feat: reduce Hatchet payload size by removing words from topic chunk workflows

Remove ~6.5MB of redundant Word data from Hatchet task boundaries: - Remove words from TopicChunkInput/TopicChunkResult (child workflow I/O) - detect_topics maps words from local chunks by chunk_index instead - TopicsResult carries empty transcript words (persisted to DB already) - extract_subjects refetches topics from DB instead of task output - Clear topics at detect_topics start for retry idempotency
feat: local llm support + standalone-script doc/draft (#856 )
2026-03-22 07:06:47 +00:00 · 2026-02-12 09:50:26 -05:00 · 2026-02-11 18:20:36 -05:00 · 2026-02-06 18:12:06 -05:00 · 2026-02-05 18:38:08 -05:00
47 changed files with 1623 additions and 1143 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,17 @@
 # Changelog

+## [0.33.0](https://github.com/Monadical-SAS/reflector/compare/v0.32.2...v0.33.0) (2026-02-05)
+
+
+### Features
+
+* Daily+hatchet default ([#846](https://github.com/Monadical-SAS/reflector/issues/846)) ([15ab2e3](https://github.com/Monadical-SAS/reflector/commit/15ab2e306eacf575494b4b5d2b2ad779d44a1c7f))
+
+
+### Bug Fixes
+
+* websocket tests ([#825](https://github.com/Monadical-SAS/reflector/issues/825)) ([1ce1c7a](https://github.com/Monadical-SAS/reflector/commit/1ce1c7a910b6c374115d2437b17f9d288ef094dc))
+
 ## [0.32.2](https://github.com/Monadical-SAS/reflector/compare/v0.32.1...v0.32.2) (2026-02-03)


--- a/docker-compose.standalone.yml
+++ b/docker-compose.standalone.yml
@@ -0,0 +1,120 @@
+# Standalone services for fully local deployment (no external dependencies).
+# Usage: docker compose -f docker-compose.yml -f docker-compose.standalone.yml up -d
+#
+# On Linux with NVIDIA GPU, also pass: --profile ollama-gpu
+# On Linux without GPU:                --profile ollama-cpu
+# On Mac: Ollama runs natively (Metal GPU) — no profile needed, services here unused.
+
+services:
+  garage:
+    image: dxflrs/garage:v1.1.0
+    ports:
+      - "3900:3900"   # S3 API
+      - "3903:3903"   # Admin API
+    volumes:
+      - garage_data:/var/lib/garage/data
+      - garage_meta:/var/lib/garage/meta
+      - ./data/garage.toml:/etc/garage.toml:ro
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "/garage", "stats"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+      start_period: 5s
+
+  ollama:
+    image: ollama/ollama:latest
+    profiles: ["ollama-gpu"]
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  ollama-cpu:
+    image: ollama/ollama:latest
+    profiles: ["ollama-cpu"]
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  # Override server/worker/beat to use self-hosted GPU service for transcription+diarization.
+  # compose `environment:` overrides values from `env_file:` — no need to edit server/.env.
+  server:
+    environment:
+      TRANSCRIPT_BACKEND: modal
+      TRANSCRIPT_URL: http://localhost:8100
+      TRANSCRIPT_MODAL_API_KEY: local
+      DIARIZATION_BACKEND: modal
+      DIARIZATION_URL: http://localhost:8100
+
+  worker:
+    environment:
+      TRANSCRIPT_BACKEND: modal
+      TRANSCRIPT_URL: http://cpu:8000
+      TRANSCRIPT_MODAL_API_KEY: local
+      DIARIZATION_BACKEND: modal
+      DIARIZATION_URL: http://cpu:8000
+
+  cpu:
+    build:
+      context: ./gpu/self_hosted
+      dockerfile: Dockerfile.cpu
+    ports:
+      - "8100:8000"
+    volumes:
+      - gpu_cache:/root/.cache
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
+      interval: 15s
+      timeout: 5s
+      retries: 10
+      start_period: 120s
+
+  gpu-nvidia:
+    build:
+      context: ./gpu/self_hosted
+    profiles: ["gpu-nvidia"]
+    volumes:
+      - gpu_cache:/root/.cache
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
+      interval: 15s
+      timeout: 5s
+      retries: 10
+      start_period: 120s
+
+volumes:
+  garage_data:
+  garage_meta:
+  ollama_data:
+  gpu_cache:
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -2,8 +2,7 @@ services:
  server:
    build:
      context: server
-    ports:
-      - 1250:1250
+    network_mode: host
    volumes:
      - ./server/:/app/
      - /app/.venv
@@ -11,6 +10,12 @@ services:
      - ./server/.env
    environment:
      ENTRYPOINT: server
+      DATABASE_URL: postgresql+asyncpg://reflector:reflector@localhost:5432/reflector
+      REDIS_HOST: localhost
+      CELERY_BROKER_URL: redis://localhost:6379/1
+      CELERY_RESULT_BACKEND: redis://localhost:6379/1
+      HATCHET_CLIENT_SERVER_URL: http://localhost:8889
+      HATCHET_CLIENT_HOST_PORT: localhost:7078

  worker:
    build:
@@ -22,6 +27,11 @@ services:
      - ./server/.env
    environment:
      ENTRYPOINT: worker
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
+    depends_on:
+      redis:
+        condition: service_started

  beat:
    build:
@@ -33,6 +43,9 @@ services:
      - ./server/.env
    environment:
      ENTRYPOINT: beat
+    depends_on:
+      redis:
+        condition: service_started

  hatchet-worker-cpu:
    build:
@@ -44,6 +57,8 @@ services:
      - ./server/.env
    environment:
      ENTRYPOINT: hatchet-worker-cpu
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
    depends_on:
      hatchet:
        condition: service_healthy
@@ -57,6 +72,8 @@ services:
      - ./server/.env
    environment:
      ENTRYPOINT: hatchet-worker-llm
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
    depends_on:
      hatchet:
        condition: service_healthy
@@ -75,10 +92,16 @@ services:
    volumes:
      - ./www:/app/
      - /app/node_modules
+      - next_cache:/app/.next
    env_file:
      - ./www/.env.local
    environment:
      - NODE_ENV=development
+      - SERVER_API_URL=http://host.docker.internal:1250
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    depends_on:
+      - server

  postgres:
    image: postgres:17
@@ -94,13 +117,14 @@ services:
      - ./server/docker/init-hatchet-db.sql:/docker-entrypoint-initdb.d/init-hatchet-db.sql:ro
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -d reflector -U reflector"]
-      interval: 10s
-      timeout: 10s
-      retries: 5
-      start_period: 10s
+      interval: 5s
+      timeout: 5s
+      retries: 10
+      start_period: 15s

  hatchet:
    image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
+    restart: on-failure
    ports:
      - "8889:8888"
      - "7078:7077"
@@ -108,7 +132,7 @@ services:
      postgres:
        condition: service_healthy
    environment:
-      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable"
+      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable&connect_timeout=30"
      SERVER_AUTH_COOKIE_DOMAIN: localhost
      SERVER_AUTH_COOKIE_INSECURE: "t"
      SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
@@ -128,6 +152,5 @@ services:
      retries: 5
      start_period: 30s

-networks:
-  default:
-    attachable: true
+volumes:
+  next_cache:
--- a/docs/docs/installation/setup-standalone.md
+++ b/docs/docs/installation/setup-standalone.md
@@ -0,0 +1,214 @@
+---
+sidebar_position: 2
+title: Standalone Local Setup
+---
+
+# Standalone Local Setup
+
+**The goal**: a clueless user clones the repo, runs one script, and has a working Reflector instance locally. No cloud accounts, no API keys, no manual env file editing.
+
+```bash
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector
+./scripts/setup-standalone.sh
+```
+
+The script is idempotent — safe to re-run at any time. It detects what's already set up and skips completed steps.
+
+## Prerequisites
+
+- Docker / OrbStack / Docker Desktop (any)
+- Mac (Apple Silicon) or Linux
+- 16GB+ RAM (32GB recommended for 14B LLM models)
+- **Mac only**: [Ollama](https://ollama.com/download) installed (`brew install ollama`)
+
+## What the script does
+
+### 1. LLM inference via Ollama
+
+**Mac**: starts Ollama natively (Metal GPU acceleration). Pulls the LLM model. Docker containers reach it via `host.docker.internal:11434`.
+
+**Linux**: starts containerized Ollama via `docker-compose.standalone.yml` profile (`ollama-gpu` with NVIDIA, `ollama-cpu` without). Pulls model inside the container.
+
+### 2. Environment files
+
+Generates `server/.env` and `www/.env.local` with standalone defaults:
+
+**`server/.env`** — key settings:
+
+| Variable | Value | Why |
+|----------|-------|-----|
+| `DATABASE_URL` | `postgresql+asyncpg://...@postgres:5432/reflector` | Docker-internal hostname |
+| `REDIS_HOST` | `redis` | Docker-internal hostname |
+| `CELERY_BROKER_URL` | `redis://redis:6379/1` | Docker-internal hostname |
+| `AUTH_BACKEND` | `none` | No Authentik in standalone |
+| `TRANSCRIPT_BACKEND` | `modal` | HTTP API to self-hosted CPU service |
+| `TRANSCRIPT_URL` | `http://cpu:8000` | Docker-internal CPU service |
+| `DIARIZATION_BACKEND` | `modal` | HTTP API to self-hosted CPU service |
+| `DIARIZATION_URL` | `http://cpu:8000` | Docker-internal CPU service |
+| `TRANSLATION_BACKEND` | `passthrough` | No Modal |
+| `LLM_URL` | `http://host.docker.internal:11434/v1` (Mac) | Ollama endpoint |
+
+**`www/.env.local`** — key settings:
+
+| Variable | Value |
+|----------|-------|
+| `API_URL` | `http://localhost:1250` |
+| `SERVER_API_URL` | `http://server:1250` |
+| `WEBSOCKET_URL` | `ws://localhost:1250` |
+| `FEATURE_REQUIRE_LOGIN` | `false` |
+| `NEXTAUTH_SECRET` | `standalone-dev-secret-not-for-production` |
+
+If env files already exist (including symlinks from worktree setup), the script resolves symlinks and ensures all standalone-critical vars are set. Existing vars not related to standalone are preserved.
+
+### 3. Object storage (Garage)
+
+Standalone uses [Garage](https://garagehq.deuxfleurs.fr/) — a lightweight S3-compatible object store running in Docker. The setup script starts Garage, initializes the layout, creates a bucket and access key, and writes the credentials to `server/.env`.
+
+**`server/.env`** — storage settings added by the script:
+
+| Variable | Value | Why |
+|----------|-------|-----|
+| `TRANSCRIPT_STORAGE_BACKEND` | `aws` | Uses the S3-compatible storage driver |
+| `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL` | `http://garage:3900` | Docker-internal Garage S3 API |
+| `TRANSCRIPT_STORAGE_AWS_BUCKET_NAME` | `reflector-media` | Created by the script |
+| `TRANSCRIPT_STORAGE_AWS_REGION` | `garage` | Must match Garage config |
+| `TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID` | *(auto-generated)* | Created by `garage key create` |
+| `TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY` | *(auto-generated)* | Created by `garage key create` |
+
+The `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL` setting enables S3-compatible backends. When set, the storage driver uses path-style addressing and routes all requests to the custom endpoint. When unset (production AWS), behavior is unchanged.
+
+Garage config template lives at `scripts/garage.toml`. The setup script generates `data/garage.toml` (gitignored) with a random RPC secret and mounts it read-only into the container. Single-node, `replication_factor=1`.
+
+> **Note**: Presigned URLs embed the Garage Docker hostname (`http://garage:3900`). This is fine — the server proxies S3 responses to the browser. Modal GPU workers cannot reach internal Garage, but standalone doesn't use Modal.
+
+### 4. Transcription and diarization
+
+Standalone runs the self-hosted ML service (`gpu/self_hosted/`) in a CPU-only Docker container named `cpu`. This is the same FastAPI service used for Modal.com GPU deployments, but built with `Dockerfile.cpu` (no NVIDIA CUDA dependencies). The compose service is named `cpu` (not `gpu`) to make clear it runs without GPU acceleration; the source code lives in `gpu/self_hosted/` because it's shared with the GPU deployment.
+
+The `modal` backend name is reused — it just means "HTTP API client". Setting `TRANSCRIPT_URL` / `DIARIZATION_URL` to `http://cpu:8000` routes requests to the local container instead of Modal.com.
+
+On first start, the service downloads pyannote speaker diarization models (~1GB) from a public S3 bundle. Models are cached in a Docker volume (`gpu_cache`) so subsequent starts are fast. No HuggingFace token or API key needed.
+
+> **Performance**: CPU-only transcription and diarization work but are slow (~15 min for a 3 min file). For faster processing on Linux with NVIDIA GPU, use `--profile gpu-nvidia` instead (see `docker-compose.standalone.yml`).
+
+### 5. Docker services
+
+```bash
+docker compose up -d postgres redis garage cpu server worker beat web
+```
+
+All services start in a single command. Garage and `cpu` are already started by earlier steps but included for idempotency. No Hatchet in standalone mode — LLM processing (summaries, topics, titles) runs via Celery tasks.
+
+### 6. Database migrations
+
+Run automatically by the `server` container on startup (`runserver.sh` calls `alembic upgrade head`). No manual step needed.
+
+### 7. Health check
+
+Verifies:
+- CPU service responds (transcription + diarization ready)
+- Server responds at `http://localhost:1250/health`
+- Frontend serves at `http://localhost:3000`
+- LLM endpoint reachable from inside containers
+
+## Services
+
+| Service | Port | Purpose |
+|---------|------|---------|
+| `server` | 1250 | FastAPI backend (runs migrations on start) |
+| `web` | 3000 | Next.js frontend |
+| `postgres` | 5432 | PostgreSQL database |
+| `redis` | 6379 | Cache + Celery broker |
+| `garage` | 3900, 3903 | S3-compatible object storage (S3 API + admin API) |
+| `cpu` | — | Self-hosted transcription + diarization (CPU-only) |
+| `worker` | — | Celery worker (live pipeline post-processing) |
+| `beat` | — | Celery beat (scheduled tasks) |
+
+## Testing programmatically
+
+After the setup script completes, verify the full pipeline (upload, transcription, diarization, LLM summary) via the API:
+
+```bash
+# 1. Create a transcript
+TRANSCRIPT_ID=$(curl -s -X POST 'http://localhost:1250/v1/transcripts' \
+  -H 'Content-Type: application/json' \
+  -d '{"name":"test-upload"}' | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])")
+echo "Created: $TRANSCRIPT_ID"
+
+# 2. Upload an audio file (single-chunk upload)
+curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}/record/upload?chunk_number=0&total_chunks=1" \
+  -X POST -F "chunk=@/path/to/audio.mp3"
+
+# 3. Poll until processing completes (status: ended or error)
+while true; do
+  STATUS=$(curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" \
+    | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
+  echo "Status: $STATUS"
+  case "$STATUS" in ended|error) break;; esac
+  sleep 10
+done
+
+# 4. Check the result
+curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" | python3 -m json.tool
+```
+
+Expected result: status `ended`, auto-generated `title`, `short_summary`, `long_summary`, and `transcript` text with `Speaker 0` / `Speaker 1` labels.
+
+CPU-only processing is slow (~15 min for a 3 min audio file). Diarization finishes in ~3 min, transcription takes the rest.
+
+## Troubleshooting
+
+### Port conflicts (most common issue)
+
+If the frontend or backend behaves unexpectedly (e.g., env vars seem ignored, changes don't take effect), **check for port conflicts first**:
+
+```bash
+# Check what's listening on key ports
+lsof -i :3000   # frontend
+lsof -i :1250   # backend
+lsof -i :5432   # postgres
+lsof -i :3900   # Garage S3 API
+lsof -i :6379   # Redis
+
+# Kill stale processes on a port
+lsof -ti :3000 | xargs kill
+```
+
+Common causes:
+- A stale `next dev` or `pnpm dev` process from another terminal/worktree
+- Another Docker Compose project (different worktree) with containers on the same ports — the setup script only manages its own project; containers from other projects must be stopped manually (`docker ps` to find them, `docker stop` to kill them)
+
+The setup script checks ports 3000, 1250, 5432, 6379, 3900, 3903 for conflicts before starting services. It ignores OrbStack/Docker Desktop port forwarding processes (which always bind these ports but are not real conflicts).
+
+### OrbStack false port-conflict warnings (Mac)
+
+If you use OrbStack as your Docker runtime, `lsof` will show OrbStack binding ports like 3000, 1250, etc. even when no containers are running. This is OrbStack's port forwarding mechanism — not a real conflict. The setup script filters these out automatically.
+
+### Re-enabling authentication
+
+Standalone runs without authentication (`FEATURE_REQUIRE_LOGIN=false`, `AUTH_BACKEND=none`). To re-enable:
+
+1. In `www/.env.local`: set `FEATURE_REQUIRE_LOGIN=true`, uncomment `AUTHENTIK_ISSUER` and `AUTHENTIK_REFRESH_TOKEN_URL`
+2. In `server/.env`: set `AUTH_BACKEND=authentik` (or your backend), configure `AUTH_JWT_AUDIENCE`
+3. Restart: `docker compose -f docker-compose.yml -f docker-compose.standalone.yml up -d --force-recreate web server`
+
+## What's NOT covered
+
+These require external accounts and infrastructure that can't be scripted:
+
+- **Live meeting rooms** — requires Daily.co account, S3 bucket, IAM roles
+- **Authentication** — requires Authentik deployment and OAuth configuration
+- **Hatchet workflows** — requires separate Hatchet setup for multitrack processing
+- **Production deployment** — see [Deployment Guide](./overview)
+
+## Current status
+
+All steps implemented. The setup script handles everything end-to-end:
+
+- Step 1 (Ollama/LLM) — implemented
+- Step 2 (environment files) — implemented
+- Step 3 (object storage / Garage) — implemented
+- Step 4 (transcription/diarization) — implemented (self-hosted GPU service)
+- Steps 5-7 (Docker, migrations, health) — implemented
+- **Unified script**: `scripts/setup-standalone.sh`
--- a/gpu/self_hosted/Dockerfile.cpu
+++ b/gpu/self_hosted/Dockerfile.cpu
@@ -0,0 +1,39 @@
+FROM python:3.12-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    UV_LINK_MODE=copy \
+    UV_NO_CACHE=1
+
+WORKDIR /tmp
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+ --mount=type=cache,target=/var/lib/apt,sharing=locked \
+ apt-get update \
+ && apt-get install -y \
+    ffmpeg \
+    curl \
+    ca-certificates \
+    gnupg \
+    wget
+ADD https://astral.sh/uv/install.sh /uv-installer.sh
+RUN sh /uv-installer.sh && rm /uv-installer.sh
+ENV PATH="/root/.local/bin/:$PATH"
+
+RUN mkdir -p /app
+WORKDIR /app
+COPY pyproject.toml uv.lock /app/
+
+
+COPY ./app /app/app
+COPY ./main.py /app/
+COPY ./runserver.sh /app/
+
+# prevent uv failing with too many open files on big cpus
+ENV UV_CONCURRENT_INSTALLS=16
+
+# first install
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --compile-bytecode --locked
+
+EXPOSE 8000
+
+CMD ["sh", "/app/runserver.sh"]
--- a/gpu/self_hosted/app/auth.py
+++ b/gpu/self_hosted/app/auth.py
@@ -3,14 +3,14 @@ import os
 from fastapi import Depends, HTTPException, status
 from fastapi.security import OAuth2PasswordBearer

-oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)


-def apikey_auth(apikey: str = Depends(oauth2_scheme)):
+def apikey_auth(apikey: str | None = Depends(oauth2_scheme)):
    required_key = os.environ.get("REFLECTOR_GPU_APIKEY")
    if not required_key:
        return
-    if apikey == required_key:
+    if apikey and apikey == required_key:
        return
    raise HTTPException(
        status_code=status.HTTP_401_UNAUTHORIZED,
--- a/gpu/self_hosted/app/services/diarizer.py
+++ b/gpu/self_hosted/app/services/diarizer.py
@@ -1,10 +1,65 @@
+import logging
 import os
+import tarfile
 import threading
+from pathlib import Path
+from urllib.request import urlopen

 import torch
 import torchaudio
+import yaml
 from pyannote.audio import Pipeline

+logger = logging.getLogger(__name__)
+
+S3_BUNDLE_URL = "https://reflector-public.s3.us-east-1.amazonaws.com/pyannote-speaker-diarization-3.1.tar.gz"
+BUNDLE_CACHE_DIR = Path("/root/.cache/pyannote-bundle")
+
+
+def _ensure_model(cache_dir: Path) -> str:
+    """Download and extract S3 model bundle if not cached."""
+    model_dir = cache_dir / "pyannote-speaker-diarization-3.1"
+    config_path = model_dir / "config.yaml"
+
+    if config_path.exists():
+        logger.info("Using cached model bundle at %s", model_dir)
+        return str(model_dir)
+
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    tarball_path = cache_dir / "model.tar.gz"
+
+    logger.info("Downloading model bundle from %s", S3_BUNDLE_URL)
+    with urlopen(S3_BUNDLE_URL) as response, open(tarball_path, "wb") as f:
+        while chunk := response.read(8192):
+            f.write(chunk)
+
+    logger.info("Extracting model bundle")
+    with tarfile.open(tarball_path, "r:gz") as tar:
+        tar.extractall(path=cache_dir, filter="data")
+    tarball_path.unlink()
+
+    _patch_config(model_dir, cache_dir)
+    return str(model_dir)
+
+
+def _patch_config(model_dir: Path, cache_dir: Path) -> None:
+    """Rewrite config.yaml to reference local pytorch_model.bin paths."""
+    config_path = model_dir / "config.yaml"
+    with open(config_path) as f:
+        config = yaml.safe_load(f)
+
+    config["pipeline"]["params"]["segmentation"] = str(
+        cache_dir / "pyannote-segmentation-3.0" / "pytorch_model.bin"
+    )
+    config["pipeline"]["params"]["embedding"] = str(
+        cache_dir / "pyannote-wespeaker-voxceleb-resnet34-LM" / "pytorch_model.bin"
+    )
+
+    with open(config_path, "w") as f:
+        yaml.dump(config, f)
+
+    logger.info("Patched config.yaml with local model paths")
+

 class PyannoteDiarizationService:
    def __init__(self):
@@ -14,10 +69,20 @@ class PyannoteDiarizationService:

    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"),
-        )
+        hf_token = os.environ.get("HF_TOKEN")
+
+        if hf_token:
+            logger.info("Loading pyannote model from HuggingFace (HF_TOKEN set)")
+            self._pipeline = Pipeline.from_pretrained(
+                "pyannote/speaker-diarization-3.1",
+                use_auth_token=hf_token,
+            )
+        else:
+            logger.info("HF_TOKEN not set — loading model from S3 bundle")
+            model_path = _ensure_model(BUNDLE_CACHE_DIR)
+            config_path = Path(model_path) / "config.yaml"
+            self._pipeline = Pipeline.from_pretrained(str(config_path))
+
        self._pipeline.to(torch.device(self._device))

    def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:
--- a/scripts/garage.toml
+++ b/scripts/garage.toml
@@ -0,0 +1,14 @@
+metadata_dir = "/var/lib/garage/meta"
+data_dir = "/var/lib/garage/data"
+replication_factor = 1
+
+rpc_secret = "__GARAGE_RPC_SECRET__"
+rpc_bind_addr = "[::]:3901"
+
+[s3_api]
+api_bind_addr = "[::]:3900"
+s3_region = "garage"
+root_domain = ".s3.garage.localhost"
+
+[admin]
+api_bind_addr = "[::]:3903"
--- a/scripts/setup-standalone.sh
+++ b/scripts/setup-standalone.sh
@@ -0,0 +1,417 @@
+#!/usr/bin/env bash
+#
+# Standalone local development setup for Reflector.
+# Takes a fresh clone to a working instance — no cloud accounts, no API keys.
+#
+# Usage:
+#   ./scripts/setup-standalone.sh
+#
+# Idempotent — safe to re-run at any time.
+#
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
+
+SERVER_ENV="$ROOT_DIR/server/.env"
+WWW_ENV="$ROOT_DIR/www/.env.local"
+
+MODEL="${LLM_MODEL:-qwen2.5:14b}"
+OLLAMA_PORT="${OLLAMA_PORT:-11434}"
+
+OS="$(uname -s)"
+
+# --- Colors ---
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+CYAN='\033[0;36m'
+NC='\033[0m'
+
+info()  { echo -e "${CYAN}==>${NC} $*"; }
+ok()    { echo -e "${GREEN}  ✓${NC} $*"; }
+warn()  { echo -e "${YELLOW}  !${NC} $*"; }
+err()   { echo -e "${RED}  ✗${NC} $*" >&2; }
+
+# --- Helpers ---
+
+wait_for_url() {
+    local url="$1" label="$2" retries="${3:-30}" interval="${4:-2}"
+    for i in $(seq 1 "$retries"); do
+        if curl -sf "$url" > /dev/null 2>&1; then
+            return 0
+        fi
+        echo -ne "\r  Waiting for $label... ($i/$retries)"
+        sleep "$interval"
+    done
+    echo ""
+    err "$label not responding at $url after $retries attempts"
+    return 1
+}
+
+env_has_key() {
+    local file="$1" key="$2"
+    grep -q "^${key}=" "$file" 2>/dev/null
+}
+
+env_set() {
+    local file="$1" key="$2" value="$3"
+    if env_has_key "$file" "$key"; then
+        # Replace existing value (portable sed)
+        if [[ "$OS" == "Darwin" ]]; then
+            sed -i '' "s|^${key}=.*|${key}=${value}|" "$file"
+        else
+            sed -i "s|^${key}=.*|${key}=${value}|" "$file"
+        fi
+    else
+        echo "${key}=${value}" >> "$file"
+    fi
+}
+
+resolve_symlink() {
+    local file="$1"
+    if [[ -L "$file" ]]; then
+        warn "$(basename "$file") is a symlink — creating standalone copy"
+        cp -L "$file" "$file.tmp"
+        rm "$file"
+        mv "$file.tmp" "$file"
+    fi
+}
+
+compose_cmd() {
+    local compose_files="-f $ROOT_DIR/docker-compose.yml -f $ROOT_DIR/docker-compose.standalone.yml"
+    if [[ "$OS" == "Linux" ]] && [[ -n "${OLLAMA_PROFILE:-}" ]]; then
+        docker compose $compose_files --profile "$OLLAMA_PROFILE" "$@"
+    else
+        docker compose $compose_files "$@"
+    fi
+}
+
+# =========================================================
+# Step 1: LLM / Ollama
+# =========================================================
+step_llm() {
+    info "Step 1: LLM setup (Ollama + $MODEL)"
+
+    case "$OS" in
+        Darwin)
+            if ! command -v ollama &> /dev/null; then
+                err "Ollama not found. Install it:"
+                err "  brew install ollama"
+                err "  # or https://ollama.com/download"
+                exit 1
+            fi
+
+            # Start if not running
+            if ! curl -sf "http://localhost:$OLLAMA_PORT/api/tags" > /dev/null 2>&1; then
+                info "Starting Ollama..."
+                ollama serve &
+                disown
+            fi
+
+            wait_for_url "http://localhost:$OLLAMA_PORT/api/tags" "Ollama"
+            echo ""
+
+            # Pull model if not already present
+            if ollama list 2>/dev/null | awk '{print $1}' | grep -qx "$MODEL"; then
+                ok "Model $MODEL already pulled"
+            else
+                info "Pulling model $MODEL (this may take a while)..."
+                ollama pull "$MODEL"
+            fi
+
+            LLM_URL_VALUE="http://host.docker.internal:$OLLAMA_PORT/v1"
+            ;;
+
+        Linux)
+            if command -v nvidia-smi &> /dev/null && nvidia-smi > /dev/null 2>&1; then
+                ok "NVIDIA GPU detected — using ollama-gpu profile"
+                OLLAMA_PROFILE="ollama-gpu"
+                OLLAMA_SVC="ollama"
+                LLM_URL_VALUE="http://ollama:$OLLAMA_PORT/v1"
+            else
+                warn "No NVIDIA GPU — using ollama-cpu profile"
+                OLLAMA_PROFILE="ollama-cpu"
+                OLLAMA_SVC="ollama-cpu"
+                LLM_URL_VALUE="http://ollama-cpu:$OLLAMA_PORT/v1"
+            fi
+
+            info "Starting Ollama container..."
+            compose_cmd up -d
+
+            wait_for_url "http://localhost:$OLLAMA_PORT/api/tags" "Ollama"
+            echo ""
+
+            # Pull model inside container
+            if compose_cmd exec "$OLLAMA_SVC" ollama list 2>/dev/null | awk '{print $1}' | grep -qx "$MODEL"; then
+                ok "Model $MODEL already pulled"
+            else
+                info "Pulling model $MODEL inside container (this may take a while)..."
+                compose_cmd exec "$OLLAMA_SVC" ollama pull "$MODEL"
+            fi
+            ;;
+
+        *)
+            err "Unsupported OS: $OS"
+            exit 1
+            ;;
+    esac
+
+    ok "LLM ready ($MODEL via Ollama)"
+}
+
+# =========================================================
+# Step 2: Generate server/.env
+# =========================================================
+step_server_env() {
+    info "Step 2: Generating server/.env"
+
+    resolve_symlink "$SERVER_ENV"
+
+    if [[ -f "$SERVER_ENV" ]]; then
+        ok "server/.env already exists — ensuring standalone vars"
+    else
+        cat > "$SERVER_ENV" << 'ENVEOF'
+# Generated by setup-standalone.sh — standalone local development
+# Source of truth for settings: server/reflector/settings.py
+ENVEOF
+        ok "Created server/.env"
+    fi
+
+    # Ensure all standalone-critical vars (appends if missing, replaces if present)
+    env_set "$SERVER_ENV" "DATABASE_URL" "postgresql+asyncpg://reflector:reflector@postgres:5432/reflector"
+    env_set "$SERVER_ENV" "REDIS_HOST" "redis"
+    env_set "$SERVER_ENV" "CELERY_BROKER_URL" "redis://redis:6379/1"
+    env_set "$SERVER_ENV" "CELERY_RESULT_BACKEND" "redis://redis:6379/1"
+    env_set "$SERVER_ENV" "AUTH_BACKEND" "none"
+    env_set "$SERVER_ENV" "PUBLIC_MODE" "true"
+    # TRANSCRIPT_BACKEND, TRANSCRIPT_URL, DIARIZATION_BACKEND, DIARIZATION_URL
+    # are set via docker-compose.standalone.yml `environment:` overrides — not written here
+    # so we don't clobber the user's server/.env for non-standalone use.
+    env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "passthrough"
+    env_set "$SERVER_ENV" "LLM_URL" "$LLM_URL_VALUE"
+    env_set "$SERVER_ENV" "LLM_MODEL" "$MODEL"
+    env_set "$SERVER_ENV" "LLM_API_KEY" "not-needed"
+
+    ok "Standalone vars set (LLM_URL=$LLM_URL_VALUE)"
+}
+
+# =========================================================
+# Step 3: Object storage (Garage)
+# =========================================================
+step_storage() {
+    info "Step 3: Object storage (Garage)"
+
+    # Generate garage.toml from template (fill in RPC secret)
+    GARAGE_TOML="$ROOT_DIR/scripts/garage.toml"
+    GARAGE_TOML_RUNTIME="$ROOT_DIR/data/garage.toml"
+    if [[ ! -f "$GARAGE_TOML_RUNTIME" ]]; then
+        mkdir -p "$ROOT_DIR/data"
+        RPC_SECRET=$(openssl rand -hex 32)
+        sed "s|__GARAGE_RPC_SECRET__|${RPC_SECRET}|" "$GARAGE_TOML" > "$GARAGE_TOML_RUNTIME"
+    fi
+
+    compose_cmd up -d garage
+
+    wait_for_url "http://localhost:3903/health" "Garage admin API"
+    echo ""
+
+    # Layout: get node ID, assign, apply (skip if already applied)
+    NODE_ID=$(compose_cmd exec -T garage /garage node id -q 2>/dev/null | tr -d '[:space:]')
+    LAYOUT_STATUS=$(compose_cmd exec -T garage /garage layout show 2>&1 || true)
+    if echo "$LAYOUT_STATUS" | grep -q "No nodes"; then
+        compose_cmd exec -T garage /garage layout assign "$NODE_ID" -c 1G -z dc1
+        compose_cmd exec -T garage /garage layout apply --version 1
+    fi
+
+    # Create bucket (idempotent — skip if exists)
+    if ! compose_cmd exec -T garage /garage bucket info reflector-media &>/dev/null; then
+        compose_cmd exec -T garage /garage bucket create reflector-media
+    fi
+
+    # Create key (idempotent — skip if exists)
+    CREATED_KEY=false
+    if compose_cmd exec -T garage /garage key info reflector &>/dev/null; then
+        ok "Key 'reflector' already exists"
+    else
+        KEY_OUTPUT=$(compose_cmd exec -T garage /garage key create reflector)
+        CREATED_KEY=true
+    fi
+
+    # Grant bucket permissions (idempotent)
+    compose_cmd exec -T garage /garage bucket allow reflector-media --read --write --key reflector
+
+    # Set env vars (only parse key on first create — key info redacts the secret)
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_BACKEND" "aws"
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL" "http://garage:3900"
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_BUCKET_NAME" "reflector-media"
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_REGION" "garage"
+    if [[ "$CREATED_KEY" == "true" ]]; then
+        KEY_ID=$(echo "$KEY_OUTPUT" | grep -i "key id" | awk '{print $NF}')
+        KEY_SECRET=$(echo "$KEY_OUTPUT" | grep -i "secret key" | awk '{print $NF}')
+        env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID" "$KEY_ID"
+        env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY" "$KEY_SECRET"
+    fi
+
+    ok "Object storage ready (Garage)"
+}
+
+# =========================================================
+# Step 4: Generate www/.env.local
+# =========================================================
+step_www_env() {
+    info "Step 4: Generating www/.env.local"
+
+    resolve_symlink "$WWW_ENV"
+
+    if [[ -f "$WWW_ENV" ]]; then
+        ok "www/.env.local already exists — ensuring standalone vars"
+    else
+        cat > "$WWW_ENV" << 'ENVEOF'
+# Generated by setup-standalone.sh — standalone local development
+ENVEOF
+        ok "Created www/.env.local"
+    fi
+
+    env_set "$WWW_ENV" "SITE_URL" "http://localhost:3000"
+    env_set "$WWW_ENV" "NEXTAUTH_URL" "http://localhost:3000"
+    env_set "$WWW_ENV" "NEXTAUTH_SECRET" "standalone-dev-secret-not-for-production"
+    env_set "$WWW_ENV" "API_URL" "http://localhost:1250"
+    env_set "$WWW_ENV" "WEBSOCKET_URL" "ws://localhost:1250"
+    env_set "$WWW_ENV" "SERVER_API_URL" "http://server:1250"
+    env_set "$WWW_ENV" "FEATURE_REQUIRE_LOGIN" "false"
+
+    ok "Standalone www vars set"
+}
+
+# =========================================================
+# Step 5: Start all services
+# =========================================================
+step_services() {
+    info "Step 5: Starting Docker services"
+
+    # Check for port conflicts — stale processes silently shadow Docker port mappings.
+    # OrbStack/Docker Desktop bind ports for forwarding; ignore those PIDs.
+    local ports_ok=true
+    for port in 3000 1250 5432 6379 3900 3903; do
+        local pids
+        pids=$(lsof -ti :"$port" 2>/dev/null || true)
+        for pid in $pids; do
+            local pname
+            pname=$(ps -p "$pid" -o comm= 2>/dev/null || true)
+            # OrbStack and Docker Desktop own port forwarding — not real conflicts
+            if [[ "$pname" == *"OrbStack"* ]] || [[ "$pname" == *"com.docker"* ]] || [[ "$pname" == *"vpnkit"* ]]; then
+                continue
+            fi
+            warn "Port $port already in use by PID $pid ($pname)"
+            warn "Kill it with: lsof -ti :$port | xargs kill"
+            ports_ok=false
+        done
+    done
+    if [[ "$ports_ok" == "false" ]]; then
+        warn "Port conflicts detected — Docker containers may not be reachable"
+        warn "Continuing anyway (services will start but may be shadowed)"
+    fi
+
+    # server runs alembic migrations on startup automatically (see runserver.sh)
+    compose_cmd up -d postgres redis garage cpu server worker beat web
+    ok "Containers started"
+    info "Server is running migrations (alembic upgrade head)..."
+}
+
+# =========================================================
+# Step 6: Health checks
+# =========================================================
+step_health() {
+    info "Step 6: Health checks"
+
+    # CPU service may take a while on first start (model download + load).
+    # No host port exposed — check via docker exec.
+    info "Waiting for CPU service (first start downloads ~1GB of models)..."
+    local cpu_ok=false
+    for i in $(seq 1 120); do
+        if compose_cmd exec -T cpu curl -sf http://localhost:8000/docs > /dev/null 2>&1; then
+            cpu_ok=true
+            break
+        fi
+        echo -ne "\r  Waiting for CPU service... ($i/120)"
+        sleep 5
+    done
+    echo ""
+    if [[ "$cpu_ok" == "true" ]]; then
+        ok "CPU service healthy (transcription + diarization)"
+    else
+        warn "CPU service not ready yet — it will keep loading in the background"
+        warn "Check with: docker compose logs cpu"
+    fi
+
+    wait_for_url "http://localhost:1250/health" "Server API" 60 3
+    echo ""
+    ok "Server API healthy"
+
+    wait_for_url "http://localhost:3000" "Frontend" 90 3
+    echo ""
+    ok "Frontend responding"
+
+    # Check LLM reachability from inside a container
+    if compose_cmd exec -T server \
+        curl -sf "$LLM_URL_VALUE/models" > /dev/null 2>&1; then
+        ok "LLM reachable from containers"
+    else
+        warn "LLM not reachable from containers at $LLM_URL_VALUE"
+        warn "Summaries/topics/titles won't work until LLM is accessible"
+    fi
+}
+
+# =========================================================
+# Main
+# =========================================================
+main() {
+    echo ""
+    echo "=========================================="
+    echo " Reflector — Standalone Local Setup"
+    echo "=========================================="
+    echo ""
+
+    # Ensure we're in the repo root
+    if [[ ! -f "$ROOT_DIR/docker-compose.yml" ]]; then
+        err "docker-compose.yml not found in $ROOT_DIR"
+        err "Run this script from the repo root: ./scripts/setup-standalone.sh"
+        exit 1
+    fi
+
+
+    # LLM_URL_VALUE is set by step_llm, used by later steps
+    LLM_URL_VALUE=""
+    OLLAMA_PROFILE=""
+
+    # docker-compose.yml may reference env_files that don't exist yet;
+    # touch them so compose_cmd works before the steps that populate them.
+    touch "$SERVER_ENV" "$WWW_ENV"
+
+    step_llm
+    echo ""
+    step_server_env
+    echo ""
+    step_storage
+    echo ""
+    step_www_env
+    echo ""
+    step_services
+    echo ""
+    step_health
+
+    echo ""
+    echo "=========================================="
+    echo -e " ${GREEN}Reflector is running!${NC}"
+    echo "=========================================="
+    echo ""
+    echo "  Frontend:  http://localhost:3000"
+    echo "  API:       http://localhost:1250"
+    echo ""
+    echo "  To stop:   docker compose down"
+    echo "  To re-run: ./scripts/setup-standalone.sh"
+    echo ""
+}
+
+main "$@"
--- a/server/.env.example
+++ b/server/.env.example
@@ -66,15 +66,22 @@ TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
 ## LLM backend (Required)
 ##
 ## Responsible for generating titles, summaries, and topic detection
-## Requires OpenAI API key
+## Supports any OpenAI-compatible endpoint.
 ## =======================================================

-## 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
+## --- Option A: Local LLM via Ollama (recommended for dev) ---
+## Setup: ./scripts/setup-standalone.sh
+## Mac: Ollama runs natively (Metal GPU). Containers reach it via host.docker.internal.
+## Linux: docker compose --profile ollama-gpu up -d (or ollama-cpu for no GPU)
+LLM_URL=http://host.docker.internal:11434/v1
+LLM_MODEL=qwen2.5:14b
+LLM_API_KEY=not-needed
+## Linux with containerized Ollama: LLM_URL=http://ollama:11434/v1

-## Optional: Custom endpoint (defaults to OpenAI)
-# LLM_URL=https://api.openai.com/v1
+## --- Option B: Remote/cloud LLM ---
+#LLM_API_KEY=sk-your-openai-api-key
+#LLM_MODEL=gpt-4o-mini
+## LLM_URL defaults to OpenAI when unset

 ## Context size for summary generation (tokens)
 LLM_CONTEXT_WINDOW=16000
--- a/server/migrations/versions/3aa20b96d963_drop_use_celery_column.py
+++ b/server/migrations/versions/3aa20b96d963_drop_use_celery_column.py
@@ -0,0 +1,35 @@
+"""drop_use_celery_column
+
+Revision ID: 3aa20b96d963
+Revises: e69f08ead8ea
+Create Date: 2026-02-05 10:12:44.065279
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "3aa20b96d963"
+down_revision: Union[str, None] = "e69f08ead8ea"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.drop_column("use_celery")
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.add_column(
+            sa.Column(
+                "use_celery",
+                sa.Boolean(),
+                server_default=sa.text("false"),
+                nullable=False,
+            )
+        )
--- a/server/pyproject.toml
+++ b/server/pyproject.toml
@@ -68,7 +68,6 @@ evaluation = [
    "pydantic>=2.1.1",
 ]
 local = [
-    "pyannote-audio>=3.3.2",
    "faster-whisper>=0.10.0",
 ]
 silero-vad = [
--- a/server/reflector/asynctask.py
+++ b/server/reflector/asynctask.py
@@ -22,6 +22,8 @@ def asynctask(f):
                await database.disconnect()

        coro = run_with_db()
+        if current_task:
+            return asyncio.run(coro)
        try:
            loop = asyncio.get_running_loop()
        except RuntimeError:
--- a/server/reflector/auth/auth_none.py
+++ b/server/reflector/auth/auth_none.py
@@ -1,11 +1,5 @@
-from typing import Annotated
-
-from fastapi import Depends
-from fastapi.security import OAuth2PasswordBearer
 from pydantic import BaseModel

-oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
-

 class UserInfo(BaseModel):
    sub: str
@@ -15,13 +9,13 @@ class AccessTokenInfo(BaseModel):
    pass


-def authenticated(token: Annotated[str, Depends(oauth2_scheme)]):
+def authenticated():
    return None


-def current_user(token: Annotated[str, Depends(oauth2_scheme)]):
+def current_user():
    return None


-def current_user_optional(token: Annotated[str, Depends(oauth2_scheme)]):
+def current_user_optional():
    return None
--- a/server/reflector/dailyco_api/client.py
+++ b/server/reflector/dailyco_api/client.py
@@ -146,6 +146,8 @@ class DailyApiClient:
            )
            raise DailyApiError(operation, response)

+        if not response.content:
+            return {}
        return response.json()

    # ============================================================================
--- a/server/reflector/dailyco_api/webhook_utils.py
+++ b/server/reflector/dailyco_api/webhook_utils.py
@@ -99,7 +99,7 @@ def extract_room_name(event: DailyWebhookEvent) -> str | None:
        >>> event = DailyWebhookEvent(**webhook_payload)
        >>> room_name = extract_room_name(event)
    """
-    room = event.payload.get("room_name")
+    room = event.payload.get("room_name") or event.payload.get("room")
    # Ensure we return a string, not any falsy value that might be in payload
    return room if isinstance(room, str) else None

--- a/server/reflector/dailyco_api/webhooks.py
+++ b/server/reflector/dailyco_api/webhooks.py
@@ -6,7 +6,7 @@ Reference: https://docs.daily.co/reference/rest-api/webhooks

 from typing import Annotated, Any, Dict, Literal, Union

-from pydantic import BaseModel, Field, field_validator
+from pydantic import AliasChoices, BaseModel, ConfigDict, Field, field_validator

 from reflector.utils.string import NonEmptyString

@@ -41,6 +41,8 @@ class DailyTrack(BaseModel):
    Reference: https://docs.daily.co/reference/rest-api/recordings
    """

+    model_config = ConfigDict(extra="ignore")
+
    type: Literal["audio", "video"]
    s3Key: NonEmptyString = Field(description="S3 object key for the track file")
    size: int = Field(description="File size in bytes")
@@ -54,6 +56,8 @@ class DailyWebhookEvent(BaseModel):
    Reference: https://docs.daily.co/reference/rest-api/webhooks
    """

+    model_config = ConfigDict(extra="ignore")
+
    version: NonEmptyString = Field(
        description="Represents the version of the event. This uses semantic versioning to inform a consumer if the payload has introduced any breaking changes"
    )
@@ -82,7 +86,13 @@ class ParticipantJoinedPayload(BaseModel):
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/participant-joined
    """

-    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
+    model_config = ConfigDict(extra="ignore")
+
+    room_name: NonEmptyString | None = Field(
+        None,
+        description="Daily.co room name",
+        validation_alias=AliasChoices("room_name", "room"),
+    )
    session_id: NonEmptyString = Field(description="Daily.co session identifier")
    user_id: NonEmptyString = Field(description="User identifier (may be encoded)")
    user_name: NonEmptyString | None = Field(None, description="User display name")
@@ -100,7 +110,13 @@ class ParticipantLeftPayload(BaseModel):
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/participant-left
    """

-    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
+    model_config = ConfigDict(extra="ignore")
+
+    room_name: NonEmptyString | None = Field(
+        None,
+        description="Daily.co room name",
+        validation_alias=AliasChoices("room_name", "room"),
+    )
    session_id: NonEmptyString = Field(description="Daily.co session identifier")
    user_id: NonEmptyString = Field(description="User identifier (may be encoded)")
    user_name: NonEmptyString | None = Field(None, description="User display name")
@@ -112,6 +128,9 @@ class ParticipantLeftPayload(BaseModel):
    _normalize_joined_at = field_validator("joined_at", mode="before")(
        normalize_timestamp_to_int
    )
+    _normalize_duration = field_validator("duration", mode="before")(
+        normalize_timestamp_to_int
+    )


 class RecordingStartedPayload(BaseModel):
@@ -121,6 +140,8 @@ class RecordingStartedPayload(BaseModel):
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-started
    """

+    model_config = ConfigDict(extra="ignore")
+
    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
    recording_id: NonEmptyString = Field(description="Recording identifier")
    start_ts: int | None = Field(None, description="Recording start timestamp")
@@ -138,7 +159,9 @@ class RecordingReadyToDownloadPayload(BaseModel):
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-ready-to-download
    """

-    type: Literal["cloud", "raw-tracks"] = Field(
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["cloud", "cloud-audio-only", "raw-tracks"] = Field(
        description="The type of recording that was generated"
    )
    recording_id: NonEmptyString = Field(
@@ -153,8 +176,9 @@ class RecordingReadyToDownloadPayload(BaseModel):
    status: Literal["finished"] = Field(
        description="The status of the given recording (always 'finished' in ready-to-download webhook, see RecordingStatus in responses.py for full API statuses)"
    )
-    max_participants: int = Field(
-        description="The number of participants on the call that were recorded"
+    max_participants: int | None = Field(
+        None,
+        description="The number of participants on the call that were recorded (optional; Daily may omit it in some webhook versions)",
    )
    duration: int = Field(description="The duration in seconds of the call")
    s3_key: NonEmptyString = Field(
@@ -180,6 +204,8 @@ class RecordingErrorPayload(BaseModel):
    Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-error
    """

+    model_config = ConfigDict(extra="ignore")
+
    action: Literal["clourd-recording-err", "cloud-recording-error"] = Field(
        description="A string describing the event that was emitted (both variants are documented)"
    )
@@ -200,6 +226,8 @@ class RecordingErrorPayload(BaseModel):


 class ParticipantJoinedEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
    version: NonEmptyString
    type: Literal["participant.joined"]
    id: NonEmptyString
@@ -212,6 +240,8 @@ class ParticipantJoinedEvent(BaseModel):


 class ParticipantLeftEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
    version: NonEmptyString
    type: Literal["participant.left"]
    id: NonEmptyString
@@ -224,6 +254,8 @@ class ParticipantLeftEvent(BaseModel):


 class RecordingStartedEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
    version: NonEmptyString
    type: Literal["recording.started"]
    id: NonEmptyString
@@ -236,6 +268,8 @@ class RecordingStartedEvent(BaseModel):


 class RecordingReadyEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
    version: NonEmptyString
    type: Literal["recording.ready-to-download"]
    id: NonEmptyString
@@ -248,6 +282,8 @@ class RecordingReadyEvent(BaseModel):


 class RecordingErrorEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
    version: NonEmptyString
    type: Literal["recording.error"]
    id: NonEmptyString
--- a/server/reflector/db/rooms.py
+++ b/server/reflector/db/rooms.py
@@ -57,12 +57,6 @@ rooms = sqlalchemy.Table(
        sqlalchemy.String,
        nullable=False,
    ),
-    sqlalchemy.Column(
-        "use_celery",
-        sqlalchemy.Boolean,
-        nullable=False,
-        server_default=false(),
-    ),
    sqlalchemy.Column(
        "skip_consent",
        sqlalchemy.Boolean,
@@ -97,7 +91,6 @@ class Room(BaseModel):
    ics_last_sync: datetime | None = None
    ics_last_etag: str | None = None
    platform: Platform = Field(default_factory=lambda: settings.DEFAULT_VIDEO_PLATFORM)
-    use_celery: bool = False
    skip_consent: bool = False


--- a/server/reflector/db/search.py
+++ b/server/reflector/db/search.py
@@ -26,6 +26,7 @@ from reflector.db.rooms import rooms
 from reflector.db.transcripts import SourceKind, TranscriptStatus, transcripts
 from reflector.db.utils import is_postgresql
 from reflector.logger import logger
+from reflector.settings import settings
 from reflector.utils.string import NonEmptyString, try_parse_non_empty_string

 DEFAULT_SEARCH_LIMIT = 20
@@ -396,7 +397,7 @@ class SearchController:
                    transcripts.c.user_id == params.user_id, rooms.c.is_shared
                )
            )
-        else:
+        elif not settings.PUBLIC_MODE:
            base_query = base_query.where(rooms.c.is_shared)
        if params.room_id:
            base_query = base_query.where(transcripts.c.room_id == params.room_id)
--- a/server/reflector/db/transcripts.py
+++ b/server/reflector/db/transcripts.py
@@ -406,7 +406,7 @@ class TranscriptController:
            query = query.where(
                or_(transcripts.c.user_id == user_id, rooms.c.is_shared)
            )
-        else:
+        elif not settings.PUBLIC_MODE:
            query = query.where(rooms.c.is_shared)

        if source_kind:
--- a/server/reflector/hatchet/client.py
+++ b/server/reflector/hatchet/client.py
@@ -12,7 +12,9 @@ import threading

 from hatchet_sdk import ClientConfig, Hatchet
 from hatchet_sdk.clients.rest.models import V1TaskStatus
+from hatchet_sdk.rate_limit import RateLimitDuration

+from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, LLM_RATE_LIMIT_PER_SECOND
 from reflector.logger import logger
 from reflector.settings import settings

@@ -113,3 +115,26 @@ class HatchetClientManager:
        """Reset the client instance (for testing)."""
        with cls._lock:
            cls._instance = None
+
+    @classmethod
+    async def ensure_rate_limit(cls) -> None:
+        """Ensure the LLM rate limit exists in Hatchet.
+
+        Uses the Hatchet SDK rate_limits client (aio_put). See:
+        https://docs.hatchet.run/sdks/python/feature-clients/rate_limits
+        """
+        logger.info(
+            "[Hatchet] Ensuring rate limit exists",
+            rate_limit_key=LLM_RATE_LIMIT_KEY,
+            limit=LLM_RATE_LIMIT_PER_SECOND,
+        )
+        client = cls.get_client()
+        await client.rate_limits.aio_put(
+            key=LLM_RATE_LIMIT_KEY,
+            limit=LLM_RATE_LIMIT_PER_SECOND,
+            duration=RateLimitDuration.SECOND,
+        )
+        logger.info(
+            "[Hatchet] Rate limit put successfully",
+            rate_limit_key=LLM_RATE_LIMIT_KEY,
+        )
--- a/server/reflector/hatchet/run_workers_llm.py
+++ b/server/reflector/hatchet/run_workers_llm.py
@@ -3,6 +3,8 @@ LLM/I/O worker pool for all non-CPU tasks.
 Handles: all tasks except mixdown_tracks (transcription, LLM inference, orchestration)
 """

+import asyncio
+
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.daily_multitrack_pipeline import (
    daily_multitrack_pipeline,
@@ -20,6 +22,15 @@ POOL = "llm-io"
 def main():
    hatchet = HatchetClientManager.get_client()

+    try:
+        asyncio.run(HatchetClientManager.ensure_rate_limit())
+    except Exception as e:
+        logger.warning(
+            "[Hatchet] Rate limit initialization failed, but continuing. "
+            "If workflows fail to register, rate limits may need to be created manually.",
+            error=str(e),
+        )
+
    logger.info(
        "Starting Hatchet LLM worker pool (all tasks except mixdown)",
        worker_name=WORKER_NAME,
--- a/server/reflector/hatchet/workflows/daily_multitrack_pipeline.py
+++ b/server/reflector/hatchet/workflows/daily_multitrack_pipeline.py
@@ -171,11 +171,13 @@ async def set_workflow_error_status(transcript_id: NonEmptyString) -> bool:

 def _spawn_storage():
    """Create fresh storage instance."""
+    # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
    return AwsStorage(
        aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
        aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
        aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
        aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+        aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
    )


@@ -718,7 +720,6 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
                chunk_text=chunk["text"],
                timestamp=chunk["timestamp"],
                duration=chunk["duration"],
-                words=chunk["words"],
            )
        )
        for chunk in chunks
@@ -730,31 +731,41 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
        TopicChunkResult(**result[TaskName.DETECT_CHUNK_TOPIC]) for result in results
    ]

+    # Build index-to-words map from local chunks (words not in child workflow results)
+    chunks_by_index = {chunk["index"]: chunk["words"] for chunk in chunks}
+
    async with fresh_db_connection():
        transcript = await transcripts_controller.get_by_id(input.transcript_id)
        if not transcript:
            raise ValueError(f"Transcript {input.transcript_id} not found")

+        # Clear topics for idempotency on retry (each topic gets a fresh UUID,
+        # so upsert_topic would append duplicates without this)
+        await transcripts_controller.update(transcript, {"topics": []})
+
        for chunk in topic_chunks:
+            chunk_words = chunks_by_index[chunk.chunk_index]
            topic = TranscriptTopic(
                title=chunk.title,
                summary=chunk.summary,
                timestamp=chunk.timestamp,
-                transcript=" ".join(w.text for w in chunk.words),
-                words=chunk.words,
+                transcript=" ".join(w.text for w in chunk_words),
+                words=chunk_words,
            )
            await transcripts_controller.upsert_topic(transcript, topic)
            await append_event_and_broadcast(
                input.transcript_id, transcript, "TOPIC", topic, logger=logger
            )

+    # Words omitted from TopicsResult — already persisted to DB above.
+    # Downstream tasks that need words refetch from DB.
    topics_list = [
        TitleSummary(
            title=chunk.title,
            summary=chunk.summary,
            timestamp=chunk.timestamp,
            duration=chunk.duration,
-            transcript=TranscriptType(words=chunk.words),
+            transcript=TranscriptType(words=[]),
        )
        for chunk in topic_chunks
    ]
@@ -840,9 +851,8 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
    ctx.log(f"extract_subjects: starting for transcript_id={input.transcript_id}")

    topics_result = ctx.task_output(detect_topics)
-    topics = topics_result.topics

-    if not topics:
+    if not topics_result.topics:
        ctx.log("extract_subjects: no topics, returning empty subjects")
        return SubjectsResult(
            subjects=[],
@@ -855,11 +865,13 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
    # sharing DB connections and LLM HTTP pools across forks
    from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
    from reflector.llm import LLM  # noqa: PLC0415
+    from reflector.processors.types import words_to_segments  # noqa: PLC0415

    async with fresh_db_connection():
        transcript = await transcripts_controller.get_by_id(input.transcript_id)

-        # Build transcript text from topics (same logic as TranscriptFinalSummaryProcessor)
+        # Build transcript text from DB topics (words omitted from task output
+        # to reduce Hatchet payload size — refetch from DB where they were persisted)
        speakermap = {}
        if transcript and transcript.participants:
            speakermap = {
@@ -869,8 +881,8 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
            }

        text_lines = []
-        for topic in topics:
-            for segment in topic.transcript.as_segments():
+        for db_topic in transcript.topics:
+            for segment in words_to_segments(db_topic.words):
                name = speakermap.get(segment.speaker, f"Speaker {segment.speaker}")
                text_lines.append(f"{name}: {segment.text}")

--- a/server/reflector/hatchet/workflows/models.py
+++ b/server/reflector/hatchet/workflows/models.py
@@ -95,7 +95,6 @@ class TopicChunkResult(BaseModel):
    summary: str
    timestamp: float
    duration: float
-    words: list[Word]


 class TopicsResult(BaseModel):
--- a/server/reflector/hatchet/workflows/padding_workflow.py
+++ b/server/reflector/hatchet/workflows/padding_workflow.py
@@ -49,11 +49,13 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
        from reflector.settings import settings  # noqa: PLC0415
        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415

+        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
        storage = AwsStorage(
            aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
            aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
            aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
            aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
        )

        source_url = await storage.get_file_url(
--- a/server/reflector/hatchet/workflows/topic_chunk_processing.py
+++ b/server/reflector/hatchet/workflows/topic_chunk_processing.py
@@ -20,7 +20,6 @@ from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_MEDIUM
 from reflector.hatchet.workflows.models import TopicChunkResult
 from reflector.logger import logger
 from reflector.processors.prompts import TOPIC_PROMPT
-from reflector.processors.types import Word


 class TopicChunkInput(BaseModel):
@@ -30,7 +29,6 @@ class TopicChunkInput(BaseModel):
    chunk_text: str
    timestamp: float
    duration: float
-    words: list[Word]


 hatchet = HatchetClientManager.get_client()
@@ -99,5 +97,4 @@ async def detect_chunk_topic(input: TopicChunkInput, ctx: Context) -> TopicChunk
        summary=response.summary,
        timestamp=input.timestamp,
        duration=input.duration,
-        words=input.words,
    )
--- a/server/reflector/hatchet/workflows/track_processing.py
+++ b/server/reflector/hatchet/workflows/track_processing.py
@@ -60,6 +60,7 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:

    try:
        # Create fresh storage instance to avoid aioboto3 fork issues
+        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
        from reflector.settings import settings  # noqa: PLC0415
        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415

@@ -68,6 +69,7 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
            aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
            aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
            aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
        )

        source_url = await storage.get_file_url(
@@ -159,6 +161,7 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
            raise ValueError("Missing padded_key from pad_track")

        # Presign URL on demand (avoids stale URLs on workflow replay)
+        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
        from reflector.settings import settings  # noqa: PLC0415
        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415

@@ -167,6 +170,7 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
            aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
            aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
            aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
        )

        audio_url = await storage.get_file_url(
--- a/server/reflector/llm.py
+++ b/server/reflector/llm.py
@@ -144,7 +144,18 @@ class StructuredOutputWorkflow(Workflow, Generic[OutputT]):
        )

        # Network retries handled by OpenAILike (max_retries=3)
-        response = await Settings.llm.acomplete(json_prompt)
+        # response_format enables grammar-based constrained decoding on backends
+        # that support it (DMR/llama.cpp, vLLM, Ollama, OpenAI).
+        response = await Settings.llm.acomplete(
+            json_prompt,
+            response_format={
+                "type": "json_schema",
+                "json_schema": {
+                    "name": self.output_cls.__name__,
+                    "schema": self.output_cls.model_json_schema(),
+                },
+            },
+        )
        return ExtractionDone(output=response.text)

    @step
--- a/server/reflector/processors/audio_diarization_pyannote.py
+++ b/server/reflector/processors/audio_diarization_pyannote.py
@@ -1,74 +0,0 @@
-import os
-
-import torch
-import torchaudio
-from pyannote.audio import Pipeline
-
-from reflector.processors.audio_diarization import AudioDiarizationProcessor
-from reflector.processors.audio_diarization_auto import AudioDiarizationAutoProcessor
-from reflector.processors.types import AudioDiarizationInput, DiarizationSegment
-
-
-class AudioDiarizationPyannoteProcessor(AudioDiarizationProcessor):
-    """Local diarization processor using pyannote.audio library"""
-
-    def __init__(
-        self,
-        model_name: str = "pyannote/speaker-diarization-3.1",
-        pyannote_auth_token: str | None = None,
-        device: str | None = None,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.model_name = model_name
-        self.auth_token = pyannote_auth_token or os.environ.get("HF_TOKEN")
-        self.device = device
-
-        if device is None:
-            self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        self.logger.info(f"Loading pyannote diarization model: {self.model_name}")
-        self.diarization_pipeline = Pipeline.from_pretrained(
-            self.model_name, use_auth_token=self.auth_token
-        )
-        self.diarization_pipeline.to(torch.device(self.device))
-        self.logger.info(f"Diarization model loaded on device: {self.device}")
-
-    async def _diarize(self, data: AudioDiarizationInput) -> list[DiarizationSegment]:
-        try:
-            # Load audio file (audio_url is assumed to be a local file path)
-            self.logger.info(f"Loading local audio file: {data.audio_url}")
-            waveform, sample_rate = torchaudio.load(data.audio_url)
-            audio_input = {"waveform": waveform, "sample_rate": sample_rate}
-            self.logger.info("Running speaker diarization")
-            diarization = self.diarization_pipeline(audio_input)
-
-            # Convert pyannote diarization output to our format
-            segments = []
-            for segment, _, speaker in diarization.itertracks(yield_label=True):
-                # Extract speaker number from label (e.g., "SPEAKER_00" -> 0)
-                speaker_id = 0
-                if speaker.startswith("SPEAKER_"):
-                    try:
-                        speaker_id = int(speaker.split("_")[-1])
-                    except (ValueError, IndexError):
-                        # Fallback to hash-based ID if parsing fails
-                        speaker_id = hash(speaker) % 1000
-
-                segments.append(
-                    {
-                        "start": round(segment.start, 3),
-                        "end": round(segment.end, 3),
-                        "speaker": speaker_id,
-                    }
-                )
-
-            self.logger.info(f"Diarization completed with {len(segments)} segments")
-            return segments
-
-        except Exception as e:
-            self.logger.exception(f"Diarization failed: {e}")
-            raise
-
-
-AudioDiarizationAutoProcessor.register("pyannote", AudioDiarizationPyannoteProcessor)
--- a/server/reflector/services/transcript_process.py
+++ b/server/reflector/services/transcript_process.py
@@ -15,14 +15,10 @@ from hatchet_sdk.clients.rest.exceptions import ApiException, NotFoundException
 from hatchet_sdk.clients.rest.models import V1TaskStatus

 from reflector.db.recordings import recordings_controller
-from reflector.db.rooms import rooms_controller
 from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.hatchet.client import HatchetClientManager
 from reflector.logger import logger
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
-from reflector.pipelines.main_multitrack_pipeline import (
-    task_pipeline_multitrack_process,
-)
 from reflector.utils.string import NonEmptyString


@@ -101,8 +97,11 @@ async def validate_transcript_for_processing(
    if transcript.locked:
        return ValidationLocked(detail="Recording is locked")

-    # Check if recording is ready for processing
-    if transcript.status == "idle" and not transcript.workflow_run_id:
+    if (
+        transcript.status == "idle"
+        and not transcript.workflow_run_id
+        and not transcript.recording_id
+    ):
        return ValidationNotReady(detail="Recording is not ready for processing")

    # Check Celery tasks
@@ -181,124 +180,98 @@ async def dispatch_transcript_processing(
    Returns AsyncResult for Celery tasks, None for Hatchet workflows.
    """
    if isinstance(config, MultitrackProcessingConfig):
-        use_celery = False
-        if config.room_id:
-            room = await rooms_controller.get_by_id(config.room_id)
-            use_celery = room.use_celery if room else False
-
-        use_hatchet = not use_celery
-
-        if use_celery:
-            logger.info(
-                "Room uses legacy Celery processing",
-                room_id=config.room_id,
-                transcript_id=config.transcript_id,
+        # Multitrack processing always uses Hatchet (no Celery fallback)
+        # First check if we can replay (outside transaction since it's read-only)
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
+        if transcript and transcript.workflow_run_id and not force:
+            can_replay = await HatchetClientManager.can_replay(
+                transcript.workflow_run_id
            )
-
-        if use_hatchet:
-            # First check if we can replay (outside transaction since it's read-only)
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            if transcript and transcript.workflow_run_id and not force:
-                can_replay = await HatchetClientManager.can_replay(
-                    transcript.workflow_run_id
+            if can_replay:
+                await HatchetClientManager.replay_workflow(transcript.workflow_run_id)
+                logger.info(
+                    "Replaying Hatchet workflow",
+                    workflow_id=transcript.workflow_run_id,
                )
-                if can_replay:
-                    await HatchetClientManager.replay_workflow(
-                        transcript.workflow_run_id
-                    )
-                    logger.info(
-                        "Replaying Hatchet workflow",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                    return None
-                else:
-                    # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
-                    # Log and proceed to start new workflow
-                    try:
-                        status = await HatchetClientManager.get_workflow_run_status(
-                            transcript.workflow_run_id
-                        )
-                        logger.info(
-                            "Old workflow not replayable, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                            old_status=status.value,
-                        )
-                    except NotFoundException:
-                        # Workflow deleted from Hatchet but ID still in DB
-                        logger.info(
-                            "Old workflow not found in Hatchet, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                        )
-
-            # Force: cancel old workflow if exists
-            if force and transcript and transcript.workflow_run_id:
-                try:
-                    await HatchetClientManager.cancel_workflow(
-                        transcript.workflow_run_id
-                    )
-                    logger.info(
-                        "Cancelled old workflow (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                except NotFoundException:
-                    logger.info(
-                        "Old workflow already deleted (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": None}
-                )
-
-            # Re-fetch and check for concurrent dispatch (optimistic approach).
-            # No database lock - worst case is duplicate dispatch, but Hatchet
-            # workflows are idempotent so this is acceptable.
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            if transcript and transcript.workflow_run_id:
-                # Another process started a workflow between validation and now
+                return None
+            else:
+                # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
+                # Log and proceed to start new workflow
                try:
                    status = await HatchetClientManager.get_workflow_run_status(
                        transcript.workflow_run_id
                    )
-                    if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
-                        logger.info(
-                            "Concurrent workflow detected, skipping dispatch",
-                            workflow_id=transcript.workflow_run_id,
-                        )
-                        return None
-                except ApiException:
-                    # Workflow might be gone (404) or API issue - proceed with new workflow
-                    pass
+                    logger.info(
+                        "Old workflow not replayable, starting new",
+                        old_workflow_id=transcript.workflow_run_id,
+                        old_status=status.value,
+                    )
+                except NotFoundException:
+                    # Workflow deleted from Hatchet but ID still in DB
+                    logger.info(
+                        "Old workflow not found in Hatchet, starting new",
+                        old_workflow_id=transcript.workflow_run_id,
+                    )

-            workflow_id = await HatchetClientManager.start_workflow(
-                workflow_name="DiarizationPipeline",
-                input_data={
-                    "recording_id": config.recording_id,
-                    "tracks": [{"s3_key": k} for k in config.track_keys],
-                    "bucket_name": config.bucket_name,
-                    "transcript_id": config.transcript_id,
-                    "room_id": config.room_id,
-                },
-                additional_metadata={
-                    "transcript_id": config.transcript_id,
-                    "recording_id": config.recording_id,
-                    "daily_recording_id": config.recording_id,
-                },
+        # Force: cancel old workflow if exists
+        if force and transcript and transcript.workflow_run_id:
+            try:
+                await HatchetClientManager.cancel_workflow(transcript.workflow_run_id)
+                logger.info(
+                    "Cancelled old workflow (--force)",
+                    workflow_id=transcript.workflow_run_id,
+                )
+            except NotFoundException:
+                logger.info(
+                    "Old workflow already deleted (--force)",
+                    workflow_id=transcript.workflow_run_id,
+                )
+            await transcripts_controller.update(transcript, {"workflow_run_id": None})
+
+        # Re-fetch and check for concurrent dispatch (optimistic approach).
+        # No database lock - worst case is duplicate dispatch, but Hatchet
+        # workflows are idempotent so this is acceptable.
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
+        if transcript and transcript.workflow_run_id:
+            # Another process started a workflow between validation and now
+            try:
+                status = await HatchetClientManager.get_workflow_run_status(
+                    transcript.workflow_run_id
+                )
+                if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                    logger.info(
+                        "Concurrent workflow detected, skipping dispatch",
+                        workflow_id=transcript.workflow_run_id,
+                    )
+                    return None
+            except ApiException:
+                # Workflow might be gone (404) or API issue - proceed with new workflow
+                pass
+
+        workflow_id = await HatchetClientManager.start_workflow(
+            workflow_name="DiarizationPipeline",
+            input_data={
+                "recording_id": config.recording_id,
+                "tracks": [{"s3_key": k} for k in config.track_keys],
+                "bucket_name": config.bucket_name,
+                "transcript_id": config.transcript_id,
+                "room_id": config.room_id,
+            },
+            additional_metadata={
+                "transcript_id": config.transcript_id,
+                "recording_id": config.recording_id,
+                "daily_recording_id": config.recording_id,
+            },
+        )
+
+        if transcript:
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
            )

-            if transcript:
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": workflow_id}
-                )
+        logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
+        return None

-            logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
-            return None
-
-        # Celery pipeline (durable workflows disabled)
-        return task_pipeline_multitrack_process.delay(
-            transcript_id=config.transcript_id,
-            bucket_name=config.bucket_name,
-            track_keys=config.track_keys,
-        )
    elif isinstance(config, FileProcessingConfig):
        return task_pipeline_file_process.delay(transcript_id=config.transcript_id)
    else:
--- a/server/reflector/settings.py
+++ b/server/reflector/settings.py
@@ -1,7 +1,7 @@
 from pydantic.types import PositiveInt
 from pydantic_settings import BaseSettings, SettingsConfigDict

-from reflector.schemas.platform import WHEREBY_PLATFORM, Platform
+from reflector.schemas.platform import DAILY_PLATFORM, Platform
 from reflector.utils.string import NonEmptyString


@@ -49,6 +49,7 @@ class Settings(BaseSettings):
    TRANSCRIPT_STORAGE_AWS_REGION: str = "us-east-1"
    TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
    TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
+    TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL: str | None = None

    # Platform-specific recording storage (follows {PREFIX}_STORAGE_AWS_{CREDENTIAL} pattern)
    # Whereby storage configuration
@@ -84,9 +85,7 @@ class Settings(BaseSettings):
    )

    # Diarization
-    # backends:
-    #   - pyannote: in-process model loading (no HTTP, runs in same process)
-    #   - modal: HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/)
+    # backend: modal — HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/)
    DIARIZATION_ENABLED: bool = True
    DIARIZATION_BACKEND: str = "modal"
    DIARIZATION_URL: str | None = None
@@ -95,9 +94,6 @@ class Settings(BaseSettings):
    # Diarization: modal backend
    DIARIZATION_MODAL_API_KEY: str | None = None

-    # Diarization: local pyannote.audio
-    DIARIZATION_PYANNOTE_AUTH_TOKEN: str | None = None
-
    # Audio Padding (Modal.com backend)
    PADDING_URL: str | None = None
    PADDING_MODAL_API_KEY: str | None = None
@@ -155,7 +151,7 @@ class Settings(BaseSettings):
        None  # Webhook UUID for this environment. Not used by production code
    )
    # Platform Configuration
-    DEFAULT_VIDEO_PLATFORM: Platform = WHEREBY_PLATFORM
+    DEFAULT_VIDEO_PLATFORM: Platform = DAILY_PLATFORM

    # Zulip integration
    ZULIP_REALM: str | None = None
--- a/server/reflector/storage/storage_aws.py
+++ b/server/reflector/storage/storage_aws.py
@@ -53,6 +53,7 @@ class AwsStorage(Storage):
        aws_access_key_id: str | None = None,
        aws_secret_access_key: str | None = None,
        aws_role_arn: str | None = None,
+        aws_endpoint_url: str | None = None,
    ):
        if not aws_bucket_name:
            raise ValueError("Storage `aws_storage` require `aws_bucket_name`")
@@ -73,17 +74,26 @@ class AwsStorage(Storage):
        self._access_key_id = aws_access_key_id
        self._secret_access_key = aws_secret_access_key
        self._role_arn = aws_role_arn
+        self._endpoint_url = aws_endpoint_url

        self.aws_folder = ""
        if "/" in aws_bucket_name:
            self._bucket_name, self.aws_folder = aws_bucket_name.split("/", 1)
-        self.boto_config = Config(retries={"max_attempts": 3, "mode": "adaptive"})
+
+        config_kwargs: dict = {"retries": {"max_attempts": 3, "mode": "adaptive"}}
+        if aws_endpoint_url:
+            config_kwargs["s3"] = {"addressing_style": "path"}
+        self.boto_config = Config(**config_kwargs)
+
        self.session = aioboto3.Session(
            aws_access_key_id=aws_access_key_id,
            aws_secret_access_key=aws_secret_access_key,
            region_name=aws_region,
        )
-        self.base_url = f"https://{self._bucket_name}.s3.amazonaws.com/"
+        if aws_endpoint_url:
+            self.base_url = f"{aws_endpoint_url}/{self._bucket_name}/"
+        else:
+            self.base_url = f"https://{self._bucket_name}.s3.amazonaws.com/"

    # Implement credential properties
    @property
@@ -139,7 +149,9 @@ class AwsStorage(Storage):
        s3filename = f"{folder}/{filename}" if folder else filename
        logger.info(f"Uploading {filename} to S3 {actual_bucket}/{folder}")

-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
            if isinstance(data, bytes):
                await client.put_object(Bucket=actual_bucket, Key=s3filename, Body=data)
            else:
@@ -162,7 +174,9 @@ class AwsStorage(Storage):
        actual_bucket = bucket or self._bucket_name
        folder = self.aws_folder
        s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
            presigned_url = await client.generate_presigned_url(
                operation,
                Params={"Bucket": actual_bucket, "Key": s3filename},
@@ -177,7 +191,9 @@ class AwsStorage(Storage):
        folder = self.aws_folder
        logger.info(f"Deleting {filename} from S3 {actual_bucket}/{folder}")
        s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
            await client.delete_object(Bucket=actual_bucket, Key=s3filename)

    @handle_s3_client_errors("download")
@@ -186,7 +202,9 @@ class AwsStorage(Storage):
        folder = self.aws_folder
        logger.info(f"Downloading {filename} from S3 {actual_bucket}/{folder}")
        s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
            response = await client.get_object(Bucket=actual_bucket, Key=s3filename)
            return await response["Body"].read()

@@ -201,7 +219,9 @@ class AwsStorage(Storage):
        logger.info(f"Listing objects from S3 {actual_bucket} with prefix '{s3prefix}'")

        keys = []
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
            paginator = client.get_paginator("list_objects_v2")
            async for page in paginator.paginate(Bucket=actual_bucket, Prefix=s3prefix):
                if "Contents" in page:
@@ -227,7 +247,9 @@ class AwsStorage(Storage):
        folder = self.aws_folder
        logger.info(f"Streaming {filename} from S3 {actual_bucket}/{folder}")
        s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
            await client.download_fileobj(
                Bucket=actual_bucket, Key=s3filename, Fileobj=fileobj
            )
--- a/server/reflector/views/daily.py
+++ b/server/reflector/views/daily.py
@@ -80,7 +80,14 @@ async def webhook(request: Request):
    try:
        event = event_adapter.validate_python(body_json)
    except Exception as e:
-        logger.error("Failed to parse webhook event", error=str(e), body=body.decode())
+        err_detail = str(e)
+        if hasattr(e, "errors"):
+            err_detail = f"{err_detail}; errors={e.errors()!r}"
+        logger.error(
+            "Failed to parse webhook event",
+            error=err_detail,
+            body=body.decode(),
+        )
        raise HTTPException(status_code=422, detail="Invalid event format")

    match event:
--- a/server/reflector/views/transcripts_upload.py
+++ b/server/reflector/views/transcripts_upload.py
@@ -5,7 +5,7 @@ from fastapi import APIRouter, Depends, HTTPException, UploadFile
 from pydantic import BaseModel

 import reflector.auth as auth
-from reflector.db.transcripts import transcripts_controller
+from reflector.db.transcripts import SourceKind, transcripts_controller
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process

 router = APIRouter()
@@ -88,8 +88,10 @@ async def transcript_record_upload(
    finally:
        container.close()

-    # set the status to "uploaded"
-    await transcripts_controller.update(transcript, {"status": "uploaded"})
+    # set the status to "uploaded" and mark as file source
+    await transcripts_controller.update(
+        transcript, {"status": "uploaded", "source_kind": SourceKind.FILE}
+    )

    # launch a background task to process the file
    task_pipeline_file_process.delay(transcript_id=transcript_id)
--- a/server/reflector/views/user_websocket.py
+++ b/server/reflector/views/user_websocket.py
@@ -1,6 +1,6 @@
 from typing import Optional

-from fastapi import APIRouter, WebSocket
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect

 from reflector.auth.auth_jwt import JWTAuth  # type: ignore
 from reflector.db.users import user_controller
@@ -60,6 +60,8 @@ async def user_events_websocket(websocket: WebSocket):
    try:
        while True:
            await websocket.receive()
+    except (RuntimeError, WebSocketDisconnect):
+        pass
    finally:
        if room_id:
            await ws_manager.remove_user_from_room(room_id, websocket)
--- a/server/reflector/worker/process.py
+++ b/server/reflector/worker/process.py
@@ -27,9 +27,6 @@ from reflector.db.transcripts import (
 from reflector.hatchet.client import HatchetClientManager
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 from reflector.pipelines.main_live_pipeline import asynctask
-from reflector.pipelines.main_multitrack_pipeline import (
-    task_pipeline_multitrack_process,
-)
 from reflector.pipelines.topic_processing import EmptyPipeline
 from reflector.processors import AudioFileWriterProcessor
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
@@ -351,49 +348,29 @@ async def _process_multitrack_recording_inner(
            room_id=room.id,
        )

-    use_celery = room and room.use_celery
-    use_hatchet = not use_celery
-
-    if use_celery:
-        logger.info(
-            "Room uses legacy Celery processing",
-            room_id=room.id,
-            transcript_id=transcript.id,
-        )
-
-    if use_hatchet:
-        workflow_id = await HatchetClientManager.start_workflow(
-            workflow_name="DiarizationPipeline",
-            input_data={
-                "recording_id": recording_id,
-                "tracks": [{"s3_key": k} for k in filter_cam_audio_tracks(track_keys)],
-                "bucket_name": bucket_name,
-                "transcript_id": transcript.id,
-                "room_id": room.id,
-            },
-            additional_metadata={
-                "transcript_id": transcript.id,
-                "recording_id": recording_id,
-                "daily_recording_id": recording_id,
-            },
-        )
-        logger.info(
-            "Started Hatchet workflow",
-            workflow_id=workflow_id,
-            transcript_id=transcript.id,
-        )
-
-        await transcripts_controller.update(
-            transcript, {"workflow_run_id": workflow_id}
-        )
-        return
-
-    # Celery pipeline (runs when durable workflows disabled)
-    task_pipeline_multitrack_process.delay(
-        transcript_id=transcript.id,
-        bucket_name=bucket_name,
-        track_keys=filter_cam_audio_tracks(track_keys),
+    # Multitrack processing always uses Hatchet (no Celery fallback)
+    workflow_id = await HatchetClientManager.start_workflow(
+        workflow_name="DiarizationPipeline",
+        input_data={
+            "recording_id": recording_id,
+            "tracks": [{"s3_key": k} for k in filter_cam_audio_tracks(track_keys)],
+            "bucket_name": bucket_name,
+            "transcript_id": transcript.id,
+            "room_id": room.id,
+        },
+        additional_metadata={
+            "transcript_id": transcript.id,
+            "recording_id": recording_id,
+            "daily_recording_id": recording_id,
+        },
    )
+    logger.info(
+        "Started Hatchet workflow",
+        workflow_id=workflow_id,
+        transcript_id=transcript.id,
+    )
+
+    await transcripts_controller.update(transcript, {"workflow_run_id": workflow_id})


@shared_task
@@ -1072,66 +1049,43 @@ async def reprocess_failed_daily_recordings():
                )
                continue

-            use_celery = room and room.use_celery
-            use_hatchet = not use_celery
-
-            if use_hatchet:
-                if not transcript:
-                    logger.warning(
-                        "No transcript for Hatchet reprocessing, skipping",
-                        recording_id=recording.id,
-                    )
-                    continue
-
-                workflow_id = await HatchetClientManager.start_workflow(
-                    workflow_name="DiarizationPipeline",
-                    input_data={
-                        "recording_id": recording.id,
-                        "tracks": [
-                            {"s3_key": k}
-                            for k in filter_cam_audio_tracks(recording.track_keys)
-                        ],
-                        "bucket_name": bucket_name,
-                        "transcript_id": transcript.id,
-                        "room_id": room.id if room else None,
-                    },
-                    additional_metadata={
-                        "transcript_id": transcript.id,
-                        "recording_id": recording.id,
-                        "reprocess": True,
-                    },
-                )
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": workflow_id}
-                )
-
-                logger.info(
-                    "Queued Daily recording for Hatchet reprocessing",
+            # Multitrack reprocessing always uses Hatchet (no Celery fallback)
+            if not transcript:
+                logger.warning(
+                    "No transcript for Hatchet reprocessing, skipping",
                    recording_id=recording.id,
-                    workflow_id=workflow_id,
-                    room_name=meeting.room_name,
-                    track_count=len(recording.track_keys),
-                )
-            else:
-                logger.info(
-                    "Queueing Daily recording for Celery reprocessing",
-                    recording_id=recording.id,
-                    room_name=meeting.room_name,
-                    track_count=len(recording.track_keys),
-                    transcript_status=transcript.status if transcript else None,
                )
+                continue

-                # For reprocessing, pass actual recording time (though it's ignored - see _process_multitrack_recording_inner)
-                # Reprocessing uses recording.meeting_id directly instead of time-based matching
-                recording_start_ts = int(recording.recorded_at.timestamp())
+            workflow_id = await HatchetClientManager.start_workflow(
+                workflow_name="DiarizationPipeline",
+                input_data={
+                    "recording_id": recording.id,
+                    "tracks": [
+                        {"s3_key": k}
+                        for k in filter_cam_audio_tracks(recording.track_keys)
+                    ],
+                    "bucket_name": bucket_name,
+                    "transcript_id": transcript.id,
+                    "room_id": room.id if room else None,
+                },
+                additional_metadata={
+                    "transcript_id": transcript.id,
+                    "recording_id": recording.id,
+                    "reprocess": True,
+                },
+            )
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
+            )

-                process_multitrack_recording.delay(
-                    bucket_name=bucket_name,
-                    daily_room_name=meeting.room_name,
-                    recording_id=recording.id,
-                    track_keys=recording.track_keys,
-                    recording_start_ts=recording_start_ts,
-                )
+            logger.info(
+                "Queued Daily recording for Hatchet reprocessing",
+                recording_id=recording.id,
+                workflow_id=workflow_id,
+                room_name=meeting.room_name,
+                track_count=len(recording.track_keys),
+            )

            reprocessed_count += 1

--- a/server/reflector/ws_manager.py
+++ b/server/reflector/ws_manager.py
@@ -48,7 +48,15 @@ class RedisPubSubManager:
        if not self.redis_connection:
            await self.connect()
        message = json.dumps(message)
-        await self.redis_connection.publish(room_id, message)
+        try:
+            await self.redis_connection.publish(room_id, message)
+        except RuntimeError:
+            # Celery workers run each task in a new event loop (asyncio.run),
+            # which closes the previous loop. Cached Redis connection is dead.
+            # Reconnect on the current loop and retry.
+            self.redis_connection = None
+            await self.connect()
+            await self.redis_connection.publish(room_id, message)

    async def subscribe(self, room_id: str) -> redis.Redis:
        await self.pubsub.subscribe(room_id)
--- a/server/scripts/recreate_daily_webhook.py
+++ b/server/scripts/recreate_daily_webhook.py
@@ -15,8 +15,7 @@ from reflector.settings import settings

 async def setup_webhook(webhook_url: str):
    """
-    Create or update Daily.co webhook for this environment using dailyco_api module.
-    Uses DAILY_WEBHOOK_UUID to identify existing webhook.
+    Create Daily.co webhook. Deletes any existing webhooks first, then creates the new one.
    """
    if not settings.DAILY_API_KEY:
        print("Error: DAILY_API_KEY not set")
@@ -35,79 +34,37 @@ async def setup_webhook(webhook_url: str):
    ]

    async with DailyApiClient(api_key=settings.DAILY_API_KEY) as client:
-        webhook_uuid = settings.DAILY_WEBHOOK_UUID
+        webhooks = await client.list_webhooks()
+        for wh in webhooks:
+            await client.delete_webhook(wh.uuid)
+            print(f"Deleted webhook {wh.uuid}")

-        if webhook_uuid:
-            print(f"Updating existing webhook {webhook_uuid}...")
-            try:
-                # Note: Daily.co doesn't support PATCH well, so we delete + recreate
-                await client.delete_webhook(webhook_uuid)
-                print(f"Deleted old webhook {webhook_uuid}")
+        request = CreateWebhookRequest(
+            url=webhook_url,
+            eventTypes=event_types,
+            hmac=settings.DAILY_WEBHOOK_SECRET,
+        )
+        result = await client.create_webhook(request)
+        webhook_uuid = result.uuid

-                request = CreateWebhookRequest(
-                    url=webhook_url,
-                    eventTypes=event_types,
-                    hmac=settings.DAILY_WEBHOOK_SECRET,
-                )
-                result = await client.create_webhook(request)
+        print(f"✓ Created webhook {webhook_uuid} (state: {result.state})")
+        print(f"  URL: {result.url}")

-                print(
-                    f"✓ Created replacement webhook {result.uuid} (state: {result.state})"
-                )
-                print(f"  URL: {result.url}")
+        env_file = Path(__file__).parent.parent / ".env"
+        if env_file.exists():
+            lines = env_file.read_text().splitlines()
+            updated = False
+            for i, line in enumerate(lines):
+                if line.startswith("DAILY_WEBHOOK_UUID="):
+                    lines[i] = f"DAILY_WEBHOOK_UUID={webhook_uuid}"
+                    updated = True
+                    break
+            if not updated:
+                lines.append(f"DAILY_WEBHOOK_UUID={webhook_uuid}")
+            env_file.write_text("\n".join(lines) + "\n")
+            print("✓ Saved DAILY_WEBHOOK_UUID to .env")

-                webhook_uuid = result.uuid
-
-            except Exception as e:
-                if hasattr(e, "response") and e.response.status_code == 404:
-                    print(f"Webhook {webhook_uuid} not found, creating new one...")
-                    webhook_uuid = None  # Fall through to creation
-                else:
-                    print(f"Error updating webhook: {e}")
-                    return 1
-
-        if not webhook_uuid:
-            print("Creating new webhook...")
-            request = CreateWebhookRequest(
-                url=webhook_url,
-                eventTypes=event_types,
-                hmac=settings.DAILY_WEBHOOK_SECRET,
-            )
-            result = await client.create_webhook(request)
-            webhook_uuid = result.uuid
-
-            print(f"✓ Created webhook {webhook_uuid} (state: {result.state})")
-            print(f"  URL: {result.url}")
-            print()
-            print("=" * 60)
-            print("IMPORTANT: Add this to your environment variables:")
-            print("=" * 60)
-            print(f"DAILY_WEBHOOK_UUID: {webhook_uuid}")
-            print("=" * 60)
-            print()
-
-            # Try to write UUID to .env file
-            env_file = Path(__file__).parent.parent / ".env"
-            if env_file.exists():
-                lines = env_file.read_text().splitlines()
-                updated = False
-
-                # Update existing DAILY_WEBHOOK_UUID line or add it
-                for i, line in enumerate(lines):
-                    if line.startswith("DAILY_WEBHOOK_UUID="):
-                        lines[i] = f"DAILY_WEBHOOK_UUID={webhook_uuid}"
-                        updated = True
-                        break
-
-                if not updated:
-                    lines.append(f"DAILY_WEBHOOK_UUID={webhook_uuid}")
-
-                env_file.write_text("\n".join(lines) + "\n")
-                print(f"✓ Also saved to local .env file")
-            else:
-                print(f"⚠ Local .env file not found - please add manually")
-
-            return 0
+        return 0


 if __name__ == "__main__":
@@ -117,11 +74,7 @@ if __name__ == "__main__":
            "Example: python recreate_daily_webhook.py https://example.com/v1/daily/webhook"
        )
        print()
-        print("Behavior:")
-        print("  - If DAILY_WEBHOOK_UUID set: Deletes old webhook, creates new one")
-        print(
-            "  - If DAILY_WEBHOOK_UUID empty: Creates new webhook, saves UUID to .env"
-        )
+        print("Deletes all existing webhooks, then creates a new one.")
        sys.exit(1)

    sys.exit(asyncio.run(setup_webhook(sys.argv[1])))
--- a/server/tests/conftest.py
+++ b/server/tests/conftest.py
@@ -4,7 +4,7 @@ from unittest.mock import patch

 import pytest

-from reflector.schemas.platform import WHEREBY_PLATFORM
+from reflector.schemas.platform import DAILY_PLATFORM, WHEREBY_PLATFORM


@pytest.fixture(scope="session", autouse=True)
@@ -14,6 +14,7 @@ def register_mock_platform():
    from reflector.video_platforms.registry import register_platform

    register_platform(WHEREBY_PLATFORM, MockPlatformClient)
+    register_platform(DAILY_PLATFORM, MockPlatformClient)
    yield


--- a/server/tests/test_hatchet_dispatch.py
+++ b/server/tests/test_hatchet_dispatch.py
@@ -255,7 +255,7 @@ async def test_validation_locked_transcript():
@pytest.mark.usefixtures("setup_database")
@pytest.mark.asyncio
 async def test_validation_idle_transcript():
-    """Test that validation rejects idle transcripts (not ready)."""
+    """Test that validation rejects idle transcripts without recording (file upload not ready)."""
    from reflector.services.transcript_process import (
        ValidationNotReady,
        validate_transcript_for_processing,
@@ -274,6 +274,34 @@ async def test_validation_idle_transcript():
    assert "not ready" in result.detail.lower()


+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_validation_idle_transcript_with_recording_allowed():
+    """Test that validation allows idle transcripts with recording_id (multitrack ready/retry)."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="idle",
+        source_kind="room",
+        recording_id="test-recording-id",
+    )
+
+    with patch(
+        "reflector.services.transcript_process.task_is_scheduled_or_active"
+    ) as mock_celery_check:
+        mock_celery_check.return_value = False
+
+        result = await validate_transcript_for_processing(mock_transcript)
+
+    assert isinstance(result, ValidationOk)
+    assert result.recording_id == "test-recording-id"
+
+
@pytest.mark.usefixtures("setup_database")
@pytest.mark.asyncio
 async def test_prepare_multitrack_config():
--- a/server/tests/test_hatchet_payload_thinning.py
+++ b/server/tests/test_hatchet_payload_thinning.py
@@ -0,0 +1,185 @@
+"""
+Tests for Hatchet payload thinning optimizations.
+
+Verifies that:
+1. TopicChunkInput no longer carries words
+2. TopicChunkResult no longer carries words
+3. words_to_segments() matches Transcript.as_segments(is_multitrack=False) — behavioral equivalence
+   for the extract_subjects refactoring
+4. TopicsResult can be constructed with empty transcript words
+"""
+
+from reflector.hatchet.workflows.models import TopicChunkResult
+from reflector.hatchet.workflows.topic_chunk_processing import TopicChunkInput
+from reflector.processors.types import Word
+
+
+def _make_words(speaker: int = 0, start: float = 0.0) -> list[Word]:
+    return [
+        Word(text="Hello", start=start, end=start + 0.5, speaker=speaker),
+        Word(text=" world.", start=start + 0.5, end=start + 1.0, speaker=speaker),
+    ]
+
+
+class TestTopicChunkInputNoWords:
+    """TopicChunkInput must not have a words field."""
+
+    def test_no_words_field(self):
+        assert "words" not in TopicChunkInput.model_fields
+
+    def test_construction_without_words(self):
+        inp = TopicChunkInput(
+            chunk_index=0, chunk_text="Hello world.", timestamp=0.0, duration=1.0
+        )
+        assert inp.chunk_index == 0
+        assert inp.chunk_text == "Hello world."
+
+    def test_rejects_words_kwarg(self):
+        """Passing words= should raise a validation error (field doesn't exist)."""
+        import pydantic
+
+        try:
+            TopicChunkInput(
+                chunk_index=0,
+                chunk_text="text",
+                timestamp=0.0,
+                duration=1.0,
+                words=_make_words(),
+            )
+            # If pydantic is configured to ignore extra, this won't raise.
+            # Verify the field is still absent from the model.
+            assert "words" not in TopicChunkInput.model_fields
+        except pydantic.ValidationError:
+            pass  # Expected
+
+
+class TestTopicChunkResultNoWords:
+    """TopicChunkResult must not have a words field."""
+
+    def test_no_words_field(self):
+        assert "words" not in TopicChunkResult.model_fields
+
+    def test_construction_without_words(self):
+        result = TopicChunkResult(
+            chunk_index=0,
+            title="Test",
+            summary="Summary",
+            timestamp=0.0,
+            duration=1.0,
+        )
+        assert result.title == "Test"
+        assert result.chunk_index == 0
+
+    def test_serialization_roundtrip(self):
+        """Serialized TopicChunkResult has no words key."""
+        result = TopicChunkResult(
+            chunk_index=0,
+            title="Test",
+            summary="Summary",
+            timestamp=0.0,
+            duration=1.0,
+        )
+        data = result.model_dump()
+        assert "words" not in data
+        reconstructed = TopicChunkResult(**data)
+        assert reconstructed == result
+
+
+class TestWordsToSegmentsBehavioralEquivalence:
+    """words_to_segments() must produce same output as Transcript.as_segments(is_multitrack=False).
+
+    This ensures the extract_subjects refactoring (from task output topic.transcript.as_segments()
+    to words_to_segments(db_topic.words)) preserves identical behavior.
+    """
+
+    def test_single_speaker(self):
+        from reflector.processors.types import Transcript as TranscriptType
+        from reflector.processors.types import words_to_segments
+
+        words = _make_words(speaker=0)
+        direct = words_to_segments(words)
+        via_transcript = TranscriptType(words=words).as_segments(is_multitrack=False)
+
+        assert len(direct) == len(via_transcript)
+        for d, v in zip(direct, via_transcript):
+            assert d.text == v.text
+            assert d.speaker == v.speaker
+            assert d.start == v.start
+            assert d.end == v.end
+
+    def test_multiple_speakers(self):
+        from reflector.processors.types import Transcript as TranscriptType
+        from reflector.processors.types import words_to_segments
+
+        words = [
+            Word(text="Hello", start=0.0, end=0.5, speaker=0),
+            Word(text=" world.", start=0.5, end=1.0, speaker=0),
+            Word(text=" How", start=1.0, end=1.5, speaker=1),
+            Word(text=" are", start=1.5, end=2.0, speaker=1),
+            Word(text=" you?", start=2.0, end=2.5, speaker=1),
+        ]
+
+        direct = words_to_segments(words)
+        via_transcript = TranscriptType(words=words).as_segments(is_multitrack=False)
+
+        assert len(direct) == len(via_transcript)
+        for d, v in zip(direct, via_transcript):
+            assert d.text == v.text
+            assert d.speaker == v.speaker
+
+    def test_empty_words(self):
+        from reflector.processors.types import Transcript as TranscriptType
+        from reflector.processors.types import words_to_segments
+
+        assert words_to_segments([]) == []
+        assert TranscriptType(words=[]).as_segments(is_multitrack=False) == []
+
+
+class TestTopicsResultEmptyWords:
+    """TopicsResult can carry topics with empty transcript words."""
+
+    def test_construction_with_empty_words(self):
+        from reflector.hatchet.workflows.models import TopicsResult
+        from reflector.processors.types import TitleSummary
+        from reflector.processors.types import Transcript as TranscriptType
+
+        topics = [
+            TitleSummary(
+                title="Topic A",
+                summary="Summary A",
+                timestamp=0.0,
+                duration=5.0,
+                transcript=TranscriptType(words=[]),
+            ),
+            TitleSummary(
+                title="Topic B",
+                summary="Summary B",
+                timestamp=5.0,
+                duration=5.0,
+                transcript=TranscriptType(words=[]),
+            ),
+        ]
+        result = TopicsResult(topics=topics)
+        assert len(result.topics) == 2
+        for t in result.topics:
+            assert t.transcript.words == []
+
+    def test_serialization_roundtrip(self):
+        from reflector.hatchet.workflows.models import TopicsResult
+        from reflector.processors.types import TitleSummary
+        from reflector.processors.types import Transcript as TranscriptType
+
+        topics = [
+            TitleSummary(
+                title="Topic",
+                summary="Summary",
+                timestamp=0.0,
+                duration=1.0,
+                transcript=TranscriptType(words=[]),
+            )
+        ]
+        result = TopicsResult(topics=topics)
+        data = result.model_dump()
+        reconstructed = TopicsResult(**data)
+        assert len(reconstructed.topics) == 1
+        assert reconstructed.topics[0].transcript.words == []
--- a/server/tests/test_storage.py
+++ b/server/tests/test_storage.py
@@ -319,3 +319,51 @@ def test_aws_storage_constructor_rejects_mixed_auth():
            aws_secret_access_key="test-secret",
            aws_role_arn="arn:aws:iam::123456789012:role/test-role",
        )
+
+
+@pytest.mark.asyncio
+async def test_aws_storage_custom_endpoint_url():
+    """Test that custom endpoint_url configures path-style addressing and passes endpoint to client."""
+    storage = AwsStorage(
+        aws_bucket_name="reflector-media",
+        aws_region="garage",
+        aws_access_key_id="GKtest",
+        aws_secret_access_key="secret",
+        aws_endpoint_url="http://garage:3900",
+    )
+    assert storage._endpoint_url == "http://garage:3900"
+    assert storage.boto_config.s3["addressing_style"] == "path"
+    assert storage.base_url == "http://garage:3900/reflector-media/"
+    # retries config preserved (merge, not replace)
+    assert storage.boto_config.retries["max_attempts"] == 3
+
+    mock_client = AsyncMock()
+    mock_client.put_object = AsyncMock()
+    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
+    mock_client.__aexit__ = AsyncMock(return_value=None)
+    mock_client.generate_presigned_url = AsyncMock(
+        return_value="http://garage:3900/reflector-media/test.txt"
+    )
+
+    with patch.object(
+        storage.session, "client", return_value=mock_client
+    ) as mock_session_client:
+        await storage.put_file("test.txt", b"data")
+        mock_session_client.assert_called_with(
+            "s3", config=storage.boto_config, endpoint_url="http://garage:3900"
+        )
+
+
+@pytest.mark.asyncio
+async def test_aws_storage_none_endpoint_url():
+    """Test that None endpoint preserves current AWS behavior."""
+    storage = AwsStorage(
+        aws_bucket_name="reflector-bucket",
+        aws_region="us-east-1",
+        aws_access_key_id="AKIAtest",
+        aws_secret_access_key="secret",
+    )
+    assert storage._endpoint_url is None
+    assert storage.base_url == "https://reflector-bucket.s3.amazonaws.com/"
+    # No s3 addressing_style override — boto_config should only have retries
+    assert not hasattr(storage.boto_config, "s3") or storage.boto_config.s3 is None
--- a/server/tests/test_transcripts_process.py
+++ b/server/tests/test_transcripts_process.py
@@ -1,6 +1,6 @@
 import asyncio
 import time
-from unittest.mock import patch
+from unittest.mock import AsyncMock, patch

 import pytest
 from httpx import ASGITransport, AsyncClient
@@ -142,17 +142,17 @@ async def test_whereby_recording_uses_file_pipeline(client):
            "reflector.services.transcript_process.task_pipeline_file_process"
        ) as mock_file_pipeline,
        patch(
-            "reflector.services.transcript_process.task_pipeline_multitrack_process"
-        ) as mock_multitrack_pipeline,
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet,
    ):
        response = await client.post(f"/transcripts/{transcript.id}/process")

        assert response.status_code == 200
        assert response.json()["status"] == "ok"

-        # Whereby recordings should use file pipeline
+        # Whereby recordings should use file pipeline, not Hatchet
        mock_file_pipeline.delay.assert_called_once_with(transcript_id=transcript.id)
-        mock_multitrack_pipeline.delay.assert_not_called()
+        mock_hatchet.start_workflow.assert_not_called()


@pytest.mark.usefixtures("setup_database")
@@ -177,8 +177,6 @@ async def test_dailyco_recording_uses_multitrack_pipeline(client):
        recording_trigger="automatic-2nd-participant",
        is_shared=False,
    )
-    # Force Celery backend for test
-    await rooms_controller.update(room, {"use_celery": True})

    transcript = await transcripts_controller.add(
        "",
@@ -213,18 +211,23 @@ async def test_dailyco_recording_uses_multitrack_pipeline(client):
            "reflector.services.transcript_process.task_pipeline_file_process"
        ) as mock_file_pipeline,
        patch(
-            "reflector.services.transcript_process.task_pipeline_multitrack_process"
-        ) as mock_multitrack_pipeline,
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet,
    ):
+        mock_hatchet.start_workflow = AsyncMock(return_value="test-workflow-id")
+
        response = await client.post(f"/transcripts/{transcript.id}/process")

        assert response.status_code == 200
        assert response.json()["status"] == "ok"

-        # Daily.co multitrack recordings should use multitrack pipeline
-        mock_multitrack_pipeline.delay.assert_called_once_with(
-            transcript_id=transcript.id,
-            bucket_name="daily-bucket",
-            track_keys=track_keys,
-        )
+        # Daily.co multitrack recordings should use Hatchet workflow
+        mock_hatchet.start_workflow.assert_called_once()
+        call_kwargs = mock_hatchet.start_workflow.call_args.kwargs
+        assert call_kwargs["workflow_name"] == "DiarizationPipeline"
+        assert call_kwargs["input_data"]["transcript_id"] == transcript.id
+        assert call_kwargs["input_data"]["bucket_name"] == "daily-bucket"
+        assert call_kwargs["input_data"]["tracks"] == [
+            {"s3_key": k} for k in track_keys
+        ]
        mock_file_pipeline.delay.assert_not_called()
--- a/server/uv.lock
+++ b/server/uv.lock
@@ -235,12 +235,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]

-[[package]]
-name = "antlr4-python3-runtime"
-version = "4.9.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 }
-
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -267,21 +261,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/2f/f5/c36551e93acba41a59939ae6a0fb77ddb3f2e8e8caa716410c65f7341f72/asgi_lifespan-2.1.0-py3-none-any.whl", hash = "sha256:ed840706680e28428c01e14afb3875d7d76d3206f3d5b2f2294e059b5c23804f", size = 10895 },
 ]

-[[package]]
-name = "asteroid-filterbanks"
-version = "0.4.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/90/fa/5c2be1f96dc179f83cdd3bb267edbd1f47d08f756785c016d5c2163901a7/asteroid-filterbanks-0.4.0.tar.gz", hash = "sha256:415f89d1dcf2b13b35f03f7a9370968ac4e6fa6800633c522dac992b283409b9", size = 24599 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c5/7c/83ff6046176a675e6a1e8aeefed8892cd97fe7c46af93cc540d1b24b8323/asteroid_filterbanks-0.4.0-py3-none-any.whl", hash = "sha256:4932ac8b6acc6e08fb87cbe8ece84215b5a74eee284fe83acf3540a72a02eaf5", size = 29912 },
-]
-
 [[package]]
 name = "async-timeout"
 version = "5.0.1"
@@ -603,56 +582,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934", size = 46018 },
 ]

-[[package]]
-name = "colorlog"
-version = "6.9.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/d3/7a/359f4d5df2353f26172b3cc39ea32daa39af8de522205f512f458923e677/colorlog-6.9.0.tar.gz", hash = "sha256:bfba54a1b93b94f54e1f4fe48395725a3d92fd2a4af702f6bd70946bdc0c6ac2", size = 16624 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl", hash = "sha256:5906e71acd67cb07a71e779c47c4bcb45fb8c2993eebe9e5adcd6a6f1b283eff", size = 11424 },
-]
-
-[[package]]
-name = "contourpy"
-version = "1.3.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 }
-wheels = [
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--- a/www/app/(app)/transcripts/[transcriptId]/processing/page.tsx
+++ b/www/app/(app)/transcripts/[transcriptId]/processing/page.tsx
@@ -11,6 +11,7 @@ import {
 import { useRouter } from "next/navigation";
 import { useTranscriptGet } from "../../../../lib/apiHooks";
 import { parseNonEmptyString } from "../../../../lib/utils";
+import { useWebSockets } from "../../useWebSockets";

 type TranscriptProcessing = {
  params: Promise<{
@@ -24,6 +25,7 @@ export default function TranscriptProcessing(details: TranscriptProcessing) {
  const router = useRouter();

  const transcript = useTranscriptGet(transcriptId);
+  useWebSockets(transcriptId);

  useEffect(() => {
    const status = transcript.data?.status;
--- a/www/app/(app)/transcripts/useWebRTC.ts
+++ b/www/app/(app)/transcripts/useWebRTC.ts
@@ -23,7 +23,16 @@ const useWebRTC = (
    let p: Peer;

    try {
-      p = new Peer({ initiator: true, stream: stream });
+      p = new Peer({
+        initiator: true,
+        stream: stream,
+        // Disable trickle ICE: single SDP exchange (offer + answer) with all candidates.
+        // Required for HTTP-based signaling; trickle needs WebSocket for candidate exchange.
+        trickle: false,
+        config: {
+          iceServers: [{ urls: "stun:stun.l.google.com:19302" }],
+        },
+      });
    } catch (error) {
      setError(error as Error, "Error creating WebRTC");
      return;
--- a/www/app/(app)/transcripts/useWebSockets.ts
+++ b/www/app/(app)/transcripts/useWebSockets.ts
@@ -431,6 +431,7 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
              );
            }
            setStatus(message.data);
+            invalidateTranscript(queryClient, transcriptId as NonEmptyString);
            if (message.data.value === "ended") {
              ws.close();
            }
Author	SHA1	Message	Date
Igor Loskutov	26f1e5f6dd	feat: reduce Hatchet payload size by removing words from topic chunk workflows Remove ~6.5MB of redundant Word data from Hatchet task boundaries: - Remove words from TopicChunkInput/TopicChunkResult (child workflow I/O) - detect_topics maps words from local chunks by chunk_index instead - TopicsResult carries empty transcript words (persisted to DB already) - extract_subjects refetches topics from DB instead of task output - Clear topics at detect_topics start for retry idempotency	2026-02-12 09:50:26 -05:00
Igor Monadical	b468427f1b	feat: local llm support + standalone-script doc/draft (#856 ) * feat: local LLM via Ollama + structured output response_format - Add setup script (scripts/setup-local-llm.sh) for one-command Ollama setup Mac: native Metal GPU, Linux: containerized via docker-compose profiles - Add ollama-gpu and ollama-cpu docker-compose profiles for Linux - Add extra_hosts to server/hatchet-worker-llm for host.docker.internal - Pass response_format JSON schema in StructuredOutputWorkflow.extract() enabling grammar-based constrained decoding on Ollama/llama.cpp/vLLM/OpenAI - Update .env.example with Ollama as default LLM option - Add Ollama PRD and local dev setup docs * refactor: move Ollama services to docker-compose.standalone.yml Ollama profiles (ollama-gpu, ollama-cpu) are only for Linux standalone deployment. Mac devs never use them. Separate file keeps the main compose clean and provides a natural home for future standalone services (MinIO, etc.). Linux: docker compose -f docker-compose.yml -f docker-compose.standalone.yml --profile ollama-gpu up -d Mac: docker compose up -d (native Ollama, no standalone file needed) * fix: correct PRD goal (demo/eval, not dev replacement) and processor naming * chore: remove completed PRD, rename setup doc, drop response_format tests - Remove docs/01_ollama.prd.md (implementation complete) - Rename local-dev-setup.md -> standalone-local-setup.md - Remove TestResponseFormat class from test_llm_retry.py * docs: resolve standalone storage step — skip S3 for live-only mode * docs: add TASKS.md for standalone env defaults + setup script work * feat: add unified setup-local-dev.sh for standalone deployment Single script takes fresh clone to working Reflector: Ollama/LLM setup, env file generation (server/.env + www/.env.local), docker compose up, health checks. No Hatchet in standalone — live pipeline is pure Celery. * chore: rename to setup-standalone, remove redundant setup-local-llm.sh * feat: add custom S3 endpoint support + Garage standalone storage Add TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL setting to enable S3-compatible backends (Garage, MinIO). When set, uses path-style addressing and routes all requests to the custom endpoint. When unset, AWS behavior is unchanged. - AwsStorage: accept aws_endpoint_url, pass to all 6 session.client() calls, configure path-style addressing and base_url - Fix 4 direct AwsStorage constructions in Hatchet workflows to pass endpoint_url (would have silently targeted wrong endpoint) - Standalone: add Garage service to docker-compose.standalone.yml, setup script initializes layout/bucket/key and writes credentials - Fix compose_cmd() bug: Mac path was missing standalone yml - garage.toml template with runtime secret generation via openssl * fix: standalone setup — garage config, symlink handling, healthcheck - garage.toml: fix rpc_secret field name (was secret_transmitter), move to top-level per Garage v1.1.0 spec, remove unused [s3_web] - setup-standalone.sh: resolve symlinked .env files before writing, always ensure all standalone-critical vars via env_set, fix garage key create/info syntax (positional arg, not --name), avoid overwriting key secret with "(redacted)" on re-run, use compose_cmd in health check - docker-compose.standalone.yml: fix garage healthcheck (no curl in image, use /garage stats instead) * docs: update standalone md — symlink handling, garage config template * docs: add troubleshooting section + port conflict check in setup script Port conflicts from stale next dev / other worktree processes silently shadow Docker container port mappings, causing env vars to appear ignored. * fix: invalidate transcript query on STATUS websocket event Without this, the processing page never redirects after completion because the redirect logic watches the REST query data, not the WebSocket status state. Cherry-picked from feat-dag-progress (`faec509a`). * fix: local env setup (#855) * Ensure rate limit * Increase nextjs compilation speed * Fix daily no content handling * Simplify daily webhook creation * Fix webhook request validation * feat: add local pyannote file diarization processor (#858) * feat: add local pyannote file diarization processor Enables file diarization without Modal by using pyannote.audio locally. Downloads model bundle from S3 on first use, caches locally, patches config to use local paths. Set DIARIZATION_BACKEND=pyannote to enable. * fix: standalone setup enables pyannote diarization and public mode Replace DIARIZATION_ENABLED=false with DIARIZATION_BACKEND=pyannote so file uploads get speaker diarization out of the box. Add PUBLIC_MODE=true so unauthenticated users can list/browse transcripts. * fix: touch env files before first compose_cmd in standalone setup docker-compose.yml references www/.env.local as env_file, but the setup script only creates it in step 4. compose_cmd calls in step 3 (Garage) fail on a fresh clone when the file doesn't exist yet. * feat: standalone uses self-hosted GPU service for transcription+diarization Replace in-process pyannote approach with self-hosted gpu/self_hosted/ service. Same HTTP API as Modal — just TRANSCRIPT_URL/DIARIZATION_URL point to local container. - Add gpu/self_hosted/Dockerfile.cpu (GPU Dockerfile minus NVIDIA CUDA) - Add S3 model bundle fallback in diarizer.py when HF_TOKEN not set - Add gpu service to docker-compose.standalone.yml with compose env overrides - Fix /browse empty in PUBLIC_MODE (search+list queries filtered out roomless transcripts) - Remove audio_diarization_pyannote.py, file_diarization_pyannote.py and tests - Remove pyannote-audio from server local deps * fix: allow unauthenticated GPU requests when no API key configured OAuth2PasswordBearer with auto_error=True rejects requests without Authorization header before apikey_auth can check if auth is needed. * fix: rename standalone gpu service to cpu to match Dockerfile.cpu usage * docs: add programmatic testing section and fix gpu->cpu naming in setup script/docs - Add "Testing programmatically" section to standalone docs with curl commands for creating transcript, uploading audio, polling status, checking result - Fix setup-standalone.sh to reference `cpu` service (was still `gpu` after rename) - Update all docs references from gpu to cpu service naming --------- Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com> * Fix websocket disconnect errors * Fix event loop is closed in Celery workers * Allow reprocessing idle multitrack transcripts * feat: add local pyannote file diarization processor Enables file diarization without Modal by using pyannote.audio locally. Downloads model bundle from S3 on first use, caches locally, patches config to use local paths. Set DIARIZATION_BACKEND=pyannote to enable. * feat: standalone uses self-hosted GPU service for transcription+diarization Replace in-process pyannote approach with self-hosted gpu/self_hosted/ service. Same HTTP API as Modal — just TRANSCRIPT_URL/DIARIZATION_URL point to local container. - Add gpu/self_hosted/Dockerfile.cpu (GPU Dockerfile minus NVIDIA CUDA) - Add S3 model bundle fallback in diarizer.py when HF_TOKEN not set - Add gpu service to docker-compose.standalone.yml with compose env overrides - Fix /browse empty in PUBLIC_MODE (search+list queries filtered out roomless transcripts) - Remove audio_diarization_pyannote.py, file_diarization_pyannote.py and tests - Remove pyannote-audio from server local deps * fix: set source_kind to FILE on audio file upload The upload endpoint left source_kind as the default LIVE even when a file was uploaded. Now sets it to FILE when the upload completes. * Add hatchet env vars * fix: improve port conflict detection and ollama model check in standalone setup - Filter OrbStack/Docker Desktop PIDs from port conflict check (false positives on Mac) - Check all infra ports (5432, 6379, 3900, 3903) not just app ports - Fix ollama model detection to match on name column only - Document OrbStack and cross-project port conflicts in troubleshooting * fix: processing page auto-redirect after file upload completes Three fixes for the processing page not redirecting when status becomes "ended": - Add useWebSockets to processing page so it receives STATUS events - Remove OAuth2PasswordBearer from auth_none — broke WebSocket endpoints (500) - Reconnect stale Redis in ws_manager when Celery worker reuses dead event loop * fix: mock Celery broker in idle transcript validation test test_validation_idle_transcript_with_recording_allowed called validate_transcript_for_processing without mocking task_is_scheduled_or_active, which attempts a real Celery broker connection (AMQP port 5672). Other tests in the same file already mock this — apply the same pattern here. * Enable server host mode * Fix webrtc connection * Remove turbopack * fix: standalone GPU service connectivity with host network mode Server runs with network_mode: host and can't resolve Docker service names. Publish cpu port as 8100 on host, point server at localhost:8100. Worker stays on bridge network using cpu:8000. Add dummy TRANSCRIPT_MODAL_API_KEY since OpenAI SDK requires it even for local endpoints. --------- Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com> Co-authored-by: Sergey Mankovsky <sergey@mankovsky.dev>	2026-02-11 18:20:36 -05:00
Mathieu Virbel	cd2255cfbc	chore(main): release 0.33.0 (#847 )	2026-02-06 18:12:06 -05:00
Igor Monadical	15ab2e306e	feat: Daily+hatchet default (#846 ) * feat: set Daily as default video platform Daily.co has been battle-tested and is ready to be the default. Whereby remains available for rooms that explicitly set it. * feat: enforce Hatchet for all multitrack processing Remove use_celery option from rooms - multitrack (Daily) recordings now always use Hatchet workflows. Celery remains for single-track (Whereby) file processing only. - Remove use_celery column from room table - Simplify dispatch logic to always use Hatchet for multitracks - Update tests to mock Hatchet instead of Celery * fix: update whereby test to patch Hatchet instead of removed Celery import --------- Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>	2026-02-05 18:38:08 -05:00