgraph "Master Credentials"
        MASTER[TRANSCRIPT_STORAGE_AWS_*<br/>Access Key ID + Secret]
    end
    subgraph "Whereby Upload Credentials"
        WHEREBY_CREDS[AWS_WHEREBY_ACCESS_KEY_*<br/>Access Key ID + Secret]
    end
    subgraph "Daily.co Upload Role"
        DAILY_ROLE[DAILY_STORAGE_AWS_ROLE_ARN<br/>IAM Role ARN]
    end
    subgraph "Our App Uses"
        MASTER -->|Read/Write/Delete| TRANSCRIPT_BUCKET[Transcript Bucket]
        MASTER -->|Read/Delete| WHEREBY_BUCKET[Whereby Bucket]
        MASTER -->|Read/Delete| DAILY_BUCKET[Daily.co Bucket]
        MASTER -->|Poll/Delete| SQS[SQS Queue]
    end
    subgraph "We Give To Services"
        WHEREBY_CREDS -->|Passed in API call| WHEREBY_SERVICE[Whereby Service]
        WHEREBY_SERVICE -->|Write Only| WHEREBY_BUCKET
        DAILY_ROLE -->|Passed in API call| DAILY_SERVICE[Daily.co Service]
        DAILY_SERVICE -->|Assume Role| DAILY_ROLE
        DAILY_SERVICE -->|Write Only| DAILY_BUCKET
    end
 ```
 # Video Platform Recording Integration
 This document explains how Reflector receives and identifies multitrack audio recordings from different video platforms.
 ## Platform Comparison
 | Platform | Delivery Method | Track Identification |
 |----------|----------------|---------------------|
 | **Daily.co** | Webhook | Explicit track list in payload |
 | **Whereby** | SQS (S3 notifications) | Single file per notification |
 ---
 ## Daily.co
 **Note:** Primary discovery via polling (`poll_daily_recordings`), webhooks as backup.
 Daily.co uses **webhooks** to notify Reflector when recordings are ready.
 ### How It Works
 1. **Daily.co sends webhook** when recording is ready
   - Event type: `recording.ready-to-download`
   - Endpoint: `/v1/daily/webhook` (`reflector/views/daily.py:46-102`)
 2. **Webhook payload explicitly includes track list**:
 ```json
 {
  "recording_id": "7443ee0a-dab1-40eb-b316-33d6c0d5ff88",
  "room_name": "daily-20251020193458",
  "tracks": [
    {
      "type": "audio",
      "s3Key": "monadical/daily-20251020193458/1760988935484-52f7f48b-fbab-431f-9a50-87b9abfc8255-cam-audio-1760988935922",
      "size": 831843
    },
    {
      "type": "audio",
      "s3Key": "monadical/daily-20251020193458/1760988935484-a37c35e3-6f8e-4274-a482-e9d0f102a732-cam-audio-1760988943823",
      "size": 408438
    },
    {
      "type": "video",
      "s3Key": "monadical/daily-20251020193458/...-video.webm",
      "size": 30000000
    }
  ]
 }
 ```
 3. **System extracts audio tracks** (`daily.py:211`):
 ```python
 track_keys = [t.s3Key for t in tracks if t.type == "audio"]
 ```
 4. **Triggers multitrack processing** (`daily.py:213-218`):
 ```python
 process_multitrack_recording.delay(
    bucket_name=bucket_name,  # reflector-dailyco-local
    room_name=room_name,      # daily-20251020193458
    recording_id=recording_id, # 7443ee0a-dab1-40eb-b316-33d6c0d5ff88
    track_keys=track_keys      # Only audio s3Keys
 )
 ```
 ### Key Advantage: No Ambiguity
 Even though multiple meetings may share the same S3 bucket/folder (`monadical/`), **there's no ambiguity** because:
 - Each webhook payload contains the exact `s3Key` list for that specific `recording_id`
 - No need to scan folders or guess which files belong together
 - Each track's s3Key includes the room timestamp subfolder (e.g., `daily-20251020193458/`)
 The room name includes timestamp (`daily-20251020193458`) to keep recordings organized, but **the webhook's explicit track list is what prevents mixing files from different meetings**.
 ### Track Timeline Extraction
 Daily.co provides timing information in two places:
 **1. PyAV WebM Metadata (current approach)**:
 ```python
 # Read from WebM container stream metadata
 stream.start_time = 8.130s  # Meeting-relative timing
 ```
 **2. Filename Timestamps (alternative approach, commit 3bae9076)**:
 ```
 Filename format: {recording_start_ts}-{uuid}-cam-audio-{track_start_ts}.webm
 Example: 1760988935484-52f7f48b-fbab-431f-9a50-87b9abfc8255-cam-audio-1760988935922.webm
 Parse timestamps:
 - recording_start_ts: 1760988935484 (Unix ms)
 - track_start_ts: 1760988935922 (Unix ms)
 - offset: (1760988935922 - 1760988935484) / 1000 = 0.438s
 ```
 **Time Difference (PyAV vs Filename)**:
 ```
 Track 0:
  Filename offset: 438ms
  PyAV metadata:   229ms
  Difference:      ~200ms
 Track 1:
  Filename offset: 8339ms
  PyAV metadata:   8130ms
  Difference:      ~200ms
 ```
 **Consistent ~200ms delta** suggests network/encoding delay between file upload initiation (filename) and actual audio stream start (metadata).
 **Note:** The ~200ms difference observed in this test recording is not crucial for timing accuracy. Either method (filename timestamps or PyAV metadata) works well for multi-track alignment. Filename timestamps are preferable as they are better officially documented by Daily.co.
 **Current implementation uses PyAV metadata** because:
 - More accurate (represents when audio actually started)
 - Padding BEFORE transcription produces correct Whisper timestamps automatically
 - No manual offset adjustment needed during transcript merge
 ### Why Re-encoding During Padding
 Padding coincidentally involves re-encoding, which is important for Daily.co + Whisper:
 **Problem:** Daily.co skips frames in recordings when microphone is muted or paused
 - WebM containers have gaps where audio frames should be
 - Whisper doesn't understand these gaps and produces incorrect timestamps
 - Example: 5s of audio with 2s muted → file has frames only for 3s, Whisper thinks duration is 3s
 **Solution:** Re-encoding via PyAV filter graph (`adelay` + `aresample`)
 - Restores missing frames as silence
 - Produces continuous audio stream without gaps
 - Whisper now sees correct duration and produces accurate timestamps
 **Why combined with padding:**
 - Already re-encoding for padding (adding initial silence)
 - More performant to do both operations in single PyAV pipeline
 - Padded values needed for mixdown anyway (creating final MP3)
 Implementation: `main_multitrack_pipeline.py:_apply_audio_padding_streaming()`
 ---
 ## Whereby (SQS-based)
 Whereby uses **AWS SQS** (via S3 notifications) to notify Reflector when files are uploaded.
 ### How It Works
 1. **Whereby uploads recording** to S3
 2. **S3 sends notification** to SQS queue (one notification per file)
 3. **Reflector polls SQS queue** (`worker/process.py:process_messages()`)
 4. **System processes single file** (`worker/process.py:process_recording()`)
 ### Key Difference from Daily.co
 **Whereby (SQS):** System receives S3 notification "file X was created" - only knows about one file at a time, would need to scan folder to find related files
 **Daily.co (Webhook):** Daily explicitly tells system which files belong together in the webhook payload
 ---
--- a/server/.env.example
+++ b/server/.env.example
@@ -3,29 +3,6 @@
 # All the settings are described here: reflector/settings.py
 #
 ## =======================================================
 ## Core Configuration (Required for Production)
 ## =======================================================
 ## Database (for docker-compose.prod.yml, use postgres hostname)
 #DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
 ## Redis (for docker-compose.prod.yml, use redis hostname)
 #REDIS_HOST=redis
 #REDIS_PORT=6379
 #CELERY_BROKER_URL=redis://redis:6379/1
 #CELERY_RESULT_BACKEND=redis://redis:6379/1
 ## Base URL - your API domain with https
 #BASE_URL=https://api.example.com
 ## CORS - required when frontend and API are on different domains
 #CORS_ORIGIN=https://app.example.com
 #CORS_ALLOW_CREDENTIALS=true
 ## Secret key - generate with: openssl rand -hex 32
 #SECRET_KEY=changeme-generate-a-secure-random-string
 ## =======================================================
 ## User authentication
 ## =======================================================
@@ -63,21 +40,18 @@ TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
 #TRANSLATION_MODAL_API_KEY=xxxxx
 ## =======================================================
-## LLM backend (Required)
+## LLM backend
 ##
-## Responsible for generating titles, summaries, and topic detection
+## Responsible for titles and short summary
-## Requires OpenAI API key
+## Check reflector/llm/* for the full list of available
 ## llm backend implementation
 ## =======================================================
 ## OpenAI API key - get from https://platform.openai.com/account/api-keys
 LLM_API_KEY=sk-your-openai-api-key
 LLM_MODEL=gpt-4o-mini
 ## Optional: Custom endpoint (defaults to OpenAI)
 # LLM_URL=https://api.openai.com/v1
 ## Context size for summary generation (tokens)
 # LLM_MODEL=microsoft/phi-4
 LLM_CONTEXT_WINDOW=16000
 LLM_URL=
 LLM_API_KEY=sk-
 ## =======================================================
 ## Diarization
@@ -91,19 +65,6 @@ DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
 #DIARIZATION_MODAL_API_KEY=xxxxx
 ## =======================================================
 ## Transcript Storage
 ##
 ## Where to store audio files and transcripts
 ## AWS S3 is required for production
 ## =======================================================
 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
 ## =======================================================
 ## Sentry
 ## =======================================================
@@ -118,22 +79,19 @@ TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
 ## Whereby
 #WHEREBY_API_KEY=your-whereby-api-key
 #WHEREBY_WEBHOOK_SECRET=your-whereby-webhook-secret
-#WHEREBY_STORAGE_AWS_ACCESS_KEY_ID=your-aws-key
+#AWS_WHEREBY_ACCESS_KEY_ID=your-aws-key
-#WHEREBY_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret
+#AWS_WHEREBY_ACCESS_KEY_SECRET=your-aws-secret
 #AWS_PROCESS_RECORDING_QUEUE_URL=https://sqs.us-west-2.amazonaws.com/...
 ## Daily.co
 #DAILY_API_KEY=your-daily-api-key
 #DAILY_WEBHOOK_SECRET=your-daily-webhook-secret
 #DAILY_SUBDOMAIN=your-subdomain
-#DAILY_WEBHOOK_UUID=  # Auto-populated by recreate_daily_webhook.py script
+#AWS_DAILY_S3_BUCKET=your-daily-bucket
-#DAILYCO_STORAGE_AWS_ROLE_ARN=...  # IAM role ARN for Daily.co S3 access
+#AWS_DAILY_S3_REGION=us-west-2
-#DAILYCO_STORAGE_AWS_BUCKET_NAME=reflector-dailyco
+#AWS_DAILY_ROLE_ARN=arn:aws:iam::ACCOUNT:role/DailyRecording
 #DAILYCO_STORAGE_AWS_REGION=us-west-2
-## Whereby (optional separate bucket)
+## Platform Selection
-#WHEREBY_STORAGE_AWS_BUCKET_NAME=reflector-whereby
+#DAILY_MIGRATION_ENABLED=false           # Enable Daily.co support
-#WHEREBY_STORAGE_AWS_REGION=us-east-1
+#DAILY_MIGRATION_ROOM_IDS=[]            # Specific rooms to use Daily
 ## Platform Configuration
 #DEFAULT_VIDEO_PLATFORM=whereby          # Default platform for new rooms
--- a/server/migrations/versions/05f8688d6895_add_action_items.py
+++ b/server/migrations/versions/05f8688d6895_add_action_items.py
@@ -1,26 +0,0 @@
 """add_action_items
 Revision ID: 05f8688d6895
 Revises: bbafedfa510c
 Create Date: 2025-12-12 11:57:50.209658
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "05f8688d6895"
 down_revision: Union[str, None] = "bbafedfa510c"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    op.add_column("transcript", sa.Column("action_items", sa.JSON(), nullable=True))
 def downgrade() -> None:
    op.drop_column("transcript", "action_items")
--- a/server/migrations/versions/0f943fede0e0_add_workflow_run_id_to_transcript.py
+++ b/server/migrations/versions/0f943fede0e0_add_workflow_run_id_to_transcript.py
@@ -1,28 +0,0 @@
 """add workflow_run_id to transcript
 Revision ID: 0f943fede0e0
 Revises: 20251217000000
 Create Date: 2025-12-16 01:54:13.855106
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "0f943fede0e0"
 down_revision: Union[str, None] = "20251217000000"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    with op.batch_alter_table("transcript", schema=None) as batch_op:
        batch_op.add_column(sa.Column("workflow_run_id", sa.String(), nullable=True))
 def downgrade() -> None:
    with op.batch_alter_table("transcript", schema=None) as batch_op:
        batch_op.drop_column("workflow_run_id")
--- a/server/migrations/versions/1e49625677e4_add_platform_support.py
+++ b/server/migrations/versions/1e49625677e4_add_platform_support.py
@@ -1,7 +1,7 @@
 """add_platform_support
 Revision ID: 1e49625677e4
-Revises: 9e3f7b2a4c8e
+Revises: dc035ff72fd5
 Create Date: 2025-10-08 13:17:29.943612
 """
@@ -13,7 +13,7 @@ from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "1e49625677e4"
-down_revision: Union[str, None] = "9e3f7b2a4c8e"
+down_revision: Union[str, None] = "dc035ff72fd5"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
@@ -25,8 +25,8 @@ def upgrade() -> None:
            sa.Column(
                "platform",
                sa.String(),
-                nullable=True,
+                nullable=False,
-                server_default=None,
+                server_default="whereby",
            )
        )
--- a/server/migrations/versions/20251217000000_add_skip_consent_to_room.py
+++ b/server/migrations/versions/20251217000000_add_skip_consent_to_room.py
@@ -1,35 +0,0 @@
 """add skip_consent to room
 Revision ID: 20251217000000
 Revises: 05f8688d6895
 Create Date: 2025-12-17 00:00:00.000000
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "20251217000000"
 down_revision: Union[str, None] = "05f8688d6895"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    with op.batch_alter_table("room", schema=None) as batch_op:
        batch_op.add_column(
            sa.Column(
                "skip_consent",
                sa.Boolean(),
                nullable=False,
                server_default=sa.text("false"),
            )
        )
 def downgrade() -> None:
    with op.batch_alter_table("room", schema=None) as batch_op:
        batch_op.drop_column("skip_consent")
--- a/server/migrations/versions/2b92a1b03caa_add_daily_participant_session_table_.py
+++ b/server/migrations/versions/2b92a1b03caa_add_daily_participant_session_table_.py
@@ -1,79 +0,0 @@
 """add daily participant session table with immutable left_at
 Revision ID: 2b92a1b03caa
 Revises: f8294b31f022
 Create Date: 2025-11-13 20:29:30.486577
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "2b92a1b03caa"
 down_revision: Union[str, None] = "f8294b31f022"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    # Create table
    op.create_table(
        "daily_participant_session",
        sa.Column("id", sa.String(), nullable=False),
        sa.Column("meeting_id", sa.String(), nullable=False),
        sa.Column("room_id", sa.String(), nullable=False),
        sa.Column("session_id", sa.String(), nullable=False),
        sa.Column("user_id", sa.String(), nullable=True),
        sa.Column("user_name", sa.String(), nullable=False),
        sa.Column("joined_at", sa.DateTime(timezone=True), nullable=False),
        sa.Column("left_at", sa.DateTime(timezone=True), nullable=True),
        sa.ForeignKeyConstraint(["meeting_id"], ["meeting.id"], ondelete="CASCADE"),
        sa.ForeignKeyConstraint(["room_id"], ["room.id"], ondelete="CASCADE"),
        sa.PrimaryKeyConstraint("id"),
    )
    with op.batch_alter_table("daily_participant_session", schema=None) as batch_op:
        batch_op.create_index(
            "idx_daily_session_meeting_left", ["meeting_id", "left_at"], unique=False
        )
        batch_op.create_index("idx_daily_session_room", ["room_id"], unique=False)
    # Create trigger function to prevent left_at from being updated once set
    op.execute("""
        CREATE OR REPLACE FUNCTION prevent_left_at_update()
        RETURNS TRIGGER AS $$
        BEGIN
            IF OLD.left_at IS NOT NULL THEN
                RAISE EXCEPTION 'left_at is immutable once set';
            END IF;
            RETURN NEW;
        END;
        $$ LANGUAGE plpgsql;
    """)
    # Create trigger
    op.execute("""
        CREATE TRIGGER prevent_left_at_update_trigger
        BEFORE UPDATE ON daily_participant_session
        FOR EACH ROW
        EXECUTE FUNCTION prevent_left_at_update();
    """)
 def downgrade() -> None:
    # Drop trigger
    op.execute(
        "DROP TRIGGER IF EXISTS prevent_left_at_update_trigger ON daily_participant_session;"
    )
    # Drop trigger function
    op.execute("DROP FUNCTION IF EXISTS prevent_left_at_update();")
    # Drop indexes and table
    with op.batch_alter_table("daily_participant_session", schema=None) as batch_op:
        batch_op.drop_index("idx_daily_session_room")
        batch_op.drop_index("idx_daily_session_meeting_left")
    op.drop_table("daily_participant_session")
--- a/server/migrations/versions/5d6b9df9b045_make_room_platform_non_nullable_with_.py
+++ b/server/migrations/versions/5d6b9df9b045_make_room_platform_non_nullable_with_.py
@@ -1,30 +0,0 @@
 """Make room platform non-nullable with dynamic default
 Revision ID: 5d6b9df9b045
 Revises: 2b92a1b03caa
 Create Date: 2025-11-21 13:22:25.756584
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "5d6b9df9b045"
 down_revision: Union[str, None] = "2b92a1b03caa"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    op.execute("UPDATE room SET platform = 'whereby' WHERE platform IS NULL")
    with op.batch_alter_table("room", schema=None) as batch_op:
        batch_op.alter_column("platform", existing_type=sa.String(), nullable=False)
 def downgrade() -> None:
    with op.batch_alter_table("room", schema=None) as batch_op:
        batch_op.alter_column("platform", existing_type=sa.String(), nullable=True)
--- a/server/migrations/versions/9e3f7b2a4c8e_add_user_api_keys.py
+++ b/server/migrations/versions/9e3f7b2a4c8e_add_user_api_keys.py
@@ -1,38 +0,0 @@
 """add user api keys
 Revision ID: 9e3f7b2a4c8e
 Revises: dc035ff72fd5
 Create Date: 2025-10-17 00:00:00.000000
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "9e3f7b2a4c8e"
 down_revision: Union[str, None] = "dc035ff72fd5"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    op.create_table(
        "user_api_key",
        sa.Column("id", sa.String(), nullable=False),
        sa.Column("user_id", sa.String(), nullable=False),
        sa.Column("key_hash", sa.String(), nullable=False),
        sa.Column("name", sa.String(), nullable=True),
        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False),
        sa.PrimaryKeyConstraint("id"),
    )
    with op.batch_alter_table("user_api_key", schema=None) as batch_op:
        batch_op.create_index("idx_user_api_key_hash", ["key_hash"], unique=True)
        batch_op.create_index("idx_user_api_key_user_id", ["user_id"], unique=False)
 def downgrade() -> None:
    op.drop_table("user_api_key")
--- a/server/migrations/versions/bbafedfa510c_add_user_table.py
+++ b/server/migrations/versions/bbafedfa510c_add_user_table.py
@@ -1,38 +0,0 @@
 """add user table
 Revision ID: bbafedfa510c
 Revises: 5d6b9df9b045
 Create Date: 2025-11-19 21:06:30.543262
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "bbafedfa510c"
 down_revision: Union[str, None] = "5d6b9df9b045"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    op.create_table(
        "user",
        sa.Column("id", sa.String(), nullable=False),
        sa.Column("email", sa.String(), nullable=False),
        sa.Column("authentik_uid", sa.String(), nullable=False),
        sa.Column("created_at", sa.DateTime(timezone=True), nullable=False),
        sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False),
        sa.PrimaryKeyConstraint("id"),
    )
    with op.batch_alter_table("user", schema=None) as batch_op:
        batch_op.create_index("idx_user_authentik_uid", ["authentik_uid"], unique=True)
        batch_op.create_index("idx_user_email", ["email"], unique=False)
 def downgrade() -> None:
    op.drop_table("user")
--- a/server/migrations/versions/bd3a729bb379_add_use_hatchet_to_room.py
+++ b/server/migrations/versions/bd3a729bb379_add_use_hatchet_to_room.py
@@ -1,35 +0,0 @@
 """add use_hatchet to room
 Revision ID: bd3a729bb379
 Revises: 0f943fede0e0
 Create Date: 2025-12-16 16:34:03.594231
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "bd3a729bb379"
 down_revision: Union[str, None] = "0f943fede0e0"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    with op.batch_alter_table("room", schema=None) as batch_op:
        batch_op.add_column(
            sa.Column(
                "use_hatchet",
                sa.Boolean(),
                server_default=sa.text("false"),
                nullable=False,
            )
        )
 def downgrade() -> None:
    with op.batch_alter_table("room", schema=None) as batch_op:
        batch_op.drop_column("use_hatchet")
--- a/server/migrations/versions/f8294b31f022_add_track_keys.py
+++ b/server/migrations/versions/f8294b31f022_add_track_keys.py
@@ -1,28 +0,0 @@
 """add_track_keys
 Revision ID: f8294b31f022
 Revises: 1e49625677e4
 Create Date: 2025-10-27 18:52:17.589167
 """
 from typing import Sequence, Union
 import sqlalchemy as sa
 from alembic import op
 # revision identifiers, used by Alembic.
 revision: str = "f8294b31f022"
 down_revision: Union[str, None] = "1e49625677e4"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    with op.batch_alter_table("recording", schema=None) as batch_op:
        batch_op.add_column(sa.Column("track_keys", sa.JSON(), nullable=True))
 def downgrade() -> None:
    with op.batch_alter_table("recording", schema=None) as batch_op:
        batch_op.drop_column("track_keys")
--- a/server/pyproject.toml
+++ b/server/pyproject.toml
@@ -39,7 +39,6 @@ dependencies = [
    "pytest-env>=1.1.5",
    "webvtt-py>=0.5.0",
    "icalendar>=6.0.0",
    "hatchet-sdk>=0.47.0",
 ]
 [dependency-groups]
@@ -127,7 +126,6 @@ markers = [
 select = [
    "I",       # isort - import sorting
    "F401",    # unused imports
    "E402",    # module level import not at top of file
    "PLC0415", # import-outside-top-level - detect inline imports
 ]
--- a/server/reflector/app.py
+++ b/server/reflector/app.py
@@ -18,7 +18,6 @@ from reflector.views.rooms import router as rooms_router
 from reflector.views.rtc_offer import router as rtc_offer_router
 from reflector.views.transcripts import router as transcripts_router
 from reflector.views.transcripts_audio import router as transcripts_audio_router
 from reflector.views.transcripts_chat import router as transcripts_chat_router
 from reflector.views.transcripts_participants import (
    router as transcripts_participants_router,
 )
@@ -28,7 +27,6 @@ from reflector.views.transcripts_upload import router as transcripts_upload_rout
 from reflector.views.transcripts_webrtc import router as transcripts_webrtc_router
 from reflector.views.transcripts_websocket import router as transcripts_websocket_router
 from reflector.views.user import router as user_router
 from reflector.views.user_api_keys import router as user_api_keys_router
 from reflector.views.user_websocket import router as user_ws_router
 from reflector.views.whereby import router as whereby_router
 from reflector.views.zulip import router as zulip_router
@@ -91,11 +89,9 @@ app.include_router(transcripts_participants_router, prefix="/v1")
 app.include_router(transcripts_speaker_router, prefix="/v1")
 app.include_router(transcripts_upload_router, prefix="/v1")
 app.include_router(transcripts_websocket_router, prefix="/v1")
 app.include_router(transcripts_chat_router, prefix="/v1")
 app.include_router(transcripts_webrtc_router, prefix="/v1")
 app.include_router(transcripts_process_router, prefix="/v1")
 app.include_router(user_router, prefix="/v1")
 app.include_router(user_api_keys_router, prefix="/v1")
 app.include_router(user_ws_router, prefix="/v1")
 app.include_router(zulip_router, prefix="/v1")
 app.include_router(whereby_router, prefix="/v1")
--- a/server/reflector/asynctask.py
+++ b/server/reflector/asynctask.py
@@ -1,19 +1,13 @@
 import asyncio
 import functools
 from uuid import uuid4
 from celery import current_task
 from reflector.db import get_database
 from reflector.llm import llm_session_id
 def asynctask(f):
    @functools.wraps(f)
    def wrapper(*args, **kwargs):
        async def run_with_db():
            task_id = current_task.request.id if current_task else None
            llm_session_id.set(task_id or f"random-{uuid4().hex}")
            database = get_database()
            await database.connect()
            try:
--- a/server/reflector/auth/auth_jwt.py
+++ b/server/reflector/auth/auth_jwt.py
@@ -1,18 +1,14 @@
-from typing import Annotated, List, Optional
+from typing import Annotated, Optional
 from fastapi import Depends, HTTPException
-from fastapi.security import APIKeyHeader, OAuth2PasswordBearer
+from fastapi.security import OAuth2PasswordBearer
 from jose import JWTError, jwt
 from pydantic import BaseModel
 from reflector.db.user_api_keys import user_api_keys_controller
 from reflector.db.users import user_controller
 from reflector.logger import logger
 from reflector.settings import settings
 from reflector.utils import generate_uuid4
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
 api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
 jwt_public_key = open(f"reflector/auth/jwt/keys/{settings.AUTH_JWT_PUBLIC_KEY}").read()
 jwt_algorithm = settings.AUTH_JWT_ALGORITHM
@@ -30,7 +26,7 @@ class JWTException(Exception):
 class UserInfo(BaseModel):
    sub: str
-    email: Optional[str] = None
+    email: str
    def __getitem__(self, key):
        return getattr(self, key)
@@ -62,65 +58,34 @@ def authenticated(token: Annotated[str, Depends(oauth2_scheme)]):
    return None
-async def _authenticate_user(
+def current_user(
-    jwt_token: Optional[str],
+    token: Annotated[Optional[str], Depends(oauth2_scheme)],
    api_key: Optional[str],
    jwtauth: JWTAuth,
 ) -> UserInfo | None:
    user_infos: List[UserInfo] = []
    if api_key:
        user_api_key = await user_api_keys_controller.verify_key(api_key)
        if user_api_key:
            user_infos.append(UserInfo(sub=user_api_key.user_id, email=None))
    if jwt_token:
        try:
            payload = jwtauth.verify_token(jwt_token)
            authentik_uid = payload["sub"]
            email = payload["email"]
            user = await user_controller.get_by_authentik_uid(authentik_uid)
            if not user:
                logger.info(
                    f"Creating new user on first login: {authentik_uid} ({email})"
                )
                user = await user_controller.create_or_update(
                    id=generate_uuid4(),
                    authentik_uid=authentik_uid,
                    email=email,
                )
            user_infos.append(UserInfo(sub=user.id, email=email))
        except JWTError as e:
            logger.error(f"JWT error: {e}")
            raise HTTPException(status_code=401, detail="Invalid authentication")
    if len(user_infos) == 0:
        return None
    if len(set([x.sub for x in user_infos])) > 1:
        raise JWTException(
            status_code=401,
            detail="Invalid authentication: more than one user provided",
        )
    return user_infos[0]
 async def current_user(
    jwt_token: Annotated[Optional[str], Depends(oauth2_scheme)],
    api_key: Annotated[Optional[str], Depends(api_key_header)],
    jwtauth: JWTAuth = Depends(),
 ):
-    user = await _authenticate_user(jwt_token, api_key, jwtauth)
+    if token is None:
    if user is None:
        raise HTTPException(status_code=401, detail="Not authenticated")
-    return user
+    try:
        payload = jwtauth.verify_token(token)
        sub = payload["sub"]
        email = payload["email"]
        return UserInfo(sub=sub, email=email)
    except JWTError as e:
        logger.error(f"JWT error: {e}")
        raise HTTPException(status_code=401, detail="Invalid authentication")
-async def current_user_optional(
+def current_user_optional(
-    jwt_token: Annotated[Optional[str], Depends(oauth2_scheme)],
+    token: Annotated[Optional[str], Depends(oauth2_scheme)],
    api_key: Annotated[Optional[str], Depends(api_key_header)],
    jwtauth: JWTAuth = Depends(),
 ):
-    return await _authenticate_user(jwt_token, api_key, jwtauth)
+    # we accept no token, but if one is provided, it must be a valid one.
    if token is None:
        return None
    try:
        payload = jwtauth.verify_token(token)
        sub = payload["sub"]
        email = payload["email"]
        return UserInfo(sub=sub, email=email)
    except JWTError as e:
        logger.error(f"JWT error: {e}")
        raise HTTPException(status_code=401, detail="Invalid authentication")
--- a/server/reflector/dailyco_api/README.md
+++ b/server/reflector/dailyco_api/README.md
@@ -1,6 +0,0 @@
 anything about Daily.co api interaction
 - webhook event shapes
 - REST api client
 No REST api client existing found in the wild; the official lib is about working with videocall as a bot
--- a/server/reflector/dailyco_api/init.py
+++ b/server/reflector/dailyco_api/init.py
@@ -1,110 +0,0 @@
 """
 Daily.co API Module
 """
 # Client
 from .client import DailyApiClient, DailyApiError
 # Request models
 from .requests import (
    CreateMeetingTokenRequest,
    CreateRoomRequest,
    CreateWebhookRequest,
    MeetingTokenProperties,
    RecordingsBucketConfig,
    RoomProperties,
    UpdateWebhookRequest,
 )
 # Response models
 from .responses import (
    FinishedRecordingResponse,
    MeetingParticipant,
    MeetingParticipantsResponse,
    MeetingResponse,
    MeetingTokenResponse,
    RecordingResponse,
    RecordingS3Info,
    RoomPresenceParticipant,
    RoomPresenceResponse,
    RoomResponse,
    WebhookResponse,
 )
 # Webhook utilities
 from .webhook_utils import (
    extract_room_name,
    parse_participant_joined,
    parse_participant_left,
    parse_recording_error,
    parse_recording_ready,
    parse_recording_started,
    parse_webhook_payload,
    verify_webhook_signature,
 )
 # Webhook models
 from .webhooks import (
    DailyTrack,
    DailyWebhookEvent,
    DailyWebhookEventUnion,
    ParticipantJoinedEvent,
    ParticipantJoinedPayload,
    ParticipantLeftEvent,
    ParticipantLeftPayload,
    RecordingErrorEvent,
    RecordingErrorPayload,
    RecordingReadyEvent,
    RecordingReadyToDownloadPayload,
    RecordingStartedEvent,
    RecordingStartedPayload,
 )
 __all__ = [
    # Client
    "DailyApiClient",
    "DailyApiError",
    # Requests
    "CreateRoomRequest",
    "RoomProperties",
    "RecordingsBucketConfig",
    "CreateMeetingTokenRequest",
    "MeetingTokenProperties",
    "CreateWebhookRequest",
    "UpdateWebhookRequest",
    # Responses
    "RoomResponse",
    "RoomPresenceResponse",
    "RoomPresenceParticipant",
    "MeetingParticipantsResponse",
    "MeetingParticipant",
    "MeetingResponse",
    "RecordingResponse",
    "FinishedRecordingResponse",
    "RecordingS3Info",
    "MeetingTokenResponse",
    "WebhookResponse",
    # Webhooks
    "DailyWebhookEvent",
    "DailyWebhookEventUnion",
    "DailyTrack",
    "ParticipantJoinedEvent",
    "ParticipantJoinedPayload",
    "ParticipantLeftEvent",
    "ParticipantLeftPayload",
    "RecordingStartedEvent",
    "RecordingStartedPayload",
    "RecordingReadyEvent",
    "RecordingReadyToDownloadPayload",
    "RecordingErrorEvent",
    "RecordingErrorPayload",
    # Webhook utilities
    "verify_webhook_signature",
    "extract_room_name",
    "parse_webhook_payload",
    "parse_participant_joined",
    "parse_participant_left",
    "parse_recording_started",
    "parse_recording_ready",
    "parse_recording_error",
 ]
--- a/server/reflector/dailyco_api/client.py
+++ b/server/reflector/dailyco_api/client.py
@@ -1,573 +0,0 @@
 """
 Daily.co API Client
 Complete async client for Daily.co REST API with Pydantic models.
 Reference: https://docs.daily.co/reference/rest-api
 """
 from http import HTTPStatus
 from typing import Any
 import httpx
 import structlog
 from reflector.utils.string import NonEmptyString
 from .requests import (
    CreateMeetingTokenRequest,
    CreateRoomRequest,
    CreateWebhookRequest,
    UpdateWebhookRequest,
 )
 from .responses import (
    MeetingParticipantsResponse,
    MeetingResponse,
    MeetingTokenResponse,
    RecordingResponse,
    RoomPresenceResponse,
    RoomResponse,
    WebhookResponse,
 )
 logger = structlog.get_logger(__name__)
 class DailyApiError(Exception):
    """Daily.co API error with full request/response context."""
    def __init__(self, operation: str, response: httpx.Response):
        self.operation = operation
        self.response = response
        self.status_code = response.status_code
        self.response_body = response.text
        self.url = str(response.url)
        self.request_body = (
            response.request.content.decode() if response.request.content else None
        )
        super().__init__(
            f"Daily.co API error: {operation} failed with status {self.status_code}: {response.text}"
        )
 class DailyApiClient:
    """
    Complete async client for Daily.co REST API.
    Usage:
        # Direct usage
        client = DailyApiClient(api_key="your_api_key")
        room = await client.create_room(CreateRoomRequest(name="my-room"))
        await client.close()  # Clean up when done
        # Context manager (recommended)
        async with DailyApiClient(api_key="your_api_key") as client:
            room = await client.create_room(CreateRoomRequest(name="my-room"))
    """
    BASE_URL = "https://api.daily.co/v1"
    DEFAULT_TIMEOUT = 10.0
    def __init__(
        self,
        api_key: NonEmptyString,
        webhook_secret: NonEmptyString | None = None,
        timeout: float = DEFAULT_TIMEOUT,
        base_url: NonEmptyString | None = None,
    ):
        """
        Initialize Daily.co API client.
        Args:
            api_key: Daily.co API key (Bearer token)
            webhook_secret: Base64-encoded HMAC secret for webhook verification.
                Must match the 'hmac' value provided when creating webhooks.
                Generate with: base64.b64encode(os.urandom(32)).decode()
            timeout: Default request timeout in seconds
            base_url: Override base URL (for testing)
        """
        self.api_key = api_key
        self.webhook_secret = webhook_secret
        self.timeout = timeout
        self.base_url = base_url or self.BASE_URL
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json",
        }
        self._client: httpx.AsyncClient | None = None
    async def __aenter__(self):
        return self
    async def __aexit__(self, exc_type, exc_val, exc_tb):
        await self.close()
    async def _get_client(self) -> httpx.AsyncClient:
        if self._client is None:
            self._client = httpx.AsyncClient(timeout=self.timeout)
        return self._client
    async def close(self):
        if self._client is not None:
            await self._client.aclose()
            self._client = None
    async def _handle_response(
        self, response: httpx.Response, operation: str
    ) -> dict[str, Any]:
        """
        Handle API response with error logging.
        Args:
            response: HTTP response
            operation: Operation name for logging (e.g., "create_room")
        Returns:
            Parsed JSON response
        Raises:
            DailyApiError: If request failed with full context
        """
        if response.status_code >= 400:
            logger.error(
                f"Daily.co API error: {operation}",
                status_code=response.status_code,
                response_body=response.text,
                request_body=response.request.content.decode()
                if response.request.content
                else None,
                url=str(response.url),
            )
            raise DailyApiError(operation, response)
        return response.json()
    # ============================================================================
    # ROOMS
    # ============================================================================
    async def create_room(self, request: CreateRoomRequest) -> RoomResponse:
        """
        Create a new Daily.co room.
        Reference: https://docs.daily.co/reference/rest-api/rooms/create-room
        Args:
            request: Room creation request with name, privacy, and properties
        Returns:
            Created room data including URL and ID
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.post(
            f"{self.base_url}/rooms",
            headers=self.headers,
            json=request.model_dump(exclude_none=True),
        )
        data = await self._handle_response(response, "create_room")
        return RoomResponse(**data)
    async def get_room(self, room_name: NonEmptyString) -> RoomResponse:
        """
        Get room configuration.
        Args:
            room_name: Daily.co room name
        Returns:
            Room configuration data
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.get(
            f"{self.base_url}/rooms/{room_name}",
            headers=self.headers,
        )
        data = await self._handle_response(response, "get_room")
        return RoomResponse(**data)
    async def get_room_presence(
        self, room_name: NonEmptyString
    ) -> RoomPresenceResponse:
        """
        Get current participants in a room (real-time presence).
        Reference: https://docs.daily.co/reference/rest-api/rooms/get-room-presence
        Args:
            room_name: Daily.co room name
        Returns:
            List of currently present participants with join time and duration
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.get(
            f"{self.base_url}/rooms/{room_name}/presence",
            headers=self.headers,
        )
        data = await self._handle_response(response, "get_room_presence")
        return RoomPresenceResponse(**data)
    async def delete_room(self, room_name: NonEmptyString) -> None:
        """
        Delete a room (idempotent - succeeds even if room doesn't exist).
        Reference: https://docs.daily.co/reference/rest-api/rooms/delete-room
        Args:
            room_name: Daily.co room name
        Raises:
            httpx.HTTPStatusError: If API request fails (except 404)
        """
        client = await self._get_client()
        response = await client.delete(
            f"{self.base_url}/rooms/{room_name}",
            headers=self.headers,
        )
        # Idempotent delete - 404 means already deleted
        if response.status_code == HTTPStatus.NOT_FOUND:
            logger.debug("Room not found (already deleted)", room_name=room_name)
            return
        await self._handle_response(response, "delete_room")
    # ============================================================================
    # MEETINGS
    # ============================================================================
    async def get_meeting(self, meeting_id: NonEmptyString) -> MeetingResponse:
        """
        Get full meeting information including participants.
        Reference: https://docs.daily.co/reference/rest-api/meetings/get-meeting-information
        Args:
            meeting_id: Daily.co meeting/session ID
        Returns:
            Meeting metadata including room, duration, participants, and status
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.get(
            f"{self.base_url}/meetings/{meeting_id}",
            headers=self.headers,
        )
        data = await self._handle_response(response, "get_meeting")
        return MeetingResponse(**data)
    async def get_meeting_participants(
        self,
        meeting_id: NonEmptyString,
        limit: int | None = None,
        joined_after: NonEmptyString | None = None,
        joined_before: NonEmptyString | None = None,
    ) -> MeetingParticipantsResponse:
        """
        Get historical participant data from a completed meeting (paginated).
        Reference: https://docs.daily.co/reference/rest-api/meetings/get-meeting-participants
        Args:
            meeting_id: Daily.co meeting/session ID
            limit: Maximum number of participant records to return
            joined_after: Return participants who joined after this participant_id
            joined_before: Return participants who joined before this participant_id
        Returns:
            List of participants with join times and duration
        Raises:
            httpx.HTTPStatusError: If API request fails (404 when no more participants)
        Note:
            For pagination, use joined_after with the last participant_id from previous response.
            Returns 404 when no more participants remain.
        """
        params = {}
        if limit is not None:
            params["limit"] = limit
        if joined_after is not None:
            params["joined_after"] = joined_after
        if joined_before is not None:
            params["joined_before"] = joined_before
        client = await self._get_client()
        response = await client.get(
            f"{self.base_url}/meetings/{meeting_id}/participants",
            headers=self.headers,
            params=params,
        )
        data = await self._handle_response(response, "get_meeting_participants")
        return MeetingParticipantsResponse(**data)
    # ============================================================================
    # RECORDINGS
    # ============================================================================
    async def get_recording(self, recording_id: NonEmptyString) -> RecordingResponse:
        """
        https://docs.daily.co/reference/rest-api/recordings/get-recording-information
        Get recording metadata and status.
        """
        client = await self._get_client()
        response = await client.get(
            f"{self.base_url}/recordings/{recording_id}",
            headers=self.headers,
        )
        data = await self._handle_response(response, "get_recording")
        return RecordingResponse(**data)
    async def list_recordings(
        self,
        room_name: NonEmptyString | None = None,
        starting_after: str | None = None,
        ending_before: str | None = None,
        limit: int = 100,
    ) -> list[RecordingResponse]:
        """
        List recordings with optional filters.
        Reference: https://docs.daily.co/reference/rest-api/recordings
        Args:
            room_name: Filter by room name
            starting_after: Pagination cursor - recording ID to start after
            ending_before: Pagination cursor - recording ID to end before
            limit: Max results per page (default 100, max 100)
        Note: starting_after/ending_before are pagination cursors (recording IDs),
        NOT time filters. API returns recordings in reverse chronological order.
        """
        client = await self._get_client()
        params = {"limit": limit}
        if room_name:
            params["room_name"] = room_name
        if starting_after:
            params["starting_after"] = starting_after
        if ending_before:
            params["ending_before"] = ending_before
        response = await client.get(
            f"{self.base_url}/recordings",
            headers=self.headers,
            params=params,
        )
        data = await self._handle_response(response, "list_recordings")
        if not isinstance(data, dict) or "data" not in data:
            logger.error(
                "Daily.co API returned unexpected format for list_recordings",
                data_type=type(data).__name__,
                data_keys=list(data.keys()) if isinstance(data, dict) else None,
                data_sample=str(data)[:500],
                room_name=room_name,
                operation="list_recordings",
            )
            raise httpx.HTTPStatusError(
                message=f"Unexpected response format from list_recordings: {type(data).__name__}",
                request=response.request,
                response=response,
            )
        return [RecordingResponse(**r) for r in data["data"]]
    # ============================================================================
    # MEETING TOKENS
    # ============================================================================
    async def create_meeting_token(
        self, request: CreateMeetingTokenRequest
    ) -> MeetingTokenResponse:
        """
        Create a meeting token for participant authentication.
        Reference: https://docs.daily.co/reference/rest-api/meeting-tokens/create-meeting-token
        Args:
            request: Token properties including room name, user_id, permissions
        Returns:
            JWT meeting token
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.post(
            f"{self.base_url}/meeting-tokens",
            headers=self.headers,
            json=request.model_dump(exclude_none=True),
        )
        data = await self._handle_response(response, "create_meeting_token")
        return MeetingTokenResponse(**data)
    # ============================================================================
    # WEBHOOKS
    # ============================================================================
    async def list_webhooks(self) -> list[WebhookResponse]:
        """
        List all configured webhooks for this account.
        Reference: https://docs.daily.co/reference/rest-api/webhooks
        Returns:
            List of webhook configurations
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.get(
            f"{self.base_url}/webhooks",
            headers=self.headers,
        )
        data = await self._handle_response(response, "list_webhooks")
        # Daily.co returns array directly (not paginated)
        if isinstance(data, list):
            return [WebhookResponse(**wh) for wh in data]
        # Future-proof: handle potential pagination envelope
        if isinstance(data, dict) and "data" in data:
            return [WebhookResponse(**wh) for wh in data["data"]]
        logger.warning("Unexpected webhook list response format", data=data)
        return []
    async def create_webhook(self, request: CreateWebhookRequest) -> WebhookResponse:
        """
        Create a new webhook subscription.
        Reference: https://docs.daily.co/reference/rest-api/webhooks
        Args:
            request: Webhook configuration with URL, event types, and HMAC secret
        Returns:
            Created webhook with UUID and state
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.post(
            f"{self.base_url}/webhooks",
            headers=self.headers,
            json=request.model_dump(exclude_none=True),
        )
        data = await self._handle_response(response, "create_webhook")
        return WebhookResponse(**data)
    async def update_webhook(
        self, webhook_uuid: NonEmptyString, request: UpdateWebhookRequest
    ) -> WebhookResponse:
        """
        Update webhook configuration.
        Note: Daily.co may not support PATCH for all fields.
        Common pattern is delete + recreate.
        Reference: https://docs.daily.co/reference/rest-api/webhooks
        Args:
            webhook_uuid: Webhook UUID to update
            request: Updated webhook configuration
        Returns:
            Updated webhook configuration
        Raises:
            httpx.HTTPStatusError: If API request fails
        """
        client = await self._get_client()
        response = await client.patch(
            f"{self.base_url}/webhooks/{webhook_uuid}",
            headers=self.headers,
            json=request.model_dump(exclude_none=True),
        )
        data = await self._handle_response(response, "update_webhook")
        return WebhookResponse(**data)
    async def delete_webhook(self, webhook_uuid: NonEmptyString) -> None:
        """
        Delete a webhook.
        Reference: https://docs.daily.co/reference/rest-api/webhooks
        Args:
            webhook_uuid: Webhook UUID to delete
        Raises:
            httpx.HTTPStatusError: If webhook not found or deletion fails
        """
        client = await self._get_client()
        response = await client.delete(
            f"{self.base_url}/webhooks/{webhook_uuid}",
            headers=self.headers,
        )
        await self._handle_response(response, "delete_webhook")
    # ============================================================================
    # HELPER METHODS
    # ============================================================================
    async def find_webhook_by_url(self, url: NonEmptyString) -> WebhookResponse | None:
        """
        Find a webhook by its URL.
        Args:
            url: Webhook endpoint URL to search for
        Returns:
            Webhook if found, None otherwise
        """
        webhooks = await self.list_webhooks()
        for webhook in webhooks:
            if webhook.url == url:
                return webhook
        return None
    async def find_webhooks_by_pattern(
        self, pattern: NonEmptyString
    ) -> list[WebhookResponse]:
        """
        Find webhooks matching a URL pattern (e.g., 'ngrok').
        Args:
            pattern: String to match in webhook URLs
        Returns:
            List of matching webhooks
        """
        webhooks = await self.list_webhooks()
        return [wh for wh in webhooks if pattern in wh.url]
--- a/server/reflector/dailyco_api/requests.py
+++ b/server/reflector/dailyco_api/requests.py
@@ -1,166 +0,0 @@
 """
 Daily.co API Request Models
 Reference: https://docs.daily.co/reference/rest-api
 """
 from typing import List, Literal
 from pydantic import BaseModel, Field
 from reflector.utils.string import NonEmptyString
 class RecordingsBucketConfig(BaseModel):
    """
    S3 bucket configuration for raw-tracks recordings.
    Reference: https://docs.daily.co/reference/rest-api/rooms/create-room
    """
    bucket_name: NonEmptyString = Field(description="S3 bucket name")
    bucket_region: NonEmptyString = Field(description="AWS region (e.g., 'us-east-1')")
    assume_role_arn: NonEmptyString = Field(
        description="AWS IAM role ARN that Daily.co will assume to write recordings"
    )
    allow_api_access: bool = Field(
        default=True,
        description="Whether to allow API access to recording metadata",
    )
 class RoomProperties(BaseModel):
    """
    Room configuration properties.
    """
    enable_recording: Literal["cloud", "local", "raw-tracks"] | None = Field(
        default=None,
        description="Recording mode: 'cloud' for mixed, 'local' for local recording, 'raw-tracks' for multitrack, None to disable",
    )
    enable_chat: bool = Field(default=True, description="Enable in-meeting chat")
    enable_screenshare: bool = Field(default=True, description="Enable screen sharing")
    enable_knocking: bool = Field(
        default=False,
        description="Enable knocking for private rooms (allows participants to request access)",
    )
    start_video_off: bool = Field(
        default=False, description="Start with video off for all participants"
    )
    start_audio_off: bool = Field(
        default=False, description="Start with audio muted for all participants"
    )
    exp: int | None = Field(
        None, description="Room expiration timestamp (Unix epoch seconds)"
    )
    recordings_bucket: RecordingsBucketConfig | None = Field(
        None, description="S3 bucket configuration for raw-tracks recordings"
    )
 class CreateRoomRequest(BaseModel):
    """
    Request to create a new Daily.co room.
    Reference: https://docs.daily.co/reference/rest-api/rooms/create-room
    """
    name: NonEmptyString = Field(description="Room name (must be unique within domain)")
    privacy: Literal["public", "private"] = Field(
        default="public", description="Room privacy setting"
    )
    properties: RoomProperties = Field(
        default_factory=RoomProperties, description="Room configuration properties"
    )
 class MeetingTokenProperties(BaseModel):
    """
    Properties for meeting token creation.
    Reference: https://docs.daily.co/reference/rest-api/meeting-tokens/create-meeting-token
    """
    room_name: NonEmptyString = Field(description="Room name this token is valid for")
    user_id: NonEmptyString | None = Field(
        None, description="User identifier to associate with token"
    )
    is_owner: bool = Field(
        default=False, description="Grant owner privileges to token holder"
    )
    start_cloud_recording: bool = Field(
        default=False, description="Automatically start cloud recording on join"
    )
    start_cloud_recording_opts: dict | None = Field(
        default=None,
        description="Options for startRecording when start_cloud_recording is true (e.g., maxDuration)",
    )
    enable_recording_ui: bool = Field(
        default=True, description="Show recording controls in UI"
    )
    eject_at_token_exp: bool = Field(
        default=False, description="Eject participant when token expires"
    )
    nbf: int | None = Field(
        None, description="Not-before timestamp (Unix epoch seconds)"
    )
    exp: int | None = Field(
        None, description="Expiration timestamp (Unix epoch seconds)"
    )
 class CreateMeetingTokenRequest(BaseModel):
    """
    Request to create a meeting token for participant authentication.
    Reference: https://docs.daily.co/reference/rest-api/meeting-tokens/create-meeting-token
    """
    properties: MeetingTokenProperties = Field(description="Token properties")
 class CreateWebhookRequest(BaseModel):
    """
    Request to create a webhook subscription.
    Reference: https://docs.daily.co/reference/rest-api/webhooks
    """
    url: NonEmptyString = Field(description="Webhook endpoint URL (must be HTTPS)")
    eventTypes: List[
        Literal[
            "participant.joined",
            "participant.left",
            "recording.started",
            "recording.ready-to-download",
            "recording.error",
        ]
    ] = Field(
        description="Array of event types to subscribe to (only events we handle)"
    )
    hmac: NonEmptyString = Field(
        description="Base64-encoded HMAC secret for webhook signature verification"
    )
    basicAuth: NonEmptyString | None = Field(
        None, description="Optional basic auth credentials for webhook endpoint"
    )
 class UpdateWebhookRequest(BaseModel):
    """
    Request to update an existing webhook.
    Note: Daily.co API may not support PATCH for webhooks.
    Common pattern is to delete and recreate.
    Reference: https://docs.daily.co/reference/rest-api/webhooks
    """
    url: NonEmptyString | None = Field(None, description="New webhook endpoint URL")
    eventTypes: List[NonEmptyString] | None = Field(
        None, description="New array of event types"
    )
    hmac: NonEmptyString | None = Field(None, description="New HMAC secret")
    basicAuth: NonEmptyString | None = Field(
        None, description="New basic auth credentials"
    )
--- a/server/reflector/dailyco_api/responses.py
+++ b/server/reflector/dailyco_api/responses.py
@@ -1,217 +0,0 @@
 """
 Daily.co API Response Models
 """
 from typing import Any, Dict, List, Literal
 from pydantic import BaseModel, Field
 from reflector.dailyco_api.webhooks import DailyTrack
 from reflector.utils.string import NonEmptyString
 # not documented in daily; we fill it according to observations
 RecordingStatus = Literal["in-progress", "finished"]
 class RoomResponse(BaseModel):
    """
    Response from room creation or retrieval.
    Reference: https://docs.daily.co/reference/rest-api/rooms/create-room
    """
    id: NonEmptyString = Field(description="Unique room identifier (UUID)")
    name: NonEmptyString = Field(description="Room name used in URLs")
    api_created: bool = Field(description="Whether room was created via API")
    privacy: Literal["public", "private"] = Field(description="Room privacy setting")
    url: NonEmptyString = Field(description="Full room URL")
    created_at: NonEmptyString = Field(description="ISO 8601 creation timestamp")
    config: Dict[NonEmptyString, Any] = Field(
        default_factory=dict, description="Room configuration properties"
    )
 class RoomPresenceParticipant(BaseModel):
    """
    Participant presence information in a room.
    Reference: https://docs.daily.co/reference/rest-api/rooms/get-room-presence
    """
    room: NonEmptyString = Field(description="Room name")
    id: NonEmptyString = Field(description="Participant session ID")
    userId: NonEmptyString | None = Field(None, description="User ID if provided")
    userName: NonEmptyString | None = Field(None, description="User display name")
    joinTime: NonEmptyString = Field(description="ISO 8601 join timestamp")
    duration: int = Field(description="Duration in room (seconds)")
 class RoomPresenceResponse(BaseModel):
    """
    Response from room presence endpoint.
    Reference: https://docs.daily.co/reference/rest-api/rooms/get-room-presence
    """
    total_count: int = Field(
        description="Total number of participants currently in room"
    )
    data: List[RoomPresenceParticipant] = Field(
        default_factory=list, description="Array of participant presence data"
    )
 class MeetingParticipant(BaseModel):
    """
    Historical participant data from a meeting.
    Reference: https://docs.daily.co/reference/rest-api/meetings/get-meeting-participants
    """
    user_id: NonEmptyString | None = Field(None, description="User identifier")
    participant_id: NonEmptyString = Field(description="Participant session identifier")
    user_name: NonEmptyString | None = Field(None, description="User display name")
    join_time: int = Field(description="Join timestamp (Unix epoch seconds)")
    duration: int = Field(description="Duration in meeting (seconds)")
 class MeetingParticipantsResponse(BaseModel):
    """
    Response from meeting participants endpoint.
    Reference: https://docs.daily.co/reference/rest-api/meetings/get-meeting-participants
    """
    data: List[MeetingParticipant] = Field(
        default_factory=list, description="Array of participant data"
    )
 class MeetingResponse(BaseModel):
    """
    Response from meeting information endpoint.
    Reference: https://docs.daily.co/reference/rest-api/meetings/get-meeting-information
    """
    id: NonEmptyString = Field(description="Meeting session identifier (UUID)")
    room: NonEmptyString = Field(description="Room name where meeting occurred")
    start_time: int = Field(
        description="Meeting start Unix timestamp (~15s granularity)"
    )
    duration: int = Field(description="Total meeting duration in seconds")
    ongoing: bool = Field(description="Whether meeting is currently active")
    max_participants: int = Field(description="Peak concurrent participant count")
    participants: List[MeetingParticipant] = Field(
        default_factory=list, description="Array of participant session data"
    )
 class RecordingS3Info(BaseModel):
    """
    S3 bucket information for a recording.
    Reference: https://docs.daily.co/reference/rest-api/recordings
    """
    bucket_name: NonEmptyString
    bucket_region: NonEmptyString
    endpoint: NonEmptyString | None = None
 class RecordingResponse(BaseModel):
    """
    Response from recording retrieval endpoint (network layer).
    Duration may be None for recordings still being processed by Daily.
    Use FinishedRecordingResponse for recordings ready for processing.
    Reference: https://docs.daily.co/reference/rest-api/recordings
    """
    id: NonEmptyString = Field(description="Recording identifier")
    room_name: NonEmptyString = Field(description="Room where recording occurred")
    start_ts: int = Field(description="Recording start timestamp (Unix epoch seconds)")
    status: RecordingStatus = Field(
        description="Recording status ('in-progress' or 'finished')"
    )
    max_participants: int | None = Field(
        None, description="Maximum participants during recording (may be missing)"
    )
    duration: int | None = Field(
        None, description="Recording duration in seconds (None if still processing)"
    )
    share_token: NonEmptyString | None = Field(
        None, description="Token for sharing recording"
    )
    s3: RecordingS3Info | None = Field(None, description="S3 bucket information")
    tracks: list[DailyTrack] = Field(
        default_factory=list,
        description="Track list for raw-tracks recordings (always array, never null)",
    )
    # this is not a mistake but a deliberate Daily.co naming decision
    mtgSessionId: NonEmptyString | None = Field(
        None, description="Meeting session identifier (may be missing)"
    )
    def to_finished(self) -> "FinishedRecordingResponse | None":
        """Convert to FinishedRecordingResponse if duration is available and status is finished."""
        if self.duration is None or self.status != "finished":
            return None
        return FinishedRecordingResponse(**self.model_dump())
 class FinishedRecordingResponse(RecordingResponse):
    """
    Recording with confirmed duration - ready for processing.
    This model guarantees duration is present and status is finished.
    """
    status: Literal["finished"] = Field(
        description="Recording status (always 'finished')"
    )
    duration: int = Field(description="Recording duration in seconds")
 class MeetingTokenResponse(BaseModel):
    """
    Response from meeting token creation.
    Reference: https://docs.daily.co/reference/rest-api/meeting-tokens/create-meeting-token
    """
    token: NonEmptyString = Field(
        description="JWT meeting token for participant authentication"
    )
 class WebhookResponse(BaseModel):
    """
    Response from webhook creation or retrieval.
    Reference: https://docs.daily.co/reference/rest-api/webhooks
    """
    uuid: NonEmptyString = Field(description="Unique webhook identifier")
    url: NonEmptyString = Field(description="Webhook endpoint URL")
    hmac: NonEmptyString | None = Field(
        None, description="Base64-encoded HMAC secret for signature verification"
    )
    basicAuth: NonEmptyString | None = Field(
        None, description="Basic auth credentials if configured"
    )
    eventTypes: List[NonEmptyString] = Field(
        default_factory=list,
        description="Array of event types (e.g., ['recording.started', 'participant.joined'])",
    )
    state: Literal["ACTIVE", "FAILED"] = Field(
        description="Webhook state - FAILED after 3+ consecutive failures"
    )
    failedCount: int = Field(default=0, description="Number of consecutive failures")
    lastMomentPushed: NonEmptyString | None = Field(
        None, description="ISO 8601 timestamp of last successful push"
    )
    domainId: NonEmptyString = Field(description="Daily.co domain/account identifier")
    createdAt: NonEmptyString = Field(description="ISO 8601 creation timestamp")
    updatedAt: NonEmptyString = Field(description="ISO 8601 last update timestamp")
--- a/server/reflector/dailyco_api/webhook_utils.py
+++ b/server/reflector/dailyco_api/webhook_utils.py
@@ -1,228 +0,0 @@
 """
 Daily.co Webhook Utilities
 Utilities for verifying and parsing Daily.co webhook events.
 Reference: https://docs.daily.co/reference/rest-api/webhooks
 """
 import base64
 import hmac
 from hashlib import sha256
 import structlog
 from .webhooks import (
    DailyWebhookEvent,
    ParticipantJoinedPayload,
    ParticipantLeftPayload,
    RecordingErrorPayload,
    RecordingReadyToDownloadPayload,
    RecordingStartedPayload,
 )
 logger = structlog.get_logger(__name__)
 def verify_webhook_signature(
    body: bytes,
    signature: str,
    timestamp: str,
    webhook_secret: str,
 ) -> bool:
    """
    Verify Daily.co webhook signature using HMAC-SHA256.
    Daily.co signature verification:
    1. Base64-decode the webhook secret
    2. Create signed content: timestamp + '.' + body
    3. Compute HMAC-SHA256(secret, signed_content)
    4. Base64-encode the result
    5. Compare with provided signature using constant-time comparison
    Reference: https://docs.daily.co/reference/rest-api/webhooks
    Args:
        body: Raw request body bytes
        signature: X-Webhook-Signature header value
        timestamp: X-Webhook-Timestamp header value
        webhook_secret: Base64-encoded HMAC secret
    Returns:
        True if signature is valid, False otherwise
    Example:
        >>> body = b'{"version":"1.0.0","type":"participant.joined",...}'
        >>> signature = "abc123..."
        >>> timestamp = "1234567890"
        >>> secret = "your-base64-secret"
        >>> is_valid = verify_webhook_signature(body, signature, timestamp, secret)
    """
    if not signature or not timestamp or not webhook_secret:
        logger.warning(
            "Missing required data for webhook verification",
            has_signature=bool(signature),
            has_timestamp=bool(timestamp),
            has_secret=bool(webhook_secret),
        )
        return False
    try:
        secret_bytes = base64.b64decode(webhook_secret)
        signed_content = timestamp.encode() + b"." + body
        expected = hmac.new(secret_bytes, signed_content, sha256).digest()
        expected_b64 = base64.b64encode(expected).decode()
        # Constant-time comparison to prevent timing attacks
        return hmac.compare_digest(expected_b64, signature)
    except (base64.binascii.Error, ValueError, TypeError, UnicodeDecodeError) as e:
        logger.error(
            "Webhook signature verification failed",
            error=str(e),
            error_type=type(e).__name__,
        )
        return False
 def extract_room_name(event: DailyWebhookEvent) -> str | None:
    """
    Extract room name from Daily.co webhook event payload.
    Args:
        event: Parsed webhook event
    Returns:
        Room name if present and is a string, None otherwise
    Example:
        >>> event = DailyWebhookEvent(**webhook_payload)
        >>> room_name = extract_room_name(event)
    """
    room = event.payload.get("room_name")
    # Ensure we return a string, not any falsy value that might be in payload
    return room if isinstance(room, str) else None
 def parse_participant_joined(event: DailyWebhookEvent) -> ParticipantJoinedPayload:
    """
    Parse participant.joined webhook event payload.
    Args:
        event: Webhook event with type "participant.joined"
    Returns:
        Parsed participant joined payload
    Raises:
        pydantic.ValidationError: If payload doesn't match expected schema
    """
    return ParticipantJoinedPayload(**event.payload)
 def parse_participant_left(event: DailyWebhookEvent) -> ParticipantLeftPayload:
    """
    Parse participant.left webhook event payload.
    Args:
        event: Webhook event with type "participant.left"
    Returns:
        Parsed participant left payload
    Raises:
        pydantic.ValidationError: If payload doesn't match expected schema
    """
    return ParticipantLeftPayload(**event.payload)
 def parse_recording_started(event: DailyWebhookEvent) -> RecordingStartedPayload:
    """
    Parse recording.started webhook event payload.
    Args:
        event: Webhook event with type "recording.started"
    Returns:
        Parsed recording started payload
    Raises:
        pydantic.ValidationError: If payload doesn't match expected schema
    """
    return RecordingStartedPayload(**event.payload)
 def parse_recording_ready(
    event: DailyWebhookEvent,
 ) -> RecordingReadyToDownloadPayload:
    """
    Parse recording.ready-to-download webhook event payload.
    This event is sent when raw-tracks recordings are complete and uploaded to S3.
    The payload includes a 'tracks' array with individual audio/video files.
    Args:
        event: Webhook event with type "recording.ready-to-download"
    Returns:
        Parsed recording ready payload with tracks array
    Raises:
        pydantic.ValidationError: If payload doesn't match expected schema
    Example:
        >>> event = DailyWebhookEvent(**webhook_payload)
        >>> if event.type == "recording.ready-to-download":
        ...     payload = parse_recording_ready(event)
        ...     audio_tracks = [t for t in payload.tracks if t.type == "audio"]
    """
    return RecordingReadyToDownloadPayload(**event.payload)
 def parse_recording_error(event: DailyWebhookEvent) -> RecordingErrorPayload:
    """
    Parse recording.error webhook event payload.
    Args:
        event: Webhook event with type "recording.error"
    Returns:
        Parsed recording error payload
    Raises:
        pydantic.ValidationError: If payload doesn't match expected schema
    """
    return RecordingErrorPayload(**event.payload)
 WEBHOOK_PARSERS = {
    "participant.joined": parse_participant_joined,
    "participant.left": parse_participant_left,
    "recording.started": parse_recording_started,
    "recording.ready-to-download": parse_recording_ready,
    "recording.error": parse_recording_error,
 }
 def parse_webhook_payload(event: DailyWebhookEvent):
    """
    Parse webhook event payload based on event type.
    Args:
        event: Webhook event
    Returns:
        Typed payload model based on event type, or raw dict if unknown
    Example:
        >>> event = DailyWebhookEvent(**webhook_payload)
        >>> payload = parse_webhook_payload(event)
        >>> if isinstance(payload, ParticipantJoinedPayload):
        ...     print(f"User {payload.user_name} joined")
    """
    parser = WEBHOOK_PARSERS.get(event.type)
    if parser:
        return parser(event)
    else:
        logger.warning("Unknown webhook event type", event_type=event.type)
        return event.payload
--- a/server/reflector/dailyco_api/webhooks.py
+++ b/server/reflector/dailyco_api/webhooks.py
@@ -1,271 +0,0 @@
 """
 Daily.co Webhook Event Models
 Reference: https://docs.daily.co/reference/rest-api/webhooks
 """
 from typing import Annotated, Any, Dict, Literal, Union
 from pydantic import BaseModel, Field, field_validator
 from reflector.utils.string import NonEmptyString
 def normalize_timestamp_to_int(v):
    """
    Normalize float timestamps to int by truncating decimal part.
    Daily.co sometimes sends timestamps as floats (e.g., 1708972279.96).
    Pydantic expects int for fields typed as `int`.
    """
    if v is None:
        return v
    if isinstance(v, float):
        return int(v)
    return v
 WebhookEventType = Literal[
    "participant.joined",
    "participant.left",
    "recording.started",
    "recording.ready-to-download",
    "recording.error",
 ]
 class DailyTrack(BaseModel):
    """
    Individual audio or video track from a multitrack recording.
    Reference: https://docs.daily.co/reference/rest-api/recordings
    """
    type: Literal["audio", "video"]
    s3Key: NonEmptyString = Field(description="S3 object key for the track file")
    size: int = Field(description="File size in bytes")
 class DailyWebhookEvent(BaseModel):
    """
    Base structure for all Daily.co webhook events.
    All events share five common fields documented below.
    Reference: https://docs.daily.co/reference/rest-api/webhooks
    """
    version: NonEmptyString = Field(
        description="Represents the version of the event. This uses semantic versioning to inform a consumer if the payload has introduced any breaking changes"
    )
    type: WebhookEventType = Field(
        description="Represents the type of the event described in the payload"
    )
    id: NonEmptyString = Field(
        description="An identifier representing this specific event"
    )
    payload: Dict[NonEmptyString, Any] = Field(
        description="An object representing the event, whose fields are described in the corresponding payload class"
    )
    event_ts: int = Field(
        description="Documenting when the webhook itself was sent. This timestamp is different than the time of the event the webhook describes. For example, a recording.started event will contain a start_ts timestamp of when the actual recording started, and a slightly later event_ts timestamp indicating when the webhook event was sent"
    )
    _normalize_event_ts = field_validator("event_ts", mode="before")(
        normalize_timestamp_to_int
    )
 class ParticipantJoinedPayload(BaseModel):
    """
    Payload for participant.joined webhook event.
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/participant-joined
    """
    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
    session_id: NonEmptyString = Field(description="Daily.co session identifier")
    user_id: NonEmptyString = Field(description="User identifier (may be encoded)")
    user_name: NonEmptyString | None = Field(None, description="User display name")
    joined_at: int = Field(description="Join timestamp in Unix epoch seconds")
    _normalize_joined_at = field_validator("joined_at", mode="before")(
        normalize_timestamp_to_int
    )
 class ParticipantLeftPayload(BaseModel):
    """
    Payload for participant.left webhook event.
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/participant-left
    """
    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
    session_id: NonEmptyString = Field(description="Daily.co session identifier")
    user_id: NonEmptyString = Field(description="User identifier (may be encoded)")
    user_name: NonEmptyString | None = Field(None, description="User display name")
    joined_at: int = Field(description="Join timestamp in Unix epoch seconds")
    duration: int | None = Field(
        None, description="Duration of participation in seconds"
    )
    _normalize_joined_at = field_validator("joined_at", mode="before")(
        normalize_timestamp_to_int
    )
 class RecordingStartedPayload(BaseModel):
    """
    Payload for recording.started webhook event.
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-started
    """
    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
    recording_id: NonEmptyString = Field(description="Recording identifier")
    start_ts: int | None = Field(None, description="Recording start timestamp")
    _normalize_start_ts = field_validator("start_ts", mode="before")(
        normalize_timestamp_to_int
    )
 class RecordingReadyToDownloadPayload(BaseModel):
    """
    Payload for recording.ready-to-download webhook event.
    This is sent when raw-tracks recordings are complete and uploaded to S3.
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-ready-to-download
    """
    type: Literal["cloud", "raw-tracks"] = Field(
        description="The type of recording that was generated"
    )
    recording_id: NonEmptyString = Field(
        description="An ID identifying the recording that was generated"
    )
    room_name: NonEmptyString = Field(
        description="The name of the room where the recording was made"
    )
    start_ts: int = Field(
        description="The Unix epoch time in seconds representing when the recording started"
    )
    status: Literal["finished"] = Field(
        description="The status of the given recording (always 'finished' in ready-to-download webhook, see RecordingStatus in responses.py for full API statuses)"
    )
    max_participants: int = Field(
        description="The number of participants on the call that were recorded"
    )
    duration: int = Field(description="The duration in seconds of the call")
    s3_key: NonEmptyString = Field(
        description="The location of the recording in the provided S3 bucket"
    )
    share_token: NonEmptyString | None = Field(
        None, description="undocumented documented secret field"
    )
    tracks: list[DailyTrack] | None = Field(
        None,
        description="If the recording is a raw-tracks recording, a tracks field will be provided. If role permissions have been removed, the tracks field may be null",
    )
    _normalize_start_ts = field_validator("start_ts", mode="before")(
        normalize_timestamp_to_int
    )
 class RecordingErrorPayload(BaseModel):
    """
    Payload for recording.error webhook event.
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-error
    """
    action: Literal["clourd-recording-err", "cloud-recording-error"] = Field(
        description="A string describing the event that was emitted (both variants are documented)"
    )
    error_msg: NonEmptyString = Field(description="The error message returned")
    instance_id: NonEmptyString = Field(
        description="The recording instance ID that was passed into the start recording command"
    )
    room_name: NonEmptyString = Field(
        description="The name of the room where the recording was made"
    )
    timestamp: int = Field(
        description="The Unix epoch time in seconds representing when the error was emitted"
    )
    _normalize_timestamp = field_validator("timestamp", mode="before")(
        normalize_timestamp_to_int
    )
 class ParticipantJoinedEvent(BaseModel):
    version: NonEmptyString
    type: Literal["participant.joined"]
    id: NonEmptyString
    payload: ParticipantJoinedPayload
    event_ts: int
    _normalize_event_ts = field_validator("event_ts", mode="before")(
        normalize_timestamp_to_int
    )
 class ParticipantLeftEvent(BaseModel):
    version: NonEmptyString
    type: Literal["participant.left"]
    id: NonEmptyString
    payload: ParticipantLeftPayload
    event_ts: int
    _normalize_event_ts = field_validator("event_ts", mode="before")(
        normalize_timestamp_to_int
    )
 class RecordingStartedEvent(BaseModel):
    version: NonEmptyString
    type: Literal["recording.started"]
    id: NonEmptyString
    payload: RecordingStartedPayload
    event_ts: int
    _normalize_event_ts = field_validator("event_ts", mode="before")(
        normalize_timestamp_to_int
    )
 class RecordingReadyEvent(BaseModel):
    version: NonEmptyString
    type: Literal["recording.ready-to-download"]
    id: NonEmptyString
    payload: RecordingReadyToDownloadPayload
    event_ts: int
    _normalize_event_ts = field_validator("event_ts", mode="before")(
        normalize_timestamp_to_int
    )
 class RecordingErrorEvent(BaseModel):
    version: NonEmptyString
    type: Literal["recording.error"]
    id: NonEmptyString
    payload: RecordingErrorPayload
    event_ts: int
    _normalize_event_ts = field_validator("event_ts", mode="before")(
        normalize_timestamp_to_int
    )
 DailyWebhookEventUnion = Annotated[
    Union[
        ParticipantJoinedEvent,
        ParticipantLeftEvent,
        RecordingStartedEvent,
        RecordingReadyEvent,
        RecordingErrorEvent,
    ],
    Field(discriminator="type"),
 ]
--- a/server/reflector/db/init.py
+++ b/server/reflector/db/init.py
@@ -25,13 +25,10 @@ def get_database() -> databases.Database:
 # import models
 import reflector.db.calendar_events  # noqa
 import reflector.db.daily_participant_sessions  # noqa
 import reflector.db.meetings  # noqa
 import reflector.db.recordings  # noqa
 import reflector.db.rooms  # noqa
 import reflector.db.transcripts  # noqa
 import reflector.db.user_api_keys  # noqa
 import reflector.db.users  # noqa
 kwargs = {}
 if "postgres" not in settings.DATABASE_URL:
--- a/server/reflector/db/daily_participant_sessions.py
+++ b/server/reflector/db/daily_participant_sessions.py
@@ -1,229 +0,0 @@
 """Daily.co participant session tracking.
 Stores webhook data for participant.joined and participant.left events to provide
 historical session information (Daily.co API only returns current participants).
 """
 from datetime import datetime
 import sqlalchemy as sa
 from pydantic import BaseModel
 from sqlalchemy.dialects.postgresql import insert
 from reflector.db import get_database, metadata
 from reflector.utils.string import NonEmptyString
 daily_participant_sessions = sa.Table(
    "daily_participant_session",
    metadata,
    sa.Column("id", sa.String, primary_key=True),
    sa.Column(
        "meeting_id",
        sa.String,
        sa.ForeignKey("meeting.id", ondelete="CASCADE"),
        nullable=False,
    ),
    sa.Column(
        "room_id",
        sa.String,
        sa.ForeignKey("room.id", ondelete="CASCADE"),
        nullable=False,
    ),
    sa.Column("session_id", sa.String, nullable=False),
    sa.Column("user_id", sa.String, nullable=True),
    sa.Column("user_name", sa.String, nullable=False),
    sa.Column("joined_at", sa.DateTime(timezone=True), nullable=False),
    sa.Column("left_at", sa.DateTime(timezone=True), nullable=True),
    sa.Index("idx_daily_session_meeting_left", "meeting_id", "left_at"),
    sa.Index("idx_daily_session_room", "room_id"),
 )
 class DailyParticipantSession(BaseModel):
    """Daily.co participant session record.
    Tracks when a participant joined and left a meeting. Populated from webhooks:
    - participant.joined: Creates record with left_at=None
    - participant.left: Updates record with left_at
    ID format: {meeting_id}:{user_id}:{joined_at_ms}
    - Ensures idempotency (duplicate webhooks don't create duplicates)
    - Allows same user to rejoin (different joined_at = different session)
    Duration is calculated as: left_at - joined_at (not stored)
    """
    id: NonEmptyString
    meeting_id: NonEmptyString
    room_id: NonEmptyString
    session_id: NonEmptyString  # Daily.co's session_id (identifies room session)
    user_id: NonEmptyString | None = None
    user_name: str
    joined_at: datetime
    left_at: datetime | None = None
 class DailyParticipantSessionController:
    """Controller for Daily.co participant session persistence."""
    async def get_by_id(self, id: str) -> DailyParticipantSession | None:
        """Get a session by its ID."""
        query = daily_participant_sessions.select().where(
            daily_participant_sessions.c.id == id
        )
        result = await get_database().fetch_one(query)
        return DailyParticipantSession(**result) if result else None
    async def get_open_session(
        self, meeting_id: NonEmptyString, session_id: NonEmptyString
    ) -> DailyParticipantSession | None:
        """Get the open (not left) session for a user in a meeting."""
        query = daily_participant_sessions.select().where(
            sa.and_(
                daily_participant_sessions.c.meeting_id == meeting_id,
                daily_participant_sessions.c.session_id == session_id,
                daily_participant_sessions.c.left_at.is_(None),
            )
        )
        results = await get_database().fetch_all(query)
        if len(results) > 1:
            raise ValueError(
                f"Multiple open sessions for daily session {session_id} in meeting {meeting_id}: "
                f"found {len(results)} sessions"
            )
        return DailyParticipantSession(**results[0]) if results else None
    async def upsert_joined(self, session: DailyParticipantSession) -> None:
        """Insert or update when participant.joined webhook arrives.
        Idempotent: Duplicate webhooks with same ID are safely ignored.
        Out-of-order: If left webhook arrived first, preserves left_at.
        """
        query = insert(daily_participant_sessions).values(**session.model_dump())
        query = query.on_conflict_do_update(
            index_elements=["id"],
            set_={"user_name": session.user_name},
        )
        await get_database().execute(query)
    async def upsert_left(self, session: DailyParticipantSession) -> None:
        """Update session when participant.left webhook arrives.
        Finds the open session for this user in this meeting and updates left_at.
        Works around Daily.co webhook timestamp inconsistency (joined_at differs by ~4ms between webhooks).
        Handles three cases:
        1. Normal flow: open session exists → updates left_at
        2. Out-of-order: left arrives first → creates new record with left data
        3. Duplicate: left arrives again → idempotent (DB trigger prevents left_at modification)
        """
        if session.left_at is None:
            raise ValueError("left_at is required for upsert_left")
        if session.left_at <= session.joined_at:
            raise ValueError(
                f"left_at ({session.left_at}) must be after joined_at ({session.joined_at})"
            )
        # Find existing open session (works around timestamp mismatch in webhooks)
        existing = await self.get_open_session(session.meeting_id, session.session_id)
        if existing:
            # Update existing open session
            query = (
                daily_participant_sessions.update()
                .where(daily_participant_sessions.c.id == existing.id)
                .values(left_at=session.left_at)
            )
            await get_database().execute(query)
        else:
            # Out-of-order or first webhook: insert new record
            query = insert(daily_participant_sessions).values(**session.model_dump())
            query = query.on_conflict_do_nothing(index_elements=["id"])
            await get_database().execute(query)
    async def get_by_meeting(self, meeting_id: str) -> list[DailyParticipantSession]:
        """Get all participant sessions for a meeting (active and ended)."""
        query = daily_participant_sessions.select().where(
            daily_participant_sessions.c.meeting_id == meeting_id
        )
        results = await get_database().fetch_all(query)
        return [DailyParticipantSession(**result) for result in results]
    async def get_active_by_meeting(
        self, meeting_id: str
    ) -> list[DailyParticipantSession]:
        """Get only active (not left) participant sessions for a meeting."""
        query = daily_participant_sessions.select().where(
            sa.and_(
                daily_participant_sessions.c.meeting_id == meeting_id,
                daily_participant_sessions.c.left_at.is_(None),
            )
        )
        results = await get_database().fetch_all(query)
        return [DailyParticipantSession(**result) for result in results]
    async def get_all_sessions_for_meeting(
        self, meeting_id: NonEmptyString
    ) -> dict[NonEmptyString, DailyParticipantSession]:
        query = daily_participant_sessions.select().where(
            daily_participant_sessions.c.meeting_id == meeting_id
        )
        results = await get_database().fetch_all(query)
        # TODO DailySessionId custom type
        return {row["session_id"]: DailyParticipantSession(**row) for row in results}
    async def batch_upsert_sessions(
        self, sessions: list[DailyParticipantSession]
    ) -> None:
        """Upsert multiple sessions in single query.
        Uses ON CONFLICT for idempotency. Updates user_name on conflict since they may change it during a meeting.
        """
        if not sessions:
            return
        values = [session.model_dump() for session in sessions]
        query = insert(daily_participant_sessions).values(values)
        query = query.on_conflict_do_update(
            index_elements=["id"],
            set_={
                # Preserve existing left_at to prevent race conditions
                "left_at": sa.func.coalesce(
                    daily_participant_sessions.c.left_at,
                    query.excluded.left_at,
                ),
                "user_name": query.excluded.user_name,
            },
        )
        await get_database().execute(query)
    async def batch_close_sessions(
        self, session_ids: list[NonEmptyString], left_at: datetime
    ) -> None:
        """Mark multiple sessions as left in single query.
        Only updates sessions where left_at is NULL (protects already-closed sessions).
        Left_at mismatch for existing sessions is ignored, assumed to be not important issue if ever happens.
        """
        if not session_ids:
            return
        query = (
            daily_participant_sessions.update()
            .where(
                sa.and_(
                    daily_participant_sessions.c.id.in_(session_ids),
                    daily_participant_sessions.c.left_at.is_(None),
                )
            )
            .values(left_at=left_at)
        )
        await get_database().execute(query)
 daily_participant_sessions_controller = DailyParticipantSessionController()
--- a/server/reflector/db/meetings.py
+++ b/server/reflector/db/meetings.py
@@ -7,9 +7,8 @@ from sqlalchemy.dialects.postgresql import JSONB
 from reflector.db import get_database, metadata
 from reflector.db.rooms import Room
-from reflector.schemas.platform import WHEREBY_PLATFORM, Platform
+from reflector.platform_types import Platform
 from reflector.utils import generate_uuid4
 from reflector.utils.string import assert_equal
 meetings = sa.Table(
    "meeting",
@@ -61,7 +60,7 @@ meetings = sa.Table(
        "platform",
        sa.String,
        nullable=False,
-        server_default=assert_equal(WHEREBY_PLATFORM, "whereby"),
+        server_default="whereby",
    ),
    sa.Index("idx_meeting_room_id", "room_id"),
    sa.Index("idx_meeting_calendar_event", "calendar_event_id"),
@@ -109,7 +108,7 @@ class Meeting(BaseModel):
    is_active: bool = True
    calendar_event_id: str | None = None
    calendar_metadata: dict[str, Any] | None = None
-    platform: Platform = WHEREBY_PLATFORM
+    platform: Platform = "whereby"
 class MeetingController:
@@ -124,6 +123,7 @@ class MeetingController:
        room: Room,
        calendar_event_id: str | None = None,
        calendar_metadata: dict[str, Any] | None = None,
        platform: Platform = "whereby",
    ):
        meeting = Meeting(
            id=id,
@@ -139,19 +139,15 @@ class MeetingController:
            recording_trigger=room.recording_trigger,
            calendar_event_id=calendar_event_id,
            calendar_metadata=calendar_metadata,
-            platform=room.platform,
+            platform=platform,
        )
        query = meetings.insert().values(**meeting.model_dump())
        await get_database().execute(query)
        return meeting
-    async def get_all_active(self, platform: str | None = None) -> list[Meeting]:
+    async def get_all_active(self) -> list[Meeting]:
-        conditions = [meetings.c.is_active]
+        query = meetings.select().where(meetings.c.is_active)
-        if platform is not None:
+        return await get_database().fetch_all(query)
            conditions.append(meetings.c.platform == platform)
        query = meetings.select().where(sa.and_(*conditions))
        results = await get_database().fetch_all(query)
        return [Meeting(**result) for result in results]
    async def get_by_room_name(
        self,
@@ -161,14 +157,16 @@ class MeetingController:
        Get a meeting by room name.
        For backward compatibility, returns the most recent meeting.
        """
        end_date = getattr(meetings.c, "end_date")
        query = (
            meetings.select()
            .where(meetings.c.room_name == room_name)
-            .order_by(meetings.c.end_date.desc())
+            .order_by(end_date.desc())
        )
        result = await get_database().fetch_one(query)
        if not result:
            return None
        return Meeting(**result)
    async def get_active(self, room: Room, current_time: datetime) -> Meeting | None:
@@ -191,6 +189,7 @@ class MeetingController:
        result = await get_database().fetch_one(query)
        if not result:
            return None
        return Meeting(**result)
    async def get_all_active_for_room(
@@ -230,27 +229,17 @@ class MeetingController:
            return None
        return Meeting(**result)
-    async def get_by_id(
+    async def get_by_id(self, meeting_id: str, **kwargs) -> Meeting | None:
        self, meeting_id: str, room: Room | None = None
    ) -> Meeting | None:
        query = meetings.select().where(meetings.c.id == meeting_id)
        if room:
            query = query.where(meetings.c.room_id == room.id)
        result = await get_database().fetch_one(query)
        if not result:
            return None
        return Meeting(**result)
-    async def get_by_calendar_event(
+    async def get_by_calendar_event(self, calendar_event_id: str) -> Meeting | None:
        self, calendar_event_id: str, room: Room
    ) -> Meeting | None:
        query = meetings.select().where(
            meetings.c.calendar_event_id == calendar_event_id
        )
        if room:
            query = query.where(meetings.c.room_id == room.id)
        result = await get_database().fetch_one(query)
        if not result:
            return None
@@ -260,7 +249,7 @@ class MeetingController:
        query = meetings.update().where(meetings.c.id == meeting_id).values(**kwargs)
        await get_database().execute(query)
-    async def increment_num_clients(self, meeting_id: str) -> None:
+    async def increment_num_clients(self, meeting_id: str):
        """Atomically increment participant count."""
        query = (
            meetings.update()
@@ -269,7 +258,7 @@ class MeetingController:
        )
        await get_database().execute(query)
-    async def decrement_num_clients(self, meeting_id: str) -> None:
+    async def decrement_num_clients(self, meeting_id: str):
        """Atomically decrement participant count (min 0)."""
        query = (
            meetings.update()
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Igor Loskutov	1bf73c8199	sync with parent	2025-10-21 11:59:26 -04:00
Sergey Mankovsky	d82abf65ba	Emit multriack pipeline events	2025-10-21 16:31:31 +02:00
Igor Loskutov	7d239fe380	dailico track merge vibe	2025-10-21 10:30:19 -04:00
Sergey Mankovsky	acb6e90f28	Generate waveforms for the mixed audio	2025-10-21 13:33:31 +02:00
Igor Loskutov	f844b9fc1f	Merge branch 'igor/dailico-2' of github-monadical:Monadical-SAS/reflector into igor/dailico-2	2025-10-17 10:00:40 -04:00
Sergey Mankovsky	96f05020cc	Align tracks of a multitrack recording	2025-10-17 15:27:27 +02:00
Sergey Mankovsky	fc79ff3114	Use explicit track keys for processing	2025-10-17 14:42:07 +02:00
Igor Loskutov	3641e2e599	apply platform from envs in priority: non-dry	2025-10-16 15:08:19 -04:00
Sergey Mankovsky	c23518d2e3	Trigger multitrack processing for daily recordings	2025-10-16 20:05:26 +02:00
Sergey Mankovsky	23edffe2a2	Mixdown with pyav filter graph	2025-10-16 17:14:55 +02:00
Sergey Mankovsky	e59770ecc9	Mixdown audio tracks	2025-10-16 17:14:55 +02:00
Sergey Mankovsky	6301f2afa6	Add multitrack pipeline	2025-10-16 17:14:55 +02:00
Mathieu Virbel	9ac7f0e8e2	chore(main): release 0.14.0 (#670 )	2025-10-16 17:14:55 +02:00
Igor Loskutov	0a84a9351a	stub processor (vibe) self-review	2025-10-10 20:41:08 -04:00
Igor Loskutov	ca22084845	stub processor (vibe) self-review	2025-10-10 18:45:19 -04:00
Igor Loskutov	f945f84be9	stub processor (vibe)	2025-10-10 18:05:31 -04:00
Igor Loskutov	4c523c8eec	dont show recording ui on call	2025-10-10 12:45:10 -04:00
Igor Loskutov	0fcf8b6875	doc update (vibe)	2025-10-10 10:57:35 -04:00
Igor Loskutov	446cb748ae	vibe dailyco	2025-10-09 17:04:16 -04:00
Igor Loskutov	3e1339a8ea	vibe dailyco	2025-10-09 15:52:23 -04:00
Igor Loskutov	807819bb2f	llm instructions	2025-10-08 13:06:04 -04:00
		`@@ -1,2 +0,0 @@`
			`-- Create hatchet database for Hatchet workflow engine`
			`CREATE DATABASE hatchet;`