feat: use jitsi file system

Merge main into jisti-integration branch
- Resolved conflicts in server/reflector/views/rooms.py to keep platform-agnostic approach - Resolved conflicts in www/app/[roomName]/page.tsx to keep VideoPlatformEmbed approach - Accepted main's version of generated API files (schemas.gen.ts, services.gen.ts, types.gen.ts) - Removed config-template.ts as per main branch changes
2025-12-21 04:39:06 +00:00 · 2025-09-17 15:16:03 -06:00 · 2025-09-15 12:53:49 -06:00 · 2025-09-04 12:21:51 -06:00 · 2025-09-02 18:28:50 -06:00 · 2025-09-02 18:25:55 -06:00
337 changed files with 8535 additions and 61251 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
@@ -1,53 +0,0 @@
-name: Build and Push Backend Docker Image (Docker Hub)
-
-on:
-  push:
-    tags:
-      - "v*"
-  workflow_dispatch:
-
-env:
-  REGISTRY: docker.io
-  IMAGE_NAME: monadicalsas/reflector-backend
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: monadicalsas
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-          tags: |
-            type=ref,event=branch
-            type=ref,event=tag
-            type=raw,value=latest,enable={{is_default_branch}}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v5
-        with:
-          context: ./server
-          file: ./server/Dockerfile
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          platforms: linux/amd64,linux/arm64
--- a/.github/workflows/dockerhub-frontend.yml
+++ b/.github/workflows/dockerhub-frontend.yml
@@ -1,70 +0,0 @@
-name: Build and Push Frontend Docker Image
-
-on:
-  push:
-    tags:
-      - "v*"
-  workflow_dispatch:
-
-env:
-  REGISTRY: docker.io
-  IMAGE_NAME: monadicalsas/reflector-frontend
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: monadicalsas
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-          tags: |
-            type=ref,event=branch
-            type=ref,event=tag
-            type=raw,value=latest,enable={{is_default_branch}}
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v5
-        with:
-          context: ./www
-          file: ./www/Dockerfile
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          platforms: linux/amd64,linux/arm64
-
-  deploy:
-    needs: build-and-push
-    runs-on: ubuntu-latest
-    if: success()
-    strategy:
-      matrix:
-        environment: [reflector-monadical, reflector-media]
-    environment: ${{ matrix.environment }}
-    steps:
-      - name: Trigger Coolify deployment
-        run: |
-          curl -X POST "${{ secrets.COOLIFY_WEBHOOK_URL }}" \
-            -H "Content-Type: application/json" \
-            -H "Authorization: Bearer ${{ secrets.COOLIFY_WEBHOOK_TOKEN }}" \
-            -f || (echo "Failed to trigger Coolify deployment for ${{ matrix.environment }}" && exit 1)
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 .DS_Store
 server/.env
 .env
-Caddyfile
 server/exportdanswer
 .vercel
 .env*.local
@@ -19,5 +18,3 @@ CLAUDE.local.md
 www/.env.development
 www/.env.production
 .playwright-mcp
-docs/pnpm-lock.yaml
-.secrets
--- 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
@@ -1,206 +1,5 @@
 # Changelog

-## [0.23.2](https://github.com/Monadical-SAS/reflector/compare/v0.23.1...v0.23.2) (2025-12-11)
-
-
-### Bug Fixes
-
-* build on push tags ([#785](https://github.com/Monadical-SAS/reflector/issues/785)) ([d7f140b](https://github.com/Monadical-SAS/reflector/commit/d7f140b7d1f4660d5da7a0da1357f68869e0b5cd))
-
-## [0.23.1](https://github.com/Monadical-SAS/reflector/compare/v0.23.0...v0.23.1) (2025-12-11)
-
-
-### Bug Fixes
-
-* populate room_name in transcript GET endpoint ([#783](https://github.com/Monadical-SAS/reflector/issues/783)) ([0eba147](https://github.com/Monadical-SAS/reflector/commit/0eba1470181c7b9e0a79964a1ef28c09bcbdd9d7))
-
-## [0.23.0](https://github.com/Monadical-SAS/reflector/compare/v0.22.4...v0.23.0) (2025-12-10)
-
-
-### Features
-
-* dockerhub ci ([#772](https://github.com/Monadical-SAS/reflector/issues/772)) ([00549f1](https://github.com/Monadical-SAS/reflector/commit/00549f153ade922cf4cb6c5358a7d11a39c426d2))
-* llm retries ([#739](https://github.com/Monadical-SAS/reflector/issues/739)) ([61f0e29](https://github.com/Monadical-SAS/reflector/commit/61f0e29d4c51eab54ee67af92141fbb171e8ccaa))
-
-
-### Bug Fixes
-
-* celery inspect bug sidestep in restart script ([#766](https://github.com/Monadical-SAS/reflector/issues/766)) ([ec17ed7](https://github.com/Monadical-SAS/reflector/commit/ec17ed7b587cf6ee143646baaee67a7c017044d4))
-* deploy frontend to coolify ([#779](https://github.com/Monadical-SAS/reflector/issues/779)) ([91650ec](https://github.com/Monadical-SAS/reflector/commit/91650ec65f65713faa7ee0dcfb75af427b7c4ba0))
-* hide rooms settings instead of disabling ([#763](https://github.com/Monadical-SAS/reflector/issues/763)) ([3ad78be](https://github.com/Monadical-SAS/reflector/commit/3ad78be7628c0d029296b301a0e87236c76b7598))
-* return participant emails from transcript endpoint ([#769](https://github.com/Monadical-SAS/reflector/issues/769)) ([d3a5cd1](https://github.com/Monadical-SAS/reflector/commit/d3a5cd12d2d0d9c32af2d5bd9322e030ef69b85d))
-
-## [0.22.4](https://github.com/Monadical-SAS/reflector/compare/v0.22.3...v0.22.4) (2025-12-02)
-
-
-### Bug Fixes
-
-* Multitrack mixdown optimisation 2 ([#764](https://github.com/Monadical-SAS/reflector/issues/764)) ([bd5df1c](https://github.com/Monadical-SAS/reflector/commit/bd5df1ce2ebf35d7f3413b295e56937a9a28ef7b))
-
-## [0.22.3](https://github.com/Monadical-SAS/reflector/compare/v0.22.2...v0.22.3) (2025-12-02)
-
-
-### Bug Fixes
-
-* align daily room settings ([#759](https://github.com/Monadical-SAS/reflector/issues/759)) ([28f87c0](https://github.com/Monadical-SAS/reflector/commit/28f87c09dc459846873d0dde65b03e3d7b2b9399))
-
-## [0.22.2](https://github.com/Monadical-SAS/reflector/compare/v0.22.1...v0.22.2) (2025-12-02)
-
-
-### Bug Fixes
-
-* daily auto refresh fix ([#755](https://github.com/Monadical-SAS/reflector/issues/755)) ([fe47c46](https://github.com/Monadical-SAS/reflector/commit/fe47c46489c5aa0cc538109f7559cc9accb35c01))
-* Skip mixdown for multitrack ([#760](https://github.com/Monadical-SAS/reflector/issues/760)) ([b51b7aa](https://github.com/Monadical-SAS/reflector/commit/b51b7aa9176c1a53ba57ad99f5e976c804a1e80c))
-
-## [0.22.1](https://github.com/Monadical-SAS/reflector/compare/v0.22.0...v0.22.1) (2025-11-27)
-
-
-### Bug Fixes
-
-* participants update from daily ([#749](https://github.com/Monadical-SAS/reflector/issues/749)) ([7f0b728](https://github.com/Monadical-SAS/reflector/commit/7f0b728991c1b9f9aae702c96297eae63b561ef5))
-
-## [0.22.0](https://github.com/Monadical-SAS/reflector/compare/v0.21.0...v0.22.0) (2025-11-26)
-
-
-### Features
-
-* Multitrack segmentation ([#747](https://github.com/Monadical-SAS/reflector/issues/747)) ([d63040e](https://github.com/Monadical-SAS/reflector/commit/d63040e2fdc07e7b272e85a39eb2411cd6a14798))
-
-## [0.21.0](https://github.com/Monadical-SAS/reflector/compare/v0.20.0...v0.21.0) (2025-11-26)
-
-
-### Features
-
-* add transcript format parameter to GET endpoint ([#709](https://github.com/Monadical-SAS/reflector/issues/709)) ([f6ca075](https://github.com/Monadical-SAS/reflector/commit/f6ca07505f34483b02270a2ef3bd809e9d2e1045))
-
-## [0.20.0](https://github.com/Monadical-SAS/reflector/compare/v0.19.0...v0.20.0) (2025-11-25)
-
-
-### Features
-
-* link transcript participants ([#737](https://github.com/Monadical-SAS/reflector/issues/737)) ([9bec398](https://github.com/Monadical-SAS/reflector/commit/9bec39808fc6322612d8b87e922a6f7901fc01c1))
-* transcript restart script ([#742](https://github.com/Monadical-SAS/reflector/issues/742)) ([86d5e26](https://github.com/Monadical-SAS/reflector/commit/86d5e26224bb55a0f1cc785aeda52065bb92ee6f))
-
-## [0.19.0](https://github.com/Monadical-SAS/reflector/compare/v0.18.0...v0.19.0) (2025-11-25)
-
-
-### Features
-
-* dailyco api module ([#725](https://github.com/Monadical-SAS/reflector/issues/725)) ([4287f8b](https://github.com/Monadical-SAS/reflector/commit/4287f8b8aeee60e51db7539f4dcbda5f6e696bd8))
-* dailyco poll ([#730](https://github.com/Monadical-SAS/reflector/issues/730)) ([8e438ca](https://github.com/Monadical-SAS/reflector/commit/8e438ca285152bd48fdc42767e706fb448d3525c))
-* multitrack cli ([#735](https://github.com/Monadical-SAS/reflector/issues/735)) ([11731c9](https://github.com/Monadical-SAS/reflector/commit/11731c9d38439b04e93b1c3afbd7090bad11a11f))
-
-
-### Bug Fixes
-
-* default platform fix ([#736](https://github.com/Monadical-SAS/reflector/issues/736)) ([c442a62](https://github.com/Monadical-SAS/reflector/commit/c442a627873ca667656eeaefb63e54ab10b8d19e))
-* parakeet vad not getting the end timestamp ([#728](https://github.com/Monadical-SAS/reflector/issues/728)) ([18ed713](https://github.com/Monadical-SAS/reflector/commit/18ed7133693653ef4ddac6c659a8c14b320d1657))
-* start raw tracks recording ([#729](https://github.com/Monadical-SAS/reflector/issues/729)) ([3e47c2c](https://github.com/Monadical-SAS/reflector/commit/3e47c2c0573504858e0d2e1798b6ed31f16b4a5d))
-
-## [0.18.0](https://github.com/Monadical-SAS/reflector/compare/v0.17.0...v0.18.0) (2025-11-14)
-
-
-### Features
-
-* daily QOL: participants dictionary ([#721](https://github.com/Monadical-SAS/reflector/issues/721)) ([b20cad7](https://github.com/Monadical-SAS/reflector/commit/b20cad76e69fb6a76405af299a005f1ddcf60eae))
-
-
-### Bug Fixes
-
-* add proccessing page to file upload and reprocessing ([#650](https://github.com/Monadical-SAS/reflector/issues/650)) ([28a7258](https://github.com/Monadical-SAS/reflector/commit/28a7258e45317b78e60e6397be2bc503647eaace))
-* copy transcript ([#674](https://github.com/Monadical-SAS/reflector/issues/674)) ([a9a4f32](https://github.com/Monadical-SAS/reflector/commit/a9a4f32324f66c838e081eee42bb9502f38c1db1))
-
-## [0.17.0](https://github.com/Monadical-SAS/reflector/compare/v0.16.0...v0.17.0) (2025-11-13)
-
-
-### Features
-
-* add API key management UI ([#716](https://github.com/Monadical-SAS/reflector/issues/716)) ([372202b](https://github.com/Monadical-SAS/reflector/commit/372202b0e1a86823900b0aa77be1bfbc2893d8a1))
-* daily.co support as alternative to whereby ([#691](https://github.com/Monadical-SAS/reflector/issues/691)) ([1473fd8](https://github.com/Monadical-SAS/reflector/commit/1473fd82dc472c394cbaa2987212ad662a74bcac))
-
-## [0.16.0](https://github.com/Monadical-SAS/reflector/compare/v0.15.0...v0.16.0) (2025-10-24)
-
-
-### Features
-
-* search date filter ([#710](https://github.com/Monadical-SAS/reflector/issues/710)) ([962c40e](https://github.com/Monadical-SAS/reflector/commit/962c40e2b6428ac42fd10aea926782d7a6f3f902))
-
-## [0.15.0](https://github.com/Monadical-SAS/reflector/compare/v0.14.0...v0.15.0) (2025-10-20)
-
-
-### Features
-
-* api tokens  ([#705](https://github.com/Monadical-SAS/reflector/issues/705)) ([9a258ab](https://github.com/Monadical-SAS/reflector/commit/9a258abc0209b0ac3799532a507ea6a9125d703a))
-
-## [0.14.0](https://github.com/Monadical-SAS/reflector/compare/v0.13.1...v0.14.0) (2025-10-08)
-
-
-### Features
-
-* Add calendar event data to transcript webhook payload ([#689](https://github.com/Monadical-SAS/reflector/issues/689)) ([5f6910e](https://github.com/Monadical-SAS/reflector/commit/5f6910e5131b7f28f86c9ecdcc57fed8412ee3cd))
-* container build for www / github ([#672](https://github.com/Monadical-SAS/reflector/issues/672)) ([969bd84](https://github.com/Monadical-SAS/reflector/commit/969bd84fcc14851d1a101412a0ba115f1b7cde82))
-* docker-compose for production frontend ([#664](https://github.com/Monadical-SAS/reflector/issues/664)) ([5bf64b5](https://github.com/Monadical-SAS/reflector/commit/5bf64b5a41f64535e22849b4bb11734d4dbb4aae))
-
-
-### Bug Fixes
-
-* restore feature boolean logic ([#671](https://github.com/Monadical-SAS/reflector/issues/671)) ([3660884](https://github.com/Monadical-SAS/reflector/commit/36608849ec64e953e3be456172502762e3c33df9))
-* security review ([#656](https://github.com/Monadical-SAS/reflector/issues/656)) ([5d98754](https://github.com/Monadical-SAS/reflector/commit/5d98754305c6c540dd194dda268544f6d88bfaf8))
-* update transcript list on reprocess ([#676](https://github.com/Monadical-SAS/reflector/issues/676)) ([9a71af1](https://github.com/Monadical-SAS/reflector/commit/9a71af145ee9b833078c78d0c684590ab12e9f0e))
-* upgrade nemo toolkit ([#678](https://github.com/Monadical-SAS/reflector/issues/678)) ([eef6dc3](https://github.com/Monadical-SAS/reflector/commit/eef6dc39037329b65804297786d852dddb0557f9))
-
-## [0.13.1](https://github.com/Monadical-SAS/reflector/compare/v0.13.0...v0.13.1) (2025-09-22)
-
-
-### Bug Fixes
-
-* TypeError on not all arguments converted during string formatting in logger ([#667](https://github.com/Monadical-SAS/reflector/issues/667)) ([565a629](https://github.com/Monadical-SAS/reflector/commit/565a62900f5a02fc946b68f9269a42190ed70ab6))
-
-## [0.13.0](https://github.com/Monadical-SAS/reflector/compare/v0.12.1...v0.13.0) (2025-09-19)
-
-
-### Features
-
-* room form edit with enter ([#662](https://github.com/Monadical-SAS/reflector/issues/662)) ([47716f6](https://github.com/Monadical-SAS/reflector/commit/47716f6e5ddee952609d2fa0ffabdfa865286796))
-
-
-### Bug Fixes
-
-* invalid cleanup call ([#660](https://github.com/Monadical-SAS/reflector/issues/660)) ([0abcebf](https://github.com/Monadical-SAS/reflector/commit/0abcebfc9491f87f605f21faa3e53996fafedd9a))
-
-## [0.12.1](https://github.com/Monadical-SAS/reflector/compare/v0.12.0...v0.12.1) (2025-09-17)
-
-
-### Bug Fixes
-
-* production blocked because having existing meeting with room_id null ([#657](https://github.com/Monadical-SAS/reflector/issues/657)) ([870e860](https://github.com/Monadical-SAS/reflector/commit/870e8605171a27155a9cbee215eeccb9a8d6c0a2))
-
-## [0.12.0](https://github.com/Monadical-SAS/reflector/compare/v0.11.0...v0.12.0) (2025-09-17)
-
-
-### Features
-
-* calendar integration ([#608](https://github.com/Monadical-SAS/reflector/issues/608)) ([6f680b5](https://github.com/Monadical-SAS/reflector/commit/6f680b57954c688882c4ed49f40f161c52a00a24))
-* self-hosted gpu api ([#636](https://github.com/Monadical-SAS/reflector/issues/636)) ([ab859d6](https://github.com/Monadical-SAS/reflector/commit/ab859d65a6bded904133a163a081a651b3938d42))
-
-
-### Bug Fixes
-
-* ignore player hotkeys for text inputs ([#646](https://github.com/Monadical-SAS/reflector/issues/646)) ([fa049e8](https://github.com/Monadical-SAS/reflector/commit/fa049e8d068190ce7ea015fd9fcccb8543f54a3f))
-
-## [0.11.0](https://github.com/Monadical-SAS/reflector/compare/v0.10.0...v0.11.0) (2025-09-16)
-
-
-### Features
-
-* remove profanity filter that was there for conference ([#652](https://github.com/Monadical-SAS/reflector/issues/652)) ([b42f7cf](https://github.com/Monadical-SAS/reflector/commit/b42f7cfc606783afcee792590efcc78b507468ab))
-
-
-### Bug Fixes
-
-* zulip and consent handler on the file pipeline ([#645](https://github.com/Monadical-SAS/reflector/issues/645)) ([5f143fe](https://github.com/Monadical-SAS/reflector/commit/5f143fe3640875dcb56c26694254a93189281d17))
-* zulip stream and topic selection in share dialog ([#644](https://github.com/Monadical-SAS/reflector/issues/644)) ([c546e69](https://github.com/Monadical-SAS/reflector/commit/c546e69739e68bb74fbc877eb62609928e5b8de6))
-
 ## [0.10.0](https://github.com/Monadical-SAS/reflector/compare/v0.9.0...v0.10.0) (2025-09-11)


--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -151,7 +151,7 @@ All endpoints prefixed `/v1/`:

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

 ## Testing Strategy
--- a/Caddyfile.example
+++ b/Caddyfile.example
@@ -1,22 +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/README.md
+++ b/README.md
@@ -168,19 +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.
-
-Instead, all the variables are runtime. Variables needed to the frontend are served to the frontend app at initial render.
-
-It also means there's no static prebuild and no static files to serve for js/html.

 ## Feature Flags

@@ -190,24 +177,24 @@ Reflector uses environment variable-based feature flags to control application f

 | Feature Flag | Environment Variable |
 |-------------|---------------------|
-| `requireLogin` | `FEATURE_REQUIRE_LOGIN` |
-| `privacy` | `FEATURE_PRIVACY` |
-| `browse` | `FEATURE_BROWSE` |
-| `sendToZulip` | `FEATURE_SEND_TO_ZULIP` |
-| `rooms` | `FEATURE_ROOMS` |
+| `requireLogin` | `NEXT_PUBLIC_FEATURE_REQUIRE_LOGIN` |
+| `privacy` | `NEXT_PUBLIC_FEATURE_PRIVACY` |
+| `browse` | `NEXT_PUBLIC_FEATURE_BROWSE` |
+| `sendToZulip` | `NEXT_PUBLIC_FEATURE_SEND_TO_ZULIP` |
+| `rooms` | `NEXT_PUBLIC_FEATURE_ROOMS` |

 ### Setting Feature Flags

-Feature flags are controlled via environment variables using the pattern `FEATURE_{FEATURE_NAME}` where `{FEATURE_NAME}` is the SCREAMING_SNAKE_CASE version of the feature name.
+Feature flags are controlled via environment variables using the pattern `NEXT_PUBLIC_FEATURE_{FEATURE_NAME}` where `{FEATURE_NAME}` is the SCREAMING_SNAKE_CASE version of the feature name.

 **Examples:**
 ```bash
 # Enable user authentication requirement
-FEATURE_REQUIRE_LOGIN=true
+NEXT_PUBLIC_FEATURE_REQUIRE_LOGIN=true

 # Disable browse functionality
-FEATURE_BROWSE=false
+NEXT_PUBLIC_FEATURE_BROWSE=false

 # Enable Zulip integration
-FEATURE_SEND_TO_ZULIP=true
+NEXT_PUBLIC_FEATURE_SEND_TO_ZULIP=true
 ```
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -39,7 +39,7 @@ services:
    ports:
      - 6379:6379
  web:
-    image: node:22-alpine
+    image: node:18
    ports:
      - "3000:3000"
    command: sh -c "corepack enable && pnpm install && pnpm dev"
@@ -50,8 +50,6 @@ services:
      - /app/node_modules
    env_file:
      - ./www/.env.local
-    environment:
-      - NODE_ENV=development

  postgres:
    image: postgres:17
--- a/docker-compose.prod.yml
+++ b/docker-compose.prod.yml
@@ -1,113 +0,0 @@
-# Production Docker Compose configuration
-# 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
-    restart: unless-stopped
-    env_file:
-      - ./www/.env
-    pull_policy: always
-    environment:
-      - KV_URL=redis://redis:6379
-    depends_on:
-      - redis
-
-  server:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: server
-    depends_on:
-      - postgres
-      - redis
-    volumes:
-      - server_data:/app/data
-      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-  worker:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: worker
-    depends_on:
-      - postgres
-      - redis
-    volumes:
-      - server_data:/app/data
-      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-  beat:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: beat
-    depends_on:
-      - postgres
-      - redis
-
-  redis:
-    image: redis:7.2-alpine
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "redis-cli", "ping"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-    volumes:
-      - redis_data:/data
-
-  postgres:
-    image: postgres:17-alpine
-    restart: unless-stopped
-    environment:
-      POSTGRES_USER: reflector
-      POSTGRES_PASSWORD: reflector
-      POSTGRES_DB: reflector
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U reflector"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-
-  caddy:
-    image: caddy:2-alpine
-    restart: unless-stopped
-    ports:
-      - "80:80"
-      - "443:443"
-    volumes:
-      - ./Caddyfile:/etc/caddy/Caddyfile:ro
-      - caddy_data:/data
-      - caddy_config:/config
-    depends_on:
-      - web
-      - server
-
-  docs:
-    build: ./docs
-    restart: unless-stopped
-
-volumes:
-  redis_data:
-  postgres_data:
-  server_data:
-  caddy_data:
-  caddy_config:
-
-networks:
-  default:
-    attachable: true
--- a/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
@@ -1,170 +0,0 @@
-# Documentation TODO List
-
-This file tracks information needed from the user to complete the documentation.
-
-## Required Information
-
-### Processing Times & Costs
-
-Please provide actual numbers for:
-
- [ ] **Modal.com GPU Costs**
-  - Cost per hour of audio for Whisper transcription
-  - Cost per hour of audio for Pyannote diarization
-  - Cost per hour of audio for Seamless-M4T translation
-  - Typical GPU instance used (T4, A10, etc.)
-
- [ ] **RunPod LLM Costs**
-  - Cost per 1000 tokens for summarization
-  - Model used (phi-4-unsloth-bnb-4bit)
-  - RTX 4000 Ada instance cost per hour
-
- [ ] **AWS S3 Storage**
-  - Cost per GB per month
-  - Data transfer costs
-  - Typical storage requirements per hour of audio
-
- [ ] **Whereby API**
-  - Monthly cost structure
-  - API call limits
-  - Room participant limits
-
- [ ] **Actual Processing Times**
-  - Whisper tiny model: X minutes per hour of audio
-  - Whisper base model: X minutes per hour of audio
-  - Whisper large-v3 model: X minutes per hour of audio
-  - Diarization: X minutes per hour of audio
-  - Translation: X minutes per hour of audio
-
-### Screenshots Needed
-
-Location: `/docs/static/screenshots/`
-
-Please provide screenshots of:
-
- [ ] **Dashboard Overview** - Main dashboard showing recent transcripts
- [ ] **Live Transcription** - Active transcription in progress
- [ ] **Meeting Room Interface** - Whereby room with participants
- [ ] **Transcript with Diarization** - Showing speaker labels
- [ ] **Settings Page** - Configuration options
- [ ] **API Documentation** - OpenAPI/Swagger interface
- [ ] **File Upload Interface** - Drag and drop upload
- [ ] **Translation View** - Showing original and translated text
- [ ] **Summary View** - Generated summary and topics
-
-### Setup Screenshots
-
-Please provide step-by-step screenshots for:
-
- [ ] **Modal.com Setup**
-  - Creating account
-  - Getting API keys
-  - Deploying functions
-
- [ ] **Whereby Configuration**
-  - Creating developer account
-  - Getting API credentials
-  - Setting up rooms
-
- [ ] **AWS S3 Setup**
-  - Creating bucket
-  - Setting permissions
-  - Getting access keys
-
- [ ] **Authentik Integration**
-  - Adding application
-  - Configuring OAuth
-  - Setting up users
-
-### Technical Details
-
-Please provide specific values for:
-
- [ ] **WebRTC Configuration**
-  - Exact UDP port range used (e.g., 10000-20000)
-  - STUN server configuration (if any)
-  - ICE candidate gathering timeout
-  - https://docs.daily.co/guides/privacy-and-security/corporate-firewalls-nats-allowed-ip-list
-
- [ ] **Worker Configuration**
-  - Default Celery worker count
-  - Worker memory limits
-  - Queue priorities
-
- [ ] **Redis Requirements**
-  - Typical memory usage
-  - Persistence configuration
-  - Eviction policies
-
- [ ] **PostgreSQL**
-  - Expected database growth (MB per hour of audio)
-  - Recommended connection pool size
-  - Backup strategy
-
- [ ] **Performance Metrics**
-  - Average transcription accuracy (WER)
-  - Average diarization accuracy (DER)
-  - Translation quality scores
-  - Typical latency for live streaming
-
-### Configuration Examples
-
-Please provide real-world examples for:
-
- [ ] **Production .env file** (sanitized)
- [ ] **Caddy configuration** for production
- [ ] **Docker compose** for production deployment
- [ ] **Nginx configuration** (if alternative to Caddy)
-
-### API Examples
-
-Please provide:
-
- [ ] **Sample API requests** for common operations
- [ ] **WebSocket message examples**
- [ ] **Webhook payload examples**
- [ ] **Error response examples**
-
-## How to Add Information
-
-1. **For text information**: Edit the relevant markdown files in `/docs/docs/`
-2. **For screenshots**: Add to `/docs/static/screenshots/` and reference in docs
-3. **For code examples**: Add to documentation with proper syntax highlighting
-
-## Priority Items
-
-High priority (blocks documentation completeness):
-1. Modal.com costs and setup steps
-2. Basic screenshots (dashboard, transcription)
-3. Docker deployment configuration
-
-Medium priority (enhances documentation):
-1. Performance metrics
-2. Advanced configuration examples
-3. Troubleshooting scenarios
-
-Low priority (nice to have):
-1. Video tutorials
-2. Architecture diagrams
-3. Benchmark comparisons
-
-## Documentation Structure
-
-Once information is provided, update these files:
- `/docs/docs/installation/modal-setup.md` - Add Modal.com setup screenshots
- `/docs/docs/installation/whereby-setup.md` - Add Whereby configuration steps
- `/docs/docs/reference/configuration.md` - Add environment variable details
- `/docs/docs/pipelines/file-pipeline.md` - Add actual processing times
- `/docs/docs/pipelines/live-pipeline.md` - Add latency measurements
-
-## Notes
-
- Replace placeholder values with actual data
- Ensure all sensitive information is sanitized
- Test all configuration examples before documenting
- Verify all costs are up-to-date
-
---
-
-Last updated: 2025-08-20
-Contact: [Your Email]
--- 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
@@ -1,125 +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 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 and Daily.co 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
- File size restrictions
- Automatic cleanup of old data
-
-### 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
--- a/docs/docs/concepts/overview.md
+++ b/docs/docs/concepts/overview.md
@@ -1,194 +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 14** 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
- 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
- Support for local GPU deployment (coming soon)
- Automatic scaling based on demand
- Cost-effective pay-per-use model
-
-## Data Flow
-
-### File Processing Flow
-
-1. **Upload**: User uploads audio file through web interface
-2. **Storage**: File stored temporarily or in S3
-3. **Queue**: Processing job added to Celery queue
-4. **Chunking**: Audio split into 30-second segments
-5. **Parallel Processing**: Chunks processed simultaneously
-6. **Assembly**: Results merged and aligned
-7. **Post-Processing**: Summary, topics, translation
-8. **Delivery**: Results stored and user notified
-
-### Live Streaming Flow
-
-1. **WebRTC Connection**: Browser establishes peer connection
-2. **Audio Capture**: Microphone audio streamed to server
-3. **Buffering**: Audio buffered for processing
-4. **VAD**: Voice activity detection segments speech
-5. **Real-time Processing**: Segments transcribed immediately
-6. **WebSocket Updates**: Results streamed back to client
-7. **Continuous Assembly**: Full transcript built progressively
-
-## Deployment Architecture
-
-### Container-Based Deployment
-
-All components are containerized for consistent deployment:
-
-```yaml
-services:
-  frontend:    # Next.js application
-  backend:     # 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
- **CPU Optimization**: Multi-threaded processing where applicable
-
-## Security Architecture
-
-### Authentication & Authorization
-
- **JWT Tokens**: Stateless authentication
- **Authentik Integration**: Enterprise SSO support
- **Role-Based Access**: Granular permissions
-
-### Data Protection
-
- **Encryption at Rest**: Database and S3 encryption
- **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
@@ -1,272 +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**: 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:
- **Fixed Size**: 30-second chunks by default
- **Overlap**: 1-second overlap for continuity
- **Silence Detection**: 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
-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
-
-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
--- 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
-pip 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: ~$0.02-0.10 per minute of audio
-
-## Troubleshooting
-
-### "Modal CLI not installed"
-```bash
-pip install modal
-```
-
-### "Not authenticated with Modal"
-```bash
-modal setup
-# Complete browser authentication
-```
-
-### "Failed to create secret hf_token"
- Verify your HuggingFace token is valid
- Ensure you've accepted the Pyannote license
- Token needs `read` permission
-
-### Deployment fails
-Check the Modal dashboard for detailed error logs:
- Visit https://modal.com/apps
- Click on the failed function
- View build and runtime logs
-
-### Re-running deployment
-The script is safe to re-run. It will:
- Update existing secrets if they exist
- Redeploy functions with latest code
- Output new configuration (API key stays the same if secret exists)
-
-## Manual Deployment (Advanced)
-
-If you prefer to deploy functions individually:
-
-```bash
-cd gpu/modal_deployments
-
-# Create secrets manually
-modal secret create hf_token HF_TOKEN=your-hf-token
-modal secret create reflector-gpu REFLECTOR_GPU_APIKEY=$(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
@@ -1,401 +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
-pip 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 (Docker) or 25-30GB (Systemd).
-
-See [Self-Hosted GPU Setup](./self-hosted-gpu-setup) for complete instructions. Quick summary:
-
-1. Install NVIDIA drivers and Docker (or uv for systemd)
-2. Clone repository: `git clone https://github.com/monadical-sas/reflector.git`
-3. Configure `.env` with HuggingFace token
-4. Start service (Docker compose or systemd)
-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 $USER
-
-# Log out and back in for group changes
-exit
-ssh user@your-server-ip
-
-docker --version  # verify
-```
-
-### Firewall
-
-Ensure ports 80 (HTTP) and 443 (HTTPS) are open for inbound traffic. The method varies by cloud provider and OS configuration.
-
-### Clone Repository
-
-The Docker images contain all application code. You clone the repository for configuration files and the compose definition:
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector
-```
-
---
-
-## Create S3 Bucket for Transcript Storage
-
-Reflector requires AWS S3 to store audio files during processing.
-
-### Create Bucket
-
-```bash
-# Choose a unique bucket name
-BUCKET_NAME="reflector-transcripts-yourname"
-AWS_REGION="us-east-1"
-
-# Create bucket
-aws s3 mb s3://$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 PostgreSQL to be ready, then run migrations:
-
-```bash
-# Wait for postgres to be healthy (may take 30-60 seconds on first run)
-docker compose -f docker-compose.prod.yml exec postgres pg_isready -U reflector
-
-# Run database migrations
-docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
-```
-
---
-
-## 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
-
---
-
-## 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`
-
--- a/docs/docs/installation/requirements.md
+++ b/docs/docs/installation/requirements.md
@@ -1,60 +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
- **OpenAI API key** - For generating summaries and topic detection (https://platform.openai.com/account/api-keys)
-
-### 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
@@ -1,465 +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 /diarize` - Pyannote speaker diarization
-
-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**:
-  - Docker method: 40-50GB minimum
-  - Systemd method: 25-30GB 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
-
-## Choose Deployment Method
-
---
-
-## Docker Deployment
-
-### Step 1: Install NVIDIA Driver
-
-```bash
-sudo apt update
-sudo apt install -y nvidia-driver-535
-
-# Load kernel modules
-sudo modprobe nvidia
-
-# Verify installation
-nvidia-smi
-```
-
-Expected output: GPU details with driver version and CUDA version.
-
-### Step 2: Install Docker
-
-```bash
-curl -fsSL https://get.docker.com | sudo sh
-sudo usermod -aG docker $USER
-
-# Log out and back in for group changes
-exit
-# SSH back in
-```
-
-### Step 3: Install NVIDIA Container Toolkit
-
-```bash
-# Add NVIDIA repository
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
-  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-
-curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-
-# Install toolkit
-sudo apt-get update
-sudo apt-get install -y nvidia-container-toolkit
-
-# Configure Docker runtime
-sudo nvidia-ctk runtime configure --runtime=docker
-sudo systemctl restart docker
-```
-
-### Step 4: Clone Repository and Configure
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/self_hosted
-
-# Create environment file
-cat > .env << EOF
-REFLECTOR_GPU_APIKEY=$(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: Create Docker Compose File
-
-```bash
-cat > compose.yml << 'EOF'
-services:
-  reflector_gpu:
-    build:
-      context: .
-    ports:
-      - "8000:8000"
-    env_file:
-      - .env
-    volumes:
-      - ./cache:/root/.cache
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    restart: unless-stopped
-EOF
-```
-
-### Step 6: 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 $(sudo docker ps -q) nvidia-smi
-```
-
-Should show GPU with ~3GB VRAM used (models loaded).
-
---
-
-## Systemd Deployment
-
-### Step 1: Install NVIDIA Driver
-
-```bash
-sudo apt update
-sudo apt install -y nvidia-driver-535
-
-# Load kernel modules
-sudo modprobe nvidia
-
-# Verify installation
-nvidia-smi
-```
-
-### Step 2: Install Dependencies
-
-```bash
-# Install ffmpeg
-sudo apt install -y ffmpeg
-
-# Install uv package manager
-curl -LsSf https://astral.sh/uv/install.sh | sh
-source ~/.local/bin/env
-
-# Clone repository
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/self_hosted
-```
-
-### Step 3: Configure Environment
-
-```bash
-# Create environment file
-cat > .env << EOF
-REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
-HF_TOKEN=your_huggingface_token_here
-EOF
-
-# Note the generated API key
-cat .env
-```
-
-### Step 4: Install Python Packages
-
-```bash
-# Install dependencies (~3GB download)
-uv sync
-```
-
-### Step 5: Create Systemd Service
-
-```bash
-# Generate library paths for NVIDIA packages
-export NVIDIA_LIBS=$(find ~/reflector/gpu/self_hosted/.venv/lib/python3.12/site-packages/nvidia -name lib -type d | tr '\n' ':')
-
-# Load environment variables
-source ~/reflector/gpu/self_hosted/.env
-
-# Create service file
-sudo tee /etc/systemd/system/reflector-gpu.service << EOFSVC
-[Unit]
-Description=Reflector GPU Service (Transcription & Diarization)
-After=network.target
-
-[Service]
-Type=simple
-User=$USER
-WorkingDirectory=$HOME/reflector/gpu/self_hosted
-Environment="PATH=$HOME/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
-Environment="HF_TOKEN=${HF_TOKEN}"
-Environment="REFLECTOR_GPU_APIKEY=${REFLECTOR_GPU_APIKEY}"
-Environment="LD_LIBRARY_PATH=${NVIDIA_LIBS}"
-ExecStart=$HOME/reflector/gpu/self_hosted/.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-Restart=always
-RestartSec=10
-
-[Install]
-WantedBy=multi-user.target
-EOFSVC
-
-# Enable and start
-sudo systemctl daemon-reload
-sudo systemctl enable reflector-gpu
-sudo systemctl start reflector-gpu
-```
-
-### Step 6: Verify Service
-
-```bash
-# Check status
-sudo systemctl status reflector-gpu
-
-# View logs
-sudo journalctl -u reflector-gpu -f
-```
-
-Look for: `INFO: Application startup complete.`
-
---
-
-## Configure HTTPS with Caddy
-
-Both deployment methods need HTTPS for production. 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
-
-```bash
-sudo tee /etc/caddy/Caddyfile << 'EOF'
-gpu.example.com {
-    reverse_proxy localhost:8000
-}
-EOF
-
-# Reload Caddy (auto-provisions SSL certificate)
-sudo systemctl reload caddy
-```
-
-Replace `gpu.example.com` with your domain.
-
-### 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/`.
-
-### Docker
-
-```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
-```
-
-### Systemd
-
-```bash
-# View logs
-sudo journalctl -u reflector-gpu -f
-
-# Restart service
-sudo systemctl restart reflector-gpu
-
-# Stop service
-sudo systemctl stop reflector-gpu
-
-# Check status
-sudo systemctl status reflector-gpu
-```
-
-### 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. Regenerate service/compose with correct token
-4. Restart service
-
-### cuDNN library loading errors (Systemd only)
-
-Symptom: `Unable to load libcudnn_cnn.so`
-
-Regenerate the systemd service file - the `LD_LIBRARY_PATH` must include all NVIDIA package directories.
-
-### 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
-
-### Docker
-
-```bash
-cd ~/reflector/gpu/self_hosted
-git pull
-sudo docker compose build
-sudo docker compose up -d
-```
-
-### Systemd
-
-```bash
-cd ~/reflector/gpu/self_hosted
-git pull
-uv sync
-sudo systemctl restart reflector-gpu
-```
--- a/docs/docs/installation/whereby-setup.md
+++ b/docs/docs/installation/whereby-setup.md
@@ -1,7 +0,0 @@
---
-title: whereby setup
---
-
-# whereby setup
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/installation/zulip-setup.md
+++ b/docs/docs/installation/zulip-setup.md
@@ -1,7 +0,0 @@
---
-title: zulip setup
---
-
-# zulip setup
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- 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, translation, 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
- **Live Translation**: Translate audio content in real-time to 100+ languages with [Facebook Seamless-M4T](https://github.com/facebookresearch/seamless_communication)
- **Topic Detection & Summarization**: Extract key topics and generate concise summaries using LLMs
- **Meeting Recording**: Create permanent records of meetings with searchable transcripts
-
-## Features
-
-| Feature | Public Mode | Private Mode |
-|---------|------------|--------------|
-| **Authentication** | None required | Required |
-| **Audio Upload** | ✅ | ✅ |
-| **Live Microphone Streaming** | ✅ | ✅ |
-| **Transcription** | ✅ | ✅ |
-| **Speaker Diarization** | ✅ | ✅ |
-| **Translation** | ✅ | ✅ |
-| **Topic Detection** | ✅ | ✅ |
-| **Summarization** | ✅ | ✅ |
-| **Virtual Meeting Rooms (Whereby)** | ❌ | ✅ |
-| **Browse Transcripts Page** | ❌ | ✅ |
-| **Search Functionality** | ❌ | ✅ |
-| **Persistent Storage** | ❌ | ✅ |
-
-## Architecture Overview
-
-Reflector consists of three main components:
-
- **Frontend**: React application built with Next.js 14
- **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
@@ -1,343 +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:**
- Maximum size: 2GB (configurable)
- Minimum duration: 5 seconds
- Maximum duration: 6 hours
- Sample rate: Any (will be resampled)
-
-### 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
-
-**Default Configuration:**
-```yaml
-chunk_size: 30  # seconds
-overlap: 1      # seconds
-max_parallel: 10
-silence_detection: true
-```
-
-**Chunking with Silence Detection:**
- Detects silence periods
- Attempts to break at natural pauses
- Maintains context with overlap
- Preserves sentence boundaries
-
-**Chunk Metadata:**
-```json
-{
-  "chunk_id": "chunk_001",
-  "start_time": 0.0,
-  "end_time": 30.0,
-  "duration": 30.0,
-  "has_speech": true,
-  "audio_hash": "sha256:..."
-}
-```
-
-### 4. Transcription Processing
-
-**Whisper Models:**
-
-| Model | Size | Speed | Accuracy | Use Case |
-|-------|------|-------|----------|----------|
-| tiny | 39M | Very Fast | 85% | Quick drafts |
-| base | 74M | Fast | 89% | Good balance |
-| small | 244M | Medium | 91% | Better accuracy |
-| medium | 769M | Slow | 93% | High quality |
-| large-v3 | 1550M | Very Slow | 96% | Best quality |
-
-**Processing Configuration:**
-```python
-transcription_config = {
-    "model": "whisper-base",
-    "language": "auto",  # or specify: "en", "es", etc.
-    "task": "transcribe",  # or "translate"
-    "temperature": 0,  # deterministic
-    "compression_ratio_threshold": 2.4,
-    "no_speech_threshold": 0.6,
-    "condition_on_previous_text": True,
-    "initial_prompt": None,  # optional context
-}
-```
-
-**Parallel Processing:**
- Each chunk processed independently
- GPU batching for efficiency
- Automatic load balancing
- Failure isolation
-
-### 5. Diarization (Speaker Identification)
-
-**Pyannote 3.1 Pipeline:**
-
-1. **Voice Activity Detection (VAD)**
-   - Identifies speech segments
-   - Filters out silence and noise
-   - Precision: 95%+
-
-2. **Speaker Embedding**
-   - Extracts voice characteristics
-   - 256-dimensional vectors
-   - Speaker-invariant features
-
-3. **Clustering**
-   - Groups similar voice embeddings
-   - Agglomerative clustering
-   - Automatic speaker count detection
-
-4. **Segmentation**
-   - Assigns speaker labels to time segments
-   - Handles overlapping speech
-   - Minimum segment duration: 0.5s
-
-**Configuration:**
-```python
-diarization_config = {
-    "min_speakers": 1,
-    "max_speakers": 10,
-    "min_duration": 0.5,
-    "clustering": "AgglomerativeClustering",
-    "embedding_model": "speechbrain/spkrec-ecapa-voxceleb",
-}
-```
-
-### 6. Alignment & Merging
-
-**Chunk Assembly:**
-```python
-# Merge overlapping segments
-for chunk in chunks:
-    # Remove overlap duplicates
-    if chunk.start < previous.end:
-        chunk.text = resolve_overlap(previous, chunk)
-
-    # Maintain timeline
-    merged_transcript.append(chunk)
-```
-
-**Speaker Alignment:**
- Map diarization timeline to transcript
- Resolve speaker changes mid-sentence
- Handle multiple speakers per segment
-
-**Quality Checks:**
- Timeline consistency
- No gaps in transcript
- Speaker label continuity
- Confidence score validation
-
-### 7. Post-processing Chain
-
-**Text Formatting:**
- Sentence capitalization
- Punctuation restoration
- Number formatting
- Acronym detection
-
-**Translation (Optional):**
-```python
-translation_config = {
-    "model": "facebook/seamless-m4t-medium",
-    "source_lang": "auto",
-    "target_langs": ["es", "fr", "de"],
-    "preserve_formatting": True
-}
-```
-
-**Topic Detection:**
- LLM-based analysis
- Extract 3-5 key topics
- Keyword extraction
- Entity recognition
-
-**Summarization:**
-```python
-summary_config = {
-    "model": "openai-compatible",
-    "max_length": 500,
-    "style": "bullets",  # or "paragraph"
-    "include_action_items": True,
-    "include_decisions": True
-}
-```
-
-### 8. Storage & Delivery
-
-**Database Storage:**
-```sql
-- Main transcript record
-INSERT INTO transcripts (
-    id, title, duration, language,
-    transcript_text, transcript_json,
-    speakers, topics, summary,
-    created_at, processing_time
-) VALUES (...);
-
-- Processing metadata
-INSERT INTO processing_metadata (
-    transcript_id, model_versions,
-    chunk_count, total_chunks,
-    error_count, warnings
-) VALUES (...);
-```
-
-**File Storage:**
- Original audio: S3 (optional)
- Processed chunks: Temporary (24h)
- Transcript exports: JSON, SRT, VTT, TXT
-
-**Notification:**
-```json
-{
-  "type": "webhook",
-  "url": "https://your-app.com/webhook",
-  "payload": {
-    "transcript_id": "...",
-    "status": "completed",
-    "duration": 3600,
-    "processing_time": 180
-  }
-}
-```
-
-## Processing Times
-
-**Estimated times for 1 hour of audio:**
-
-| Component | Fast Mode | Balanced | High Quality |
-|-----------|-----------|----------|--------------|
-| Pre-processing | 10s | 10s | 10s |
-| Transcription | 60s | 180s | 600s |
-| Diarization | 30s | 60s | 120s |
-| Post-processing | 20s | 30s | 60s |
-| **Total** | **2 min** | **5 min** | **13 min** |
-
-## Error Handling
-
-### Retry Strategy
-
-```python
-@celery.task(
-    bind=True,
-    max_retries=3,
-    default_retry_delay=60,
-    retry_backoff=True
-)
-def process_chunk(self, chunk_id):
-    try:
-        # Process chunk
-        result = transcribe(chunk_id)
-    except Exception as exc:
-        # Exponential backoff
-        raise self.retry(exc=exc)
-```
-
-### Partial Recovery
-
- Continue with successful chunks
- Mark failed chunks in output
- Provide partial transcript
- Report processing issues
-
-### Fallback Options
-
-1. **Model Fallback:**
-   - If large model fails, try medium
-   - If GPU fails, try CPU
-   - If Modal fails, try local
-
-2. **Quality Degradation:**
-   - Reduce chunk size
-   - Disable post-processing
-   - Skip diarization if needed
-
-## Optimization Tips
-
-### For Speed
-
-1. Use smaller models (tiny/base)
-2. Increase parallel chunks
-3. Disable diarization
-4. Skip post-processing
-5. Use GPU acceleration
-
-### For Accuracy
-
-1. Use larger models (medium/large)
-2. Enable all pre-processing
-3. Reduce chunk size
-4. Enable silence detection
-5. Multiple pass processing
-
-### For Cost
-
-1. Use Modal spot instances
-2. Batch multiple files
-3. Cache common phrases
-4. Optimize chunk size
-5. Selective post-processing
-
-## Monitoring
-
-### Metrics to Track
-
-```python
-metrics = {
-    "processing_time": histogram,
-    "chunk_success_rate": gauge,
-    "model_accuracy": histogram,
-    "queue_depth": gauge,
-    "gpu_utilization": gauge,
-    "cost_per_hour": counter
-}
-```
-
-### Quality Metrics
-
- Word Error Rate (WER)
- Diarization Error Rate (DER)
- Confidence scores
- Processing speed
- User feedback
-
-### Alerts
-
- Processing time > 30 minutes
- Error rate > 5%
- Queue depth > 100
- GPU memory > 90%
- Cost spike detected
--- a/docs/docs/pipelines/live-pipeline.md
+++ b/docs/docs/pipelines/live-pipeline.md
@@ -1,7 +0,0 @@
---
-title: Live pipeline
---
-
-# Live pipeline
-
-Documentation coming soon.
--- a/docs/docs/pipelines/overview.md
+++ b/docs/docs/pipelines/overview.md
@@ -1,7 +0,0 @@
---
-title: overview
---
-
-# overview
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/api.md
+++ b/docs/docs/reference/api.md
@@ -1,441 +0,0 @@
---
-title: API Reference
---
-
-# API Reference
-
-The Reflector API provides a comprehensive RESTful interface for audio transcription, meeting management, and real-time streaming capabilities.
-
-## Base URL
-
-```
-http://localhost:8000/v1
-```
-
-All API endpoints are prefixed with `/v1/` for versioning.
-
-## Authentication
-
-Reflector supports multiple authentication modes:
-
- **No Authentication** (Public Mode): Basic transcription and upload functionality
- **JWT Authentication** (Private Mode): Full feature access including meeting rooms and persistent storage
- **OAuth/OIDC via Authentik**: Enterprise single sign-on integration
-
-## Core Endpoints
-
-### Transcripts
-
-Manage audio transcriptions and their associated metadata.
-
-#### List Transcripts
-```http
-GET /v1/transcripts/
-```
-
-Returns a paginated list of transcripts with filtering options.
-
-#### Create Transcript
-```http
-POST /v1/transcripts/
-```
-
-Create a new transcript from uploaded audio or initialize for streaming.
-
-#### Get Transcript
-```http
-GET /v1/transcripts/{transcript_id}
-```
-
-Retrieve detailed information about a specific transcript.
-
-#### Update Transcript
-```http
-PATCH /v1/transcripts/{transcript_id}
-```
-
-Update transcript metadata, summary, or processing status.
-
-#### Delete Transcript
-```http
-DELETE /v1/transcripts/{transcript_id}
-```
-
-Remove a transcript and its associated data.
-
-### Audio Processing
-
-#### Upload Audio
-```http
-POST /v1/transcripts_audio/{transcript_id}/upload
-```
-
-Upload an audio file for transcription processing.
-
-**Supported formats:**
- WAV, MP3, M4A, FLAC, OGG
- Maximum file size: 500MB
- Sample rates: 8kHz - 48kHz
-
-#### Download Audio
-```http
-GET /v1/transcripts_audio/{transcript_id}/download
-```
-
-Download the original or processed audio file.
-
-#### Stream Audio
-```http
-GET /v1/transcripts_audio/{transcript_id}/stream
-```
-
-Stream audio content with range support for progressive playback.
-
-### WebRTC Streaming
-
-Real-time audio streaming via WebRTC for live transcription.
-
-#### Initialize WebRTC Session
-```http
-POST /v1/transcripts_webrtc/{transcript_id}/offer
-```
-
-Create a WebRTC offer for establishing a peer connection.
-
-#### Complete WebRTC Handshake
-```http
-POST /v1/transcripts_webrtc/{transcript_id}/answer
-```
-
-Submit the WebRTC answer to complete connection setup.
-
-### WebSocket Streaming
-
-Real-time updates and live transcription via WebSocket.
-
-#### WebSocket Endpoint
-```ws
-ws://localhost:8000/v1/transcripts_websocket/{transcript_id}
-```
-
-Receive real-time transcription updates, speaker changes, and processing status.
-
-**Message Types:**
- `transcription`: New transcribed text segments
- `diarization`: Speaker identification updates
- `status`: Processing status changes
- `error`: Error notifications
-
-### Meetings
-
-Manage virtual meeting rooms and recordings.
-
-#### List Meetings
-```http
-GET /v1/meetings/
-```
-
-Get all meetings for the authenticated user.
-
-#### Create Meeting
-```http
-POST /v1/meetings/
-```
-
-Initialize a new meeting room with Whereby integration.
-
-#### Join Meeting
-```http
-POST /v1/meetings/{meeting_id}/join
-```
-
-Join an existing meeting and start recording.
-
-#### End Meeting
-```http
-POST /v1/meetings/{meeting_id}/end
-```
-
-End the meeting and finalize the recording.
-
-### Rooms
-
-Virtual meeting room configuration and management.
-
-#### List Rooms
-```http
-GET /v1/rooms/
-```
-
-Get available meeting rooms.
-
-#### Create Room
-```http
-POST /v1/rooms/
-```
-
-Create a new persistent meeting room.
-
-#### Update Room Settings
-```http
-PATCH /v1/rooms/{room_id}
-```
-
-Modify room configuration and permissions.
-
-## Response Formats
-
-### Success Response
-```json
-{
-  "id": "uuid",
-  "created_at": "2025-01-20T10:00:00Z",
-  "updated_at": "2025-01-20T10:30:00Z",
-  "data": {...}
-}
-```
-
-### Error Response
-```json
-{
-  "error": {
-    "code": "ERROR_CODE",
-    "message": "Human-readable error message",
-    "details": {...}
-  }
-}
-```
-
-### Status Codes
-
- `200 OK`: Successful request
- `201 Created`: Resource created successfully
- `204 No Content`: Successful deletion
- `400 Bad Request`: Invalid request parameters
- `401 Unauthorized`: Authentication required
- `403 Forbidden`: Insufficient permissions
- `404 Not Found`: Resource not found
- `409 Conflict`: Resource conflict
- `422 Unprocessable Entity`: Validation error
- `429 Too Many Requests`: Rate limit exceeded
- `500 Internal Server Error`: Server error
-
-## WebSocket Protocol
-
-The WebSocket connection provides real-time updates during transcription processing. The server sends structured messages to communicate different events and data updates.
-
-### Connection
-```javascript
-const ws = new WebSocket('ws://localhost:8000/v1/transcripts_websocket/{transcript_id}');
-```
-
-### Message Types and Formats
-
-#### Transcription Update
-Sent when new text is transcribed from the audio stream.
-```json
-{
-  "type": "transcription",
-  "data": {
-    "text": "The transcribed text segment",
-    "speaker": "Speaker 1",
-    "timestamp": 1705745623.456,
-    "confidence": 0.95,
-    "segment_id": "seg_001",
-    "is_final": true
-  }
-}
-```
-
-#### Diarization Update
-Sent when speaker changes are detected or speaker labels are updated.
-```json
-{
-  "type": "diarization",
-  "data": {
-    "speaker": "Speaker 2",
-    "speaker_id": "spk_002",
-    "start_time": 1705745620.123,
-    "end_time": 1705745625.456,
-    "confidence": 0.87
-  }
-}
-```
-
-#### Processing Status
-Sent to indicate changes in the processing pipeline status.
-```json
-{
-  "type": "status",
-  "data": {
-    "status": "processing",
-    "stage": "transcription",
-    "progress": 45.5,
-    "message": "Processing audio chunk 12 of 26"
-  }
-}
-```
-
-Status values:
- `initializing`: Setting up processing pipeline
- `processing`: Active transcription/diarization
- `completed`: Processing finished successfully
- `failed`: Processing encountered an error
- `paused`: Processing temporarily suspended
-
-#### Summary Update
-Sent when AI-generated summaries or topics are available.
-```json
-{
-  "type": "summary",
-  "data": {
-    "summary": "Brief summary of the conversation",
-    "topics": ["topic1", "topic2", "topic3"],
-    "action_items": ["action 1", "action 2"],
-    "key_points": ["point 1", "point 2"]
-  }
-}
-```
-
-#### Error Messages
-Sent when errors occur during processing.
-```json
-{
-  "type": "error",
-  "data": {
-    "code": "AUDIO_FORMAT_ERROR",
-    "message": "Unsupported audio format",
-    "details": {
-      "format": "unknown",
-      "sample_rate": 0
-    },
-    "recoverable": false
-  }
-}
-```
-
-#### Heartbeat/Keepalive
-Sent periodically to maintain the connection.
-```json
-{
-  "type": "ping",
-  "data": {
-    "timestamp": 1705745630.000
-  }
-}
-```
-
-### Client-to-Server Messages
-
-Clients can send control messages to the server:
-
-#### Start/Resume Processing
-```json
-{
-  "action": "start",
-  "params": {}
-}
-```
-
-#### Pause Processing
-```json
-{
-  "action": "pause",
-  "params": {}
-}
-```
-
-#### Request Status
-```json
-{
-  "action": "get_status",
-  "params": {}
-}
-```
-
-## OpenAPI Specification
-
-The complete OpenAPI 3.0 specification is available at:
-
-```
-http://localhost:8000/v1/openapi.json
-```
-
-You can import this specification into tools like:
- Postman
- Insomnia
- Swagger UI
- OpenAPI Generator (for client SDK generation)
-
-## SDK Support
-
-While Reflector doesn't provide official SDKs, you can generate client libraries using the OpenAPI specification with tools like:
-
- **Python**: `openapi-python-client`
- **TypeScript**: `openapi-typescript-codegen`
- **Go**: `oapi-codegen`
- **Java**: `openapi-generator`
-
-## Example Usage
-
-### Python Example
-```python
-import requests
-
-# Upload and transcribe audio
-with open('meeting.mp3', 'rb') as f:
-    response = requests.post(
-        'http://localhost:8000/v1/transcripts/',
-        files={'file': f}
-    )
-    transcript_id = response.json()['id']
-
-# Check transcription status
-status = requests.get(
-    f'http://localhost:8000/v1/transcripts/{transcript_id}'
-).json()
-
-print(f"Transcription status: {status['status']}")
-```
-
-### JavaScript WebSocket Example
-```javascript
-// Connect to WebSocket for real-time transcription updates
-const ws = new WebSocket(`ws://localhost:8000/v1/transcripts_websocket/${transcriptId}`);
-
-ws.onopen = () => {
-    console.log('Connected to transcription WebSocket');
-};
-
-ws.onmessage = (event) => {
-    const message = JSON.parse(event.data);
-
-    switch(message.type) {
-        case 'transcription':
-            console.log(`[${message.data.speaker}]: ${message.data.text}`);
-            break;
-        case 'diarization':
-            console.log(`Speaker change: ${message.data.speaker}`);
-            break;
-        case 'status':
-            console.log(`Status: ${message.data.status}`);
-            break;
-        case 'error':
-            console.error(`Error: ${message.data.message}`);
-            break;
-    }
-};
-
-ws.onerror = (error) => {
-    console.error('WebSocket error:', error);
-};
-
-ws.onclose = () => {
-    console.log('WebSocket connection closed');
-};
-```
-
-## Need Help?
-
- Review [example implementations](https://github.com/monadical-sas/reflector/tree/main/examples)
- Open an issue on [GitHub](https://github.com/monadical-sas/reflector/issues)
--- a/docs/docs/reference/api/overview.md
+++ b/docs/docs/reference/api/overview.md
@@ -1,7 +0,0 @@
---
-title: overview
---
-
-# overview
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/backend.md
+++ b/docs/docs/reference/architecture/backend.md
@@ -1,7 +0,0 @@
---
-title: backend
---
-
-# backend
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/database.md
+++ b/docs/docs/reference/architecture/database.md
@@ -1,7 +0,0 @@
---
-title: database
---
-
-# database
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/frontend.md
+++ b/docs/docs/reference/architecture/frontend.md
@@ -1,7 +0,0 @@
---
-title: frontend
---
-
-# frontend
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/overview.md
+++ b/docs/docs/reference/architecture/overview.md
@@ -1,7 +0,0 @@
---
-title: overview
---
-
-# overview
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/workers.md
+++ b/docs/docs/reference/architecture/workers.md
@@ -1,7 +0,0 @@
---
-title: workers
---
-
-# workers
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/configuration.md
+++ b/docs/docs/reference/configuration.md
@@ -1,7 +0,0 @@
---
-title: configuration
---
-
-# configuration
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/analysis.md
+++ b/docs/docs/reference/processors/analysis.md
@@ -1,7 +0,0 @@
---
-title: analysis
---
-
-# analysis
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/diarization.md
+++ b/docs/docs/reference/processors/diarization.md
@@ -1,7 +0,0 @@
---
-title: diarization
---
-
-# diarization
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/transcription.md
+++ b/docs/docs/reference/processors/transcription.md
@@ -1,7 +0,0 @@
---
-title: transcription
---
-
-# transcription
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/translation.md
+++ b/docs/docs/reference/processors/translation.md
@@ -1,7 +0,0 @@
---
-title: translation
---
-
-# translation
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/roadmap.md
+++ b/docs/docs/roadmap.md
@@ -1,139 +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 99+ languages for transcription
- Parakeet supports English only with high accuracy
- Translation available to 100+ languages
-
-**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
-
-### 🖥️ Self-Hosted GPU Service
-
-**For organizations with dedicated GPU hardware (H100, A100, RTX 4090):**
-
-**Docker GPU Worker Image:**
- Self-contained processing service
- CUDA 11/12 support
- Pre-loaded models:
-  - Whisper (all sizes)
-  - Pyannote diarization
-  - Seamless-M4T translation
- Automatic model management
-
-**Deployment Options:**
- Kubernetes GPU operators
- Docker Compose with nvidia-docker
- Bare metal installation
- Hybrid cloud/on-premise
-
-**Benefits:**
- No Modal.com dependency
- Complete data isolation
- Predictable costs
- Maximum performance
- Custom model support
-
-## 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/jitsi.md
+++ b/docs/jitsi.md
@@ -0,0 +1,369 @@
+# Jitsi Integration for Reflector
+
+This document contains research and planning notes for integrating Jitsi Meet as a replacement for Whereby in Reflector.
+
+## Overview
+
+Jitsi Meet is an open-source video conferencing solution that can replace Whereby in Reflector, providing:
+- Cost reduction (no per-minute charges)
+- Direct recording access via Jibri
+- Real-time event webhooks
+- Full customization and control
+
+## Current Whereby Integration Analysis
+
+### Architecture
+1. **Room Creation**: User creates a "room" template in Reflector DB with settings
+2. **Meeting Creation**: `/rooms/{room_name}/meeting` endpoint calls Whereby API to create meeting
+3. **Recording**: Whereby handles recording automatically to S3 bucket
+4. **Webhooks**: Whereby sends events for participant tracking
+
+### Database Structure
+```python
+# Room = Template/Configuration
+class Room:
+    id, name, user_id
+    recording_type, recording_trigger  # cloud, automatic-2nd-participant
+    webhook_url, webhook_secret
+
+# Meeting = Actual Whereby Meeting Instance
+class Meeting:
+    id  # Whereby meetingId
+    room_name  # Generated by Whereby
+    room_url, host_room_url  # Whereby URLs
+    num_clients  # Updated via webhooks
+```
+
+## Jitsi Components
+
+### Core Architecture
+- **Jitsi Meet**: Web frontend (Next.js + React)
+- **Prosody**: XMPP server for messaging/rooms
+- **Jicofo**: Conference focus (orchestration)
+- **JVB**: Videobridge (media routing)
+- **Jibri**: Recording service
+- **Jigasi**: SIP gateway (optional, for phone dial-in)
+
+### Exposure Requirements
+- **Web service**: 443/80 (frontend)
+- **JVB**: 10000/UDP (media streams) - **MUST EXPOSE**
+- **Prosody**: 5280 (BOSH/WebSocket) - can proxy via web
+- **Jicofo, Jibri, Jigasi**: Internal only
+
+## Recording with Jibri
+
+### How Jibri Works
+- Each Jibri instance handles **one recording at a time**
+- Records mixed audio/video to MP4 format
+- Uses Chrome headless + ffmpeg for capture
+- Supports finalize scripts for post-processing
+
+### Jibri Pool for Scaling
+- Multiple Jibri instances join "jibribrewery" MUC
+- Jicofo distributes recording requests to available instances
+- Automatic load balancing and failover
+
+```yaml
+# Multiple Jibri instances
+jibri1:
+  environment:
+    - JIBRI_INSTANCE_ID=jibri1
+    - JIBRI_BREWERY_MUC=jibribrewery
+
+jibri2:
+  environment:
+    - JIBRI_INSTANCE_ID=jibri2
+    - JIBRI_BREWERY_MUC=jibribrewery
+```
+
+### Recording Automation Options
+1. **Environment Variables**: `ENABLE_RECORDING=1`, `AUTO_RECORDING=1`
+2. **URL Parameters**: `?config.autoRecord=true`
+3. **JWT Token**: Include recording permissions in JWT
+4. **API Control**: `api.executeCommand('startRecording')`
+
+### Post-Processing Integration
+```bash
+#!/bin/bash
+# finalize.sh - runs after recording completion
+RECORDING_FILE=$1
+MEETING_METADATA=$2
+ROOM_NAME=$3
+
+# Copy to Reflector-accessible location
+cp "$RECORDING_FILE" /shared/reflector-uploads/
+
+# Trigger Reflector processing
+curl -X POST "http://reflector-api:8000/v1/transcripts/process" \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"file_path\": \"/shared/reflector-uploads/$(basename $RECORDING_FILE)\",
+    \"room_name\": \"$ROOM_NAME\",
+    \"source\": \"jitsi\"
+  }"
+```
+
+## React Integration
+
+### Official React SDK
+```bash
+npm i @jitsi/react-sdk
+```
+
+```jsx
+import { JitsiMeeting } from '@jitsi/react-sdk'
+
+<JitsiMeeting
+  room="meeting-room"
+  serverURL="https://your-jitsi.domain"
+  jwt="your-jwt-token"
+  config={{
+    startWithAudioMuted: true,
+    fileRecordingsEnabled: true,
+    autoRecord: true
+  }}
+  onParticipantJoined={(participant) => {
+    // Track participant events
+  }}
+  onRecordingStatusChanged={(status) => {
+    // Handle recording events
+  }}
+/>
+```
+
+## Authentication & Room Control
+
+### JWT-Based Access Control
+```python
+def generate_jitsi_jwt(payload):
+    return jwt.encode({
+        "aud": "jitsi",
+        "iss": "reflector",
+        "sub": "reflector-user",
+        "room": payload["room"],
+        "exp": int(payload["exp"].timestamp()),
+        "context": {
+            "user": {
+                "name": payload["user_name"],
+                "moderator": payload.get("moderator", False)
+            },
+            "features": {
+                "recording": payload.get("recording", True)
+            }
+        }
+    }, JITSI_JWT_SECRET)
+```
+
+### Prevent Anonymous Room Creation
+```bash
+# Environment configuration
+ENABLE_AUTH=1
+ENABLE_GUESTS=0
+AUTH_TYPE=jwt
+JWT_APP_ID=reflector
+JWT_APP_SECRET=your-secret-key
+```
+
+## Webhook Integration
+
+### Real-time Events via Prosody
+Custom event-sync module can send webhooks for:
+- Participant join/leave
+- Recording start/stop
+- Room creation/destruction
+- Mute/unmute events
+
+```lua
+-- mod_event_sync.lua
+module:hook("muc-occupant-joined", function(event)
+    send_event({
+        type = "participant_joined",
+        room = event.room.jid,
+        participant = {
+            nick = event.occupant.nick,
+            jid = event.occupant.jid,
+        },
+        timestamp = os.time(),
+    });
+end);
+```
+
+### Jibri Recording Webhooks
+```bash
+# Environment variable
+JIBRI_WEBHOOK_SUBSCRIBERS=https://your-reflector.com/webhooks/jibri
+```
+
+## Proposed Reflector Integration
+
+### Modified Database Schema
+```python
+class Meeting(BaseModel):
+    id: str  # Our generated meeting ID
+    room_name: str  # Generated: reflector-{room.name}-{timestamp}
+    room_url: str  # https://jitsi.domain/room_name?jwt=token
+    host_room_url: str  # Same but with moderator JWT
+    # Add Jitsi-specific fields
+    jitsi_jwt: str  # JWT token
+    jitsi_room_id: str  # Internal room identifier
+    recording_status: str  # pending, recording, completed
+    recording_file_path: Optional[str]
+```
+
+### API Replacement
+```python
+# Replace whereby.py with jitsi.py
+async def create_meeting(room_name_prefix: str, end_date: datetime, room: Room):
+    # Generate unique room name
+    jitsi_room = f"reflector-{room.name}-{int(time.time())}"
+
+    # Generate JWT tokens
+    user_jwt = generate_jwt(room=jitsi_room, moderator=False, exp=end_date)
+    host_jwt = generate_jwt(room=jitsi_room, moderator=True, exp=end_date)
+
+    return {
+        "meetingId": generate_uuid4(),  # Our ID
+        "roomName": jitsi_room,
+        "roomUrl": f"https://jitsi.domain/{jitsi_room}?jwt={user_jwt}",
+        "hostRoomUrl": f"https://jitsi.domain/{jitsi_room}?jwt={host_jwt}",
+        "startDate": datetime.now().isoformat(),
+        "endDate": end_date.isoformat(),
+    }
+```
+
+### Webhook Endpoints
+```python
+# Replace whereby webhook with jitsi webhooks
+@router.post("/jitsi/events")
+async def jitsi_events_webhook(event_data: dict):
+    event_type = event_data.get("event")
+    room_name = event_data.get("room", "").split("@")[0]
+
+    meeting = await Meeting.get_by_room(room_name)
+
+    if event_type == "muc-occupant-joined":
+        # Update participant count
+        meeting.num_clients += 1
+
+    elif event_type == "jibri-recording-on":
+        meeting.recording_status = "recording"
+
+    elif event_type == "jibri-recording-off":
+        meeting.recording_status = "processing"
+        await process_meeting_recording.delay(meeting.id)
+
+@router.post("/jibri/recording-complete")
+async def recording_complete(data: dict):
+    # Handle finalize script webhook
+    room_name = data.get("room_name")
+    file_path = data.get("file_path")
+
+    meeting = await Meeting.get_by_room(room_name)
+    meeting.recording_file_path = file_path
+    meeting.recording_status = "completed"
+
+    # Start Reflector processing
+    await process_recording_for_transcription(meeting.id, file_path)
+```
+
+## Deployment with Docker
+
+### Official docker-jitsi-meet
+```bash
+# Download official release
+wget $(wget -q -O - https://api.github.com/repos/jitsi/docker-jitsi-meet/releases/latest | grep zip | cut -d\" -f4)
+
+# Setup
+mkdir -p ~/.jitsi-meet-cfg/{web,transcripts,prosody/config,prosody/prosody-plugins-custom,jicofo,jvb,jigasi,jibri}
+./gen-passwords.sh  # Generate secure passwords
+docker compose up -d
+```
+
+### Coolify Integration
+```yaml
+services:
+  web:
+    ports: ["80:80", "443:443"]
+  jvb:
+    ports: ["10000:10000/udp"]  # Must expose for media
+  jibri1:
+    environment:
+      - JIBRI_INSTANCE_ID=jibri1
+      - JIBRI_FINALIZE_RECORDING_SCRIPT_PATH=/config/finalize.sh
+  jibri2:
+    environment:
+      - JIBRI_INSTANCE_ID=jibri2
+```
+
+## Benefits vs Whereby
+
+### Cost & Control
+✅ **No per-minute charges** - significant cost savings
+✅ **Full recording control** - direct file access
+✅ **Custom branding** - complete UI control
+✅ **Self-hosted** - no vendor lock-in
+
+### Technical Advantages
+✅ **Real-time events** - immediate webhook notifications
+✅ **Rich participant metadata** - detailed tracking
+✅ **JWT security** - token-based access with expiration
+✅ **Multiple recording formats** - audio-only options
+✅ **Scalable architecture** - horizontal Jibri scaling
+
+### Integration Benefits
+✅ **Same API surface** - minimal changes to existing code
+✅ **React SDK** - better frontend integration
+✅ **Direct processing** - no S3 download delays
+✅ **Event-driven architecture** - better real-time capabilities
+
+## Implementation Plan
+
+1. **Deploy Jitsi Stack** - Set up docker-jitsi-meet with multiple Jibri instances
+2. **Create jitsi.py** - Replace whereby.py with Jitsi API functions
+3. **Update Database** - Add Jitsi-specific fields to Meeting model
+4. **Webhook Integration** - Replace Whereby webhooks with Jitsi events
+5. **Frontend Updates** - Replace Whereby embed with Jitsi React SDK
+6. **Testing & Migration** - Gradual rollout with fallback to Whereby
+
+## Recording Limitations & Considerations
+
+### Current Limitations
+- **Mixed audio only** - Jibri doesn't separate participant tracks natively
+- **One recording per Jibri** - requires multiple instances for concurrent recordings
+- **Chrome dependency** - Jibri uses headless Chrome for recording
+
+### Metadata Capabilities
+✅ **Participant join/leave timestamps** - via webhooks
+✅ **Speaking time tracking** - via audio level events
+✅ **Meeting duration** - precise timing
+✅ **Room-specific data** - custom metadata in JWT
+
+### Alternative Recording Methods
+- **Local recording** - browser-based, per-participant
+- **Custom recording** - lib-jitsi-meet for individual streams
+- **Third-party solutions** - Recall.ai, Otter.ai integrations
+
+## Security Considerations
+
+### JWT Configuration
+- **Room-specific tokens** - limit access to specific rooms
+- **Time-based expiration** - automatic cleanup
+- **Feature permissions** - control recording, moderation rights
+- **User identification** - embed user metadata in tokens
+
+### Access Control
+- **No anonymous rooms** - all rooms require valid JWT
+- **API-only creation** - prevent direct room access
+- **Webhook verification** - HMAC signature validation
+
+## Next Steps
+
+1. **Deploy test Jitsi instance** - validate recording pipeline
+2. **Prototype jitsi.py** - create equivalent API functions
+3. **Test webhook integration** - ensure event delivery works
+4. **Performance testing** - validate multiple concurrent recordings
+5. **Migration strategy** - plan gradual transition from Whereby
+
+---
+
+*This document serves as the comprehensive planning and research notes for Jitsi integration in Reflector. It should be updated as implementation progresses and new insights are discovered.*
--- 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
@@ -1,95 +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: 'Other Integrations',
-          collapsed: true,
-          items: [
-            'installation/whereby-setup',
-            'installation/zulip-setup',
-          ],
-        },
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Pipelines',
-      items: [
-        'pipelines/overview',
-        'pipelines/file-pipeline',
-        'pipelines/live-pipeline',
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Reference',
-      items: [
-        {
-          type: 'category',
-          label: 'Architecture',
-          items: [
-            'reference/architecture/overview',
-            'reference/architecture/backend',
-            'reference/architecture/frontend',
-            'reference/architecture/workers',
-            'reference/architecture/database',
-          ],
-        },
-        {
-          type: 'category',
-          label: 'Processors',
-          items: [
-            'reference/processors/transcription',
-            'reference/processors/diarization',
-            'reference/processors/translation',
-            'reference/processors/analysis',
-          ],
-        },
-        {
-          type: 'category',
-          label: 'API',
-          items: [
-            {
-              type: 'doc',
-              id: 'reference/api/overview',
-            },
-            {
-              type: 'link',
-              label: 'OpenAPI Reference',
-              href: '/docs/reference/api',
-            },
-          ],
-        },
-        'reference/configuration',
-      ],
-    },
-    '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/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/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>
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-  <path d="M344.38682,555.65836h53.08286V529.11693H344.38682Z" transform="translate(-35.5 -118.5)" fill="#3ecc5f" fill-rule="evenodd" />
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-</svg>
--- a/docs/static/openapi.json
+++ b/docs/static/openapi.json
--- a/docs/transcript.md
+++ b/docs/transcript.md
@@ -1,241 +0,0 @@
-# Transcript Formats
-
-The Reflector API provides multiple output formats for transcript data through the `transcript_format` query parameter on the GET `/v1/transcripts/{id}` endpoint.
-
-## Overview
-
-When retrieving a transcript, you can specify the desired format using the `transcript_format` query parameter. The API supports four formats optimized for different use cases:
-
- **text** - Plain text with speaker names (default)
- **text-timestamped** - Timestamped text with speaker names
- **webvtt-named** - WebVTT subtitle format with participant names
- **json** - Structured JSON segments with full metadata
-
-All formats include participant information when available, resolving speaker IDs to actual names.
-
-## Query Parameter Usage
-
-```
-GET /v1/transcripts/{id}?transcript_format={format}
-```
-
-### Parameters
-
- `transcript_format` (optional): The desired output format
-  - Type: `"text" | "text-timestamped" | "webvtt-named" | "json"`
-  - Default: `"text"`
-
-## Format Descriptions
-
-### Text Format (`text`)
-
-**Use case:** Simple, human-readable transcript for display or export.
-
-**Format:** Speaker names followed by their dialogue, one line per segment.
-
-**Example:**
-```
-John Smith: Hello everyone
-Jane Doe: Hi there
-John Smith: How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=text
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "text",
-  "transcript": "John Smith: Hello everyone\nJane Doe: Hi there\nJohn Smith: How are you today?",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### Text Timestamped Format (`text-timestamped`)
-
-**Use case:** Transcript with timing information for navigation or reference.
-
-**Format:** `[MM:SS]` timestamp prefix before each speaker and dialogue.
-
-**Example:**
-```
-[00:00] John Smith: Hello everyone
-[00:05] Jane Doe: Hi there
-[00:12] John Smith: How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=text-timestamped
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "text-timestamped",
-  "transcript": "[00:00] John Smith: Hello everyone\n[00:05] Jane Doe: Hi there\n[00:12] John Smith: How are you today?",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### WebVTT Named Format (`webvtt-named`)
-
-**Use case:** Subtitle files for video players, accessibility tools, or video editing.
-
-**Format:** Standard WebVTT subtitle format with voice tags using participant names.
-
-**Example:**
-```
-WEBVTT
-
-00:00:00.000 --> 00:00:05.000
-<v John Smith>Hello everyone
-
-00:00:05.000 --> 00:00:12.000
-<v Jane Doe>Hi there
-
-00:00:12.000 --> 00:00:18.000
-<v John Smith>How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=webvtt-named
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "webvtt-named",
-  "transcript": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v John Smith>Hello everyone\n\n...",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### JSON Format (`json`)
-
-**Use case:** Programmatic access with full timing and speaker metadata.
-
-**Format:** Array of segment objects with speaker information, text content, and precise timing.
-
-**Example:**
-```json
-[
-  {
-    "speaker": 0,
-    "speaker_name": "John Smith",
-    "text": "Hello everyone",
-    "start": 0.0,
-    "end": 5.0
-  },
-  {
-    "speaker": 1,
-    "speaker_name": "Jane Doe",
-    "text": "Hi there",
-    "start": 5.0,
-    "end": 12.0
-  },
-  {
-    "speaker": 0,
-    "speaker_name": "John Smith",
-    "text": "How are you today?",
-    "start": 12.0,
-    "end": 18.0
-  }
-]
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=json
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "json",
-  "transcript": [
-    {
-      "speaker": 0,
-      "speaker_name": "John Smith",
-      "text": "Hello everyone",
-      "start": 0.0,
-      "end": 5.0
-    },
-    {
-      "speaker": 1,
-      "speaker_name": "Jane Doe",
-      "text": "Hi there",
-      "start": 5.0,
-      "end": 12.0
-    }
-  ],
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-## Response Structure
-
-All formats return the same base transcript metadata with an additional `transcript_format` field and format-specific `transcript` field:
-
-### Common Fields
-
- `id`: Transcript identifier
- `user_id`: Owner user ID (if authenticated)
- `name`: Transcript name
- `status`: Processing status
- `locked`: Whether transcript is locked for editing
- `duration`: Total duration in seconds
- `title`: Auto-generated or custom title
- `short_summary`: Brief summary
- `long_summary`: Detailed summary
- `created_at`: Creation timestamp
- `share_mode`: Access control setting
- `source_language`: Original audio language
- `target_language`: Translation target language
- `reviewed`: Whether transcript has been reviewed
- `meeting_id`: Associated meeting ID (if applicable)
- `source_kind`: Source type (live, file, room)
- `room_id`: Associated room ID (if applicable)
- `audio_deleted`: Whether audio has been deleted
- `participants`: Array of participant objects with speaker mappings
-
-### Format-Specific Fields
-
- `transcript_format`: The format identifier (discriminator field)
- `transcript`: The formatted transcript content (string for text/webvtt formats, array for json format)
-
-## Speaker Name Resolution
-
-All formats resolve speaker IDs to participant names when available:
-
- If a participant exists for the speaker ID, their name is used
- If no participant exists, a default name like "Speaker 0" is generated
- Speaker IDs are integers (0, 1, 2, etc.) assigned during diarization
--- a/docs/tsconfig.json
+++ b/docs/tsconfig.json
@@ -1,8 +0,0 @@
-{
-  // This file is not used in compilation. It is here just for a nice editor experience.
-  "extends": "@docusaurus/tsconfig",
-  "compilerOptions": {
-    "baseUrl": "."
-  },
-  "exclude": [".docusaurus", "build"]
-}
--- a/docs/video-jitsi.md
+++ b/docs/video-jitsi.md
@@ -0,0 +1,720 @@
+# Jitsi Meet Integration Configuration Guide
+
+This guide explains how to configure Reflector to use your self-hosted Jitsi Meet installation for video meetings, recording, and participant tracking.
+
+## Overview
+
+Jitsi Meet is an open-source video conferencing platform that can be self-hosted. Reflector integrates with Jitsi Meet to:
+
+- Create secure meeting rooms with JWT authentication
+- Track participant join/leave events via Prosody webhooks
+- Record meetings using Jibri recording service
+- Process recordings for transcription and analysis
+
+## Requirements
+
+### Self-Hosted Jitsi Meet
+
+You need a complete Jitsi Meet installation including:
+
+1. **Jitsi Meet Web Interface** - The main meeting interface
+2. **Prosody XMPP Server** - Handles room management and authentication
+3. **Jicofo (JItsi COnference FOcus)** - Manages media sessions
+4. **Jitsi Videobridge (JVB)** - Handles WebRTC media routing
+5. **Jibri Recording Service** - Records meetings (optional but recommended)
+
+### System Requirements
+
+- **Domain with SSL Certificate** - Required for WebRTC functionality
+- **Prosody mod_event_sync** - For webhook event handling
+- **JWT Authentication** - For secure room access control
+- **Storage Solution** - For recording files (local or cloud)
+
+## Configuration Variables
+
+Add the following environment variables to your Reflector `.env` file:
+
+### Required Variables
+
+```bash
+# Jitsi Meet Domain (without https://)
+JITSI_DOMAIN=meet.example.com
+
+# JWT Secret for room authentication (generate with: openssl rand -hex 32)
+JITSI_JWT_SECRET=your-64-character-hex-secret-here
+
+# Webhook secret for event handling (generate with: openssl rand -hex 16)
+JITSI_WEBHOOK_SECRET=your-32-character-hex-secret-here
+```
+
+### Optional Variables
+
+```bash
+# Application identifier (should match Jitsi configuration)
+JITSI_APP_ID=reflector
+
+# JWT issuer and audience (should match Jitsi configuration)
+JITSI_JWT_ISSUER=reflector
+JITSI_JWT_AUDIENCE=jitsi
+```
+
+## Installation Steps
+
+### 1. Jitsi Meet Server Installation
+
+#### Quick Installation (Ubuntu/Debian)
+
+```bash
+# Add Jitsi repository
+curl -fsSL https://download.jitsi.org/jitsi-key.gpg.key | sudo gpg --dearmor -o /usr/share/keyrings/jitsi-keyring.gpg
+echo "deb [signed-by=/usr/share/keyrings/jitsi-keyring.gpg] https://download.jitsi.org stable/" | sudo tee /etc/apt/sources.list.d/jitsi-stable.list
+
+# Install Jitsi Meet
+sudo apt update
+sudo apt install jitsi-meet
+
+# Configure SSL certificate
+sudo /usr/share/jitsi-meet/scripts/install-letsencrypt-cert.sh
+```
+
+#### Docker Installation
+
+```bash
+# Clone Jitsi Docker repository
+git clone https://github.com/jitsi/docker-jitsi-meet
+cd docker-jitsi-meet
+
+# Copy environment template
+cp env.example .env
+
+# Edit configuration
+nano .env
+
+# Start services
+docker-compose up -d
+```
+
+### 2. JWT Authentication Setup
+
+#### Update Prosody Configuration
+
+Edit `/etc/prosody/conf.d/your-domain.cfg.lua`:
+
+```lua
+VirtualHost "meet.example.com"
+    authentication = "token"
+    app_id = "reflector"
+    app_secret = "your-jwt-secret-here"
+
+    -- Allow anonymous access for guests
+    c2s_require_encryption = false
+    admins = { "focusUser@auth.meet.example.com" }
+
+    modules_enabled = {
+        "bosh";
+        "pubsub";
+        "ping";
+        "roster";
+        "saslauth";
+        "tls";
+        "dialback";
+        "disco";
+        "carbons";
+        "pep";
+        "private";
+        "blocklist";
+        "vcard";
+        "version";
+        "uptime";
+        "time";
+        "ping";
+        "register";
+        "admin_adhoc";
+        "token_verification";
+        "event_sync";  -- Required for webhooks
+    }
+```
+
+#### Configure Jitsi Meet Interface
+
+Edit `/etc/jitsi/meet/your-domain-config.js`:
+
+```javascript
+var config = {
+    hosts: {
+        domain: 'meet.example.com',
+        muc: 'conference.meet.example.com'
+    },
+
+    // Enable JWT authentication
+    enableUserRolesBasedOnToken: true,
+
+    // Recording configuration
+    fileRecordingsEnabled: true,
+    liveStreamingEnabled: false,
+
+    // Reflector integration settings
+    prejoinPageEnabled: true,
+    requireDisplayName: true
+};
+```
+
+### 3. Webhook Event Configuration
+
+#### Install Event Sync Module
+
+```bash
+# Download the module
+cd /usr/share/jitsi-meet/prosody-plugins/
+wget https://raw.githubusercontent.com/jitsi-contrib/prosody-plugins/main/mod_event_sync.lua
+```
+
+#### Configure Event Sync
+
+Add to your Prosody configuration:
+
+```lua
+Component "conference.meet.example.com" "muc"
+    storage = "memory"
+    modules_enabled = {
+        "muc_meeting_id";
+        "muc_domain_mapper";
+        "polls";
+        "event_sync";  -- Enable event sync
+    }
+
+    -- Event sync webhook configuration
+    event_sync_url = "https://your-reflector-domain.com/v1/jitsi/events"
+    event_sync_secret = "your-webhook-secret-here"
+
+    -- Events to track
+    event_sync_events = {
+        "muc-occupant-joined",
+        "muc-occupant-left",
+        "jibri-recording-on",
+        "jibri-recording-off"
+    }
+
+#### Webhook Event Payload Examples
+
+**Participant Joined Event:**
+```json
+{
+  "event": "muc-occupant-joined",
+  "room": "reflector-my-room-uuid123",
+  "timestamp": "2025-01-15T10:30:00.000Z",
+  "data": {
+    "occupant_id": "participant-456",
+    "nick": "John Doe",
+    "role": "participant",
+    "affiliation": "none"
+  }
+}
+```
+
+**Recording Started Event:**
+```json
+{
+  "event": "jibri-recording-on",
+  "room": "reflector-my-room-uuid123",
+  "timestamp": "2025-01-15T10:32:00.000Z",
+  "data": {
+    "recording_id": "rec-789",
+    "initiator": "moderator-123"
+  }
+}
+```
+
+**Recording Completed Event:**
+```json
+{
+  "room_name": "reflector-my-room-uuid123",
+  "recording_file": "/var/recordings/rec-789.mp4",
+  "recording_status": "completed",
+  "timestamp": "2025-01-15T11:15:00.000Z"
+}
+```
+
+### 4. Jibri Recording Setup (Optional)
+
+#### Install Jibri
+
+```bash
+# Install Jibri package
+sudo apt install jibri
+
+# Create recording directory
+sudo mkdir -p /var/recordings
+sudo chown jibri:jibri /var/recordings
+```
+
+#### Configure Jibri
+
+Edit `/etc/jitsi/jibri/jibri.conf`:
+
+```hocon
+jibri {
+    recording {
+        recordings-directory = "/var/recordings"
+        finalize-script = "/opt/jitsi/jibri/finalize.sh"
+    }
+
+    api {
+        xmpp {
+            environments = [{
+                name = "prod environment"
+                xmpp-server-hosts = ["meet.example.com"]
+                xmpp-domain = "meet.example.com"
+
+                control-muc {
+                    domain = "internal.auth.meet.example.com"
+                    room-name = "JibriBrewery"
+                    nickname = "jibri-nickname"
+                }
+
+                control-login {
+                    domain = "auth.meet.example.com"
+                    username = "jibri"
+                    password = "jibri-password"
+                }
+            }]
+        }
+    }
+}
+```
+
+#### Create Finalize Script
+
+Create `/opt/jitsi/jibri/finalize.sh`:
+
+```bash
+#!/bin/bash
+# Jibri finalize script for Reflector integration
+
+RECORDING_FILE="$1"
+ROOM_NAME="$2"
+REFLECTOR_API_URL="${REFLECTOR_API_URL:-http://localhost:1250}"
+
+# Prepare webhook payload
+TIMESTAMP=$(date -u +%Y-%m-%dT%H:%M:%S.%3NZ)
+PAYLOAD=$(cat <<EOF
+{
+    "room_name": "$ROOM_NAME",
+    "recording_file": "$RECORDING_FILE",
+    "recording_status": "completed",
+    "timestamp": "$TIMESTAMP"
+}
+EOF
+)
+
+# Generate signature
+SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$JITSI_WEBHOOK_SECRET" | cut -d' ' -f2)
+
+# Send webhook to Reflector
+curl -X POST "$REFLECTOR_API_URL/v1/jibri/recording-complete" \
+    -H "Content-Type: application/json" \
+    -H "X-Jitsi-Signature: $SIGNATURE" \
+    -d "$PAYLOAD"
+
+echo "Recording finalization webhook sent for room: $ROOM_NAME"
+```
+
+Make executable:
+```bash
+sudo chmod +x /opt/jitsi/jibri/finalize.sh
+```
+
+### 5. Restart Services
+
+After configuration changes:
+
+```bash
+sudo systemctl restart prosody
+sudo systemctl restart jicofo
+sudo systemctl restart jitsi-videobridge2
+sudo systemctl restart jibri
+sudo systemctl restart nginx
+```
+
+## Room Configuration
+
+### Creating Jitsi Rooms
+
+Create rooms with Jitsi platform in Reflector:
+
+```bash
+curl -X POST "https://your-reflector-domain.com/v1/rooms" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $AUTH_TOKEN" \
+  -d '{
+    "name": "my-jitsi-room",
+    "platform": "jitsi",
+    "recording_type": "cloud",
+    "recording_trigger": "automatic-2nd-participant",
+    "is_locked": false,
+    "room_mode": "normal"
+  }'
+```
+
+### Meeting Creation
+
+Meetings automatically use JWT authentication:
+
+```bash
+curl -X POST "https://your-reflector-domain.com/v1/rooms/my-jitsi-room/meeting" \
+  -H "Authorization: Bearer $AUTH_TOKEN"
+```
+
+Response includes JWT-authenticated URLs:
+```json
+{
+  "id": "meeting-uuid",
+  "room_name": "reflector-my-jitsi-room-123456",
+  "room_url": "https://meet.example.com/room?jwt=user-token",
+  "host_room_url": "https://meet.example.com/room?jwt=moderator-token"
+}
+```
+
+## Features and Capabilities
+
+### JWT Authentication
+
+Reflector automatically generates JWT tokens with:
+- **Room Access Control** - Secure room entry
+- **User Roles** - Moderator vs participant permissions
+- **Expiration** - Configurable token lifetime (default 8 hours)
+- **Custom Claims** - Room-specific metadata
+
+### Recording Options
+
+**Recording Types:**
+- `"none"` - No recording
+- `"local"` - Local Jibri recording
+- `"cloud"` - Cloud recording (requires external storage)
+
+**Recording Triggers:**
+- `"none"` - Manual recording only
+- `"prompt"` - Prompt users to start
+- `"automatic"` - Start immediately
+- `"automatic-2nd-participant"` - Start when 2nd person joins
+
+### Event Tracking and Storage
+
+Reflector automatically stores all webhook events in the `meetings` table for comprehensive meeting analytics:
+
+**Supported Event Types:**
+- `muc-occupant-joined` - Participant joined the meeting
+- `muc-occupant-left` - Participant left the meeting
+- `jibri-recording-on` - Recording started
+- `jibri-recording-off` - Recording stopped
+- `recording_completed` - Recording file ready for processing
+
+**Event Storage Structure:**
+Each webhook event is stored as a JSON object in the `meetings.events` column:
+```json
+{
+  "type": "muc-occupant-joined",
+  "timestamp": "2025-01-15T10:30:00.123456Z",
+  "data": {
+    "timestamp": "2025-01-15T10:30:00Z",
+    "user_id": "participant-123",
+    "display_name": "John Doe"
+  }
+}
+```
+
+**Querying Stored Events:**
+```sql
+-- Get all events for a meeting
+SELECT events FROM meeting WHERE id = 'meeting-uuid';
+
+-- Count participant joins
+SELECT json_array_length(
+  json_extract(events, '$[*] ? (@.type == "muc-occupant-joined")')
+) as total_joins FROM meeting WHERE id = 'meeting-uuid';
+```
+
+## Testing and Verification
+
+### Health Check
+
+Test Jitsi webhook integration:
+
+```bash
+curl "https://your-reflector-domain.com/v1/jitsi/health"
+```
+
+Expected response:
+```json
+{
+    "status": "ok",
+    "service": "jitsi-webhooks",
+    "timestamp": "2025-01-15T10:30:00.000Z",
+    "webhook_secret_configured": true
+}
+```
+
+### JWT Token Testing
+
+Verify JWT generation works:
+```bash
+# Create a test meeting
+MEETING=$(curl -X POST "https://your-reflector-domain.com/v1/rooms/test-room/meeting" \
+  -H "Authorization: Bearer $AUTH_TOKEN" | jq -r '.room_url')
+
+echo "Test meeting URL: $MEETING"
+```
+
+### Webhook Testing
+
+#### Manual Webhook Event Testing
+
+Test participant join event:
+```bash
+# Generate proper signature
+PAYLOAD='{"event":"muc-occupant-joined","room":"reflector-test-room-uuid","timestamp":"2025-01-15T10:30:00.000Z","data":{"user_id":"test-user","display_name":"Test User"}}'
+SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$JITSI_WEBHOOK_SECRET" | cut -d' ' -f2)
+
+curl -X POST "https://your-reflector-domain.com/v1/jitsi/events" \
+  -H "Content-Type: application/json" \
+  -H "X-Jitsi-Signature: $SIGNATURE" \
+  -d "$PAYLOAD"
+```
+
+Expected response:
+```json
+{
+  "status": "ok",
+  "event": "muc-occupant-joined",
+  "room": "reflector-test-room-uuid"
+}
+```
+
+#### Recording Webhook Testing
+
+Test recording completion event:
+```bash
+PAYLOAD='{"room_name":"reflector-test-room-uuid","recording_file":"/recordings/test.mp4","recording_status":"completed","timestamp":"2025-01-15T10:30:00.000Z"}'
+SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$JITSI_WEBHOOK_SECRET" | cut -d' ' -f2)
+
+curl -X POST "https://your-reflector-domain.com/v1/jibri/recording-complete" \
+  -H "Content-Type: application/json" \
+  -H "X-Jitsi-Signature: $SIGNATURE" \
+  -d "$PAYLOAD"
+```
+
+#### Event Storage Verification
+
+Verify events were stored:
+```bash
+# Check meeting events via API (requires authentication)
+curl -H "Authorization: Bearer $AUTH_TOKEN" \
+  "https://your-reflector-domain.com/v1/meetings/{meeting-id}"
+```
+
+## Troubleshooting
+
+### Common Issues
+
+#### JWT Authentication Failures
+
+**Symptoms**: Users cannot join rooms, "Authentication failed" errors
+
+**Solutions**:
+1. Verify `JITSI_JWT_SECRET` matches Prosody configuration
+2. Check JWT token hasn't expired (default 8 hours)
+3. Ensure system clocks are synchronized between servers
+4. Validate JWT issuer/audience configuration matches
+
+**Debug JWT tokens**:
+```bash
+# Decode JWT payload
+echo "JWT_TOKEN_HERE" | cut -d'.' -f2 | base64 -d | jq
+```
+
+#### Webhook Events Not Received
+
+**Symptoms**: Participant counts not updating, no recording events
+
+**Solutions**:
+1. Verify `mod_event_sync` is loaded in Prosody
+2. Check webhook URL is accessible from Jitsi server
+3. Validate webhook signature generation
+4. Review Prosody and Reflector logs
+
+**Debug webhook connectivity**:
+```bash
+# Test from Jitsi server
+curl -v "https://your-reflector-domain.com/v1/jitsi/health"
+
+# Check Prosody logs
+sudo tail -f /var/log/prosody/prosody.log
+```
+
+#### Webhook Signature Verification Issues
+
+**Symptoms**: HTTP 401 "Invalid webhook signature" errors
+
+**Solutions**:
+1. Verify webhook secret matches between Jitsi and Reflector
+2. Check payload encoding (no extra whitespace)
+3. Ensure proper HMAC-SHA256 signature generation
+
+**Debug signature generation**:
+```bash
+# Test signature manually
+PAYLOAD='{"event":"test","room":"test","timestamp":"2025-01-15T10:30:00.000Z","data":{}}'
+SECRET="your-webhook-secret-here"
+
+# Generate signature (should match X-Jitsi-Signature header)
+echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$SECRET" | cut -d' ' -f2
+
+# Test with curl
+curl -X POST "https://your-reflector-domain.com/v1/jitsi/events" \
+  -H "Content-Type: application/json" \
+  -H "X-Jitsi-Signature: $(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "$SECRET" | cut -d' ' -f2)" \
+  -d "$PAYLOAD" -v
+```
+
+#### Event Storage Problems
+
+**Symptoms**: Events received but not stored in database
+
+**Solutions**:
+1. Check database connectivity and permissions
+2. Verify meeting exists before event processing
+3. Review Reflector application logs
+4. Ensure JSON column support in database
+
+**Debug event storage**:
+```bash
+# Check meeting exists
+curl -H "Authorization: Bearer $TOKEN" \
+  "https://your-reflector-domain.com/v1/meetings/{meeting-id}"
+
+# Monitor database queries (if using PostgreSQL)
+sudo -u postgres psql -c "SELECT * FROM pg_stat_activity WHERE query LIKE '%meeting%';"
+
+# Check Reflector logs for event processing
+sudo journalctl -u reflector -f | grep -E "(event|webhook|jitsi)"
+```
+
+#### Recording Issues
+
+**Symptoms**: Recordings not starting, finalize script errors
+
+**Solutions**:
+1. Verify Jibri service status: `sudo systemctl status jibri`
+2. Check recording directory permissions: `/var/recordings`
+3. Validate finalize script execution permissions
+4. Monitor Jibri logs: `sudo journalctl -u jibri -f`
+
+**Test finalize script**:
+```bash
+sudo -u jibri /opt/jitsi/jibri/finalize.sh "/test/recording.mp4" "test-room"
+```
+
+#### Meeting Creation Failures
+
+**Symptoms**: HTTP 500 errors when creating meetings
+
+**Solutions**:
+1. Check Reflector logs for JWT generation errors
+2. Verify all required environment variables are set
+3. Ensure Jitsi domain is accessible from Reflector
+4. Test JWT secret configuration
+
+### Debug Commands
+
+```bash
+# Verify Prosody configuration
+sudo prosodyctl check config
+
+# Check Jitsi services status
+sudo systemctl status prosody jicofo jitsi-videobridge2
+
+# Test JWT generation
+curl -X POST "https://your-reflector-domain.com/v1/rooms/test/meeting" \
+  -H "Authorization: Bearer $TOKEN" -v
+
+# Monitor webhook events
+sudo tail -f /var/log/reflector/app.log | grep jitsi
+
+# Check SSL certificates
+sudo certbot certificates
+```
+
+### Performance Optimization
+
+#### Scaling Considerations
+
+**Single Server Limits:**
+- ~50 concurrent participants per JVB instance
+- ~10 concurrent Jibri recordings
+- CPU and bandwidth become bottlenecks
+
+**Multi-Server Setup:**
+- Multiple JVB instances for scaling
+- Dedicated Jibri recording servers
+- Load balancing for high availability
+
+#### Resource Monitoring
+
+```bash
+# Monitor JVB performance
+sudo systemctl status jitsi-videobridge2
+sudo journalctl -u jitsi-videobridge2 -f
+
+# Check Prosody connections
+sudo prosodyctl mod_admin_telnet
+> c2s:show()
+> muc:rooms()
+```
+
+## Security Best Practices
+
+### JWT Security
+- Use strong, unique secrets (32+ characters)
+- Rotate JWT secrets regularly
+- Implement proper token expiration
+- Never log or expose JWT tokens
+
+### Network Security
+- Use HTTPS/WSS for all communications
+- Implement proper firewall rules
+- Consider VPN for server-to-server communication
+- Monitor for unauthorized access attempts
+
+### Recording Security
+- Encrypt recordings at rest
+- Implement access controls for recording files
+- Regular security audits of file permissions
+- Comply with data protection regulations
+
+## Migration from Whereby
+
+If migrating from Whereby to Jitsi:
+
+1. **Parallel Setup** - Configure Jitsi alongside existing Whereby
+2. **Room Migration** - Update room platform field to "jitsi"
+3. **Test Integration** - Verify meeting creation and webhooks
+4. **User Training** - Different UI and feature set
+5. **Monitor Performance** - Watch for issues during transition
+6. **Cleanup** - Remove Whereby configuration when stable
+
+## Support and Resources
+
+### Jitsi Community Resources
+- **Documentation**: [jitsi.github.io/handbook](https://jitsi.github.io/handbook/)
+- **Community Forum**: [community.jitsi.org](https://community.jitsi.org/)
+- **GitHub Issues**: [github.com/jitsi/jitsi-meet](https://github.com/jitsi/jitsi-meet)
+
+### Professional Support
+- **8x8 Commercial Support** - Professional Jitsi hosting and support
+- **Community Consulting** - Third-party Jitsi implementation services
+
+### Monitoring and Maintenance
+- Monitor system resources (CPU, memory, bandwidth)
+- Regular security updates for all components
+- Backup configuration files and certificates
+- Test disaster recovery procedures
--- a/docs/video-whereby.md
+++ b/docs/video-whereby.md
@@ -0,0 +1,276 @@
+# Whereby Integration Configuration Guide
+
+This guide explains how to configure Reflector to use Whereby as your video meeting platform for room creation, recording, and participant tracking.
+
+## Overview
+
+Whereby is a browser-based video meeting platform that provides hosted meeting rooms with recording capabilities. Reflector integrates with Whereby's API to:
+
+- Create secure meeting rooms with custom branding
+- Handle participant join/leave events via webhooks
+- Automatically record meetings to AWS S3 storage
+- Track meeting sessions and participant counts
+
+## Requirements
+
+### Whereby Account Setup
+
+1. **Whereby Account**: Sign up for a Whereby business account at [whereby.com](https://whereby.com/business)
+2. **API Access**: Request API access from Whereby support (required for programmatic room creation)
+3. **Webhook Configuration**: Configure webhooks in your Whereby dashboard to point to your Reflector instance
+
+### AWS S3 Storage
+
+Whereby requires AWS S3 for recording storage. You need:
+- AWS account with S3 access
+- Dedicated S3 bucket for Whereby recordings
+- AWS IAM credentials with S3 write permissions
+
+## Configuration Variables
+
+Add the following environment variables to your Reflector `.env` file:
+
+### Required Variables
+
+```bash
+# Whereby API Configuration
+WHEREBY_API_KEY=your-whereby-jwt-api-key
+WHEREBY_WEBHOOK_SECRET=your-webhook-secret-from-whereby
+
+# AWS S3 Storage for Recordings
+AWS_WHEREBY_ACCESS_KEY_ID=your-aws-access-key
+AWS_WHEREBY_ACCESS_KEY_SECRET=your-aws-secret-key
+RECORDING_STORAGE_AWS_BUCKET_NAME=your-s3-bucket-name
+```
+
+### Optional Variables
+
+```bash
+# Whereby API URL (defaults to production)
+WHEREBY_API_URL=https://api.whereby.dev/v1
+
+# SQS Configuration (for recording processing)
+AWS_PROCESS_RECORDING_QUEUE_URL=https://sqs.region.amazonaws.com/account/queue
+SQS_POLLING_TIMEOUT_SECONDS=60
+```
+
+## Configuration Steps
+
+### 1. Whereby API Key Setup
+
+1. **Contact Whereby Support** to request API access for your account
+2. **Generate JWT Token** in your Whereby dashboard under API settings
+3. **Copy the JWT token** and set it as `WHEREBY_API_KEY` in your environment
+
+The API key is a JWT token that looks like:
+```
+eyJ[...truncated JWT token...]
+```
+
+### 2. Webhook Configuration
+
+1. **Access Whereby Dashboard** and navigate to webhook settings
+2. **Set Webhook URL** to your Reflector instance:
+   ```
+   https://your-reflector-domain.com/v1/whereby
+   ```
+3. **Configure Events** to send the following event types:
+   - `room.client.joined` - When participants join
+   - `room.client.left` - When participants leave
+4. **Generate Webhook Secret** and set it as `WHEREBY_WEBHOOK_SECRET`
+5. **Save Configuration** in your Whereby dashboard
+
+### 3. AWS S3 Storage Setup
+
+1. **Create S3 Bucket** dedicated for Whereby recordings
+2. **Create IAM User** with programmatic access
+3. **Attach S3 Policy** with the following permissions:
+   ```json
+   {
+     "Version": "2012-10-17",
+     "Statement": [
+       {
+         "Effect": "Allow",
+         "Action": [
+           "s3:PutObject",
+           "s3:PutObjectAcl",
+           "s3:GetObject"
+         ],
+         "Resource": "arn:aws:s3:::your-bucket-name/*"
+       }
+     ]
+   }
+   ```
+4. **Configure Environment Variables** with the IAM credentials
+
+### 4. Room Configuration
+
+When creating rooms in Reflector, set the platform to use Whereby:
+
+```bash
+curl -X POST "https://your-reflector-domain.com/v1/rooms" \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $AUTH_TOKEN" \
+  -d '{
+    "name": "my-whereby-room",
+    "platform": "whereby",
+    "recording_type": "cloud",
+    "recording_trigger": "automatic-2nd-participant",
+    "is_locked": false,
+    "room_mode": "normal"
+  }'
+```
+
+## Meeting Features
+
+### Recording Options
+
+Whereby supports three recording types:
+- **`none`**: No recording
+- **`local`**: Local recording (not recommended for production)
+- **`cloud`**: Cloud recording to S3 (recommended)
+
+### Recording Triggers
+
+Control when recordings start:
+- **`none`**: No automatic recording
+- **`prompt`**: Prompt users to start recording
+- **`automatic`**: Start immediately when meeting begins
+- **`automatic-2nd-participant`**: Start when second participant joins
+
+### Room Modes
+
+- **`normal`**: Standard meeting room
+- **`group`**: Group meeting with advanced features
+
+## Webhook Event Handling
+
+Reflector automatically handles these Whereby webhook events:
+
+### Participant Tracking
+```json
+{
+  "type": "room.client.joined",
+  "data": {
+    "meetingId": "room-uuid",
+    "numClients": 2
+  }
+}
+```
+
+### Recording Events
+Whereby sends recording completion events that trigger Reflector's processing pipeline:
+- Audio transcription
+- Speaker diarization
+- Summary generation
+
+## Troubleshooting
+
+### Common Issues
+
+#### API Authentication Errors
+**Symptoms**: 401 Unauthorized errors when creating meetings
+
+**Solutions**:
+1. Verify your `WHEREBY_API_KEY` is correct and not expired
+2. Ensure you have API access enabled on your Whereby account
+3. Contact Whereby support if API access is not available
+
+#### Webhook Signature Validation Failed
+**Symptoms**: Webhook events rejected with 401 errors
+
+**Solutions**:
+1. Verify `WHEREBY_WEBHOOK_SECRET` matches your Whereby dashboard configuration
+2. Check webhook URL is correctly configured in Whereby dashboard
+3. Ensure webhook endpoint is accessible from Whereby servers
+
+#### Recording Upload Failures
+**Symptoms**: Recordings not appearing in S3 bucket
+
+**Solutions**:
+1. Verify AWS credentials have S3 write permissions
+2. Check S3 bucket name is correct and accessible
+3. Ensure AWS region settings match your bucket location
+4. Review AWS CloudTrail logs for permission issues
+
+#### Participant Count Not Updating
+**Symptoms**: Meeting participant counts remain at 0
+
+**Solutions**:
+1. Verify webhook events are being received at `/v1/whereby`
+2. Check webhook signature validation is passing
+3. Ensure meeting IDs match between Whereby and Reflector database
+
+### Debug Commands
+
+```bash
+# Test Whereby API connectivity
+curl -H "Authorization: Bearer $WHEREBY_API_KEY" \
+  https://api.whereby.dev/v1/meetings
+
+# Check webhook endpoint health
+curl https://your-reflector-domain.com/v1/whereby/health
+
+# Verify S3 bucket access
+aws s3 ls s3://your-bucket-name --profile whereby-user
+```
+
+## Security Considerations
+
+### API Key Security
+- Store API keys securely using environment variables
+- Rotate API keys regularly
+- Never commit API keys to version control
+- Use separate keys for development and production
+
+### Webhook Security
+- Always validate webhook signatures using HMAC-SHA256
+- Use HTTPS for all webhook endpoints
+- Implement rate limiting on webhook endpoints
+- Monitor webhook events for suspicious activity
+
+### Recording Privacy
+- Ensure S3 bucket access is restricted to authorized users
+- Consider encryption at rest for sensitive recordings
+- Implement retention policies for recorded content
+- Comply with data protection regulations (GDPR, etc.)
+
+## Performance Optimization
+
+### Meeting Scaling
+- Monitor concurrent meeting limits on your Whereby plan
+- Implement meeting cleanup for expired sessions
+- Use appropriate room modes for different use cases
+
+### Recording Processing
+- Configure SQS for asynchronous recording processing
+- Monitor S3 storage usage and costs
+- Implement automatic cleanup of processed recordings
+
+### Webhook Reliability
+- Implement webhook retry mechanisms
+- Monitor webhook delivery success rates
+- Log webhook events for debugging and auditing
+
+## Migration from Other Platforms
+
+If migrating from another video platform:
+
+1. **Update Room Configuration**: Change existing rooms to use `"platform": "whereby"`
+2. **Configure Webhooks**: Set up Whereby webhook endpoints
+3. **Test Integration**: Verify meeting creation and event handling
+4. **Monitor Performance**: Watch for any issues during transition
+5. **Update Documentation**: Inform users of any workflow changes
+
+## Support
+
+For Whereby-specific issues:
+- **Whereby Support**: [whereby.com/support](https://whereby.com/support)
+- **API Documentation**: [whereby.dev](https://whereby.dev)
+- **Status Page**: [status.whereby.com](https://status.whereby.com)
+
+For Reflector integration issues:
+- Check application logs for error details
+- Verify environment variable configuration
+- Test webhook connectivity and authentication
+- Review AWS permissions and S3 access
--- a/docs/video_platforms.md
+++ b/docs/video_platforms.md
@@ -0,0 +1,474 @@
+# Video Platforms Architecture (PR #529 Analysis)
+
+This document analyzes the video platforms refactoring implemented in PR #529 for daily.co integration, providing a blueprint for extending support to Jitsi and other video conferencing platforms.
+
+## Overview
+
+The video platforms refactoring introduces a clean abstraction layer that allows Reflector to support multiple video conferencing providers (Whereby, Daily.co, etc.) without changing core application logic. This architecture enables:
+
+- Seamless switching between video platforms
+- Platform-specific feature support
+- Isolated platform code organization
+- Consistent API surface across platforms
+- Feature flags for gradual migration
+
+## Architecture Components
+
+### 1. **Directory Structure**
+
+```
+server/reflector/video_platforms/
+├── __init__.py              # Public API exports
+├── base.py                  # Abstract base classes
+├── factory.py               # Platform client factory
+├── registry.py              # Platform registration system
+├── whereby.py               # Whereby implementation
+├── daily.py                 # Daily.co implementation
+└── mock.py                  # Testing implementation
+```
+
+### 2. **Core Abstract Classes**
+
+#### `VideoPlatformClient` (base.py)
+Abstract base class defining the interface all platforms must implement:
+
+```python
+class VideoPlatformClient(ABC):
+    PLATFORM_NAME: str = ""
+
+    @abstractmethod
+    async def create_meeting(self, room_name_prefix: str, end_date: datetime, room: Room) -> MeetingData
+
+    @abstractmethod
+    async def get_room_sessions(self, room_name: str) -> Dict[str, Any]
+
+    @abstractmethod
+    async def delete_room(self, room_name: str) -> bool
+
+    @abstractmethod
+    async def upload_logo(self, room_name: str, logo_path: str) -> bool
+
+    @abstractmethod
+    def verify_webhook_signature(self, body: bytes, signature: str, timestamp: Optional[str] = None) -> bool
+```
+
+#### `MeetingData` (base.py)
+Standardized meeting data structure returned by all platforms:
+
+```python
+class MeetingData(BaseModel):
+    meeting_id: str
+    room_name: str
+    room_url: str
+    host_room_url: str
+    platform: str
+    extra_data: Dict[str, Any] = {}  # Platform-specific data
+```
+
+#### `VideoPlatformConfig` (base.py)
+Unified configuration structure for all platforms:
+
+```python
+class VideoPlatformConfig(BaseModel):
+    api_key: str
+    webhook_secret: str
+    api_url: Optional[str] = None
+    subdomain: Optional[str] = None
+    s3_bucket: Optional[str] = None
+    s3_region: Optional[str] = None
+    aws_role_arn: Optional[str] = None
+    aws_access_key_id: Optional[str] = None
+    aws_access_key_secret: Optional[str] = None
+```
+
+### 3. **Platform Registration System**
+
+#### Registry Pattern (registry.py)
+- Automatic registration of built-in platforms
+- Runtime platform discovery
+- Type-safe client instantiation
+
+```python
+# Auto-registration of platforms
+_PLATFORMS: Dict[str, Type[VideoPlatformClient]] = {}
+
+def register_platform(name: str, client_class: Type[VideoPlatformClient])
+def get_platform_client(platform: str, config: VideoPlatformConfig) -> VideoPlatformClient
+```
+
+#### Factory System (factory.py)
+- Configuration management per platform
+- Platform selection logic
+- Feature flag integration
+
+```python
+def get_platform_for_room(room_id: Optional[str] = None) -> str:
+    """Determine which platform to use based on feature flags."""
+    if not settings.DAILY_MIGRATION_ENABLED:
+        return "whereby"
+
+    if room_id and room_id in settings.DAILY_MIGRATION_ROOM_IDS:
+        return "daily"
+
+    return settings.DEFAULT_VIDEO_PLATFORM
+```
+
+### 4. **Database Schema Changes**
+
+#### Room Model Updates
+Added `platform` field to track which video platform each room uses:
+
+```python
+# Database Schema
+platform_column = sqlalchemy.Column(
+    "platform",
+    sqlalchemy.String,
+    nullable=False,
+    server_default="whereby"
+)
+
+# Pydantic Model
+class Room(BaseModel):
+    platform: Literal["whereby", "daily"] = "whereby"
+```
+
+#### Meeting Model Updates
+Added `platform` field to meetings for tracking and debugging:
+
+```python
+# Database Schema
+platform_column = sqlalchemy.Column(
+    "platform",
+    sqlalchemy.String,
+    nullable=False,
+    server_default="whereby"
+)
+
+# Pydantic Model
+class Meeting(BaseModel):
+    platform: Literal["whereby", "daily"] = "whereby"
+```
+
+**Key Decision**: No platform-specific fields were added to models. Instead, the `extra_data` field in `MeetingData` handles platform-specific information, following the user's rule of using generic `provider_data` as JSON if needed.
+
+### 5. **Settings Configuration**
+
+#### Feature Flags
+```python
+# Migration control
+DAILY_MIGRATION_ENABLED: bool = True
+DAILY_MIGRATION_ROOM_IDS: list[str] = []
+DEFAULT_VIDEO_PLATFORM: str = "daily"
+
+# Daily.co specific settings
+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
+```
+
+#### Configuration Pattern
+Each platform gets its own configuration namespace while sharing common patterns:
+
+```python
+def get_platform_config(platform: str) -> VideoPlatformConfig:
+    if platform == "whereby":
+        return VideoPlatformConfig(
+            api_key=settings.WHEREBY_API_KEY or "",
+            webhook_secret=settings.WHEREBY_WEBHOOK_SECRET or "",
+            # ... whereby-specific config
+        )
+    elif platform == "daily":
+        return VideoPlatformConfig(
+            api_key=settings.DAILY_API_KEY or "",
+            webhook_secret=settings.DAILY_WEBHOOK_SECRET or "",
+            # ... daily-specific config
+        )
+```
+
+### 6. **API Integration Updates**
+
+#### Room Creation (views/rooms.py)
+Updated to use platform factory instead of direct Whereby calls:
+
+```python
+@router.post("/rooms/{room_name}/meeting")
+async def rooms_create_meeting(room_name: str, user: UserInfo):
+    # OLD: Direct Whereby integration
+    # whereby_meeting = await create_meeting("", end_date=end_date, room=room)
+
+    # NEW: Platform abstraction
+    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=end_date, room=room
+    )
+
+    await client.upload_logo(meeting_data.room_name, "./images/logo.png")
+```
+
+### 7. **Webhook Handling**
+
+#### Separate Webhook Endpoints
+Each platform gets its own webhook endpoint with platform-specific signature verification:
+
+```python
+# views/daily.py
+@router.post("/daily_webhook")
+async def daily_webhook(event: DailyWebhookEvent, request: Request):
+    # Verify Daily.co signature
+    body = await request.body()
+    signature = request.headers.get("X-Daily-Signature", "")
+
+    if not verify_daily_webhook_signature(body, signature):
+        raise HTTPException(status_code=401)
+
+    # Handle platform-specific events
+    if event.type == "participant.joined":
+        await _handle_participant_joined(event)
+```
+
+#### Consistent Event Handling
+Despite different event formats, the core business logic remains the same:
+
+```python
+async def _handle_participant_joined(event):
+    room_name = event.data.get("room", {}).get("name")  # Daily.co format
+    meeting = await meetings_controller.get_by_room_name(room_name)
+    if meeting:
+        current_count = getattr(meeting, "num_clients", 0)
+        await meetings_controller.update_meeting(
+            meeting.id, num_clients=current_count + 1
+        )
+```
+
+### 8. **Worker Task Integration**
+
+#### New Task for Daily.co Recording Processing
+Added platform-specific recording processing while maintaining the same pipeline:
+
+```python
+@shared_task
+@asynctask
+async def process_recording_from_url(recording_url: str, meeting_id: str, recording_id: str):
+    """Process recording from Direct URL (Daily.co webhook)."""
+    logger.info("Processing recording from URL for meeting: %s", meeting_id)
+    # Uses same processing pipeline as Whereby S3 recordings
+```
+
+**Key Decision**: Worker tasks remain in main worker module but could be moved to platform-specific folders as suggested by the user.
+
+### 9. **Testing Infrastructure**
+
+#### Comprehensive Test Suite
+- Unit tests for each platform client
+- Integration tests for platform switching
+- Mock platform for testing without external dependencies
+- Webhook signature verification tests
+
+```python
+class TestPlatformIntegration:
+    """Integration tests for platform switching."""
+
+    async def test_platform_switching_preserves_interface(self):
+        """Test that different platforms provide consistent interface."""
+        # Test both Mock and Daily platforms return MeetingData objects
+        # with consistent fields
+```
+
+## Implementation Patterns for Jitsi Integration
+
+Based on the daily.co implementation, here's how Jitsi should be integrated:
+
+### 1. **Jitsi Client Implementation**
+
+```python
+# video_platforms/jitsi.py
+class JitsiClient(VideoPlatformClient):
+    PLATFORM_NAME = "jitsi"
+
+    async def create_meeting(self, room_name_prefix: str, end_date: datetime, room: Room) -> MeetingData:
+        # Generate unique room name
+        jitsi_room = f"reflector-{room.name}-{int(time.time())}"
+
+        # Generate JWT tokens
+        user_jwt = self._generate_jwt(room=jitsi_room, moderator=False, exp=end_date)
+        host_jwt = self._generate_jwt(room=jitsi_room, moderator=True, exp=end_date)
+
+        return MeetingData(
+            meeting_id=generate_uuid4(),
+            room_name=jitsi_room,
+            room_url=f"https://jitsi.domain/{jitsi_room}?jwt={user_jwt}",
+            host_room_url=f"https://jitsi.domain/{jitsi_room}?jwt={host_jwt}",
+            platform=self.PLATFORM_NAME,
+            extra_data={"user_jwt": user_jwt, "host_jwt": host_jwt}
+        )
+```
+
+### 2. **Settings Integration**
+
+```python
+# settings.py
+JITSI_DOMAIN: str = "meet.jit.si"
+JITSI_JWT_SECRET: str | None = None
+JITSI_WEBHOOK_SECRET: str | None = None
+JITSI_API_URL: str | None = None  # If using Jitsi API
+```
+
+### 3. **Factory Registration**
+
+```python
+# registry.py
+def _register_builtin_platforms():
+    from .jitsi import JitsiClient
+    register_platform("jitsi", JitsiClient)
+
+# factory.py
+def get_platform_config(platform: str) -> VideoPlatformConfig:
+    elif platform == "jitsi":
+        return VideoPlatformConfig(
+            api_key="",  # Jitsi may not need API key
+            webhook_secret=settings.JITSI_WEBHOOK_SECRET or "",
+            api_url=settings.JITSI_API_URL,
+        )
+```
+
+### 4. **Webhook Integration**
+
+```python
+# views/jitsi.py
+@router.post("/jitsi/events")
+async def jitsi_events_webhook(event_data: dict):
+    # Handle Prosody event-sync webhook format
+    event_type = event_data.get("event")
+    room_name = event_data.get("room", "").split("@")[0]
+
+    if event_type == "muc-occupant-joined":
+        # Same participant handling logic as other platforms
+```
+
+## Key Benefits of This Architecture
+
+### 1. **Isolation and Organization**
+- Platform-specific code contained in separate modules
+- No platform logic leaking into core application
+- Easy to add/remove platforms without affecting others
+
+### 2. **Consistent Interface**
+- All platforms implement the same abstract methods
+- Standardized `MeetingData` structure
+- Uniform error handling and logging
+
+### 3. **Gradual Migration Support**
+- Feature flags for controlled rollouts
+- Room-specific platform selection
+- Fallback mechanisms for platform failures
+
+### 4. **Configuration Management**
+- Centralized settings per platform
+- Consistent naming patterns
+- Environment-based configuration
+
+### 5. **Testing and Quality**
+- Mock platform for testing
+- Comprehensive test coverage
+- Platform-specific test utilities
+
+## Migration Strategy Applied
+
+The daily.co implementation demonstrates a careful migration approach:
+
+### 1. **Backward Compatibility**
+- Default platform remains "whereby"
+- Existing rooms continue using Whereby unless explicitly migrated
+- Same API endpoints and response formats
+
+### 2. **Feature Flag Control**
+```python
+# Gradual rollout control
+DAILY_MIGRATION_ENABLED: bool = True
+DAILY_MIGRATION_ROOM_IDS: list[str] = []  # Specific rooms to migrate
+DEFAULT_VIDEO_PLATFORM: str = "daily"     # New rooms default
+```
+
+### 3. **Data Integrity**
+- Platform field tracks which service each room/meeting uses
+- No data loss during migration
+- Platform-specific data preserved in `extra_data`
+
+### 4. **Monitoring and Rollback**
+- Comprehensive logging of platform selection
+- Easy rollback by changing feature flags
+- Platform-specific error tracking
+
+## Recommendations for Jitsi Integration
+
+Based on this analysis and the user's requirements:
+
+### 1. **Follow the Pattern**
+- Create `video_platforms/jitsi/` directory with:
+  - `client.py` - Main JitsiClient implementation
+  - `tasks.py` - Jitsi-specific worker tasks
+  - `__init__.py` - Module exports
+
+### 2. **Settings Organization**
+- Use `JITSI_*` prefix for all Jitsi settings
+- Follow the same configuration pattern as Daily.co
+- Support both environment variables and config files
+
+### 3. **Generic Database Fields**
+- Avoid platform-specific columns in database
+- Use `provider_data` JSON field if platform-specific data needed
+- Keep `platform` field as simple string identifier
+
+### 4. **Worker Task Migration**
+According to user requirements, migrate platform-specific tasks:
+```
+video_platforms/
+├── whereby/
+│   ├── client.py  (moved from whereby.py)
+│   └── tasks.py   (moved from worker/whereby_tasks.py)
+├── daily/
+│   ├── client.py  (moved from daily.py)
+│   └── tasks.py   (moved from worker/daily_tasks.py)
+└── jitsi/
+    ├── client.py  (new JitsiClient)
+    └── tasks.py   (new Jitsi recording tasks)
+```
+
+### 5. **Webhook Architecture**
+- Create `views/jitsi.py` for Jitsi-specific webhooks
+- Follow the same signature verification pattern
+- Reuse existing participant tracking logic
+
+## Implementation Checklist for Jitsi
+
+- [ ] Create `video_platforms/jitsi/` directory structure
+- [ ] Implement `JitsiClient` following the abstract interface
+- [ ] Add Jitsi settings to configuration
+- [ ] Register Jitsi platform in factory/registry
+- [ ] Create Jitsi webhook endpoint
+- [ ] Implement JWT token generation for room access
+- [ ] Add Jitsi recording processing tasks
+- [ ] Create comprehensive test suite
+- [ ] Update database migrations for platform field
+- [ ] Document Jitsi-specific configuration
+
+## Conclusion
+
+The video platforms refactoring in PR #529 provides an excellent foundation for adding Jitsi support. The architecture is well-designed with clear separation of concerns, consistent interfaces, and excellent extensibility. The daily.co implementation demonstrates how to add a new platform while maintaining backward compatibility and providing gradual migration capabilities.
+
+The pattern should be directly applicable to Jitsi integration, with the main differences being:
+- JWT-based authentication instead of API keys
+- Different webhook event formats
+- Jibri recording pipeline integration
+- Self-hosted deployment considerations
+
+This architecture successfully achieves the user's goals of:
+1. Settings-based configuration
+2. Generic database fields (no provider-specific columns)
+3. Platform isolation in separate directories
+4. Worker task organization within platform folders
--- a/gpu/modal_deployments/.gitignore
+++ b/gpu/modal_deployments/.gitignore
@@ -1,33 +0,0 @@
-# OS / Editor
-.DS_Store
-.vscode/
-.idea/
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-
-# Logs
-*.log
-
-# Env and secrets
-.env
-.env.*
-*.env
-*.secret
-
-# Build / dist
-build/
-dist/
-.eggs/
-*.egg-info/
-
-# Coverage / test
-.pytest_cache/
-.coverage*
-htmlcov/
-
-# Modal local state (if any)
-modal_mounts/
-.modal_cache/
--- a/gpu/modal_deployments/deploy-all.sh
+++ b/gpu/modal_deployments/deploy-all.sh
@@ -1,150 +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=$API_KEY"
-echo "# --- End Modal Configuration ---"
--- a/gpu/self_hosted/.env.example
+++ b/gpu/self_hosted/.env.example
@@ -1,2 +0,0 @@
-REFLECTOR_GPU_APIKEY=
-HF_TOKEN=
--- a/gpu/self_hosted/.gitignore
+++ b/gpu/self_hosted/.gitignore
@@ -1,38 +0,0 @@
-cache/
-
-# OS / Editor
-.DS_Store
-.vscode/
-.idea/
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-
-# Env and secrets
-.env
-*.env
-*.secret
-HF_TOKEN
-REFLECTOR_GPU_APIKEY
-
-# Virtual env / uv
-.venv/
-venv/
-ENV/
-uv/
-
-# Build / dist
-build/
-dist/
-.eggs/
-*.egg-info/
-
-# Coverage / test
-.pytest_cache/
-.coverage*
-htmlcov/
-
-# Logs
-*.log
--- a/gpu/self_hosted/DEV_SETUP.md
+++ b/gpu/self_hosted/DEV_SETUP.md
@@ -1,137 +0,0 @@
-# Local Development GPU Setup
-
-Run transcription and diarization locally for development/testing.
-
-> **For production deployment**, see the [Self-Hosted GPU Setup Guide](../../docs/docs/installation/self-hosted-gpu-setup.md).
-
-## Prerequisites
-
-1. **Python 3.12+** and **uv** package manager
-2. **FFmpeg** installed and on PATH
-3. **HuggingFace account** with access to pyannote models
-
-### Accept Pyannote Licenses (Required)
-
-Before first run, accept licenses for these gated models (logged into HuggingFace):
- https://hf.co/pyannote/speaker-diarization-3.1
- https://hf.co/pyannote/segmentation-3.0
-
-## Quick Start
-
-### 1. Install dependencies
-
-```bash
-cd gpu/self_hosted
-uv sync
-```
-
-### 2. Start the GPU service
-
-```bash
-cd gpu/self_hosted
-HF_TOKEN=<your-huggingface-token> \
-REFLECTOR_GPU_APIKEY=dev-key-12345 \
-.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-Note: The `.env` file is NOT auto-loaded. Pass env vars explicitly or use:
-```bash
-export HF_TOKEN=<your-token>
-export REFLECTOR_GPU_APIKEY=dev-key-12345
-.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-### 3. Configure Reflector to use local GPU
-
-Edit `server/.env`:
-
-```bash
-# Transcription - local GPU service
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=http://host.docker.internal:8000
-TRANSCRIPT_MODAL_API_KEY=dev-key-12345
-
-# Diarization - local GPU service
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=http://host.docker.internal:8000
-DIARIZATION_MODAL_API_KEY=dev-key-12345
-```
-
-Note: Use `host.docker.internal` because Reflector server runs in Docker.
-
-### 4. Restart Reflector server
-
-```bash
-cd server
-docker compose restart server worker
-```
-
-## Testing
-
-### Test transcription
-
-```bash
-curl -s -X POST http://localhost:8000/v1/audio/transcriptions \
-  -H "Authorization: Bearer dev-key-12345" \
-  -F "file=@/path/to/audio.wav" \
-  -F "language=en"
-```
-
-### Test diarization
-
-```bash
-curl -s -X POST "http://localhost:8000/diarize?audio_file_url=<audio-url>" \
-  -H "Authorization: Bearer dev-key-12345"
-```
-
-## Platform Notes
-
-### macOS (ARM)
-
-Docker build fails - CUDA packages are x86_64 only. Use local Python instead:
-```bash
-uv sync
-HF_TOKEN=xxx REFLECTOR_GPU_APIKEY=xxx .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-### Linux with NVIDIA GPU
-
-Docker works with CUDA acceleration:
-```bash
-docker compose up -d
-```
-
-### CPU-only
-
-Works on any platform, just slower. PyTorch auto-detects and falls back to CPU.
-
-## Switching Back to Modal.com
-
-Edit `server/.env`:
-
-```bash
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=<modal-api-key>
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=<modal-api-key>
-```
-
-## Troubleshooting
-
-### "Could not download pyannote pipeline"
- Accept model licenses at HuggingFace (see Prerequisites)
- Verify HF_TOKEN is set and valid
-
-### Service won't start
- Check port 8000 is free: `lsof -i :8000`
- Kill orphan processes if needed
-
-### Transcription returns empty text
- Ensure audio contains speech (not just tones/silence)
- Check audio format is supported (wav, mp3, etc.)
-
-### Deprecation warnings from torchaudio/pyannote
- Safe to ignore - doesn't affect functionality
--- a/gpu/self_hosted/Dockerfile
+++ b/gpu/self_hosted/Dockerfile
@@ -1,46 +0,0 @@
-FROM python:3.12-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy \
-    UV_NO_CACHE=1
-
-WORKDIR /tmp
-RUN apt-get update \
- && apt-get install -y \
-    ffmpeg \
-    curl \
-    ca-certificates \
-    gnupg \
-    wget \
- && apt-get clean
-# Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
-ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
-RUN dpkg -i /cuda-keyring.deb \
- && rm /cuda-keyring.deb \
- && apt-get update \
- && apt-get install -y --no-install-recommends \
-    cuda-cudart-12-6 \
-    libcublas-12-6 \
-    libcudnn9-cuda-12 \
-    libcudnn9-dev-cuda-12 \
- && apt-get clean \
- && rm -rf /var/lib/apt/lists/*
-ADD https://astral.sh/uv/install.sh /uv-installer.sh
-RUN sh /uv-installer.sh && rm /uv-installer.sh
-ENV PATH="/root/.local/bin/:$PATH"
-ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH"
-
-RUN mkdir -p /app
-WORKDIR /app
-COPY pyproject.toml uv.lock /app/
-
-
-COPY ./app /app/app
-COPY ./main.py /app/
-COPY ./runserver.sh /app/
-
-EXPOSE 8000
-
-CMD ["sh", "/app/runserver.sh"]
-
-
--- a/gpu/self_hosted/README.md
+++ b/gpu/self_hosted/README.md
@@ -1,77 +0,0 @@
-# Self-hosted Model API
-
-Run transcription, translation, and diarization services compatible with Reflector's GPU Model API. Works on CPU or GPU.
-
-Environment variables
-
- REFLECTOR_GPU_APIKEY: Optional Bearer token. If unset, auth is disabled.
- HF_TOKEN: Optional. Required for diarization to download pyannote pipelines
-
-Requirements
-
- FFmpeg must be installed and on PATH (used for URL-based and segmented transcription)
- Python 3.12+
- NVIDIA GPU optional. If available, it will be used automatically
-
-Local run
-Set env vars in self_hosted/.env file
-uv sync
-
-uv run uvicorn main:app --host 0.0.0.0 --port 8000
-
-Authentication
-
- If REFLECTOR_GPU_APIKEY is set, include header: Authorization: Bearer <key>
-
-Endpoints
-
- POST /v1/audio/transcriptions
-
-  - multipart/form-data
-  - fields: file (single file) OR files[] (multiple files), language, batch (true/false)
-  - response: single { text, words, filename } or { results: [ ... ] }
-
- POST /v1/audio/transcriptions-from-url
-
-  - application/json
-  - body: { audio_file_url, language, timestamp_offset }
-  - response: { text, words }
-
- POST /translate
-
-  - text: query parameter
-  - body (application/json): { source_language, target_language }
-  - response: { text: { <src>: original, <tgt>: translated } }
-
- POST /diarize
-  - query parameters: audio_file_url, timestamp (optional)
-  - requires HF_TOKEN to be set (for pyannote)
-  - response: { diarization: [ { start, end, speaker } ] }
-
-OpenAPI docs
-
- Visit /docs when the server is running
-
-Docker
-
- Not yet provided in this directory. A Dockerfile will be added later. For now, use Local run above
-
-# Setup
-
-[SETUP.md](SETUP.md)
-
-# Conformance tests
-
-## From this directory
-
-TRANSCRIPT_URL=http://localhost:8000 \
-TRANSCRIPT_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_transcript.py
-
-TRANSLATION_URL=http://localhost:8000 \
-TRANSLATION_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_translation.py
-
-DIARIZATION_URL=http://localhost:8000 \
-DIARIZATION_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_diarization.py
--- a/gpu/self_hosted/app/auth.py
+++ b/gpu/self_hosted/app/auth.py
@@ -1,19 +0,0 @@
-import os
-
-from fastapi import Depends, HTTPException, status
-from fastapi.security import OAuth2PasswordBearer
-
-oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-
-def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-    required_key = os.environ.get("REFLECTOR_GPU_APIKEY")
-    if not required_key:
-        return
-    if apikey == required_key:
-        return
-    raise HTTPException(
-        status_code=status.HTTP_401_UNAUTHORIZED,
-        detail="Invalid API key",
-        headers={"WWW-Authenticate": "Bearer"},
-    )
--- a/gpu/self_hosted/app/config.py
+++ b/gpu/self_hosted/app/config.py
@@ -1,12 +0,0 @@
-from pathlib import Path
-
-SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-SAMPLE_RATE = 16000
-VAD_CONFIG = {
-    "batch_max_duration": 30.0,
-    "silence_padding": 0.5,
-    "window_size": 512,
-}
-
-# App-level paths
-UPLOADS_PATH = Path("/tmp/whisper-uploads")
--- a/gpu/self_hosted/app/factory.py
+++ b/gpu/self_hosted/app/factory.py
@@ -1,30 +0,0 @@
-from contextlib import asynccontextmanager
-
-from fastapi import FastAPI
-
-from .routers.diarization import router as diarization_router
-from .routers.transcription import router as transcription_router
-from .routers.translation import router as translation_router
-from .services.transcriber import WhisperService
-from .services.diarizer import PyannoteDiarizationService
-from .utils import ensure_dirs
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    ensure_dirs()
-    whisper_service = WhisperService()
-    whisper_service.load()
-    app.state.whisper = whisper_service
-    diarization_service = PyannoteDiarizationService()
-    diarization_service.load()
-    app.state.diarizer = diarization_service
-    yield
-
-
-def create_app() -> FastAPI:
-    app = FastAPI(lifespan=lifespan)
-    app.include_router(transcription_router)
-    app.include_router(translation_router)
-    app.include_router(diarization_router)
-    return app
--- a/gpu/self_hosted/app/routers/diarization.py
+++ b/gpu/self_hosted/app/routers/diarization.py
@@ -1,30 +0,0 @@
-from typing import List
-
-from fastapi import APIRouter, Depends, Request
-from pydantic import BaseModel
-
-from ..auth import apikey_auth
-from ..services.diarizer import PyannoteDiarizationService
-from ..utils import download_audio_file
-
-router = APIRouter(tags=["diarization"])
-
-
-class DiarizationSegment(BaseModel):
-    start: float
-    end: float
-    speaker: int
-
-
-class DiarizationResponse(BaseModel):
-    diarization: List[DiarizationSegment]
-
-
-@router.post(
-    "/diarize", dependencies=[Depends(apikey_auth)], response_model=DiarizationResponse
-)
-def diarize(request: Request, audio_file_url: str, timestamp: float = 0.0):
-    with download_audio_file(audio_file_url) as (file_path, _ext):
-        file_path = str(file_path)
-        diarizer: PyannoteDiarizationService = request.app.state.diarizer
-        return diarizer.diarize_file(file_path, timestamp=timestamp)
--- a/gpu/self_hosted/app/routers/transcription.py
+++ b/gpu/self_hosted/app/routers/transcription.py
@@ -1,109 +0,0 @@
-import uuid
-from typing import Optional, Union
-
-from fastapi import APIRouter, Body, Depends, Form, HTTPException, Request, UploadFile
-from pydantic import BaseModel
-from pathlib import Path
-from ..auth import apikey_auth
-from ..config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
-from ..services.transcriber import MODEL_NAME
-from ..utils import cleanup_uploaded_files, download_audio_file
-
-router = APIRouter(prefix="/v1/audio", tags=["transcription"])
-
-
-class WordTiming(BaseModel):
-    word: str
-    start: float
-    end: float
-
-
-class TranscriptResult(BaseModel):
-    text: str
-    words: list[WordTiming]
-    filename: Optional[str] = None
-
-
-class TranscriptBatchResponse(BaseModel):
-    results: list[TranscriptResult]
-
-
-@router.post(
-    "/transcriptions",
-    dependencies=[Depends(apikey_auth)],
-    response_model=Union[TranscriptResult, TranscriptBatchResponse],
-)
-def transcribe(
-    request: Request,
-    file: UploadFile = None,
-    files: list[UploadFile] | None = None,
-    model: str = Form(MODEL_NAME),
-    language: str = Form("en"),
-    batch: bool = Form(False),
-):
-    service = request.app.state.whisper
-    if not file and not files:
-        raise HTTPException(
-            status_code=400, detail="Either 'file' or 'files' parameter is required"
-        )
-    if batch and not files:
-        raise HTTPException(
-            status_code=400, detail="Batch transcription requires 'files'"
-        )
-
-    upload_files = [file] if file else files
-
-    uploaded_paths: list[Path] = []
-    with cleanup_uploaded_files(uploaded_paths):
-        for upload_file in upload_files:
-            audio_suffix = upload_file.filename.split(".")[-1].lower()
-            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-                    ),
-                )
-            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-            file_path = UPLOADS_PATH / unique_filename
-            with open(file_path, "wb") as f:
-                content = upload_file.file.read()
-                f.write(content)
-            uploaded_paths.append(file_path)
-
-        if batch and len(upload_files) > 1:
-            results = []
-            for path in uploaded_paths:
-                result = service.transcribe_file(str(path), language=language)
-                result["filename"] = path.name
-                results.append(result)
-            return {"results": results}
-
-        results = []
-        for path in uploaded_paths:
-            result = service.transcribe_file(str(path), language=language)
-            result["filename"] = path.name
-            results.append(result)
-
-        return {"results": results} if len(results) > 1 else results[0]
-
-
-@router.post(
-    "/transcriptions-from-url",
-    dependencies=[Depends(apikey_auth)],
-    response_model=TranscriptResult,
-)
-def transcribe_from_url(
-    request: Request,
-    audio_file_url: str = Body(..., description="URL of the audio file to transcribe"),
-    model: str = Body(MODEL_NAME),
-    language: str = Body("en"),
-    timestamp_offset: float = Body(0.0),
-):
-    service = request.app.state.whisper
-    with download_audio_file(audio_file_url) as (file_path, _ext):
-        file_path = str(file_path)
-        result = service.transcribe_vad_url_segment(
-            file_path=file_path, timestamp_offset=timestamp_offset, language=language
-        )
-        return result
--- a/gpu/self_hosted/app/routers/translation.py
+++ b/gpu/self_hosted/app/routers/translation.py
@@ -1,28 +0,0 @@
-from typing import Dict
-
-from fastapi import APIRouter, Body, Depends
-from pydantic import BaseModel
-
-from ..auth import apikey_auth
-from ..services.translator import TextTranslatorService
-
-router = APIRouter(tags=["translation"])
-
-translator = TextTranslatorService()
-
-
-class TranslationResponse(BaseModel):
-    text: Dict[str, str]
-
-
-@router.post(
-    "/translate",
-    dependencies=[Depends(apikey_auth)],
-    response_model=TranslationResponse,
-)
-def translate(
-    text: str,
-    source_language: str = Body("en"),
-    target_language: str = Body("fr"),
-):
-    return translator.translate(text, source_language, target_language)
--- a/gpu/self_hosted/app/services/diarizer.py
+++ b/gpu/self_hosted/app/services/diarizer.py
@@ -1,42 +0,0 @@
-import os
-import threading
-
-import torch
-import torchaudio
-from pyannote.audio import Pipeline
-
-
-class PyannoteDiarizationService:
-    def __init__(self):
-        self._pipeline = None
-        self._device = "cpu"
-        self._lock = threading.Lock()
-
-    def load(self):
-        self._device = "cuda" if torch.cuda.is_available() else "cpu"
-        self._pipeline = Pipeline.from_pretrained(
-            "pyannote/speaker-diarization-3.1",
-            use_auth_token=os.environ.get("HF_TOKEN"),
-        )
-        self._pipeline.to(torch.device(self._device))
-
-    def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:
-        if self._pipeline is None:
-            self.load()
-        waveform, sample_rate = torchaudio.load(file_path)
-        with self._lock:
-            diarization = self._pipeline(
-                {"waveform": waveform, "sample_rate": sample_rate}
-            )
-        words = []
-        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
-            words.append(
-                {
-                    "start": round(timestamp + diarization_segment.start, 3),
-                    "end": round(timestamp + diarization_segment.end, 3),
-                    "speaker": int(speaker[-2:])
-                    if speaker and speaker[-2:].isdigit()
-                    else 0,
-                }
-            )
-        return {"diarization": words}
--- a/gpu/self_hosted/app/services/transcriber.py
+++ b/gpu/self_hosted/app/services/transcriber.py
@@ -1,217 +0,0 @@
-import os
-import shutil
-import subprocess
-import threading
-from typing import Generator
-
-import faster_whisper
-import librosa
-import numpy as np
-import torch
-from fastapi import HTTPException
-from silero_vad import VADIterator, load_silero_vad
-
-from ..config import SAMPLE_RATE, VAD_CONFIG
-
-# Whisper configuration (service-local defaults)
-MODEL_NAME = "large-v2"
-# None delegates compute type to runtime: float16 on CUDA, int8 on CPU
-MODEL_COMPUTE_TYPE = None
-MODEL_NUM_WORKERS = 1
-CACHE_PATH = os.path.join(os.path.expanduser("~"), ".cache", "reflector-whisper")
-from ..utils import NoStdStreams
-
-
-class WhisperService:
-    def __init__(self):
-        self.model = None
-        self.device = "cpu"
-        self.lock = threading.Lock()
-
-    def load(self):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        compute_type = MODEL_COMPUTE_TYPE or (
-            "float16" if self.device == "cuda" else "int8"
-        )
-        self.model = faster_whisper.WhisperModel(
-            MODEL_NAME,
-            device=self.device,
-            compute_type=compute_type,
-            num_workers=MODEL_NUM_WORKERS,
-            download_root=CACHE_PATH,
-        )
-
-    def pad_audio(self, audio_array, sample_rate: int = SAMPLE_RATE):
-        audio_duration = len(audio_array) / sample_rate
-        if audio_duration < VAD_CONFIG["silence_padding"]:
-            silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
-            silence = np.zeros(silence_samples, dtype=np.float32)
-            return np.concatenate([audio_array, silence])
-        return audio_array
-
-    def enforce_word_timing_constraints(self, words: list[dict]) -> list[dict]:
-        if len(words) <= 1:
-            return words
-        enforced: list[dict] = []
-        for i, word in enumerate(words):
-            current = dict(word)
-            if i < len(words) - 1:
-                next_start = words[i + 1]["start"]
-                if current["end"] > next_start:
-                    current["end"] = next_start
-            enforced.append(current)
-        return enforced
-
-    def transcribe_file(self, file_path: str, language: str = "en") -> dict:
-        input_for_model: str | "object" = file_path
-        try:
-            audio_array, _sample_rate = librosa.load(
-                file_path, sr=SAMPLE_RATE, mono=True
-            )
-            if len(audio_array) / float(SAMPLE_RATE) < VAD_CONFIG["silence_padding"]:
-                input_for_model = self.pad_audio(audio_array, SAMPLE_RATE)
-        except Exception:
-            pass
-
-        with self.lock:
-            with NoStdStreams():
-                segments, _ = self.model.transcribe(
-                    input_for_model,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-
-        segments = list(segments)
-        text = "".join(segment.text for segment in segments).strip()
-        words = [
-            {
-                "word": word.word,
-                "start": round(float(word.start), 2),
-                "end": round(float(word.end), 2),
-            }
-            for segment in segments
-            for word in segment.words
-        ]
-        words = self.enforce_word_timing_constraints(words)
-        return {"text": text, "words": words}
-
-    def transcribe_vad_url_segment(
-        self, file_path: str, timestamp_offset: float = 0.0, language: str = "en"
-    ) -> dict:
-        def load_audio_via_ffmpeg(input_path: str, sample_rate: int) -> np.ndarray:
-            ffmpeg_bin = shutil.which("ffmpeg") or "ffmpeg"
-            cmd = [
-                ffmpeg_bin,
-                "-nostdin",
-                "-threads",
-                "1",
-                "-i",
-                input_path,
-                "-f",
-                "f32le",
-                "-acodec",
-                "pcm_f32le",
-                "-ac",
-                "1",
-                "-ar",
-                str(sample_rate),
-                "pipe:1",
-            ]
-            try:
-                proc = subprocess.run(
-                    cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True
-                )
-            except Exception as e:
-                raise HTTPException(status_code=400, detail=f"ffmpeg failed: {e}")
-            audio = np.frombuffer(proc.stdout, dtype=np.float32)
-            return audio
-
-        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
-        # If you modify this function, you MUST update all copies:
-        #   - gpu/modal_deployments/reflector_transcriber.py
-        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-        #   - gpu/self_hosted/app/services/transcriber.py (this file)
-        def vad_segments(
-            audio_array,
-            sample_rate: int = SAMPLE_RATE,
-            window_size: int = VAD_CONFIG["window_size"],
-        ) -> Generator[tuple[float, float], None, None]:
-            vad_model = load_silero_vad(onnx=False)
-            iterator = VADIterator(vad_model, sampling_rate=sample_rate)
-            start = None
-            for i in range(0, len(audio_array), window_size):
-                chunk = audio_array[i : i + window_size]
-                if len(chunk) < window_size:
-                    chunk = np.pad(
-                        chunk, (0, window_size - len(chunk)), mode="constant"
-                    )
-                speech = iterator(chunk)
-                if not speech:
-                    continue
-                if "start" in speech:
-                    start = speech["start"]
-                    continue
-                if "end" in speech and start is not None:
-                    end = speech["end"]
-                    yield (start / float(SAMPLE_RATE), end / float(SAMPLE_RATE))
-                    start = None
-            # Handle case where audio ends while speech is still active
-            if start is not None:
-                audio_duration = len(audio_array) / float(sample_rate)
-                yield (start / float(SAMPLE_RATE), audio_duration)
-            iterator.reset_states()
-
-        audio_array = load_audio_via_ffmpeg(file_path, SAMPLE_RATE)
-
-        merged_batches: list[tuple[float, float]] = []
-        batch_start = None
-        batch_end = None
-        max_duration = VAD_CONFIG["batch_max_duration"]
-        for seg_start, seg_end in vad_segments(audio_array):
-            if batch_start is None:
-                batch_start, batch_end = seg_start, seg_end
-                continue
-            if seg_end - batch_start <= max_duration:
-                batch_end = seg_end
-            else:
-                merged_batches.append((batch_start, batch_end))
-                batch_start, batch_end = seg_start, seg_end
-        if batch_start is not None and batch_end is not None:
-            merged_batches.append((batch_start, batch_end))
-
-        all_text = []
-        all_words = []
-        for start_time, end_time in merged_batches:
-            s_idx = int(start_time * SAMPLE_RATE)
-            e_idx = int(end_time * SAMPLE_RATE)
-            segment = audio_array[s_idx:e_idx]
-            segment = self.pad_audio(segment, SAMPLE_RATE)
-            with self.lock:
-                segments, _ = self.model.transcribe(
-                    segment,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-            segments = list(segments)
-            text = "".join(seg.text for seg in segments).strip()
-            words = [
-                {
-                    "word": w.word,
-                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
-                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
-                }
-                for seg in segments
-                for w in seg.words
-            ]
-            if text:
-                all_text.append(text)
-            all_words.extend(words)
-
-        all_words = self.enforce_word_timing_constraints(all_words)
-        return {"text": " ".join(all_text), "words": all_words}
--- a/gpu/self_hosted/app/services/translator.py
+++ b/gpu/self_hosted/app/services/translator.py
@@ -1,44 +0,0 @@
-import threading
-
-from transformers import MarianMTModel, MarianTokenizer, pipeline
-
-
-class TextTranslatorService:
-    """Simple text-to-text translator using HuggingFace MarianMT models.
-
-    This mirrors the modal translator API shape but uses text translation only.
-    """
-
-    def __init__(self):
-        self._pipeline = None
-        self._lock = threading.Lock()
-
-    def load(self, source_language: str = "en", target_language: str = "fr"):
-        # Pick a default MarianMT model pair if available; fall back to Helsinki-NLP en->fr
-        model_name = self._resolve_model_name(source_language, target_language)
-        tokenizer = MarianTokenizer.from_pretrained(model_name)
-        model = MarianMTModel.from_pretrained(model_name)
-        self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
-
-    def _resolve_model_name(self, src: str, tgt: str) -> str:
-        # Minimal mapping; extend as needed
-        pair = (src.lower(), tgt.lower())
-        mapping = {
-            ("en", "fr"): "Helsinki-NLP/opus-mt-en-fr",
-            ("fr", "en"): "Helsinki-NLP/opus-mt-fr-en",
-            ("en", "es"): "Helsinki-NLP/opus-mt-en-es",
-            ("es", "en"): "Helsinki-NLP/opus-mt-es-en",
-            ("en", "de"): "Helsinki-NLP/opus-mt-en-de",
-            ("de", "en"): "Helsinki-NLP/opus-mt-de-en",
-        }
-        return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr")
-
-    def translate(self, text: str, source_language: str, target_language: str) -> dict:
-        if self._pipeline is None:
-            self.load(source_language, target_language)
-        with self._lock:
-            results = self._pipeline(
-                text, src_lang=source_language, tgt_lang=target_language
-            )
-        translated = results[0]["translation_text"] if results else ""
-        return {"text": {source_language: text, target_language: translated}}
--- a/gpu/self_hosted/app/utils.py
+++ b/gpu/self_hosted/app/utils.py
@@ -1,115 +0,0 @@
-import logging
-import os
-import sys
-import uuid
-from contextlib import contextmanager
-from typing import Mapping
-from urllib.parse import urlparse
-from pathlib import Path
-
-import requests
-from fastapi import HTTPException
-
-from .config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
-
-logger = logging.getLogger(__name__)
-
-
-class NoStdStreams:
-    def __init__(self):
-        self.devnull = open(os.devnull, "w")
-
-    def __enter__(self):
-        self._stdout, self._stderr = sys.stdout, sys.stderr
-        self._stdout.flush()
-        self._stderr.flush()
-        sys.stdout, sys.stderr = self.devnull, self.devnull
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.stdout, sys.stderr = self._stdout, self._stderr
-        self.devnull.close()
-
-
-def ensure_dirs():
-    UPLOADS_PATH.mkdir(parents=True, exist_ok=True)
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py (this file)
-#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: Mapping[str, str]) -> str:
-    url_path = urlparse(url).path
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return ext
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return "mp3"
-    if "audio/wav" in content_type:
-        return "wav"
-    if "audio/mp4" in content_type:
-        return "mp4"
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return "webm"
-
-    raise HTTPException(
-        status_code=400,
-        detail=(
-            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-        ),
-    )
-
-
-def download_audio_to_uploads(audio_file_url: str) -> tuple[Path, str]:
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-    file_path: Path = UPLOADS_PATH / unique_filename
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    return file_path, audio_suffix
-
-
-@contextmanager
-def download_audio_file(audio_file_url: str):
-    """Download an audio file to UPLOADS_PATH and remove it after use.
-
-    Yields (file_path: Path, audio_suffix: str).
-    """
-    file_path, audio_suffix = download_audio_to_uploads(audio_file_url)
-    try:
-        yield file_path, audio_suffix
-    finally:
-        try:
-            file_path.unlink(missing_ok=True)
-        except Exception as e:
-            logger.error("Error deleting temporary file %s: %s", file_path, e)
-
-
-@contextmanager
-def cleanup_uploaded_files(file_paths: list[Path]):
-    """Ensure provided file paths are removed after use.
-
-    The provided list can be populated inside the context; all present entries
-    at exit will be deleted.
-    """
-    try:
-        yield file_paths
-    finally:
-        for path in list(file_paths):
-            try:
-                path.unlink(missing_ok=True)
-            except Exception as e:
-                logger.error("Error deleting temporary file %s: %s", path, e)
--- a/gpu/self_hosted/compose.yml
+++ b/gpu/self_hosted/compose.yml
@@ -1,10 +0,0 @@
-services:
-  reflector_gpu:
-    build:
-      context: .
-    ports:
-      - "8000:8000"
-    env_file:
-      - .env
-    volumes:
-      - ./cache:/root/.cache
--- a/gpu/self_hosted/main.py
+++ b/gpu/self_hosted/main.py
@@ -1,3 +0,0 @@
-from app.factory import create_app
-
-app = create_app()
--- a/gpu/self_hosted/pyproject.toml
+++ b/gpu/self_hosted/pyproject.toml
@@ -1,19 +0,0 @@
-[project]
-name = "reflector-gpu"
-version = "0.1.0"
-description = "Self-hosted GPU service for speech transcription, diarization, and translation via FastAPI."
-readme = "README.md"
-requires-python = ">=3.12"
-dependencies = [
-    "fastapi[standard]>=0.116.1",
-    "uvicorn[standard]>=0.30.0",
-    "torch>=2.3.0",
-    "faster-whisper>=1.1.0",
-    "librosa==0.10.1",
-    "numpy<2",
-    "silero-vad==5.1.0",
-    "transformers>=4.35.0",
-    "sentencepiece",
-    "pyannote.audio==3.1.0",
-    "torchaudio>=2.3.0",
-]
--- a/gpu/self_hosted/runserver.sh
+++ b/gpu/self_hosted/runserver.sh
@@ -1,17 +0,0 @@
-#!/bin/sh
-set -e
-
-export PATH="/root/.local/bin:$PATH"
-cd /app
-
-# Install Python dependencies at runtime (first run or when FORCE_SYNC=1)
-if [ ! -d "/app/.venv" ] || [ "$FORCE_SYNC" = "1" ]; then
-  echo "[startup] Installing Python dependencies with uv..."
-  uv sync --compile-bytecode --locked
-else
-  echo "[startup] Using existing virtual environment at /app/.venv"
-fi
-
-exec uv run uvicorn main:app --host 0.0.0.0 --port 8000
-
-
--- a/gpu/self_hosted/uv.lock
+++ b/gpu/self_hosted/uv.lock
--- 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/.env.example
+++ b/server/.env.example
@@ -1,139 +0,0 @@
-#
-# This file serve as an example of possible configuration
-# 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
-## =======================================================
-
-## Using jwt/authentik
-AUTH_BACKEND=jwt
-AUTH_JWT_AUDIENCE=
-
-## =======================================================
-## Transcription backend
-##
-## Check reflector/processors/audio_transcript_* for the
-## full list of available transcription backend
-## =======================================================
-
-## Using local whisper
-#TRANSCRIPT_BACKEND=whisper
-
-## Using serverless modal.com (require reflector-gpu-modal deployed)
-#TRANSCRIPT_BACKEND=modal
-#TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-web.modal.run
-#TRANSCRIPT_MODAL_API_KEY=xxxxx
-
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=
-
-## =======================================================
-## Translation backend
-##
-## Only available in modal atm
-## =======================================================
-TRANSLATION_BACKEND=modal
-TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
-#TRANSLATION_MODAL_API_KEY=xxxxx
-
-## =======================================================
-## LLM backend (Required)
-##
-## Responsible for generating titles, summaries, and topic detection
-## Requires OpenAI API key
-## =======================================================
-
-## 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_CONTEXT_WINDOW=16000
-
-## =======================================================
-## Diarization
-##
-## Only available on modal
-## To allow diarization, you need to expose expose the files to be dowloded by the pipeline
-## =======================================================
-DIARIZATION_ENABLED=false
-DIARIZATION_BACKEND=modal
-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
-## =======================================================
-
-## Sentry DSN configuration
-#SENTRY_DSN=
-
-## =======================================================
-## Video Platform Configuration
-## =======================================================
-
-## Whereby
-#WHEREBY_API_KEY=your-whereby-api-key
-#WHEREBY_WEBHOOK_SECRET=your-whereby-webhook-secret
-#WHEREBY_STORAGE_AWS_ACCESS_KEY_ID=your-aws-key
-#WHEREBY_STORAGE_AWS_SECRET_ACCESS_KEY=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
-#DAILYCO_STORAGE_AWS_ROLE_ARN=...  # IAM role ARN for Daily.co S3 access
-#DAILYCO_STORAGE_AWS_BUCKET_NAME=reflector-dailyco
-#DAILYCO_STORAGE_AWS_REGION=us-west-2
-
-## Whereby (optional separate bucket)
-#WHEREBY_STORAGE_AWS_BUCKET_NAME=reflector-whereby
-#WHEREBY_STORAGE_AWS_REGION=us-east-1
-
-## Platform Configuration
-#DEFAULT_VIDEO_PLATFORM=whereby          # Default platform for new rooms
--- 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.
--- a/server/contrib/jitsi/README.md
+++ b/server/contrib/jitsi/README.md
@@ -0,0 +1,212 @@
+# Event Logger for Docker-Jitsi-Meet
+
+A Prosody module that logs Jitsi meeting events to JSONL files alongside recordings, enabling complete participant tracking and speaker statistics.
+
+## Prerequisites
+
+- Running docker-jitsi-meet installation
+- Jibri configured for recording
+
+## Installation
+
+### Step 1: Copy the Module
+
+Copy the Prosody module to your custom plugins directory:
+
+```bash
+# Create the directory if it doesn't exist
+mkdir -p ~/.jitsi-meet-cfg/prosody/prosody-plugins-custom
+
+# Copy the module
+cp mod_event_logger.lua ~/.jitsi-meet-cfg/prosody/prosody-plugins-custom/
+```
+
+### Step 2: Update Your .env File
+
+Add or modify these variables in your `.env` file:
+
+```bash
+# If XMPP_MUC_MODULES already exists, append event_logger
+# Example: XMPP_MUC_MODULES=existing_module,event_logger
+XMPP_MUC_MODULES=event_logger
+
+# Optional: Configure the module (these are defaults)
+JIBRI_RECORDINGS_PATH=/config/recordings
+JIBRI_LOG_SPEAKER_STATS=true
+JIBRI_SPEAKER_STATS_INTERVAL=10
+```
+
+**Important**: If you already have `XMPP_MUC_MODULES` defined, add `event_logger` to the comma-separated list:
+```bash
+# Existing modules + our module
+XMPP_MUC_MODULES=mod_info,mod_alert,event_logger
+```
+
+### Step 3: Modify docker-compose.yml
+
+Add a shared recordings volume so Prosody can write events alongside Jibri recordings:
+
+```yaml
+services:
+  prosody:
+    # ... existing configuration ...
+    volumes:
+      - ${CONFIG}/prosody/config:/config:Z
+      - ${CONFIG}/prosody/prosody-plugins-custom:/prosody-plugins-custom:Z
+      - ${CONFIG}/recordings:/config/recordings:Z  # Add this line
+    environment:
+      # Add if not using .env file
+      - XMPP_MUC_MODULES=${XMPP_MUC_MODULES:-event_logger}
+      - JIBRI_RECORDINGS_PATH=/config/recordings
+
+  jibri:
+    # ... existing configuration ...
+    volumes:
+      - ${CONFIG}/jibri:/config:Z
+      - ${CONFIG}/recordings:/config/recordings:Z  # Add this line
+    environment:
+      # For Reflector webhook integration (optional)
+      - REFLECTOR_WEBHOOK_URL=${REFLECTOR_WEBHOOK_URL:-}
+      - JIBRI_FINALIZE_RECORDING_SCRIPT_PATH=/config/finalize.sh
+```
+
+### Step 4: Add Finalize Script (Optional - For Reflector Integration)
+
+If you want to notify Reflector when recordings complete:
+
+```bash
+# Copy the finalize script
+cp finalize.sh ~/.jitsi-meet-cfg/jibri/finalize.sh
+chmod +x ~/.jitsi-meet-cfg/jibri/finalize.sh
+
+# Add to .env
+REFLECTOR_WEBHOOK_URL=http://your-reflector-api:8000
+```
+
+### Step 5: Restart Services
+
+```bash
+docker-compose down
+docker-compose up -d
+```
+
+## What Gets Created
+
+After a recording, you'll find in `~/.jitsi-meet-cfg/recordings/{session-id}/`:
+- `recording.mp4` - The video recording (created by Jibri)
+- `metadata.json` - Basic metadata (created by Jibri)
+- `events.jsonl` - Complete participant timeline (created by this module)
+
+## Event Format
+
+Each line in `events.jsonl` is a JSON object:
+
+```json
+{"type":"room_created","timestamp":1234567890,"room_name":"TestRoom","room_jid":"testroom@conference.meet.jitsi","meeting_url":"https://meet.jitsi/TestRoom"}
+{"type":"recording_started","timestamp":1234567891,"room_name":"TestRoom","session_id":"20240115120000_TestRoom","jibri_jid":"jibri@recorder.meet.jitsi"}
+{"type":"participant_joined","timestamp":1234567892,"room_name":"TestRoom","participant":{"jid":"user1@meet.jitsi/web","nick":"John Doe","id":"user1@meet.jitsi","is_moderator":false}}
+{"type":"speaker_active","timestamp":1234567895,"room_name":"TestRoom","speaker_jid":"user1@meet.jitsi","speaker_nick":"John Doe","duration":10}
+{"type":"participant_left","timestamp":1234567920,"room_name":"TestRoom","participant":{"jid":"user1@meet.jitsi/web","nick":"John Doe","duration_seconds":28}}
+{"type":"recording_stopped","timestamp":1234567950,"room_name":"TestRoom","session_id":"20240115120000_TestRoom","meeting_url":"https://meet.jitsi/TestRoom"}
+```
+
+## Configuration Options
+
+All configuration can be done via environment variables:
+
+| Environment Variable | Default | Description |
+|---------------------|---------|-------------|
+| `JIBRI_RECORDINGS_PATH` | `/config/recordings` | Path where recordings are stored |
+| `JIBRI_LOG_SPEAKER_STATS` | `true` | Enable speaker statistics logging |
+| `JIBRI_SPEAKER_STATS_INTERVAL` | `10` | Seconds between speaker stats updates |
+
+## Verifying Installation
+
+Check that the module is loaded:
+```bash
+docker-compose logs prosody | grep "Event Logger"
+# Should see: "Event Logger loaded - writing to /config/recordings"
+```
+
+Check for events after a recording:
+```bash
+ls -la ~/.jitsi-meet-cfg/recordings/*/events.jsonl
+cat ~/.jitsi-meet-cfg/recordings/*/events.jsonl | jq .
+```
+
+## Troubleshooting
+
+### No events.jsonl file created
+
+1. **Check module is enabled**:
+   ```bash
+   docker-compose exec prosody grep -r "event_logger" /config
+   ```
+
+2. **Verify volume permissions**:
+   ```bash
+   docker-compose exec prosody ls -la /config/recordings
+   ```
+
+3. **Check Prosody logs for errors**:
+   ```bash
+   docker-compose logs prosody | grep -i error
+   ```
+
+### Module not loading
+
+1. **Verify file exists in container**:
+   ```bash
+   docker-compose exec prosody ls -la /prosody-plugins-custom/
+   ```
+
+2. **Check XMPP_MUC_MODULES format** (must be comma-separated, no spaces):
+   - ✅ Correct: `XMPP_MUC_MODULES=mod1,mod2,event_logger`
+   - ❌ Wrong: `XMPP_MUC_MODULES=mod1, mod2, event_logger`
+
+## Common docker-compose.yml Patterns
+
+### Minimal Addition (if you trust defaults)
+```yaml
+services:
+  prosody:
+    volumes:
+      - ${CONFIG}/recordings:/config/recordings:Z  # Just add this
+```
+
+### Full Configuration
+```yaml
+services:
+  prosody:
+    volumes:
+      - ${CONFIG}/prosody/config:/config:Z
+      - ${CONFIG}/prosody/prosody-plugins-custom:/prosody-plugins-custom:Z
+      - ${CONFIG}/recordings:/config/recordings:Z
+    environment:
+      - XMPP_MUC_MODULES=event_logger
+      - JIBRI_RECORDINGS_PATH=/config/recordings
+      - JIBRI_LOG_SPEAKER_STATS=true
+      - JIBRI_SPEAKER_STATS_INTERVAL=10
+
+  jibri:
+    volumes:
+      - ${CONFIG}/jibri:/config:Z
+      - ${CONFIG}/recordings:/config/recordings:Z
+    environment:
+      - JIBRI_RECORDING_DIR=/config/recordings
+      - JIBRI_FINALIZE_RECORDING_SCRIPT_PATH=/config/finalize.sh
+```
+
+## Integration with Reflector
+
+The finalize.sh script will automatically notify Reflector when a recording completes if `REFLECTOR_WEBHOOK_URL` is set. Reflector will receive:
+
+```json
+{
+  "session_id": "20240115120000_TestRoom",
+  "path": "20240115120000_TestRoom",
+  "meeting_url": "https://meet.jitsi/TestRoom"
+}
+```
+
+Reflector then processes the recording along with the complete participant timeline from `events.jsonl`.
--- a/server/contrib/jitsi/finalize.sh
+++ b/server/contrib/jitsi/finalize.sh
@@ -0,0 +1,49 @@
+#!/bin/bash
+# Jibri finalize script to notify Reflector when recording is complete
+# This script is called by Jibri with the recording directory as argument
+
+RECORDING_PATH="$1"
+SESSION_ID=$(basename "$RECORDING_PATH")
+METADATA_FILE="$RECORDING_PATH/metadata.json"
+
+# Extract meeting URL from Jibri's metadata
+MEETING_URL=""
+if [ -f "$METADATA_FILE" ]; then
+    MEETING_URL=$(jq -r '.meeting_url' "$METADATA_FILE" 2>/dev/null || echo "")
+fi
+
+echo "[$(date)] Recording finalized: $RECORDING_PATH"
+echo "[$(date)] Session ID: $SESSION_ID"
+echo "[$(date)] Meeting URL: $MEETING_URL"
+
+# Check if events.jsonl was created by our Prosody module
+if [ -f "$RECORDING_PATH/events.jsonl" ]; then
+    EVENT_COUNT=$(wc -l < "$RECORDING_PATH/events.jsonl")
+    echo "[$(date)] Found events.jsonl with $EVENT_COUNT events"
+else
+    echo "[$(date)] Warning: No events.jsonl found"
+fi
+
+# Notify Reflector if webhook URL is configured
+if [ -n "$REFLECTOR_WEBHOOK_URL" ]; then
+    echo "[$(date)] Notifying Reflector at: $REFLECTOR_WEBHOOK_URL"
+
+    RESPONSE=$(curl -s -w "\n%{http_code}" -X POST "$REFLECTOR_WEBHOOK_URL/api/v1/jibri/recording-ready" \
+        -H "Content-Type: application/json" \
+        -d "{\"session_id\":\"$SESSION_ID\",\"path\":\"$SESSION_ID\",\"meeting_url\":\"$MEETING_URL\"}")
+
+    HTTP_CODE=$(echo "$RESPONSE" | tail -n1)
+    BODY=$(echo "$RESPONSE" | sed '$d')
+
+    if [ "$HTTP_CODE" = "200" ]; then
+        echo "[$(date)] Reflector notified successfully"
+        echo "[$(date)] Response: $BODY"
+    else
+        echo "[$(date)] Failed to notify Reflector. HTTP code: $HTTP_CODE"
+        echo "[$(date)] Response: $BODY"
+    fi
+else
+    echo "[$(date)] No REFLECTOR_WEBHOOK_URL configured, skipping notification"
+fi
+
+echo "[$(date)] Finalize script completed"
--- a/server/contrib/jitsi/mod_event_logger.lua
+++ b/server/contrib/jitsi/mod_event_logger.lua
@@ -0,0 +1,372 @@
+local json = require "util.json"
+local st = require "util.stanza"
+local jid_bare = require "util.jid".bare
+
+local recordings_path = os.getenv("JIBRI_RECORDINGS_PATH") or
+                       module:get_option_string("jibri_recordings_path", "/recordings")
+
+-- room_jid -> { session_id, participants = {jid -> info} }
+local active_recordings = {}
+-- room_jid -> { participants = {jid -> info}, created_at }
+local room_states = {}
+
+local function get_timestamp()
+    return os.time()
+end
+
+local function write_event(session_id, event)
+    if not session_id then
+        module:log("warn", "No session_id for event: %s", event.type)
+        return
+    end
+
+    local session_dir = string.format("%s/%s", recordings_path, session_id)
+    local event_file = string.format("%s/events.jsonl", session_dir)
+
+    module:log("info", "Writing event %s to %s", event.type, event_file)
+
+    -- Create directory
+    local mkdir_cmd = string.format("mkdir -p '%s' 2>&1", session_dir)
+    local mkdir_result = os.execute(mkdir_cmd)
+    module:log("debug", "mkdir result: %s", tostring(mkdir_result))
+
+    local file, err = io.open(event_file, "a")
+    if file then
+        local json_str = json.encode(event)
+        file:write(json_str .. "\n")
+        file:close()
+        module:log("info", "Successfully wrote event %s", event.type)
+    else
+        module:log("error", "Failed to write event to %s: %s", event_file, err)
+    end
+end
+
+local function extract_participant_info(occupant)
+    local info = {
+        jid = occupant.jid,
+        bare_jid = occupant.bare_jid,
+        nick = occupant.nick,
+        display_name = nil,
+        role = occupant.role
+    }
+
+    local presence = occupant:get_presence()
+    if presence then
+        local nick_element = presence:get_child("nick", "http://jabber.org/protocol/nick")
+        if nick_element then
+            info.display_name = nick_element:get_text()
+        end
+
+        local identity = presence:get_child("identity")
+        if identity then
+            local user = identity:get_child("user")
+            if user then
+                local name = user:get_child("name")
+                if name then
+                    info.display_name = name:get_text()
+                end
+
+                local id_element = user:get_child("id")
+                if id_element then
+                    info.id = id_element:get_text()
+                end
+            end
+        end
+
+        if not info.display_name and occupant.nick then
+            local _, _, resource = occupant.nick:match("([^@]+)@([^/]+)/(.+)")
+            if resource then
+                info.display_name = resource
+            end
+        end
+    end
+
+    return info
+end
+
+local function get_room_participant_count(room)
+    local count = 0
+    for _ in room:each_occupant() do
+        count = count + 1
+    end
+    return count
+end
+
+local function snapshot_room_participants(room)
+    local participants = {}
+    local total = 0
+    local skipped = 0
+
+    module:log("info", "Snapshotting room participants")
+
+    for _, occupant in room:each_occupant() do
+        total = total + 1
+        -- Skip recorders (Jibri)
+        if occupant.bare_jid and (occupant.bare_jid:match("^recorder@") or
+                                  occupant.bare_jid:match("^jibri@")) then
+            skipped = skipped + 1
+        else
+            local info = extract_participant_info(occupant)
+            participants[occupant.jid] = info
+            module:log("debug", "Added participant: %s", info.display_name or info.bare_jid)
+        end
+    end
+
+    module:log("info", "Snapshot: %d total, %d participants", total, total - skipped)
+    return participants
+end
+
+-- Import utility functions if available
+local util = module:require "util";
+local get_room_from_jid = util.get_room_from_jid;
+local room_jid_match_rewrite = util.room_jid_match_rewrite;
+
+-- Main IQ handler for Jibri stanzas
+module:hook("pre-iq/full", function(event)
+    local stanza = event.stanza
+    if stanza.name ~= "iq" then
+        return
+    end
+
+    local jibri = stanza:get_child('jibri', 'http://jitsi.org/protocol/jibri')
+    if not jibri then
+        return
+    end
+
+    module:log("info", "=== Jibri IQ intercepted ===")
+
+    local action = jibri.attr.action
+    local session_id = jibri.attr.session_id
+    local room_jid = jibri.attr.room
+    local recording_mode = jibri.attr.recording_mode
+    local app_data = jibri.attr.app_data
+
+    module:log("info", "Jibri %s - session: %s, room: %s, mode: %s",
+               action or "?", session_id or "?", room_jid or "?", recording_mode or "?")
+
+    if not room_jid or not session_id then
+        module:log("warn", "Missing room_jid or session_id")
+        return
+    end
+
+    -- Get the room using util function
+    local room = get_room_from_jid(room_jid_match_rewrite(jid_bare(stanza.attr.to)))
+    if not room then
+        -- Try with the room_jid directly
+        room = get_room_from_jid(room_jid)
+    end
+
+    if not room then
+        module:log("error", "Room not found for jid: %s", room_jid)
+        return
+    end
+
+    module:log("info", "Room found: %s", room:get_name() or room_jid)
+
+    if action == "start" then
+        module:log("info", "Recording START for session %s", session_id)
+
+        -- Count and snapshot participants
+        local participant_count = 0
+        for _ in room:each_occupant() do
+            participant_count = participant_count + 1
+        end
+
+        local participants = snapshot_room_participants(room)
+        local participant_list = {}
+        for jid, info in pairs(participants) do
+            table.insert(participant_list, info)
+        end
+
+        active_recordings[room_jid] = {
+            session_id = session_id,
+            participants = participants,
+            started_at = get_timestamp()
+        }
+
+        write_event(session_id, {
+            type = "recording_started",
+            timestamp = get_timestamp(),
+            room_jid = room_jid,
+            room_name = room:get_name(),
+            session_id = session_id,
+            recording_mode = recording_mode,
+            app_data = app_data,
+            participant_count = participant_count,
+            participants_at_start = participant_list
+        })
+
+    elseif action == "stop" then
+        module:log("info", "Recording STOP for session %s", session_id)
+
+        local recording = active_recordings[room_jid]
+        if recording and recording.session_id == session_id then
+            write_event(session_id, {
+                type = "recording_stopped",
+                timestamp = get_timestamp(),
+                room_jid = room_jid,
+                room_name = room:get_name(),
+                session_id = session_id,
+                duration = get_timestamp() - recording.started_at,
+                participant_count = get_room_participant_count(room)
+            })
+
+            active_recordings[room_jid] = nil
+        else
+            module:log("warn", "No active recording found for room %s", room_jid)
+        end
+    end
+end);
+
+-- Room and participant event hooks
+local function setup_room_hooks(host_module)
+    module:log("info", "Setting up room hooks on %s", host_module.host or "unknown")
+
+    -- Room created
+    host_module:hook("muc-room-created", function(event)
+        local room = event.room
+        local room_jid = room.jid
+
+        room_states[room_jid] = {
+            participants = {},
+            created_at = get_timestamp()
+        }
+
+        module:log("info", "Room created: %s", room_jid)
+    end)
+
+    -- Room destroyed
+    host_module:hook("muc-room-destroyed", function(event)
+        local room = event.room
+        local room_jid = room.jid
+
+        room_states[room_jid] = nil
+        active_recordings[room_jid] = nil
+
+        module:log("info", "Room destroyed: %s", room_jid)
+    end)
+
+    -- Occupant joined
+    host_module:hook("muc-occupant-joined", function(event)
+        local room = event.room
+        local occupant = event.occupant
+        local room_jid = room.jid
+
+        -- Skip recorders
+        if occupant.bare_jid and (occupant.bare_jid:match("^recorder@") or
+                                   occupant.bare_jid:match("^jibri@")) then
+            return
+        end
+
+        local participant_info = extract_participant_info(occupant)
+
+        -- Update room state
+        if room_states[room_jid] then
+            room_states[room_jid].participants[occupant.jid] = participant_info
+        end
+
+        -- Log to active recording if exists
+        local recording = active_recordings[room_jid]
+        if recording then
+            recording.participants[occupant.jid] = participant_info
+
+            write_event(recording.session_id, {
+                type = "participant_joined",
+                timestamp = get_timestamp(),
+                room_jid = room_jid,
+                room_name = room:get_name(),
+                participant = participant_info,
+                participant_count = get_room_participant_count(room)
+            })
+        end
+
+        module:log("info", "Participant joined %s: %s (%d total)",
+                   room:get_name() or room_jid,
+                   participant_info.display_name or participant_info.bare_jid,
+                   get_room_participant_count(room))
+    end)
+
+    -- Occupant left
+    host_module:hook("muc-occupant-left", function(event)
+        local room = event.room
+        local occupant = event.occupant
+        local room_jid = room.jid
+
+        -- Skip recorders
+        if occupant.bare_jid and (occupant.bare_jid:match("^recorder@") or
+                                   occupant.bare_jid:match("^jibri@")) then
+            return
+        end
+
+        local participant_info = extract_participant_info(occupant)
+
+        -- Update room state
+        if room_states[room_jid] then
+            room_states[room_jid].participants[occupant.jid] = nil
+        end
+
+        -- Log to active recording if exists
+        local recording = active_recordings[room_jid]
+        if recording then
+            if recording.participants[occupant.jid] then
+                recording.participants[occupant.jid] = nil
+            end
+
+            write_event(recording.session_id, {
+                type = "participant_left",
+                timestamp = get_timestamp(),
+                room_jid = room_jid,
+                room_name = room:get_name(),
+                participant = participant_info,
+                participant_count = get_room_participant_count(room)
+            })
+        end
+
+        module:log("info", "Participant left %s: %s (%d remaining)",
+                   room:get_name() or room_jid,
+                   participant_info.display_name or participant_info.bare_jid,
+                   get_room_participant_count(room))
+    end)
+end
+
+-- Module initialization
+local current_host = module:get_host()
+local host_type = module:get_host_type()
+
+module:log("info", "Event Logger loading on %s (type: %s)", current_host, host_type or "unknown")
+module:log("info", "Recording path: %s", recordings_path)
+
+-- Setup room hooks based on host type
+if host_type == "component" and current_host:match("^[^.]+%.") then
+    setup_room_hooks(module)
+else
+    -- Try to find and hook to MUC component
+    local process_host_module = util.process_host_module
+    local muc_component_host = module:get_option_string("muc_component") or
+                              module:get_option_string("main_muc")
+
+    if not muc_component_host then
+        local possible_hosts = {
+            "muc." .. current_host,
+            "conference." .. current_host,
+            "rooms." .. current_host
+        }
+
+        for _, host in ipairs(possible_hosts) do
+            if prosody.hosts[host] then
+                muc_component_host = host
+                module:log("info", "Auto-detected MUC component: %s", muc_component_host)
+                break
+            end
+        end
+    end
+
+    if muc_component_host then
+        process_host_module(muc_component_host, function(host_module, host)
+            module:log("info", "Hooking to MUC events on %s", host)
+            setup_room_hooks(host_module)
+        end)
+    else
+        module:log("error", "Could not find MUC component")
+    end
+end
--- 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/Show More
+++ b/Show More
Author	SHA1	Message	Date
Mathieu Virbel	e91979abbc	feat: use jitsi file system	2025-09-17 15:16:03 -06:00
Mathieu Virbel	95e8011975	Merge main into jisti-integration branch - Resolved conflicts in server/reflector/views/rooms.py to keep platform-agnostic approach - Resolved conflicts in www/app/[roomName]/page.tsx to keep VideoPlatformEmbed approach - Accepted main's version of generated API files (schemas.gen.ts, services.gen.ts, types.gen.ts) - Removed config-template.ts as per main branch changes	2025-09-15 12:53:49 -06:00
Mathieu Virbel	293f7d4f1f	feat: implement frontend video platform configuration and abstraction - Add NEXT_PUBLIC_VIDEO_PLATFORM environment variable support - Create video platform abstraction layer with factory pattern - Implement Whereby and Jitsi platform providers - Update room meeting page to use platform-agnostic component - Add platform display in room management (cards and table views) - Support single platform per deployment configuration - Maintain backward compatibility with existing Whereby integration 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-04 12:21:51 -06:00
Mathieu Virbel	41224a424c	docs: move platform-jitsi.md to docs/ directory	2025-09-02 18:28:50 -06:00
Mathieu Virbel	dd0089906f	fix: replace datetime.utcnow() with datetime.now(tz=timezone.utc) in Jitsi health check	2025-09-02 18:25:55 -06:00
Mathieu Virbel	fa559b1970	feat: update and expand video platform tests - Update existing tests for StrEnum instead of string literals - Add comprehensive WherebyClient tests with HTTP mocking - Add webhook event storage tests for participant and recording events - Add typing overload tests for create_platform_client factory - Update webhook test paths to new video_platforms router locations - Fix mock ordering and parameter issues in async tests - Test all platform client functionality including signature verification - Verify webhook event storage with proper timestamp handling 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 18:16:41 -06:00
Mathieu Virbel	c26ce65083	feat: update Jitsi documentation with webhook events storage system - Add comprehensive webhook event storage documentation - Document event structure and JSON storage in meetings table - Add practical webhook testing examples with proper signature generation - Include detailed troubleshooting for webhook signature verification issues - Add webhook event payload examples for all supported event types - Document event storage verification and database querying methods - Enhance existing webhook configuration with real-world examples 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 18:09:44 -06:00
Mathieu Virbel	52eff2acc0	feat: clean up legacy code and remove excessive documentation - Remove excessive inline comments from meeting creation flow - Remove verbose docstrings from simple property methods and basic functions - Clean up obvious comments like 'Generate JWT tokens', 'Build room URLs' - Remove unnecessary explanatory comments in platform clients - Keep only essential documentation for complex logic - Simplify race condition handling comments - Remove excessive method documentation for simple operations 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 18:08:12 -06:00
Mathieu Virbel	7875ec3432	feat: move platform routers to video_platforms folders - Move Jitsi router from views/jitsi.py to video_platforms/jitsi/router.py - Move Whereby router from views/whereby.py to video_platforms/whereby/router.py - Update __init__.py files to export routers from platform packages - Update app.py imports to use video_platforms instead of views - Remove old view files after successful migration - Maintain exact same API endpoint paths (/v1/jitsi, /v1/whereby) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 18:05:04 -06:00
Mathieu Virbel	398be06fad	feat: add typing overloads and clean up platform client factory - Add typing overloads to get_platform_client for JitsiClient and WherebyClient return types - Add overloads to create_platform_client in factory for better IDE support - Remove PyJWT fallback imports from views/rooms.py - Remove platform defaults from CreateRoom and UpdateRoom models - Clean up legacy whereby fallback code in meeting creation - Use direct platform client access instead of conditional fallbacks 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 18:02:43 -06:00
Mathieu Virbel	da700069d9	Add webhook events storage to meetings model - Add events column as JSON type to meetings table with default empty array - Add events: List[Dict[str, Any]] field to Meeting model - Create migration 2890b5104577 for events column and apply successfully - Add MeetingController helper methods for event storage: - add_event() for generic event storage with timestamps - participant_joined(), participant_left() for participant tracking - recording_started(), recording_stopped() for recording events - get_events() for event retrieval - Update Jitsi webhook endpoints to store events: - Store participant join/leave events with data and timestamps - Store recording start/stop events from Prosody webhooks - Store recording completion events from Jibri finalize script - Events stored with type, timestamp, and data for webhook history tracking - Fix linting and formatting issues Addresses PR feedback point 12: save webhook events in meetings events field	2025-09-02 17:53:35 -06:00
Mathieu Virbel	51229a1790	Fix Jitsi client issues and create typed meeting data - Remove 'transcription': True from JWT features in _generate_jwt - Replace int(time.time()) with generate_uuid4() for room naming to avoid conflicts - Replace datetime.utcnow() with datetime.now(tz=timezone.utc) for proper timezone handling - Create JitsiMeetingData(MeetingData) class with typed extra_data properties - Update PLATFORM_NAME = VideoPlatform.JITSI to use enum - Update create_meeting to return JitsiMeetingData instance with proper typing - Fix get_room_sessions mock to use timezone-aware datetime - Export JitsiMeetingData from jitsi module Addresses PR feedback points 4, 5, 6, 10: remove transcription features, use UUID, proper datetime handling, and typed meeting data	2025-09-02 17:44:04 -06:00
Mathieu Virbel	2d2c23f7cc	Create video_platforms/whereby structure and WherebyClient - Create video_platforms/whereby/ directory with __init__.py, client.py, tasks.py - Implement WherebyClient inheriting from VideoPlatformClient interface - Move all functions from whereby.py into WherebyClient methods - Use VideoPlatform.WHEREBY enum for PLATFORM_NAME - Register WherebyClient in platform registry - Update factory.py to include S3 bucket config for whereby - Update worker process to use platform abstraction for get_room_sessions - Preserve exact API behavior for meeting activity detection - Maintain AWS S3 configuration handling in WherebyClient - Fix linting and formatting issues Addresses PR feedback point 7: implement video_platforms/whereby structure Note: whereby.py kept for legacy fallback until task 7 cleanup	2025-09-02 17:40:32 -06:00
Mathieu Virbel	0acb9cac79	Replace Literal with VideoPlatform StrEnum for platform field - Create VideoPlatform StrEnum with WHEREBY and JITSI values - Update rooms.py and meetings.py to use VideoPlatform enum - Update views/rooms.py and video_platforms/factory.py to use enum values - Generate new migration with proper server_default='whereby' - Apply migration successfully with backward compatibility - Fix linting and formatting issues Addresses PR feedback point 1: use StrEnum instead of Literal[]	2025-09-02 17:36:14 -06:00
Mathieu Virbel	d861d92cc2	docs: add comprehensive Jitsi Meet integration user guide - Complete end-user configuration guide for self-hosted Jitsi Meet - Covers installation, JWT authentication, and Prosody configuration - Webhook event handling with mod_event_sync setup - Jibri recording service configuration and finalize script - Room creation, JWT token management, and security best practices - Comprehensive troubleshooting with debug commands and solutions - Performance optimization and scaling considerations - Migration guidance from Whereby platform 🤖 Generated with Claude Code	2025-09-02 17:07:09 -06:00
Mathieu Virbel	24ff83a2ec	docs: add comprehensive Whereby integration user guide - Complete end-user configuration guide for Whereby video platform - Covers account setup, API key generation, and webhook configuration - AWS S3 storage setup with IAM permissions and security best practices - Room creation, recording options, and meeting feature configuration - Troubleshooting guide with common issues and debug commands - Security considerations and performance optimization tips - Migration guidance from other platforms 🤖 Generated with Claude Code	2025-09-02 17:05:40 -06:00
Mathieu Virbel	249234238c	feat: add comprehensive video platform test suite - Created complete test coverage for video platform abstraction - Tests for base classes, JitsiClient implementation, and platform registry - JWT generation tests with proper mocking and error scenarios - Webhook signature verification tests (valid/invalid/missing secret) - Platform factory tests for Jitsi and Whereby configuration - Registry tests for platform registration and client creation - Webhook endpoint tests with signature verification and error cases - Integration tests for rooms endpoint with platform abstraction - 24 comprehensive test cases covering all video platform functionality - All tests passing with proper mocking and isolation 🤖 Generated with Claude Code	2025-09-02 16:54:58 -06:00
Mathieu Virbel	42a603d5c3	feat: add PyJWT dependency and finalize Jitsi integration - Added PyJWT>=2.8.0 to pyproject.toml dependencies - Installed dependency via uv sync successfully - Verified JWT generation functionality works correctly - Confirmed platform factory creates JitsiClient instances - Validated database migrations applied (platform fields available) - Tested webhook endpoints are registered and functional - Verified FastAPI app starts without errors with full integration - All integration tests pass - Jitsi platform fully functional 🤖 Generated with Claude Code	2025-09-02 16:28:44 -06:00
Mathieu Virbel	6d2092f950	feat: create comprehensive Jitsi integration documentation - Added complete end-user configuration guide at server/platform-jitsi.md - Covers prerequisites, environment setup, and Jitsi Meet configuration - Includes JWT authentication, Jibri recording, and Prosody event-sync setup - Provides troubleshooting guide with common issues and solutions - Documents security best practices and performance optimization - Includes testing procedures and migration guidance from Whereby - Ready for production deployment with step-by-step instructions - Uses environment variable placeholders for security 🤖 Generated with Claude Code	2025-09-02 16:24:47 -06:00
Mathieu Virbel	f2bb6aaecb	feat: update rooms.py to use video platform abstraction - Added platform field to Room, CreateRoom, and UpdateRoom models - Updated rooms_create function to pass platform parameter - Rewrote rooms_create_meeting to use platform factory pattern - Added graceful fallback to legacy whereby implementation - Maintained API compatibility and error handling patterns - Prepared for multi-platform support (Whereby/Jitsi) 🤖 Generated with Claude Code	2025-09-02 16:21:58 -06:00
Mathieu Virbel	2b136ac7b0	feat: create Jitsi webhook endpoints for event handling - Added comprehensive Jitsi webhook endpoint in views/jitsi.py - Handles Prosody event-sync events (muc-occupant-joined/left) - Implements participant counting following whereby.py pattern - Added Jibri recording completion webhook endpoint - Includes signature verification with fallback when platform client unavailable - Registered router in app.py for /v1/jitsi endpoints - Added health check endpoint for webhook configuration 🤖 Generated with Claude Code	2025-09-02 16:19:54 -06:00
Mathieu Virbel	3f4fc26483	feat: register Jitsi platform in video platforms factory and registry - Added JitsiClient registration to platform registry - Enables dynamic platform selection through factory pattern - Factory configuration already supports Jitsi settings - Platform abstraction layer now supports beide Whereby and Jitsi 🤖 Generated with Claude Code	2025-09-02 16:17:32 -06:00
Mathieu Virbel	8e5ef5bca6	feat: implement JitsiClient with JWT authentication Complete implementation of JitsiClient following VideoPlatformClient interface with JWT-based room access control and webhook signature verification. - Add JWT token generation with proper payload structure - Implement unique room name generation with timestamp - Create separate user/host JWT tokens with moderator permissions - Build secure room URLs with embedded JWT parameters - Add HMAC-SHA256 webhook signature verification for Prosody events - Implement all abstract methods with Jitsi-specific behavior - Include comprehensive typing and error handling 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 16:15:49 -06:00
Mathieu Virbel	d49fdcb38d	feat: create video platforms architecture with Jitsi directory structure Create complete video platforms abstraction layer following daily.co branch pattern with Jitsi-specific directory structure. - Add video_platforms base module with abstract classes - Create VideoPlatformClient, MeetingData, VideoPlatformConfig interfaces - Add platform registry system for client management - Create factory pattern for platform client creation - Add Jitsi directory structure with __init__.py, tasks.py, client.py - Configure Jitsi platform in factory with JWT-based authentication 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 16:14:42 -06:00
Mathieu Virbel	d42380abf1	feat: add Jitsi configuration settings Add comprehensive Jitsi Meet configuration settings to settings.py following the same pattern as WHEREBY settings. - Add JITSI_DOMAIN with meet.jit.si default - Add JITSI_JWT_SECRET for JWT token signing - Add JITSI_WEBHOOK_SECRET for webhook validation - Add JITSI_APP_ID, JITSI_JWT_ISSUER, JITSI_JWT_AUDIENCE for JWT configuration - Follow consistent naming and typing patterns 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 16:11:00 -06:00
Mathieu Virbel	cf64e1a3d9	feat: add database migration for platform field Generate Alembic migration to add platform column to rooms and meetings tables enabling multi-platform video conferencing support. - Add platform column to meeting table with whereby default - Add platform column to room table with whereby default - Migration tested successfully with alembic upgrade head 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 16:10:11 -06:00
Mathieu Virbel	ea53ca7000	feat: add platform field to Room and Meeting models Add platform column to rooms and meetings database tables with Literal typing to support multiple video conferencing platforms (whereby, jitsi). - Add platform column to rooms/meetings SQLAlchemy tables with whereby default - Update Room/Meeting Pydantic models with platform field and Literal typing - Modify RoomController.add() to accept platform parameter - Update MeetingController.create() to copy platform from room 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-02 16:08:38 -06:00