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

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:

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:

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`

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"]

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,

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:

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"

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 "$@"

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

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 = [

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:

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

server/reflector/dailyco_api/client.py

@@ -146,6 +146,8 @@ class DailyApiClient:
             )
             raise DailyApiError(operation, response)
 
+        if not response.content:
+            return {}
         return response.json()
 
     # ============================================================================

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
 

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

server/reflector/db/search.py

@@ -1,7 +1,6 @@
 """Search functionality for transcripts and other entities."""
 
 import itertools
-import json
 from dataclasses import dataclass
 from datetime import datetime
 from io import StringIO
@@ -27,6 +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
@@ -173,9 +173,6 @@ class SearchResult(BaseModel):
     total_match_count: NonNegativeInt = Field(
         default=0, description="Total number of matches found in the transcript"
     )
-    dag_status: list[dict] | None = Field(
-        default=None, description="Latest DAG task status for processing transcripts"
-    )
 
     @field_serializer("created_at", when_used="json")
     def serialize_datetime(self, dt: datetime) -> str:
@@ -332,42 +329,6 @@ class SnippetGenerator:
         return summary_snippets + webvtt_snippets, total_matches
 
 
-async def _fetch_dag_statuses(transcript_ids: list[str]) -> dict[str, list[dict]]:
-    """Fetch latest DAG_STATUS event data for given transcript IDs.
-
-    Returns dict mapping transcript_id -> tasks list from the last DAG_STATUS event.
-    """
-    if not transcript_ids:
-        return {}
-
-    db = get_database()
-    query = sqlalchemy.select(
-        [
-            transcripts.c.id,
-            transcripts.c.events,
-        ]
-    ).where(transcripts.c.id.in_(transcript_ids))
-
-    rows = await db.fetch_all(query)
-    result: dict[str, list[dict]] = {}
-
-    for row in rows:
-        events_raw = row["events"]
-        if not events_raw:
-            continue
-        # events is stored as JSON list
-        events = events_raw if isinstance(events_raw, list) else json.loads(events_raw)
-        # Find last DAG_STATUS event
-        for ev in reversed(events):
-            if isinstance(ev, dict) and ev.get("event") == "DAG_STATUS":
-                tasks = ev.get("data", {}).get("tasks")
-                if tasks:
-                    result[row["id"]] = tasks
-                break
-
-    return result
-
-
 class SearchController:
     """Controller for search operations across different entities."""
 
@@ -436,7 +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)
@@ -510,14 +471,6 @@ class SearchController:
             logger.error(f"Error processing search results: {e}", exc_info=True)
             raise
 
-        # Enrich processing transcripts with DAG status
-        processing_ids = [r.id for r in results if r.status == "processing"]
-        if processing_ids:
-            dag_statuses = await _fetch_dag_statuses(processing_ids)
-            for r in results:
-                if r.id in dag_statuses:
-                    r.dag_status = dag_statuses[r.id]
-
         return results, total
 
 

server/reflector/db/transcripts.py

@@ -234,7 +234,7 @@ class Transcript(BaseModel):
         return dt.isoformat()
 
     def add_event(self, event: str, data: BaseModel) -> TranscriptEvent:
-        ev = TranscriptEvent(event=event, data=data.model_dump(mode="json"))
+        ev = TranscriptEvent(event=event, data=data.model_dump())
         self.events.append(ev)
         return ev
 
@@ -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:

server/reflector/hatchet/broadcast.py

@@ -15,7 +15,7 @@ from reflector.utils.string import NonEmptyString
 from reflector.ws_manager import get_ws_manager
 
 # Events that should also be sent to user room (matches Celery behavior)
-USER_ROOM_EVENTS = {"STATUS", "FINAL_TITLE", "DURATION", "DAG_STATUS"}
+USER_ROOM_EVENTS = {"STATUS", "FINAL_TITLE", "DURATION"}
 
 
 async def broadcast_event(

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,
+        )

server/reflector/hatchet/dag_progress.py

@@ -1,230 +0,0 @@
-"""
-DAG Progress Reporting — models and transform.
-
-Converts Hatchet V1WorkflowRunDetails into structured DagTask list
-for frontend WebSocket/REST consumption.
-
-Ported from render_hatchet_run.py (feat-dag-zulip) which renders markdown;
-this module produces structured Pydantic models instead.
-"""
-
-from datetime import datetime
-from enum import StrEnum
-
-from hatchet_sdk.clients.rest.models import (
-    V1TaskStatus,
-    V1WorkflowRunDetails,
-    WorkflowRunShapeItemForWorkflowRunDetails,
-)
-from pydantic import BaseModel
-
-
-class DagTaskStatus(StrEnum):
-    QUEUED = "queued"
-    RUNNING = "running"
-    COMPLETED = "completed"
-    FAILED = "failed"
-    CANCELLED = "cancelled"
-
-
-_HATCHET_TO_DAG_STATUS: dict[V1TaskStatus, DagTaskStatus] = {
-    V1TaskStatus.QUEUED: DagTaskStatus.QUEUED,
-    V1TaskStatus.RUNNING: DagTaskStatus.RUNNING,
-    V1TaskStatus.COMPLETED: DagTaskStatus.COMPLETED,
-    V1TaskStatus.FAILED: DagTaskStatus.FAILED,
-    V1TaskStatus.CANCELLED: DagTaskStatus.CANCELLED,
-}
-
-
-class DagTask(BaseModel):
-    name: str
-    status: DagTaskStatus
-    started_at: datetime | None
-    finished_at: datetime | None
-    duration_seconds: float | None
-    parents: list[str]
-    error: str | None
-    children_total: int | None
-    children_completed: int | None
-    progress_pct: float | None
-
-
-class DagStatusData(BaseModel):
-    workflow_run_id: str
-    tasks: list[DagTask]
-
-
-def _topo_sort(
-    shape: list[WorkflowRunShapeItemForWorkflowRunDetails],
-) -> list[str]:
-    """Topological sort of step_ids from shape DAG (Kahn's algorithm).
-
-    Ported from render_hatchet_run.py.
-    """
-    step_ids = {s.step_id for s in shape}
-    children_map: dict[str, list[str]] = {}
-    in_degree: dict[str, int] = {sid: 0 for sid in step_ids}
-
-    for s in shape:
-        children = [c for c in (s.children_step_ids or []) if c in step_ids]
-        children_map[s.step_id] = children
-        for c in children:
-            in_degree[c] += 1
-
-    queue = sorted(sid for sid, deg in in_degree.items() if deg == 0)
-    result: list[str] = []
-    while queue:
-        node = queue.pop(0)
-        result.append(node)
-        for c in children_map.get(node, []):
-            in_degree[c] -= 1
-            if in_degree[c] == 0:
-                queue.append(c)
-                queue.sort()
-
-    return result
-
-
-def _extract_error_summary(error_message: str | None) -> str | None:
-    """Extract first meaningful line from error message, skipping traceback frames."""
-    if not error_message or not error_message.strip():
-        return None
-
-    err_lines = error_message.strip().split("\n")
-    err_summary = err_lines[0]
-    for line in err_lines:
-        stripped = line.strip()
-        if stripped and not stripped.startswith(("Traceback", "File ", "{", ")")):
-            err_summary = stripped
-    return err_summary
-
-
-def extract_dag_tasks(details: V1WorkflowRunDetails) -> list[DagTask]:
-    """Extract structured DagTask list from Hatchet workflow run details.
-
-    Returns tasks in topological order with status, timestamps, parents,
-    error summaries, and fan-out children counts.
-    """
-    shape = details.shape or []
-    tasks = details.tasks or []
-
-    if not shape:
-        return []
-
-    # Build lookups
-    step_to_shape: dict[str, WorkflowRunShapeItemForWorkflowRunDetails] = {
-        s.step_id: s for s in shape
-    }
-    step_to_name: dict[str, str] = {s.step_id: s.task_name for s in shape}
-
-    # Reverse edges: child -> parent names
-    parents_by_step: dict[str, list[str]] = {s.step_id: [] for s in shape}
-    for s in shape:
-        for child_id in s.children_step_ids or []:
-            if child_id in parents_by_step:
-                parents_by_step[child_id].append(step_to_name[s.step_id])
-
-    # Join tasks by step_id
-    from hatchet_sdk.clients.rest.models import V1TaskSummary  # noqa: PLC0415
-
-    task_by_step: dict[str, V1TaskSummary] = {}
-    for t in tasks:
-        if t.step_id and t.step_id in step_to_name:
-            task_by_step[t.step_id] = t
-
-    ordered = _topo_sort(shape)
-
-    result: list[DagTask] = []
-    for step_id in ordered:
-        name = step_to_name[step_id]
-        t = task_by_step.get(step_id)
-
-        if not t:
-            result.append(
-                DagTask(
-                    name=name,
-                    status=DagTaskStatus.QUEUED,
-                    started_at=None,
-                    finished_at=None,
-                    duration_seconds=None,
-                    parents=parents_by_step.get(step_id, []),
-                    error=None,
-                    children_total=None,
-                    children_completed=None,
-                    progress_pct=None,
-                )
-            )
-            continue
-
-        status = _HATCHET_TO_DAG_STATUS.get(t.status, DagTaskStatus.QUEUED)
-
-        duration_seconds: float | None = None
-        if t.duration is not None:
-            duration_seconds = t.duration / 1000.0
-
-        # Fan-out children
-        children_total: int | None = None
-        children_completed: int | None = None
-        if t.num_spawned_children and t.num_spawned_children > 0:
-            children_total = t.num_spawned_children
-            children_completed = sum(
-                1 for c in (t.children or []) if c.status == V1TaskStatus.COMPLETED
-            )
-
-        result.append(
-            DagTask(
-                name=name,
-                status=status,
-                started_at=t.started_at,
-                finished_at=t.finished_at,
-                duration_seconds=duration_seconds,
-                parents=parents_by_step.get(step_id, []),
-                error=_extract_error_summary(t.error_message),
-                children_total=children_total,
-                children_completed=children_completed,
-                progress_pct=None,
-            )
-        )
-
-    return result
-
-
-async def broadcast_dag_status(transcript_id: str, workflow_run_id: str) -> None:
-    """Fetch current DAG state from Hatchet and broadcast via WebSocket.
-
-    Fire-and-forget: exceptions are logged but never raised.
-    All imports are deferred for fork-safety (Hatchet workers fork processes).
-    """
-    try:
-        from reflector.db.transcripts import transcripts_controller  # noqa: I001, PLC0415
-        from reflector.hatchet.broadcast import append_event_and_broadcast  # noqa: PLC0415
-        from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (  # noqa: PLC0415
-            fresh_db_connection,
-        )
-        from reflector.logger import logger  # noqa: PLC0415
-
-        async with fresh_db_connection():
-            client = HatchetClientManager.get_client()
-            details = await client.runs.aio_get(workflow_run_id)
-            dag_tasks = extract_dag_tasks(details)
-            dag_status = DagStatusData(workflow_run_id=workflow_run_id, tasks=dag_tasks)
-
-            transcript = await transcripts_controller.get_by_id(transcript_id)
-            if transcript:
-                await append_event_and_broadcast(
-                    transcript_id,
-                    transcript,
-                    "DAG_STATUS",
-                    dag_status,
-                    logger,
-                )
-    except Exception:
-        from reflector.logger import logger  # noqa: PLC0415
-
-        logger.warning(
-            "[DAG Progress] Failed to broadcast DAG status",
-            transcript_id=transcript_id,
-            workflow_run_id=workflow_run_id,
-            exc_info=True,
-        )

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,

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,
     )
 
 
@@ -184,10 +186,7 @@ class Loggable(Protocol):
 
 
 def make_audio_progress_logger(
-    ctx: Loggable,
-    task_name: TaskName,
-    interval: float = 5.0,
-    transcript_id: str | None = None,
+    ctx: Loggable, task_name: TaskName, interval: float = 5.0
 ) -> Callable[[float | None, float], None]:
     """Create a throttled progress logger callback for audio processing.
 
@@ -195,7 +194,6 @@ def make_audio_progress_logger(
         ctx: Object with .log() method (e.g., Hatchet Context).
         task_name: Name to prefix in log messages.
         interval: Minimum seconds between log messages.
-        transcript_id: If provided, broadcasts transient DAG_TASK_PROGRESS events.
 
     Returns:
         Callback(progress_pct, audio_position) that logs at most every `interval` seconds.
@@ -217,27 +215,6 @@ def make_audio_progress_logger(
                 )
             last_log_time[0] = now
 
-        if transcript_id and progress_pct is not None:
-            try:
-                import asyncio  # noqa: PLC0415
-
-                from reflector.db.transcripts import TranscriptEvent  # noqa: PLC0415
-                from reflector.hatchet.broadcast import broadcast_event  # noqa: PLC0415
-
-                loop = asyncio.get_event_loop()
-                loop.create_task(
-                    broadcast_event(
-                        transcript_id,
-                        TranscriptEvent(
-                            event="DAG_TASK_PROGRESS",
-                            data={"task_name": task_name, "progress_pct": progress_pct},
-                        ),
-                        logger=logger,
-                    )
-                )
-            except Exception:
-                pass  # transient, never fail the callback
-
     return callback
 
 
@@ -262,15 +239,8 @@ def with_error_handling(
     ) -> Callable[[PipelineInput, Context], Coroutine[Any, Any, R]]:
         @functools.wraps(func)
         async def wrapper(input: PipelineInput, ctx: Context) -> R:
-            from reflector.hatchet.dag_progress import broadcast_dag_status  # noqa: I001, PLC0415
-
             try:
-                result = await func(input, ctx)
-                try:
-                    await broadcast_dag_status(input.transcript_id, ctx.workflow_run_id)
-                except Exception:
-                    pass
-                return result
+                return await func(input, ctx)
             except Exception as e:
                 logger.error(
                     f"[Hatchet] {step_name} failed",
@@ -278,10 +248,6 @@ def with_error_handling(
                     error=str(e),
                     exc_info=True,
                 )
-                try:
-                    await broadcast_dag_status(input.transcript_id, ctx.workflow_run_id)
-                except Exception:
-                    pass
                 if set_error_status:
                     await set_workflow_error_status(input.transcript_id)
                 raise
@@ -596,9 +562,7 @@ async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
         target_sample_rate,
         offsets_seconds=None,
         logger=logger,
-        progress_callback=make_audio_progress_logger(
-            ctx, TaskName.MIXDOWN_TRACKS, transcript_id=input.transcript_id
-        ),
+        progress_callback=make_audio_progress_logger(ctx, TaskName.MIXDOWN_TRACKS),
         expected_duration_sec=recording_duration if recording_duration > 0 else None,
     )
     await writer.flush()
@@ -756,7 +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
@@ -768,31 +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
     ]
@@ -878,9 +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=[],
@@ -893,11 +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 = {
@@ -907,8 +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}")
 

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):

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(

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,
     )

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(

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

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)

server/reflector/services/transcript_process.py

@@ -97,8 +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
@@ -267,19 +270,6 @@ async def dispatch_transcript_processing(
             )
 
         logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
-
-        try:
-            from reflector.hatchet.dag_progress import broadcast_dag_status  # noqa: I001, PLC0415
-
-            await broadcast_dag_status(config.transcript_id, workflow_id)
-        except Exception:
-            logger.warning(
-                "[DAG Progress] Failed initial broadcast",
-                transcript_id=config.transcript_id,
-                workflow_id=workflow_id,
-                exc_info=True,
-            )
-
         return None
 
     elif isinstance(config, FileProcessingConfig):

server/reflector/settings.py

@@ -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

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
             )

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:

server/reflector/views/transcripts.py

@@ -111,7 +111,6 @@ class GetTranscriptMinimal(BaseModel):
     room_id: str | None = None
     room_name: str | None = None
     audio_deleted: bool | None = None
-    dag_status: list[dict] | None = None
 
 
 class TranscriptParticipantWithEmail(TranscriptParticipant):
@@ -492,13 +491,6 @@ async def transcript_get(
                 )
             )
 
-    dag_status = None
-    if transcript.status == "processing" and transcript.events:
-        for ev in reversed(transcript.events):
-            if ev.event == "DAG_STATUS":
-                dag_status = ev.data.get("tasks") if isinstance(ev.data, dict) else None
-                break
-
     base_data = {
         "id": transcript.id,
         "user_id": transcript.user_id,
@@ -520,7 +512,6 @@ async def transcript_get(
         "room_id": transcript.room_id,
         "room_name": room_name,
         "audio_deleted": transcript.audio_deleted,
-        "dag_status": dag_status,
         "participants": participants,
     }
 

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)

server/reflector/views/transcripts_websocket.py

@@ -41,19 +41,13 @@ async def transcript_events_websocket(
 
     try:
         # on first connection, send all events only to the current user
-        # Find the last DAG_STATUS to send after other historical events
-        last_dag_status = None
         for event in transcript.events:
+            # for now, do not send TRANSCRIPT or STATUS options - theses are live event
+            # not necessary to be sent to the client; but keep the rest
             name = event.event
             if name in ("TRANSCRIPT", "STATUS"):
                 continue
-            if name == "DAG_STATUS":
-                last_dag_status = event
-                continue
             await websocket.send_json(event.model_dump(mode="json"))
-        # Send only the most recent DAG_STATUS so reconnecting clients get current state
-        if last_dag_status is not None:
-            await websocket.send_json(last_dag_status.model_dump(mode="json"))
 
         # XXX if transcript is final (locked=True and status=ended)
         # XXX send a final event to the client and close the connection

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)

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)

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])))

server/tests/test_dag_progress.py

@@ -1,959 +0,0 @@
-"""Tests for DAG progress models and transform function.
-
-Tests the extract_dag_tasks function that converts Hatchet V1WorkflowRunDetails
-into structured DagTask list for frontend consumption.
-"""
-
-from datetime import datetime, timezone
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from reflector.hatchet.constants import TaskName
-from reflector.hatchet.dag_progress import (
-    DagStatusData,
-    DagTask,
-    DagTaskStatus,
-    extract_dag_tasks,
-)
-
-
-def _make_shape_item(
-    step_id: str,
-    task_name: str,
-    children_step_ids: list[str] | None = None,
-) -> MagicMock:
-    """Create a mock WorkflowRunShapeItemForWorkflowRunDetails."""
-    item = MagicMock()
-    item.step_id = step_id
-    item.task_name = task_name
-    item.children_step_ids = children_step_ids or []
-    return item
-
-
-def _make_task_summary(
-    step_id: str,
-    status: str = "QUEUED",
-    started_at: datetime | None = None,
-    finished_at: datetime | None = None,
-    duration: int | None = None,
-    error_message: str | None = None,
-    task_external_id: str | None = None,
-    num_spawned_children: int | None = None,
-    children: list | None = None,
-) -> MagicMock:
-    """Create a mock V1TaskSummary."""
-    from hatchet_sdk.clients.rest.models import V1TaskStatus
-
-    task = MagicMock()
-    task.step_id = step_id
-    task.status = V1TaskStatus(status)
-    task.started_at = started_at
-    task.finished_at = finished_at
-    task.duration = duration
-    task.error_message = error_message
-    task.task_external_id = task_external_id or f"ext-{step_id}"
-    task.num_spawned_children = num_spawned_children
-    task.children = children or []
-    return task
-
-
-def _make_details(
-    shape: list,
-    tasks: list,
-    run_id: str = "test-run-id",
-) -> MagicMock:
-    """Create a mock V1WorkflowRunDetails."""
-    details = MagicMock()
-    details.shape = shape
-    details.tasks = tasks
-    details.task_events = []
-    details.run = MagicMock()
-    details.run.metadata = MagicMock()
-    details.run.metadata.id = run_id
-    return details
-
-
-class TestExtractDagTasksBasic:
-    """Test basic extraction of DAG tasks from workflow run details."""
-
-    def test_empty_shape_returns_empty_list(self):
-        details = _make_details(shape=[], tasks=[])
-        result = extract_dag_tasks(details)
-        assert result == []
-
-    def test_single_task_queued(self):
-        shape = [_make_shape_item("s1", "get_recording")]
-        tasks = [_make_task_summary("s1", status="QUEUED")]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert len(result) == 1
-        assert result[0].name == "get_recording"
-        assert result[0].status == DagTaskStatus.QUEUED
-        assert result[0].parents == []
-        assert result[0].started_at is None
-        assert result[0].finished_at is None
-        assert result[0].duration_seconds is None
-        assert result[0].error is None
-        assert result[0].children_total is None
-        assert result[0].children_completed is None
-        assert result[0].progress_pct is None
-
-    def test_completed_task_with_duration(self):
-        now = datetime.now(timezone.utc)
-        shape = [_make_shape_item("s1", "get_recording")]
-        tasks = [
-            _make_task_summary(
-                "s1",
-                status="COMPLETED",
-                started_at=now,
-                finished_at=now,
-                duration=1500,  # milliseconds
-            )
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert result[0].status == DagTaskStatus.COMPLETED
-        assert result[0].duration_seconds == 1.5
-        assert result[0].started_at == now
-        assert result[0].finished_at == now
-
-    def test_failed_task_with_error(self):
-        shape = [_make_shape_item("s1", "get_recording")]
-        tasks = [
-            _make_task_summary(
-                "s1",
-                status="FAILED",
-                error_message="Traceback (most recent call last):\n  File something\nConnectionError: connection refused",
-            )
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert result[0].status == DagTaskStatus.FAILED
-        assert result[0].error == "ConnectionError: connection refused"
-
-    def test_running_task(self):
-        now = datetime.now(timezone.utc)
-        shape = [_make_shape_item("s1", "mixdown_tracks")]
-        tasks = [
-            _make_task_summary(
-                "s1",
-                status="RUNNING",
-                started_at=now,
-                duration=5000,
-            )
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert result[0].status == DagTaskStatus.RUNNING
-        assert result[0].started_at == now
-        assert result[0].duration_seconds == 5.0
-
-    def test_cancelled_task(self):
-        shape = [_make_shape_item("s1", "post_zulip")]
-        tasks = [_make_task_summary("s1", status="CANCELLED")]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert result[0].status == DagTaskStatus.CANCELLED
-
-
-class TestExtractDagTasksTopology:
-    """Test topological ordering and parent extraction."""
-
-    def test_linear_chain_parents(self):
-        """A -> B -> C should produce correct parents."""
-        shape = [
-            _make_shape_item("s1", "get_recording", children_step_ids=["s2"]),
-            _make_shape_item("s2", "get_participants", children_step_ids=["s3"]),
-            _make_shape_item("s3", "process_tracks"),
-        ]
-        tasks = [
-            _make_task_summary("s1", status="COMPLETED"),
-            _make_task_summary("s2", status="COMPLETED"),
-            _make_task_summary("s3", status="QUEUED"),
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert [t.name for t in result] == [
-            "get_recording",
-            "get_participants",
-            "process_tracks",
-        ]
-        assert result[0].parents == []
-        assert result[1].parents == ["get_recording"]
-        assert result[2].parents == ["get_participants"]
-
-    def test_diamond_dag(self):
-        """
-        A -> B, A -> C, B -> D, C -> D
-        D should have parents [B, C] (or [C, B] depending on sort).
-        """
-        shape = [
-            _make_shape_item("s1", "get_recording", children_step_ids=["s2", "s3"]),
-            _make_shape_item("s2", "mixdown_tracks", children_step_ids=["s4"]),
-            _make_shape_item("s3", "detect_topics", children_step_ids=["s4"]),
-            _make_shape_item("s4", "finalize"),
-        ]
-        tasks = [
-            _make_task_summary("s1", status="COMPLETED"),
-            _make_task_summary("s2", status="RUNNING"),
-            _make_task_summary("s3", status="RUNNING"),
-            _make_task_summary("s4", status="QUEUED"),
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        # Topological: s1 first, s2/s3 in some order, s4 last
-        assert result[0].name == "get_recording"
-        assert result[-1].name == "finalize"
-        finalize = result[-1]
-        assert set(finalize.parents) == {"mixdown_tracks", "detect_topics"}
-
-    def test_topological_order_is_stable(self):
-        """Verify deterministic ordering (sorted queue in Kahn's)."""
-        shape = [
-            _make_shape_item("s_c", "task_c"),
-            _make_shape_item("s_a", "task_a", children_step_ids=["s_c"]),
-            _make_shape_item("s_b", "task_b", children_step_ids=["s_c"]),
-        ]
-        tasks = [
-            _make_task_summary("s_c", status="QUEUED"),
-            _make_task_summary("s_a", status="COMPLETED"),
-            _make_task_summary("s_b", status="COMPLETED"),
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        # s_a and s_b both roots with in-degree 0; sorted alphabetically by step_id
-        names = [t.name for t in result]
-        assert names[-1] == "task_c"
-        # First two should be task_a, task_b (sorted by step_id: s_a < s_b)
-        assert names[0] == "task_a"
-        assert names[1] == "task_b"
-
-    def test_production_dag_shape(self):
-        """Test the real 15-task pipeline topology with mixed statuses.
-
-        Simulates a mid-pipeline state where early tasks completed,
-        middle tasks running, and later tasks still queued.
-        """
-        # Production DAG edges (parent -> children):
-        # get_recording -> get_participants
-        # get_participants -> process_tracks
-        # process_tracks -> mixdown_tracks, detect_topics, finalize
-        # mixdown_tracks -> generate_waveform
-        # detect_topics -> generate_title, extract_subjects
-        # extract_subjects -> process_subjects, identify_action_items
-        # process_subjects -> generate_recap
-        # generate_title -> finalize
-        # generate_recap -> finalize
-        # identify_action_items -> finalize
-        # finalize -> cleanup_consent
-        # cleanup_consent -> post_zulip, send_webhook
-        shape = [
-            _make_shape_item(
-                "s_get_recording", TaskName.GET_RECORDING, ["s_get_participants"]
-            ),
-            _make_shape_item(
-                "s_get_participants", TaskName.GET_PARTICIPANTS, ["s_process_tracks"]
-            ),
-            _make_shape_item(
-                "s_process_tracks",
-                TaskName.PROCESS_TRACKS,
-                ["s_mixdown_tracks", "s_detect_topics", "s_finalize"],
-            ),
-            _make_shape_item(
-                "s_mixdown_tracks", TaskName.MIXDOWN_TRACKS, ["s_generate_waveform"]
-            ),
-            _make_shape_item("s_generate_waveform", TaskName.GENERATE_WAVEFORM),
-            _make_shape_item(
-                "s_detect_topics",
-                TaskName.DETECT_TOPICS,
-                ["s_generate_title", "s_extract_subjects"],
-            ),
-            _make_shape_item(
-                "s_generate_title", TaskName.GENERATE_TITLE, ["s_finalize"]
-            ),
-            _make_shape_item(
-                "s_extract_subjects",
-                TaskName.EXTRACT_SUBJECTS,
-                ["s_process_subjects", "s_identify_action_items"],
-            ),
-            _make_shape_item(
-                "s_process_subjects", TaskName.PROCESS_SUBJECTS, ["s_generate_recap"]
-            ),
-            _make_shape_item(
-                "s_generate_recap", TaskName.GENERATE_RECAP, ["s_finalize"]
-            ),
-            _make_shape_item(
-                "s_identify_action_items",
-                TaskName.IDENTIFY_ACTION_ITEMS,
-                ["s_finalize"],
-            ),
-            _make_shape_item("s_finalize", TaskName.FINALIZE, ["s_cleanup_consent"]),
-            _make_shape_item(
-                "s_cleanup_consent",
-                TaskName.CLEANUP_CONSENT,
-                ["s_post_zulip", "s_send_webhook"],
-            ),
-            _make_shape_item("s_post_zulip", TaskName.POST_ZULIP),
-            _make_shape_item("s_send_webhook", TaskName.SEND_WEBHOOK),
-        ]
-
-        # Mid-pipeline: early tasks done, middle running, later queued
-        tasks = [
-            _make_task_summary("s_get_recording", status="COMPLETED"),
-            _make_task_summary("s_get_participants", status="COMPLETED"),
-            _make_task_summary("s_process_tracks", status="COMPLETED"),
-            _make_task_summary("s_mixdown_tracks", status="RUNNING"),
-            _make_task_summary("s_generate_waveform", status="QUEUED"),
-            _make_task_summary("s_detect_topics", status="RUNNING"),
-            _make_task_summary("s_generate_title", status="QUEUED"),
-            _make_task_summary("s_extract_subjects", status="QUEUED"),
-            _make_task_summary("s_process_subjects", status="QUEUED"),
-            _make_task_summary("s_generate_recap", status="QUEUED"),
-            _make_task_summary("s_identify_action_items", status="QUEUED"),
-            _make_task_summary("s_finalize", status="QUEUED"),
-            _make_task_summary("s_cleanup_consent", status="QUEUED"),
-            _make_task_summary("s_post_zulip", status="QUEUED"),
-            _make_task_summary("s_send_webhook", status="QUEUED"),
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        # All 15 tasks present
-        assert len(result) == 15
-        result_names = [t.name for t in result]
-        assert set(result_names) == {
-            TaskName.GET_RECORDING,
-            TaskName.GET_PARTICIPANTS,
-            TaskName.PROCESS_TRACKS,
-            TaskName.MIXDOWN_TRACKS,
-            TaskName.GENERATE_WAVEFORM,
-            TaskName.DETECT_TOPICS,
-            TaskName.GENERATE_TITLE,
-            TaskName.EXTRACT_SUBJECTS,
-            TaskName.PROCESS_SUBJECTS,
-            TaskName.GENERATE_RECAP,
-            TaskName.IDENTIFY_ACTION_ITEMS,
-            TaskName.FINALIZE,
-            TaskName.CLEANUP_CONSENT,
-            TaskName.POST_ZULIP,
-            TaskName.SEND_WEBHOOK,
-        }
-
-        # Topological order invariant: no task appears before its parents
-        name_to_index = {t.name: i for i, t in enumerate(result)}
-        for task in result:
-            for parent_name in task.parents:
-                assert name_to_index[parent_name] < name_to_index[task.name], (
-                    f"Parent {parent_name} (idx {name_to_index[parent_name]}) "
-                    f"must appear before {task.name} (idx {name_to_index[task.name]})"
-                )
-
-        # finalize has exactly 4 parents
-        finalize = next(t for t in result if t.name == TaskName.FINALIZE)
-        assert set(finalize.parents) == {
-            TaskName.PROCESS_TRACKS,
-            TaskName.GENERATE_TITLE,
-            TaskName.GENERATE_RECAP,
-            TaskName.IDENTIFY_ACTION_ITEMS,
-        }
-
-        # cleanup_consent has 1 parent (finalize)
-        cleanup = next(t for t in result if t.name == TaskName.CLEANUP_CONSENT)
-        assert cleanup.parents == [TaskName.FINALIZE]
-
-        # post_zulip and send_webhook both have cleanup_consent as parent
-        post_zulip = next(t for t in result if t.name == TaskName.POST_ZULIP)
-        send_webhook = next(t for t in result if t.name == TaskName.SEND_WEBHOOK)
-        assert post_zulip.parents == [TaskName.CLEANUP_CONSENT]
-        assert send_webhook.parents == [TaskName.CLEANUP_CONSENT]
-
-        # Verify statuses propagated correctly
-        assert (
-            next(t for t in result if t.name == TaskName.GET_RECORDING).status
-            == DagTaskStatus.COMPLETED
-        )
-        assert (
-            next(t for t in result if t.name == TaskName.MIXDOWN_TRACKS).status
-            == DagTaskStatus.RUNNING
-        )
-        assert (
-            next(t for t in result if t.name == TaskName.FINALIZE).status
-            == DagTaskStatus.QUEUED
-        )
-
-    def test_topological_sort_invariant_complex_dag(self):
-        """For a complex DAG, every task's parents appear earlier in the list.
-
-        Uses a wider branching/merging DAG than diamond to stress the invariant.
-        """
-        # DAG: A -> B, A -> C, A -> D, B -> E, C -> E, C -> F, D -> F, E -> G, F -> G
-        shape = [
-            _make_shape_item("s_a", "task_a", ["s_b", "s_c", "s_d"]),
-            _make_shape_item("s_b", "task_b", ["s_e"]),
-            _make_shape_item("s_c", "task_c", ["s_e", "s_f"]),
-            _make_shape_item("s_d", "task_d", ["s_f"]),
-            _make_shape_item("s_e", "task_e", ["s_g"]),
-            _make_shape_item("s_f", "task_f", ["s_g"]),
-            _make_shape_item("s_g", "task_g"),
-        ]
-        tasks = [
-            _make_task_summary("s_a", status="COMPLETED"),
-            _make_task_summary("s_b", status="COMPLETED"),
-            _make_task_summary("s_c", status="RUNNING"),
-            _make_task_summary("s_d", status="COMPLETED"),
-            _make_task_summary("s_e", status="QUEUED"),
-            _make_task_summary("s_f", status="QUEUED"),
-            _make_task_summary("s_g", status="QUEUED"),
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert len(result) == 7
-        name_to_index = {t.name: i for i, t in enumerate(result)}
-
-        # Verify invariant: every parent appears before its child
-        for task in result:
-            for parent_name in task.parents:
-                assert name_to_index[parent_name] < name_to_index[task.name], (
-                    f"Parent {parent_name} (idx {name_to_index[parent_name]}) "
-                    f"must appear before {task.name} (idx {name_to_index[task.name]})"
-                )
-
-        # task_g has 2 parents
-        task_g = next(t for t in result if t.name == "task_g")
-        assert set(task_g.parents) == {"task_e", "task_f"}
-
-        # task_e has 2 parents
-        task_e = next(t for t in result if t.name == "task_e")
-        assert set(task_e.parents) == {"task_b", "task_c"}
-
-        # task_a is root (first in topological order)
-        assert result[0].name == "task_a"
-        assert result[0].parents == []
-
-
-class TestExtractDagTasksFanOut:
-    """Test fan-out tasks with spawned children."""
-
-    def test_fan_out_children_counts(self):
-        from hatchet_sdk.clients.rest.models import V1TaskStatus
-
-        child_mocks = []
-        for status in ["COMPLETED", "COMPLETED", "RUNNING", "QUEUED"]:
-            child = MagicMock()
-            child.status = V1TaskStatus(status)
-            child_mocks.append(child)
-
-        shape = [_make_shape_item("s1", "process_tracks")]
-        tasks = [
-            _make_task_summary(
-                "s1",
-                status="RUNNING",
-                num_spawned_children=4,
-                children=child_mocks,
-            )
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert result[0].children_total == 4
-        assert result[0].children_completed == 2
-
-    def test_no_children_when_no_spawn(self):
-        shape = [_make_shape_item("s1", "get_recording")]
-        tasks = [
-            _make_task_summary("s1", status="COMPLETED", num_spawned_children=None)
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert result[0].children_total is None
-        assert result[0].children_completed is None
-
-    def test_zero_spawned_children(self):
-        shape = [_make_shape_item("s1", "process_tracks")]
-        tasks = [_make_task_summary("s1", status="COMPLETED", num_spawned_children=0)]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert result[0].children_total is None
-        assert result[0].children_completed is None
-
-
-class TestExtractDagTasksErrorExtraction:
-    """Test error message extraction logic."""
-
-    def test_simple_error(self):
-        shape = [_make_shape_item("s1", "mixdown_tracks")]
-        tasks = [
-            _make_task_summary(
-                "s1", status="FAILED", error_message="ValueError: no tracks"
-            )
-        ]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-        assert result[0].error == "ValueError: no tracks"
-
-    def test_traceback_extracts_meaningful_line(self):
-        error = (
-            "Traceback (most recent call last):\n"
-            '  File "/app/something.py", line 42\n'
-            "RuntimeError: out of memory"
-        )
-        shape = [_make_shape_item("s1", "mixdown_tracks")]
-        tasks = [_make_task_summary("s1", status="FAILED", error_message=error)]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-        assert result[0].error == "RuntimeError: out of memory"
-
-    def test_no_error_when_none(self):
-        shape = [_make_shape_item("s1", "get_recording")]
-        tasks = [_make_task_summary("s1", status="COMPLETED", error_message=None)]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-        assert result[0].error is None
-
-    def test_empty_error_message(self):
-        shape = [_make_shape_item("s1", "get_recording")]
-        tasks = [_make_task_summary("s1", status="FAILED", error_message="")]
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-        assert result[0].error is None
-
-
-class TestExtractDagTasksMissingData:
-    """Test edge cases with missing task data."""
-
-    def test_shape_without_matching_task(self):
-        """Shape has a step but tasks list doesn't contain it."""
-        shape = [_make_shape_item("s1", "get_recording")]
-        tasks = []  # No matching task
-        details = _make_details(shape, tasks)
-
-        result = extract_dag_tasks(details)
-
-        assert len(result) == 1
-        assert result[0].name == "get_recording"
-        assert result[0].status == DagTaskStatus.QUEUED  # default when no task data
-        assert result[0].started_at is None
-
-    def test_none_shape_returns_empty(self):
-        details = _make_details(shape=[], tasks=[])
-        details.shape = None
-
-        result = extract_dag_tasks(details)
-        assert result == []
-
-
-class TestDagStatusData:
-    """Test DagStatusData model serialization."""
-
-    def test_serialization(self):
-        task = DagTask(
-            name="get_recording",
-            status=DagTaskStatus.COMPLETED,
-            started_at=datetime(2025, 1, 1, tzinfo=timezone.utc),
-            finished_at=datetime(2025, 1, 1, 0, 0, 1, tzinfo=timezone.utc),
-            duration_seconds=1.0,
-            parents=[],
-            error=None,
-            children_total=None,
-            children_completed=None,
-            progress_pct=None,
-        )
-        data = DagStatusData(workflow_run_id="test-123", tasks=[task])
-        dumped = data.model_dump(mode="json")
-
-        assert dumped["workflow_run_id"] == "test-123"
-        assert len(dumped["tasks"]) == 1
-        assert dumped["tasks"][0]["name"] == "get_recording"
-        assert dumped["tasks"][0]["status"] == "completed"
-        assert dumped["tasks"][0]["duration_seconds"] == 1.0
-
-
-class AsyncContextManager:
-    """No-op async context manager for mocking fresh_db_connection."""
-
-    async def __aenter__(self):
-        return None
-
-    async def __aexit__(self, *args):
-        return None
-
-
-class TestBroadcastDagStatus:
-    """Test broadcast_dag_status function.
-
-    broadcast_dag_status uses deferred imports inside its function body.
-    We mock the source modules/objects before calling the function.
-    Importing daily_multitrack_pipeline triggers a cascade
-    (subject_processing -> HatchetClientManager.get_client at module level),
-    so we set _instance before the import to prevent real SDK init.
-    """
-
-    @pytest.fixture(autouse=True)
-    def _setup_hatchet_mock(self):
-        """Set HatchetClientManager._instance to a mock to prevent real SDK init.
-
-        Module-level code in workflow files calls get_client() during import.
-        Setting _instance before import avoids ClientConfig validation.
-        """
-        from reflector.hatchet.client import HatchetClientManager
-
-        original = HatchetClientManager._instance
-        HatchetClientManager._instance = MagicMock()
-        yield
-        HatchetClientManager._instance = original
-
-    @pytest.mark.asyncio
-    async def test_broadcasts_dag_status(self):
-        """broadcast_dag_status fetches run, transforms, and broadcasts."""
-        mock_transcript = MagicMock()
-        mock_transcript.id = "t-123"
-
-        mock_details = _make_details(
-            shape=[_make_shape_item("s1", "get_recording")],
-            tasks=[_make_task_summary("s1", status="COMPLETED")],
-            run_id="wf-abc",
-        )
-
-        mock_client = MagicMock()
-        mock_client.runs.aio_get = AsyncMock(return_value=mock_details)
-
-        with (
-            patch(
-                "reflector.hatchet.client.HatchetClientManager.get_client",
-                return_value=mock_client,
-            ),
-            patch(
-                "reflector.hatchet.broadcast.append_event_and_broadcast",
-                new_callable=AsyncMock,
-            ) as mock_broadcast,
-            patch(
-                "reflector.db.transcripts.transcripts_controller.get_by_id",
-                new_callable=AsyncMock,
-                return_value=mock_transcript,
-            ),
-            patch(
-                "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
-                return_value=AsyncContextManager(),
-            ),
-        ):
-            from reflector.hatchet.dag_progress import broadcast_dag_status
-
-            await broadcast_dag_status("t-123", "wf-abc")
-
-            mock_client.runs.aio_get.assert_called_once_with("wf-abc")
-            mock_broadcast.assert_called_once()
-            call_args = mock_broadcast.call_args
-            assert call_args[0][0] == "t-123"  # transcript_id
-            assert call_args[0][1] is mock_transcript  # transcript
-            assert call_args[0][2] == "DAG_STATUS"  # event_name
-            data = call_args[0][3]
-            assert isinstance(data, DagStatusData)
-            assert data.workflow_run_id == "wf-abc"
-            assert len(data.tasks) == 1
-
-    @pytest.mark.asyncio
-    async def test_swallows_exceptions(self):
-        """broadcast_dag_status never raises even when internals fail."""
-        from reflector.hatchet.dag_progress import broadcast_dag_status
-
-        with patch(
-            "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
-            side_effect=RuntimeError("db exploded"),
-        ):
-            # Should not raise
-            await broadcast_dag_status("t-123", "wf-abc")
-
-    @pytest.mark.asyncio
-    async def test_no_broadcast_when_transcript_not_found(self):
-        """broadcast_dag_status does not broadcast if transcript is None."""
-        mock_details = _make_details(
-            shape=[_make_shape_item("s1", "get_recording")],
-            tasks=[_make_task_summary("s1", status="COMPLETED")],
-        )
-
-        mock_client = MagicMock()
-        mock_client.runs.aio_get = AsyncMock(return_value=mock_details)
-
-        with (
-            patch(
-                "reflector.hatchet.client.HatchetClientManager.get_client",
-                return_value=mock_client,
-            ),
-            patch(
-                "reflector.hatchet.workflows.daily_multitrack_pipeline.fresh_db_connection",
-                return_value=AsyncContextManager(),
-            ),
-            patch(
-                "reflector.db.transcripts.transcripts_controller.get_by_id",
-                new_callable=AsyncMock,
-                return_value=None,
-            ),
-            patch(
-                "reflector.hatchet.broadcast.append_event_and_broadcast",
-                new_callable=AsyncMock,
-            ) as mock_broadcast,
-        ):
-            from reflector.hatchet.dag_progress import broadcast_dag_status
-
-            await broadcast_dag_status("t-123", "wf-abc")
-
-            mock_broadcast.assert_not_called()
-
-
-class TestMakeAudioProgressLoggerWithBroadcast:
-    """Test make_audio_progress_logger with transcript_id for transient broadcasts."""
-
-    @pytest.fixture(autouse=True)
-    def _setup_hatchet_mock(self):
-        """Set HatchetClientManager._instance to prevent real SDK init on import."""
-        from reflector.hatchet.client import HatchetClientManager
-
-        original = HatchetClientManager._instance
-        if original is None:
-            HatchetClientManager._instance = MagicMock()
-        yield
-        HatchetClientManager._instance = original
-
-    def test_broadcasts_transient_progress_event(self):
-        """When transcript_id provided and progress_pct not None, broadcasts event."""
-        import asyncio
-
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            make_audio_progress_logger,
-        )
-
-        ctx = MagicMock()
-        ctx.log = MagicMock()
-
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-
-        mock_broadcast = AsyncMock()
-        tasks_created = []
-
-        original_create_task = loop.create_task
-
-        def capture_create_task(coro):
-            task = original_create_task(coro)
-            tasks_created.append(task)
-            return task
-
-        try:
-            with (
-                patch(
-                    "reflector.hatchet.broadcast.broadcast_event",
-                    mock_broadcast,
-                ),
-                patch.object(loop, "create_task", side_effect=capture_create_task),
-            ):
-                callback = make_audio_progress_logger(
-                    ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id="t-123"
-                )
-                callback(50.0, 100.0)
-
-                # Run pending tasks
-                if tasks_created:
-                    loop.run_until_complete(asyncio.gather(*tasks_created))
-
-                mock_broadcast.assert_called_once()
-                event_arg = mock_broadcast.call_args[0][1]
-                assert event_arg.event == "DAG_TASK_PROGRESS"
-                assert event_arg.data["task_name"] == TaskName.MIXDOWN_TRACKS
-                assert event_arg.data["progress_pct"] == 50.0
-        finally:
-            loop.close()
-
-    def test_no_broadcast_without_transcript_id(self):
-        """When transcript_id is None, no broadcast happens."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            make_audio_progress_logger,
-        )
-
-        ctx = MagicMock()
-
-        with patch(
-            "reflector.hatchet.broadcast.broadcast_event",
-            new_callable=AsyncMock,
-        ) as mock_broadcast:
-            callback = make_audio_progress_logger(
-                ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id=None
-            )
-            callback(50.0, 100.0)
-            mock_broadcast.assert_not_called()
-
-    def test_no_broadcast_when_progress_pct_is_none(self):
-        """When progress_pct is None, no broadcast happens even with transcript_id."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            make_audio_progress_logger,
-        )
-
-        ctx = MagicMock()
-
-        with patch(
-            "reflector.hatchet.broadcast.broadcast_event",
-            new_callable=AsyncMock,
-        ) as mock_broadcast:
-            callback = make_audio_progress_logger(
-                ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id="t-123"
-            )
-            callback(None, 100.0)
-            mock_broadcast.assert_not_called()
-
-    def test_logging_throttled_by_interval(self):
-        """With interval=5.0, rapid calls only log once until interval elapses.
-
-        The throttle applies to ctx.log() calls. Broadcasts (fire-and-forget)
-        are not throttled — they occur every call when transcript_id + progress_pct set.
-        """
-        import asyncio
-        import time as time_mod
-
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            make_audio_progress_logger,
-        )
-
-        ctx = MagicMock()
-        ctx.log = MagicMock()
-
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-
-        mock_broadcast = AsyncMock()
-        tasks_created = []
-        original_create_task = loop.create_task
-
-        def capture_create_task(coro):
-            task = original_create_task(coro)
-            tasks_created.append(task)
-            return task
-
-        # Controlled monotonic values for the 4 calls from make_audio_progress_logger:
-        # init (start_time, last_log_time), call1 (now), call2 (now), call3 (now)
-        # After those, fall back to real time.monotonic() for asyncio internals.
-        controlled_values = [100.0, 100.0, 101.0, 106.0]
-        call_index = [0]
-        real_monotonic = time_mod.monotonic
-
-        def mock_monotonic():
-            if call_index[0] < len(controlled_values):
-                val = controlled_values[call_index[0]]
-                call_index[0] += 1
-                return val
-            return real_monotonic()
-
-        try:
-            with (
-                patch(
-                    "reflector.hatchet.workflows.daily_multitrack_pipeline.time.monotonic",
-                    side_effect=mock_monotonic,
-                ),
-                patch(
-                    "reflector.hatchet.broadcast.broadcast_event",
-                    mock_broadcast,
-                ),
-                patch.object(loop, "create_task", side_effect=capture_create_task),
-            ):
-                callback = make_audio_progress_logger(
-                    ctx, TaskName.MIXDOWN_TRACKS, interval=5.0, transcript_id="t-123"
-                )
-
-                # Call 1 at t=100.0: 100.0 - 100.0 = 0.0 < 5.0 => no log
-                callback(25.0, 50.0)
-                assert ctx.log.call_count == 0
-
-                # Call 2 at t=101.0: 101.0 - 100.0 = 1.0 < 5.0 => no log
-                callback(50.0, 100.0)
-                assert ctx.log.call_count == 0
-
-                # Call 3 at t=106.0: 106.0 - 100.0 = 6.0 >= 5.0 => logs
-                callback(75.0, 150.0)
-                assert ctx.log.call_count == 1
-
-                # Run pending broadcast tasks
-                if tasks_created:
-                    loop.run_until_complete(asyncio.gather(*tasks_created))
-
-                # Broadcasts happen on every call (not throttled) — 3 calls total
-                assert mock_broadcast.call_count == 3
-        finally:
-            loop.close()
-
-    def test_uses_broadcast_event_not_append_event_and_broadcast(self):
-        """Progress events use broadcast_event (transient), not append_event_and_broadcast (persisted)."""
-        import asyncio
-
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            make_audio_progress_logger,
-        )
-
-        ctx = MagicMock()
-        ctx.log = MagicMock()
-
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-
-        mock_broadcast_event = AsyncMock()
-        mock_append = AsyncMock()
-        tasks_created = []
-        original_create_task = loop.create_task
-
-        def capture_create_task(coro):
-            task = original_create_task(coro)
-            tasks_created.append(task)
-            return task
-
-        try:
-            with (
-                patch(
-                    "reflector.hatchet.broadcast.broadcast_event",
-                    mock_broadcast_event,
-                ),
-                patch(
-                    "reflector.hatchet.broadcast.append_event_and_broadcast",
-                    mock_append,
-                ),
-                patch.object(loop, "create_task", side_effect=capture_create_task),
-            ):
-                callback = make_audio_progress_logger(
-                    ctx, TaskName.MIXDOWN_TRACKS, interval=0.0, transcript_id="t-123"
-                )
-                callback(50.0, 100.0)
-
-                if tasks_created:
-                    loop.run_until_complete(asyncio.gather(*tasks_created))
-
-                # broadcast_event (transient) IS called
-                mock_broadcast_event.assert_called_once()
-                # append_event_and_broadcast (persisted) is NOT called
-                mock_append.assert_not_called()
-        finally:
-            loop.close()

server/tests/test_dag_progress_decorator.py

@@ -1,181 +0,0 @@
-"""Tests for with_error_handling decorator integration with broadcast_dag_status.
-
-The decorator wraps each pipeline task and calls broadcast_dag_status on both
-success and failure paths. These tests verify that integration rather than
-testing broadcast_dag_status in isolation (which test_dag_progress.py covers).
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from reflector.hatchet.constants import TaskName
-
-
-class TestWithErrorHandlingBroadcast:
-    """Test with_error_handling decorator's integration with broadcast_dag_status."""
-
-    @pytest.fixture(autouse=True)
-    def _setup_hatchet_mock(self):
-        """Set HatchetClientManager._instance to a mock to prevent real SDK init.
-
-        Module-level code in workflow files calls get_client() during import.
-        Setting _instance before import avoids ClientConfig validation.
-        """
-        from reflector.hatchet.client import HatchetClientManager
-
-        original = HatchetClientManager._instance
-        HatchetClientManager._instance = MagicMock()
-        yield
-        HatchetClientManager._instance = original
-
-    def _make_input(self, transcript_id: str = "t-123") -> MagicMock:
-        """Create a mock PipelineInput with transcript_id."""
-        inp = MagicMock()
-        inp.transcript_id = transcript_id
-        return inp
-
-    def _make_ctx(self, workflow_run_id: str = "wf-abc") -> MagicMock:
-        """Create a mock Context with workflow_run_id."""
-        ctx = MagicMock()
-        ctx.workflow_run_id = workflow_run_id
-        return ctx
-
-    @pytest.mark.asyncio
-    async def test_calls_broadcast_on_success(self):
-        """Decorator calls broadcast_dag_status once when task succeeds."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            with_error_handling,
-        )
-
-        inner = AsyncMock(return_value="ok")
-        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
-
-        with patch(
-            "reflector.hatchet.dag_progress.broadcast_dag_status",
-            new_callable=AsyncMock,
-        ) as mock_broadcast:
-            result = await wrapped(self._make_input(), self._make_ctx())
-
-        assert result == "ok"
-        mock_broadcast.assert_called_once_with("t-123", "wf-abc")
-
-    @pytest.mark.asyncio
-    async def test_calls_broadcast_on_failure(self):
-        """Decorator calls broadcast_dag_status once when task raises."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            with_error_handling,
-        )
-
-        inner = AsyncMock(side_effect=RuntimeError("boom"))
-        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
-
-        with (
-            patch(
-                "reflector.hatchet.dag_progress.broadcast_dag_status",
-                new_callable=AsyncMock,
-            ) as mock_broadcast,
-            patch(
-                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
-                new_callable=AsyncMock,
-            ),
-        ):
-            with pytest.raises(RuntimeError, match="boom"):
-                await wrapped(self._make_input(), self._make_ctx())
-
-        mock_broadcast.assert_called_once_with("t-123", "wf-abc")
-
-    @pytest.mark.asyncio
-    async def test_swallows_broadcast_exception_on_success(self):
-        """Broadcast failure does not crash the task on the success path."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            with_error_handling,
-        )
-
-        inner = AsyncMock(return_value="ok")
-        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
-
-        with patch(
-            "reflector.hatchet.dag_progress.broadcast_dag_status",
-            new_callable=AsyncMock,
-            side_effect=RuntimeError("broadcast exploded"),
-        ):
-            result = await wrapped(self._make_input(), self._make_ctx())
-
-        assert result == "ok"
-
-    @pytest.mark.asyncio
-    async def test_swallows_broadcast_exception_on_failure(self):
-        """Original task exception propagates even when broadcast also fails."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            with_error_handling,
-        )
-
-        inner = AsyncMock(side_effect=ValueError("original error"))
-        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
-
-        with (
-            patch(
-                "reflector.hatchet.dag_progress.broadcast_dag_status",
-                new_callable=AsyncMock,
-                side_effect=RuntimeError("broadcast exploded"),
-            ),
-            patch(
-                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
-                new_callable=AsyncMock,
-            ),
-        ):
-            with pytest.raises(ValueError, match="original error"):
-                await wrapped(self._make_input(), self._make_ctx())
-
-    @pytest.mark.asyncio
-    async def test_calls_set_workflow_error_status_on_failure(self):
-        """On task failure with set_error_status=True (default), calls set_workflow_error_status."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            with_error_handling,
-        )
-
-        inner = AsyncMock(side_effect=RuntimeError("boom"))
-        wrapped = with_error_handling(TaskName.GET_RECORDING)(inner)
-
-        with (
-            patch(
-                "reflector.hatchet.dag_progress.broadcast_dag_status",
-                new_callable=AsyncMock,
-            ),
-            patch(
-                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
-                new_callable=AsyncMock,
-            ) as mock_set_error,
-        ):
-            with pytest.raises(RuntimeError, match="boom"):
-                await wrapped(self._make_input(), self._make_ctx())
-
-        mock_set_error.assert_called_once_with("t-123")
-
-    @pytest.mark.asyncio
-    async def test_no_set_workflow_error_status_when_disabled(self):
-        """With set_error_status=False, set_workflow_error_status is NOT called on failure."""
-        from reflector.hatchet.workflows.daily_multitrack_pipeline import (
-            with_error_handling,
-        )
-
-        inner = AsyncMock(side_effect=RuntimeError("boom"))
-        wrapped = with_error_handling(TaskName.GET_RECORDING, set_error_status=False)(
-            inner
-        )
-
-        with (
-            patch(
-                "reflector.hatchet.dag_progress.broadcast_dag_status",
-                new_callable=AsyncMock,
-            ),
-            patch(
-                "reflector.hatchet.workflows.daily_multitrack_pipeline.set_workflow_error_status",
-                new_callable=AsyncMock,
-            ) as mock_set_error,
-        ):
-            with pytest.raises(RuntimeError, match="boom"):
-                await wrapped(self._make_input(), self._make_ctx())
-
-        mock_set_error.assert_not_called()

server/tests/test_dag_progress_rest.py

@@ -1,421 +0,0 @@
-"""Tests for DAG status REST enrichment on search and transcript GET endpoints."""
-
-from datetime import datetime, timezone
-from types import SimpleNamespace
-from unittest.mock import AsyncMock, patch
-
-import pytest
-
-import reflector.db.search as search_module
-from reflector.db.search import SearchResult, _fetch_dag_statuses
-from reflector.db.transcripts import TranscriptEvent
-
-
-class TestFetchDagStatuses:
-    """Test the _fetch_dag_statuses helper."""
-
-    @pytest.mark.asyncio
-    async def test_returns_empty_for_empty_ids(self):
-        result = await _fetch_dag_statuses([])
-        assert result == {}
-
-    @pytest.mark.asyncio
-    async def test_extracts_last_dag_status(self):
-        events = [
-            {"event": "STATUS", "data": {"value": "processing"}},
-            {
-                "event": "DAG_STATUS",
-                "data": {
-                    "workflow_run_id": "r1",
-                    "tasks": [{"name": "get_recording", "status": "completed"}],
-                },
-            },
-            {
-                "event": "DAG_STATUS",
-                "data": {
-                    "workflow_run_id": "r1",
-                    "tasks": [
-                        {"name": "get_recording", "status": "completed"},
-                        {"name": "process_tracks", "status": "running"},
-                    ],
-                },
-            },
-        ]
-        mock_row = {"id": "t1", "events": events}
-
-        with patch("reflector.db.search.get_database") as mock_db:
-            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
-            result = await _fetch_dag_statuses(["t1"])
-
-        assert "t1" in result
-        assert len(result["t1"]) == 2  # Last DAG_STATUS had 2 tasks
-
-    @pytest.mark.asyncio
-    async def test_skips_transcripts_without_events(self):
-        mock_row = {"id": "t1", "events": None}
-
-        with patch("reflector.db.search.get_database") as mock_db:
-            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
-            result = await _fetch_dag_statuses(["t1"])
-
-        assert result == {}
-
-    @pytest.mark.asyncio
-    async def test_skips_transcripts_without_dag_status(self):
-        events = [
-            {"event": "STATUS", "data": {"value": "processing"}},
-            {"event": "DURATION", "data": {"duration": 1000}},
-        ]
-        mock_row = {"id": "t1", "events": events}
-
-        with patch("reflector.db.search.get_database") as mock_db:
-            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
-            result = await _fetch_dag_statuses(["t1"])
-
-        assert result == {}
-
-    @pytest.mark.asyncio
-    async def test_handles_json_string_events(self):
-        """Events stored as JSON string rather than already-parsed list."""
-        import json
-
-        events = [
-            {
-                "event": "DAG_STATUS",
-                "data": {
-                    "workflow_run_id": "r1",
-                    "tasks": [{"name": "transcribe", "status": "running"}],
-                },
-            },
-        ]
-        mock_row = {"id": "t1", "events": json.dumps(events)}
-
-        with patch("reflector.db.search.get_database") as mock_db:
-            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
-            result = await _fetch_dag_statuses(["t1"])
-
-        assert "t1" in result
-        assert len(result["t1"]) == 1
-        assert result["t1"][0]["name"] == "transcribe"
-
-    @pytest.mark.asyncio
-    async def test_multiple_transcripts(self):
-        """Handles multiple transcripts in one call."""
-        events_t1 = [
-            {
-                "event": "DAG_STATUS",
-                "data": {
-                    "workflow_run_id": "r1",
-                    "tasks": [{"name": "a", "status": "completed"}],
-                },
-            },
-        ]
-        events_t2 = [
-            {
-                "event": "DAG_STATUS",
-                "data": {
-                    "workflow_run_id": "r2",
-                    "tasks": [{"name": "b", "status": "running"}],
-                },
-            },
-        ]
-        mock_rows = [
-            {"id": "t1", "events": events_t1},
-            {"id": "t2", "events": events_t2},
-        ]
-
-        with patch("reflector.db.search.get_database") as mock_db:
-            mock_db.return_value.fetch_all = AsyncMock(return_value=mock_rows)
-            result = await _fetch_dag_statuses(["t1", "t2"])
-
-        assert "t1" in result
-        assert "t2" in result
-        assert result["t1"][0]["name"] == "a"
-        assert result["t2"][0]["name"] == "b"
-
-    @pytest.mark.asyncio
-    async def test_dag_status_without_tasks_key_skipped(self):
-        """DAG_STATUS event with no tasks key in data should be skipped."""
-        events = [
-            {"event": "DAG_STATUS", "data": {"workflow_run_id": "r1"}},
-        ]
-        mock_row = {"id": "t1", "events": events}
-
-        with patch("reflector.db.search.get_database") as mock_db:
-            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
-            result = await _fetch_dag_statuses(["t1"])
-
-        assert result == {}
-
-
-def _extract_dag_status_from_transcript(transcript):
-    """Replicate the dag_status extraction logic from transcript_get view.
-
-    This mirrors the code in reflector/views/transcripts.py lines 495-500:
-        dag_status = None
-        if transcript.status == "processing" and transcript.events:
-            for ev in reversed(transcript.events):
-                if ev.event == "DAG_STATUS":
-                    dag_status = ev.data.get("tasks") if isinstance(ev.data, dict) else None
-                    break
-    """
-    dag_status = None
-    if transcript.status == "processing" and transcript.events:
-        for ev in reversed(transcript.events):
-            if ev.event == "DAG_STATUS":
-                dag_status = ev.data.get("tasks") if isinstance(ev.data, dict) else None
-                break
-    return dag_status
-
-
-class TestTranscriptGetDagStatusExtraction:
-    """Test dag_status extraction logic from transcript_get endpoint.
-
-    The actual endpoint is complex to set up, so we test the extraction
-    logic directly using the same code pattern from the view.
-    """
-
-    def test_processing_transcript_with_dag_status_events(self):
-        """Processing transcript with DAG_STATUS events returns tasks from last event."""
-        transcript = SimpleNamespace(
-            status="processing",
-            events=[
-                TranscriptEvent(event="STATUS", data={"value": "processing"}),
-                TranscriptEvent(
-                    event="DAG_STATUS",
-                    data={
-                        "workflow_run_id": "r1",
-                        "tasks": [{"name": "get_recording", "status": "completed"}],
-                    },
-                ),
-                TranscriptEvent(
-                    event="DAG_STATUS",
-                    data={
-                        "workflow_run_id": "r1",
-                        "tasks": [
-                            {"name": "get_recording", "status": "completed"},
-                            {"name": "transcribe", "status": "running"},
-                        ],
-                    },
-                ),
-            ],
-        )
-
-        result = _extract_dag_status_from_transcript(transcript)
-
-        assert result is not None
-        assert len(result) == 2
-        assert result[0]["name"] == "get_recording"
-        assert result[1]["name"] == "transcribe"
-        assert result[1]["status"] == "running"
-
-    def test_processing_transcript_without_dag_status_events(self):
-        """Processing transcript with only non-DAG_STATUS events returns None."""
-        transcript = SimpleNamespace(
-            status="processing",
-            events=[
-                TranscriptEvent(event="STATUS", data={"value": "processing"}),
-                TranscriptEvent(event="DURATION", data={"duration": 1000}),
-            ],
-        )
-
-        result = _extract_dag_status_from_transcript(transcript)
-        assert result is None
-
-    def test_ended_transcript_with_dag_status_events(self):
-        """Ended transcript with DAG_STATUS events returns None (status check)."""
-        transcript = SimpleNamespace(
-            status="ended",
-            events=[
-                TranscriptEvent(
-                    event="DAG_STATUS",
-                    data={
-                        "workflow_run_id": "r1",
-                        "tasks": [{"name": "transcribe", "status": "completed"}],
-                    },
-                ),
-            ],
-        )
-
-        result = _extract_dag_status_from_transcript(transcript)
-        assert result is None
-
-    def test_processing_transcript_with_empty_events(self):
-        """Processing transcript with empty events list returns None."""
-        transcript = SimpleNamespace(
-            status="processing",
-            events=[],
-        )
-
-        result = _extract_dag_status_from_transcript(transcript)
-        assert result is None
-
-    def test_processing_transcript_with_none_events(self):
-        """Processing transcript with None events returns None."""
-        transcript = SimpleNamespace(
-            status="processing",
-            events=None,
-        )
-
-        result = _extract_dag_status_from_transcript(transcript)
-        assert result is None
-
-    def test_extracts_last_dag_status_not_first(self):
-        """Should pick the last DAG_STATUS event (most recent), not the first."""
-        transcript = SimpleNamespace(
-            status="processing",
-            events=[
-                TranscriptEvent(
-                    event="DAG_STATUS",
-                    data={
-                        "workflow_run_id": "r1",
-                        "tasks": [{"name": "a", "status": "running"}],
-                    },
-                ),
-                TranscriptEvent(event="STATUS", data={"value": "processing"}),
-                TranscriptEvent(
-                    event="DAG_STATUS",
-                    data={
-                        "workflow_run_id": "r1",
-                        "tasks": [
-                            {"name": "a", "status": "completed"},
-                            {"name": "b", "status": "running"},
-                        ],
-                    },
-                ),
-            ],
-        )
-
-        result = _extract_dag_status_from_transcript(transcript)
-        assert len(result) == 2
-        assert result[0]["status"] == "completed"
-        assert result[1]["name"] == "b"
-
-
-class TestSearchEnrichmentIntegration:
-    """Test DAG status enrichment in search results.
-
-    The search function enriches processing transcripts with dag_status
-    by calling _fetch_dag_statuses for processing IDs and assigning results.
-    We test this enrichment logic by mocking _fetch_dag_statuses.
-    """
-
-    def _make_search_result(self, id: str, status: str) -> SearchResult:
-        """Create a minimal SearchResult for testing."""
-        return SearchResult(
-            id=id,
-            title=f"Transcript {id}",
-            user_id="u1",
-            room_id=None,
-            room_name=None,
-            source_kind="live",
-            created_at=datetime(2024, 1, 1, tzinfo=timezone.utc),
-            status=status,
-            rank=1.0,
-            duration=60.0,
-            search_snippets=[],
-            total_match_count=0,
-            dag_status=None,
-        )
-
-    @pytest.mark.asyncio
-    async def test_processing_result_gets_dag_status(self):
-        """SearchResult with status='processing' and matching DAG_STATUS events
-        gets dag_status populated."""
-        results = [self._make_search_result("t1", "processing")]
-        dag_tasks = [
-            {"name": "get_recording", "status": "completed"},
-            {"name": "transcribe", "status": "running"},
-        ]
-
-        with patch.object(
-            search_module,
-            "_fetch_dag_statuses",
-            new_callable=AsyncMock,
-            return_value={"t1": dag_tasks},
-        ) as mock_fetch:
-            # Replicate the enrichment logic from SearchController.search_transcripts
-            processing_ids = [r.id for r in results if r.status == "processing"]
-            if processing_ids:
-                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
-                for r in results:
-                    if r.id in dag_statuses:
-                        r.dag_status = dag_statuses[r.id]
-
-            mock_fetch.assert_called_once_with(["t1"])
-
-        assert results[0].dag_status == dag_tasks
-
-    @pytest.mark.asyncio
-    async def test_ended_result_does_not_trigger_fetch(self):
-        """SearchResult with status='ended' does NOT trigger _fetch_dag_statuses."""
-        results = [self._make_search_result("t1", "ended")]
-
-        with patch.object(
-            search_module,
-            "_fetch_dag_statuses",
-            new_callable=AsyncMock,
-            return_value={},
-        ) as mock_fetch:
-            processing_ids = [r.id for r in results if r.status == "processing"]
-            if processing_ids:
-                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
-                for r in results:
-                    if r.id in dag_statuses:
-                        r.dag_status = dag_statuses[r.id]
-
-            mock_fetch.assert_not_called()
-
-        assert results[0].dag_status is None
-
-    @pytest.mark.asyncio
-    async def test_mixed_processing_and_ended_results(self):
-        """Only processing results get enriched; ended results stay None."""
-        results = [
-            self._make_search_result("t1", "processing"),
-            self._make_search_result("t2", "ended"),
-            self._make_search_result("t3", "processing"),
-        ]
-        dag_tasks_t1 = [{"name": "transcribe", "status": "running"}]
-        dag_tasks_t3 = [{"name": "diarize", "status": "completed"}]
-
-        with patch.object(
-            search_module,
-            "_fetch_dag_statuses",
-            new_callable=AsyncMock,
-            return_value={"t1": dag_tasks_t1, "t3": dag_tasks_t3},
-        ) as mock_fetch:
-            processing_ids = [r.id for r in results if r.status == "processing"]
-            if processing_ids:
-                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
-                for r in results:
-                    if r.id in dag_statuses:
-                        r.dag_status = dag_statuses[r.id]
-
-            mock_fetch.assert_called_once_with(["t1", "t3"])
-
-        assert results[0].dag_status == dag_tasks_t1
-        assert results[1].dag_status is None
-        assert results[2].dag_status == dag_tasks_t3
-
-    @pytest.mark.asyncio
-    async def test_processing_result_without_dag_events_stays_none(self):
-        """Processing result with no DAG_STATUS events in DB stays dag_status=None."""
-        results = [self._make_search_result("t1", "processing")]
-
-        with patch.object(
-            search_module,
-            "_fetch_dag_statuses",
-            new_callable=AsyncMock,
-            return_value={},
-        ) as mock_fetch:
-            processing_ids = [r.id for r in results if r.status == "processing"]
-            if processing_ids:
-                dag_statuses = await search_module._fetch_dag_statuses(processing_ids)
-                for r in results:
-                    if r.id in dag_statuses:
-                        r.dag_status = dag_statuses[r.id]
-
-            mock_fetch.assert_called_once_with(["t1"])
-
-        assert results[0].dag_status is None

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():

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 == []

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

server/tests/test_ws_dag_broadcast.py

@@ -1,331 +0,0 @@
-"""WebSocket broadcast delivery tests for STATUS and DAG_STATUS events.
-
-Tests the full chain identified in DEBUG.md:
-  broadcast_event() → ws_manager.send_json() → Redis/in-memory pub/sub
-    → _pubsub_data_reader() → socket.send_json() → WebSocket client
-
-Covers:
-1. STATUS event delivery to transcript room WS
-2. DAG_STATUS event delivery to transcript room WS
-3. Full broadcast_event() chain (requires broadcast.py patching)
-4. _pubsub_data_reader resilience when a client disconnects
-"""
-
-import asyncio
-import threading
-import time
-
-import pytest
-from httpx import AsyncClient
-from httpx_ws import aconnect_ws
-from uvicorn import Config, Server
-
-
-@pytest.fixture
-def appserver_ws_broadcast(setup_database, monkeypatch):
-    """Start real uvicorn server for WebSocket broadcast tests.
-
-    Also patches broadcast.py's get_ws_manager (missing from conftest autouse fixture).
-    """
-    # Patch broadcast.py's get_ws_manager — conftest.py misses this module.
-    # Without this, broadcast_event() creates a real Redis ws_manager.
-    import reflector.ws_manager as ws_mod
-    from reflector.app import app
-    from reflector.db import get_database
-
-    monkeypatch.setattr(
-        "reflector.hatchet.broadcast.get_ws_manager", ws_mod.get_ws_manager
-    )
-
-    host = "127.0.0.1"
-    port = 1259
-    server_started = threading.Event()
-    server_exception = None
-    server_instance = None
-
-    def run_server():
-        nonlocal server_exception, server_instance
-        try:
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
-            config = Config(app=app, host=host, port=port, loop=loop)
-            server_instance = Server(config)
-
-            async def start_server():
-                database = get_database()
-                await database.connect()
-                try:
-                    await server_instance.serve()
-                finally:
-                    await database.disconnect()
-
-            server_started.set()
-            loop.run_until_complete(start_server())
-        except Exception as e:
-            server_exception = e
-            server_started.set()
-        finally:
-            loop.close()
-
-    server_thread = threading.Thread(target=run_server, daemon=True)
-    server_thread.start()
-
-    server_started.wait(timeout=30)
-    if server_exception:
-        raise server_exception
-
-    time.sleep(0.5)
-
-    yield host, port
-
-    if server_instance:
-        server_instance.should_exit = True
-        server_thread.join(timeout=2.0)
-
-    from reflector.ws_manager import reset_ws_manager
-
-    reset_ws_manager()
-
-
-async def _create_transcript(host: str, port: int, name: str) -> str:
-    """Create a transcript via ASGI transport and return its ID."""
-    from reflector.app import app
-
-    async with AsyncClient(app=app, base_url=f"http://{host}:{port}/v1") as ac:
-        resp = await ac.post("/transcripts", json={"name": name})
-        assert resp.status_code == 200, f"Failed to create transcript: {resp.text}"
-        return resp.json()["id"]
-
-
-async def _drain_historical_events(ws, timeout: float = 0.5) -> list[dict]:
-    """Read all historical events sent on WS connect (non-blocking drain)."""
-    events = []
-    deadline = asyncio.get_event_loop().time() + timeout
-    while asyncio.get_event_loop().time() < deadline:
-        try:
-            msg = await asyncio.wait_for(ws.receive_json(), timeout=0.1)
-            events.append(msg)
-        except (asyncio.TimeoutError, Exception):
-            break
-    return events
-
-
-# ---------------------------------------------------------------------------
-# Test 1: STATUS event delivery via ws_manager.send_json
-# ---------------------------------------------------------------------------
-@pytest.mark.asyncio
-async def test_transcript_ws_receives_status_via_send_json(appserver_ws_broadcast):
-    """STATUS event published via ws_manager.send_json() arrives at transcript room WS."""
-    host, port = appserver_ws_broadcast
-    transcript_id = await _create_transcript(host, port, "Status send_json test")
-
-    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
-    async with aconnect_ws(ws_url) as ws:
-        await _drain_historical_events(ws)
-
-        import reflector.ws_manager as ws_mod
-
-        ws_manager = ws_mod.get_ws_manager()
-        await ws_manager.send_json(
-            room_id=f"ts:{transcript_id}",
-            message={"event": "STATUS", "data": {"value": "processing"}},
-        )
-
-        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
-        assert msg["event"] == "STATUS"
-        assert msg["data"]["value"] == "processing"
-
-
-# ---------------------------------------------------------------------------
-# Test 2: DAG_STATUS event delivery via ws_manager.send_json
-# ---------------------------------------------------------------------------
-@pytest.mark.asyncio
-async def test_transcript_ws_receives_dag_status_via_send_json(appserver_ws_broadcast):
-    """DAG_STATUS event published via ws_manager.send_json() arrives at transcript room WS."""
-    host, port = appserver_ws_broadcast
-    transcript_id = await _create_transcript(host, port, "DAG_STATUS send_json test")
-
-    dag_payload = {
-        "event": "DAG_STATUS",
-        "data": {
-            "workflow_run_id": "test-run-123",
-            "tasks": [
-                {
-                    "name": "get_recording",
-                    "status": "completed",
-                    "started_at": "2025-01-01T00:00:00Z",
-                    "finished_at": "2025-01-01T00:00:05Z",
-                    "duration_seconds": 5.0,
-                    "parents": [],
-                    "error": None,
-                    "children_total": None,
-                    "children_completed": None,
-                    "progress_pct": None,
-                },
-                {
-                    "name": "process_tracks",
-                    "status": "running",
-                    "started_at": "2025-01-01T00:00:05Z",
-                    "finished_at": None,
-                    "duration_seconds": None,
-                    "parents": ["get_recording"],
-                    "error": None,
-                    "children_total": 3,
-                    "children_completed": 1,
-                    "progress_pct": 33.3,
-                },
-            ],
-        },
-    }
-
-    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
-    async with aconnect_ws(ws_url) as ws:
-        await _drain_historical_events(ws)
-
-        import reflector.ws_manager as ws_mod
-
-        ws_manager = ws_mod.get_ws_manager()
-        await ws_manager.send_json(
-            room_id=f"ts:{transcript_id}",
-            message=dag_payload,
-        )
-
-        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
-        assert msg["event"] == "DAG_STATUS"
-        assert msg["data"]["workflow_run_id"] == "test-run-123"
-        assert len(msg["data"]["tasks"]) == 2
-        assert msg["data"]["tasks"][0]["name"] == "get_recording"
-        assert msg["data"]["tasks"][0]["status"] == "completed"
-        assert msg["data"]["tasks"][1]["name"] == "process_tracks"
-        assert msg["data"]["tasks"][1]["children_completed"] == 1
-
-
-# ---------------------------------------------------------------------------
-# Test 3: Full broadcast_event() chain for STATUS
-# ---------------------------------------------------------------------------
-@pytest.mark.asyncio
-async def test_broadcast_event_delivers_status_to_transcript_ws(appserver_ws_broadcast):
-    """broadcast_event() end-to-end: STATUS event reaches transcript room WS."""
-    host, port = appserver_ws_broadcast
-    transcript_id = await _create_transcript(host, port, "broadcast_event STATUS test")
-
-    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
-    async with aconnect_ws(ws_url) as ws:
-        await _drain_historical_events(ws)
-
-        from reflector.db.transcripts import TranscriptEvent
-        from reflector.hatchet.broadcast import broadcast_event
-        from reflector.logger import logger
-
-        log = logger.bind(transcript_id=transcript_id)
-        event = TranscriptEvent(event="STATUS", data={"value": "processing"})
-        await broadcast_event(transcript_id, event, logger=log)
-
-        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
-        assert msg["event"] == "STATUS"
-        assert msg["data"]["value"] == "processing"
-
-
-# ---------------------------------------------------------------------------
-# Test 4: Full broadcast_event() chain for DAG_STATUS
-# ---------------------------------------------------------------------------
-@pytest.mark.asyncio
-async def test_broadcast_event_delivers_dag_status_to_transcript_ws(
-    appserver_ws_broadcast,
-):
-    """broadcast_event() end-to-end: DAG_STATUS event reaches transcript room WS."""
-    host, port = appserver_ws_broadcast
-    transcript_id = await _create_transcript(host, port, "broadcast_event DAG test")
-
-    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
-    async with aconnect_ws(ws_url) as ws:
-        await _drain_historical_events(ws)
-
-        from reflector.db.transcripts import TranscriptEvent
-        from reflector.hatchet.broadcast import broadcast_event
-        from reflector.logger import logger
-
-        log = logger.bind(transcript_id=transcript_id)
-        event = TranscriptEvent(
-            event="DAG_STATUS",
-            data={
-                "workflow_run_id": "test-run-456",
-                "tasks": [
-                    {
-                        "name": "get_recording",
-                        "status": "running",
-                        "started_at": None,
-                        "finished_at": None,
-                        "duration_seconds": None,
-                        "parents": [],
-                        "error": None,
-                        "children_total": None,
-                        "children_completed": None,
-                        "progress_pct": None,
-                    }
-                ],
-            },
-        )
-        await broadcast_event(transcript_id, event, logger=log)
-
-        msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
-        assert msg["event"] == "DAG_STATUS"
-        assert msg["data"]["tasks"][0]["name"] == "get_recording"
-
-
-# ---------------------------------------------------------------------------
-# Test 5: Multiple rapid events arrive in order
-# ---------------------------------------------------------------------------
-@pytest.mark.asyncio
-async def test_multiple_events_arrive_in_order(appserver_ws_broadcast):
-    """Multiple STATUS then DAG_STATUS events arrive in correct order."""
-    host, port = appserver_ws_broadcast
-    transcript_id = await _create_transcript(host, port, "ordering test")
-
-    ws_url = f"http://{host}:{port}/v1/transcripts/{transcript_id}/events"
-    async with aconnect_ws(ws_url) as ws:
-        await _drain_historical_events(ws)
-
-        import reflector.ws_manager as ws_mod
-
-        ws_manager = ws_mod.get_ws_manager()
-
-        await ws_manager.send_json(
-            room_id=f"ts:{transcript_id}",
-            message={"event": "STATUS", "data": {"value": "processing"}},
-        )
-        await ws_manager.send_json(
-            room_id=f"ts:{transcript_id}",
-            message={
-                "event": "DAG_STATUS",
-                "data": {"workflow_run_id": "r1", "tasks": []},
-            },
-        )
-        await ws_manager.send_json(
-            room_id=f"ts:{transcript_id}",
-            message={
-                "event": "DAG_STATUS",
-                "data": {
-                    "workflow_run_id": "r1",
-                    "tasks": [{"name": "a", "status": "running"}],
-                },
-            },
-        )
-        await ws_manager.send_json(
-            room_id=f"ts:{transcript_id}",
-            message={"event": "STATUS", "data": {"value": "ended"}},
-        )
-
-        msgs = []
-        for _ in range(4):
-            msg = await asyncio.wait_for(ws.receive_json(), timeout=5.0)
-            msgs.append(msg)
-
-        assert msgs[0]["event"] == "STATUS"
-        assert msgs[0]["data"]["value"] == "processing"
-        assert msgs[1]["event"] == "DAG_STATUS"
-        assert msgs[1]["data"]["tasks"] == []
-        assert msgs[2]["event"] == "DAG_STATUS"
-        assert len(msgs[2]["data"]["tasks"]) == 1
-        assert msgs[3]["event"] == "STATUS"
-        assert msgs[3]["data"]["value"] == "ended"

server/uv.lock

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-    { 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 = "torchaudio", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
-    { name = "torchaudio", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-    { name = "tqdm" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/ab/10/87e666544a4e0cec7cbdc09f26948994831ae0f8bbc58de3bf53b68285ff/speechbrain-1.0.3.tar.gz", hash = "sha256:fcab3c6e90012cecb1eed40ea235733b550137e73da6bfa2340ba191ec714052", size = 747735 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/58/13/e61f1085aebee17d5fc2df19fcc5177c10379be52578afbecdd615a831c9/speechbrain-1.0.3-py3-none-any.whl", hash = "sha256:9859d4c1b1fb3af3b85523c0c89f52e45a04f305622ed55f31aa32dd2fba19e9", size = 864091 },
-]
-
 [[package]]
 name = "sqlalchemy"
 version = "1.4.54"
@@ -3883,15 +3291,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353 },
 ]
 
-[[package]]
-name = "tabulate"
-version = "0.9.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252 },
-]
-
 [[package]]
 name = "tenacity"
 version = "9.1.2"
@@ -3901,29 +3300,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e5/30/643397144bfbfec6f6ef821f36f33e57d35946c44a2352d3c9f0ae847619/tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138", size = 28248 },
 ]
 
-[[package]]
-name = "tensorboardx"
-version = "2.6.4"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "packaging" },
-    { name = "protobuf" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/2b/c5/d4cc6e293fb837aaf9f76dd7745476aeba8ef7ef5146c3b3f9ee375fe7a5/tensorboardx-2.6.4.tar.gz", hash = "sha256:b163ccb7798b31100b9f5fa4d6bc22dad362d7065c2f24b51e50731adde86828", size = 4769801 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e0/1d/b5d63f1a6b824282b57f7b581810d20b7a28ca951f2d5b59f1eb0782c12b/tensorboardx-2.6.4-py3-none-any.whl", hash = "sha256:5970cf3a1f0a6a6e8b180ccf46f3fe832b8a25a70b86e5a237048a7c0beb18e2", size = 87201 },
-]
-
-[[package]]
-name = "threadpoolctl"
-version = "3.6.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638 },
-]
-
 [[package]]
 name = "tiktoken"
 version = "0.9.0"
@@ -4064,40 +3440,6 @@ wheels = [
     { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-win_arm64.whl", hash = "sha256:99fc421a5d234580e45957a7b02effbf3e1c884a5dd077afc85352c77bf41434" },
 ]
 
-[[package]]
-name = "torch-audiomentations"
-version = "0.12.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "julius" },
-    { 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 = "torch-pitch-shift" },
-    { name = "torchaudio", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
-    { name = "torchaudio", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/31/8d/2f8fd7e34c75f5ee8de4310c3bd3f22270acd44d1f809e2fe7c12fbf35f8/torch_audiomentations-0.12.0.tar.gz", hash = "sha256:b02d4c5eb86376986a53eb405cca5e34f370ea9284411237508e720c529f7888", size = 52094 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/21/9d/1ee04f49c15d2d632f6f7102061d7c07652858e6d91b58a091531034e84f/torch_audiomentations-0.12.0-py3-none-any.whl", hash = "sha256:1b80b91d2016ccf83979622cac8f702072a79b7dcc4c2bee40f00b26433a786b", size = 48506 },
-]
-
-[[package]]
-name = "torch-pitch-shift"
-version = "1.2.5"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "packaging" },
-    { name = "primepy" },
-    { 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 = "torchaudio", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
-    { name = "torchaudio", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/79/a6/722a832bca75d5079f6731e005b3d0c2eec7c6c6863d030620952d143d57/torch_pitch_shift-1.2.5.tar.gz", hash = "sha256:6e1c7531f08d0f407a4c55e5ff8385a41355c5c5d27ab7fa08632e51defbd0ed", size = 4725 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/27/4c/96ac2a09efb56cc3c41fb3ce9b6f4d8c0604499f7481d4a13a7b03e21382/torch_pitch_shift-1.2.5-py3-none-any.whl", hash = "sha256:6f8500cbc13f1c98b11cde1805ce5084f82cdd195c285f34287541f168a7c6a7", size = 5005 },
-]
-
 [[package]]
 name = "torchaudio"
 version = "2.8.0"
@@ -4145,22 +3487,6 @@ wheels = [
     { url = "https://download.pytorch.org/whl/cpu/torchaudio-2.8.0%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:9b302192b570657c1cc787a4d487ae4bbb7f2aab1c01b1fcc46757e7f86f391e" },
 ]
 
-[[package]]
-name = "torchmetrics"
-version = "1.8.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "lightning-utilities" },
-    { name = "numpy" },
-    { name = "packaging" },
-    { 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'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/85/2e/48a887a59ecc4a10ce9e8b35b3e3c5cef29d902c4eac143378526e7485cb/torchmetrics-1.8.2.tar.gz", hash = "sha256:cf64a901036bf107f17a524009eea7781c9c5315d130713aeca5747a686fe7a5", size = 580679 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/02/21/aa0f434434c48490f91b65962b1ce863fdcce63febc166ca9fe9d706c2b6/torchmetrics-1.8.2-py3-none-any.whl", hash = "sha256:08382fd96b923e39e904c4d570f3d49e2cc71ccabd2a94e0f895d1f0dac86242", size = 983161 },
-]
-
 [[package]]
 name = "tqdm"
 version = "4.67.1"

www/app/(app)/browse/_components/DagProgressDots.tsx

@@ -1,61 +0,0 @@
-import React from "react";
-import { Box, Flex } from "@chakra-ui/react";
-import type { DagTask } from "../../../lib/UserEventsProvider";
-
-const pulseKeyframes = `
-  @keyframes dagDotPulse {
-    0%, 100% { opacity: 1; }
-    50% { opacity: 0.3; }
-  }
-`;
-
-function humanizeTaskName(name: string): string {
-  return name
-    .split("_")
-    .map((word) => word.charAt(0).toUpperCase() + word.slice(1))
-    .join(" ");
-}
-
-function dotProps(status: DagTask["status"]): Record<string, unknown> {
-  switch (status) {
-    case "completed":
-      return { bg: "green.500" };
-    case "running":
-      return {
-        bg: "blue.500",
-        style: { animation: "dagDotPulse 1.5s ease-in-out infinite" },
-      };
-    case "failed":
-      return { bg: "red.500" };
-    case "cancelled":
-      return { bg: "gray.400" };
-    case "queued":
-    default:
-      return {
-        bg: "transparent",
-        border: "1px solid",
-        borderColor: "gray.400",
-      };
-  }
-}
-
-export default function DagProgressDots({ tasks }: { tasks: DagTask[] }) {
-  return (
-    <>
-      <style>{pulseKeyframes}</style>
-      <Flex gap="2px" alignItems="center" flexWrap="wrap">
-        {tasks.map((task) => (
-          <Box
-            key={task.name}
-            w="4px"
-            h="4px"
-            borderRadius="full"
-            flexShrink={0}
-            title={humanizeTaskName(task.name)}
-            {...dotProps(task.status)}
-          />
-        ))}
-      </Flex>
-    </>
-  );
-}

www/app/(app)/browse/_components/TranscriptCards.tsx

@@ -19,7 +19,6 @@ import {
   generateTextFragment,
 } from "../../../lib/textHighlight";
 import type { components } from "../../../reflector-api";
-import type { DagTask } from "../../../lib/UserEventsProvider";
 
 type SearchResult = components["schemas"]["SearchResult"];
 type SourceKind = components["schemas"]["SourceKind"];
@@ -30,7 +29,6 @@ interface TranscriptCardsProps {
   isLoading?: boolean;
   onDelete: (transcriptId: string) => void;
   onReprocess: (transcriptId: string) => void;
-  dagStatusMap?: Map<string, DagTask[]>;
 }
 
 function highlightText(text: string, query: string): React.ReactNode {
@@ -104,13 +102,11 @@ function TranscriptCard({
   query,
   onDelete,
   onReprocess,
-  dagStatusMap,
 }: {
   result: SearchResult;
   query: string;
   onDelete: (transcriptId: string) => void;
   onReprocess: (transcriptId: string) => void;
-  dagStatusMap?: Map<string, DagTask[]>;
 }) {
   const [isExpanded, setIsExpanded] = useState(false);
 
@@ -141,16 +137,7 @@ function TranscriptCard({
     <Box borderWidth={1} p={4} borderRadius="md" fontSize="sm">
       <Flex justify="space-between" alignItems="flex-start" gap="2">
         <Box>
-          <TranscriptStatusIcon
-            status={result.status}
-            dagStatus={
-              dagStatusMap?.get(result.id) ??
-              ((result as Record<string, unknown>).dag_status as
-                | DagTask[]
-                | null) ??
-              null
-            }
-          />
+          <TranscriptStatusIcon status={result.status} />
         </Box>
         <Box flex="1">
           {/* Title with highlighting and text fragment for deep linking */}
@@ -297,7 +284,6 @@ export default function TranscriptCards({
   isLoading,
   onDelete,
   onReprocess,
-  dagStatusMap,
 }: TranscriptCardsProps) {
   return (
     <Box position="relative">
@@ -329,7 +315,6 @@ export default function TranscriptCards({
               query={query}
               onDelete={onDelete}
               onReprocess={onReprocess}
-              dagStatusMap={dagStatusMap}
             />
           ))}
         </Stack>

www/app/(app)/browse/_components/TranscriptStatusIcon.tsx

@@ -8,17 +8,13 @@ import {
   FaGear,
 } from "react-icons/fa6";
 import { TranscriptStatus } from "../../../lib/transcript";
-import type { DagTask } from "../../../lib/UserEventsProvider";
-import DagProgressDots from "./DagProgressDots";
 
 interface TranscriptStatusIconProps {
   status: TranscriptStatus;
-  dagStatus?: DagTask[] | null;
 }
 
 export default function TranscriptStatusIcon({
   status,
-  dagStatus,
 }: TranscriptStatusIconProps) {
   switch (status) {
     case "ended":
@@ -40,9 +36,6 @@ export default function TranscriptStatusIcon({
         </Box>
       );
     case "processing":
-      if (dagStatus && dagStatus.length > 0) {
-        return <DagProgressDots tasks={dagStatus} />;
-      }
       return (
         <Box as="span" title="Processing in progress">
           <Icon color="gray.500" as={FaGear} />

www/app/(app)/browse/page.tsx

@@ -43,7 +43,6 @@ import DeleteTranscriptDialog from "./_components/DeleteTranscriptDialog";
 import { formatLocalDate } from "../../lib/time";
 import { RECORD_A_MEETING_URL } from "../../api/urls";
 import { useUserName } from "../../lib/useUserName";
-import { useDagStatusMap } from "../../lib/UserEventsProvider";
 
 const SEARCH_FORM_QUERY_INPUT_NAME = "query" as const;
 
@@ -274,7 +273,6 @@ export default function TranscriptBrowser() {
   }, [JSON.stringify(searchFilters)]);
 
   const userName = useUserName();
-  const dagStatusMap = useDagStatusMap();
   const [deletionLoading, setDeletionLoading] = useState(false);
   const cancelRef = React.useRef(null);
   const [transcriptToDeleteId, setTranscriptToDeleteId] =
@@ -410,7 +408,6 @@ export default function TranscriptBrowser() {
             isLoading={searchLoading}
             onDelete={setTranscriptToDeleteId}
             onReprocess={handleProcessTranscript}
-            dagStatusMap={dagStatusMap}
           />
 
           {!searchLoading && results.length === 0 && (

www/app/(app)/transcripts/[transcriptId]/processing/DagProgressTable.tsx

@@ -1,190 +0,0 @@
-"use client";
-
-import { useEffect, useState } from "react";
-import { Table, Box, Icon, Spinner, Text, Badge } from "@chakra-ui/react";
-import { FaCheck, FaXmark, FaClock, FaMinus } from "react-icons/fa6";
-import type { DagTask, DagTaskStatus } from "../../useWebSockets";
-
-function humanizeTaskName(name: string): string {
-  return name
-    .split("_")
-    .map((word) => word.charAt(0).toUpperCase() + word.slice(1))
-    .join(" ");
-}
-
-function formatDuration(seconds: number): string {
-  if (seconds < 60) {
-    return `${Math.round(seconds)}s`;
-  }
-  const minutes = Math.floor(seconds / 60);
-  const remainingSeconds = Math.round(seconds % 60);
-  return `${minutes}m ${remainingSeconds}s`;
-}
-
-function StatusIcon({ status }: { status: DagTaskStatus }) {
-  switch (status) {
-    case "completed":
-      return (
-        <Box as="span" title="Completed">
-          <Icon color="green.500" as={FaCheck} />
-        </Box>
-      );
-    case "running":
-      return <Spinner size="sm" color="blue.500" />;
-    case "failed":
-      return (
-        <Box as="span" title="Failed">
-          <Icon color="red.500" as={FaXmark} />
-        </Box>
-      );
-    case "queued":
-      return (
-        <Box as="span" title="Queued">
-          <Icon color="gray.400" as={FaClock} />
-        </Box>
-      );
-    case "cancelled":
-      return (
-        <Box as="span" title="Cancelled">
-          <Icon color="gray.400" as={FaMinus} />
-        </Box>
-      );
-    default:
-      return null;
-  }
-}
-
-function ElapsedTimer({ startedAt }: { startedAt: string }) {
-  const [elapsed, setElapsed] = useState<number>(() => {
-    return (Date.now() - new Date(startedAt).getTime()) / 1000;
-  });
-
-  useEffect(() => {
-    const interval = setInterval(() => {
-      setElapsed((Date.now() - new Date(startedAt).getTime()) / 1000);
-    }, 1000);
-    return () => clearInterval(interval);
-  }, [startedAt]);
-
-  return <Text fontSize="sm">{formatDuration(elapsed)}</Text>;
-}
-
-function DurationCell({ task }: { task: DagTask }) {
-  if (task.status === "completed" && task.duration_seconds !== null) {
-    return <Text fontSize="sm">{formatDuration(task.duration_seconds)}</Text>;
-  }
-  if (task.status === "running" && task.started_at) {
-    return <ElapsedTimer startedAt={task.started_at} />;
-  }
-  return (
-    <Text fontSize="sm" color="gray.400">
-      --
-    </Text>
-  );
-}
-
-function ProgressCell({ task }: { task: DagTask }) {
-  if (task.progress_pct === null && task.children_total === null) {
-    return null;
-  }
-
-  return (
-    <Box>
-      {task.progress_pct !== null && (
-        <Box
-          w="100%"
-          h="6px"
-          bg="gray.200"
-          borderRadius="full"
-          overflow="hidden"
-        >
-          <Box
-            h="100%"
-            w={`${Math.min(100, Math.max(0, task.progress_pct))}%`}
-            bg={task.status === "failed" ? "red.400" : "blue.400"}
-            borderRadius="full"
-            transition="width 0.3s ease"
-          />
-        </Box>
-      )}
-      {task.children_total !== null && (
-        <Badge
-          size="sm"
-          colorPalette="gray"
-          mt={task.progress_pct !== null ? 1 : 0}
-        >
-          {task.children_completed ?? 0}/{task.children_total}
-        </Badge>
-      )}
-    </Box>
-  );
-}
-
-function TaskRow({ task }: { task: DagTask }) {
-  const [expanded, setExpanded] = useState(false);
-  const hasFailed = task.status === "failed" && task.error;
-
-  return (
-    <>
-      <Table.Row
-        cursor={hasFailed ? "pointer" : "default"}
-        onClick={hasFailed ? () => setExpanded((prev) => !prev) : undefined}
-        _hover={hasFailed ? { bg: "gray.50" } : undefined}
-      >
-        <Table.Cell>
-          <Text fontSize="sm" fontWeight="medium">
-            {humanizeTaskName(task.name)}
-          </Text>
-        </Table.Cell>
-        <Table.Cell>
-          <StatusIcon status={task.status} />
-        </Table.Cell>
-        <Table.Cell>
-          <DurationCell task={task} />
-        </Table.Cell>
-        <Table.Cell>
-          <ProgressCell task={task} />
-        </Table.Cell>
-      </Table.Row>
-      {hasFailed && expanded && (
-        <Table.Row>
-          <Table.Cell colSpan={4}>
-            <Box bg="red.50" p={3} borderRadius="md">
-              <Text fontSize="xs" color="red.700" whiteSpace="pre-wrap">
-                {task.error}
-              </Text>
-            </Box>
-          </Table.Cell>
-        </Table.Row>
-      )}
-    </>
-  );
-}
-
-export default function DagProgressTable({ tasks }: { tasks: DagTask[] }) {
-  return (
-    <Box w="100%" overflowX="auto">
-      <Table.Root size="sm">
-        <Table.Header>
-          <Table.Row>
-            <Table.ColumnHeader fontWeight="600">Task</Table.ColumnHeader>
-            <Table.ColumnHeader fontWeight="600" width="80px">
-              Status
-            </Table.ColumnHeader>
-            <Table.ColumnHeader fontWeight="600" width="100px">
-              Duration
-            </Table.ColumnHeader>
-            <Table.ColumnHeader fontWeight="600" width="140px">
-              Progress
-            </Table.ColumnHeader>
-          </Table.Row>
-        </Table.Header>
-        <Table.Body>
-          {tasks.map((task) => (
-            <TaskRow key={task.name} task={task} />
-          ))}
-        </Table.Body>
-      </Table.Root>
-    </Box>
-  );
-}

www/app/(app)/transcripts/[transcriptId]/processing/page.tsx

@@ -12,9 +12,6 @@ import { useRouter } from "next/navigation";
 import { useTranscriptGet } from "../../../../lib/apiHooks";
 import { parseNonEmptyString } from "../../../../lib/utils";
 import { useWebSockets } from "../../useWebSockets";
-import type { DagTask } from "../../useWebSockets";
-import { useDagStatusMap } from "../../../../lib/UserEventsProvider";
-import DagProgressTable from "./DagProgressTable";
 
 type TranscriptProcessing = {
   params: Promise<{
@@ -28,21 +25,10 @@ export default function TranscriptProcessing(details: TranscriptProcessing) {
   const router = useRouter();
 
   const transcript = useTranscriptGet(transcriptId);
-  const { status: wsStatus, dagStatus: wsDagStatus } =
-    useWebSockets(transcriptId);
-  const userDagStatusMap = useDagStatusMap();
-  const userDagStatus = userDagStatusMap.get(transcriptId) ?? null;
-
-  const restDagStatus: DagTask[] | null =
-    ((transcript.data as Record<string, unknown>)?.dag_status as
-      | DagTask[]
-      | null) ?? null;
-
-  // Prefer transcript room WS (most granular), then user room WS, then REST
-  const dagStatus = wsDagStatus ?? userDagStatus ?? restDagStatus;
+  useWebSockets(transcriptId);
 
   useEffect(() => {
-    const status = wsStatus?.value ?? transcript.data?.status;
+    const status = transcript.data?.status;
     if (!status) return;
 
     if (status === "ended" || status === "error") {
@@ -57,7 +43,6 @@ export default function TranscriptProcessing(details: TranscriptProcessing) {
       router.replace(dest);
     }
   }, [
-    wsStatus?.value,
     transcript.data?.status,
     transcript.data?.source_kind,
     router,
@@ -91,29 +76,11 @@ export default function TranscriptProcessing(details: TranscriptProcessing) {
         w={{ base: "full", md: "container.xl" }}
       >
         <Center h={"full"} w="full">
-          <VStack
-            gap={10}
-            bg="gray.100"
-            p={10}
-            borderRadius="md"
-            maxW="600px"
-            w="full"
-          >
-            {dagStatus ? (
-              <>
-                <Heading size={"md"} textAlign="center">
-                  Processing recording
-                </Heading>
-                <DagProgressTable tasks={dagStatus} />
-              </>
-            ) : (
-              <>
-                <Spinner size="xl" color="blue.500" />
-                <Heading size={"md"} textAlign="center">
-                  Processing recording
-                </Heading>
-              </>
-            )}
+          <VStack gap={10} bg="gray.100" p={10} borderRadius="md" maxW="500px">
+            <Spinner size="xl" color="blue.500" />
+            <Heading size={"md"} textAlign="center">
+              Processing recording
+            </Heading>
             <Text color="gray.600" textAlign="center">
               You can safely return to the library while your recording is being
               processed.

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;

www/app/(app)/transcripts/useWebSockets.ts

@@ -14,9 +14,6 @@ import {
 } from "../../lib/apiHooks";
 import { NonEmptyString } from "../../lib/utils";
 
-import type { DagTask } from "../../lib/dagTypes";
-export type { DagTask, DagTaskStatus } from "../../lib/dagTypes";
-
 export type UseWebSockets = {
   transcriptTextLive: string;
   translateText: string;
@@ -27,7 +24,6 @@ export type UseWebSockets = {
   status: Status | null;
   waveform: AudioWaveform | null;
   duration: number | null;
-  dagStatus: DagTask[] | null;
 };
 
 export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
@@ -44,7 +40,6 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
     summary: "",
   });
   const [status, setStatus] = useState<Status | null>(null);
-  const [dagStatus, setDagStatus] = useState<DagTask[] | null>(null);
   const { setError } = useError();
 
   const queryClient = useQueryClient();
@@ -442,25 +437,6 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
             }
             break;
 
-          case "DAG_STATUS":
-            if (message.data?.tasks) {
-              setDagStatus(message.data.tasks);
-            }
-            break;
-
-          case "DAG_TASK_PROGRESS":
-            if (message.data) {
-              setDagStatus(
-                (prev) =>
-                  prev?.map((t) =>
-                    t.name === message.data.task_name
-                      ? { ...t, progress_pct: message.data.progress_pct }
-                      : t,
-                  ) ?? null,
-              );
-            }
-            break;
-
           default:
             setError(
               new Error(`Received unknown WebSocket event: ${message.event}`),
@@ -518,6 +494,5 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
     status,
     waveform,
     duration,
-    dagStatus,
   };
 };

www/app/lib/UserEventsProvider.tsx

@@ -1,25 +1,11 @@
 "use client";
 
-import React, { useEffect, useRef, useState } from "react";
+import React, { useEffect, useRef } from "react";
 import { useQueryClient } from "@tanstack/react-query";
 import { WEBSOCKET_URL } from "./apiClient";
 import { useAuth } from "./AuthProvider";
 import { z } from "zod";
-import {
-  invalidateTranscript,
-  invalidateTranscriptLists,
-  TRANSCRIPT_SEARCH_URL,
-} from "./apiHooks";
-import type { NonEmptyString } from "./utils";
-
-import type { DagTask } from "./dagTypes";
-export type { DagTask, DagTaskStatus } from "./dagTypes";
-
-const DagStatusContext = React.createContext<Map<string, DagTask[]>>(new Map());
-
-export function useDagStatusMap() {
-  return React.useContext(DagStatusContext);
-}
+import { invalidateTranscriptLists, TRANSCRIPT_SEARCH_URL } from "./apiHooks";
 
 const UserEvent = z.object({
   event: z.string(),
@@ -109,9 +95,6 @@ export function UserEventsProvider({
   const queryClient = useQueryClient();
   const tokenRef = useRef<string | null>(null);
   const detachRef = useRef<(() => void) | null>(null);
-  const [dagStatusMap, setDagStatusMap] = useState<Map<string, DagTask[]>>(
-    new Map(),
-  );
 
   useEffect(() => {
     // Only tear down when the user is truly unauthenticated
@@ -150,52 +133,20 @@ export function UserEventsProvider({
     if (!detachRef.current) {
       const onMessage = (event: MessageEvent) => {
         try {
-          const fullMsg = JSON.parse(event.data);
-          const msg = UserEvent.parse(fullMsg);
+          const msg = UserEvent.parse(JSON.parse(event.data));
           const eventName = msg.event;
+
           const invalidateList = () => invalidateTranscriptLists(queryClient);
 
           switch (eventName) {
             case "TRANSCRIPT_CREATED":
             case "TRANSCRIPT_DELETED":
+            case "TRANSCRIPT_STATUS":
             case "TRANSCRIPT_FINAL_TITLE":
             case "TRANSCRIPT_DURATION":
               invalidateList().then(() => {});
               break;
 
-            case "TRANSCRIPT_STATUS": {
-              invalidateList().then(() => {});
-              const transcriptId = fullMsg.data?.id as string | undefined;
-              if (transcriptId) {
-                invalidateTranscript(
-                  queryClient,
-                  transcriptId as NonEmptyString,
-                ).then(() => {});
-              }
-              const status = fullMsg.data?.value as string | undefined;
-              if (transcriptId && status && status !== "processing") {
-                setDagStatusMap((prev) => {
-                  const next = new Map(prev);
-                  next.delete(transcriptId);
-                  return next;
-                });
-              }
-              break;
-            }
-
-            case "TRANSCRIPT_DAG_STATUS": {
-              const transcriptId = fullMsg.data?.id as string | undefined;
-              const tasks = fullMsg.data?.tasks as DagTask[] | undefined;
-              if (transcriptId && tasks) {
-                setDagStatusMap((prev) => {
-                  const next = new Map(prev);
-                  next.set(transcriptId, tasks);
-                  return next;
-                });
-              }
-              break;
-            }
-
             default:
               // Ignore other content events for list updates
               break;
@@ -225,9 +176,5 @@ export function UserEventsProvider({
     };
   }, []);
 
-  return (
-    <DagStatusContext.Provider value={dagStatusMap}>
-      {children}
-    </DagStatusContext.Provider>
-  );
+  return <>{children}</>;
 }

www/app/lib/dagTypes.ts

@@ -1,19 +0,0 @@
-export type DagTaskStatus =
-  | "queued"
-  | "running"
-  | "completed"
-  | "failed"
-  | "cancelled";
-
-export type DagTask = {
-  name: string;
-  status: DagTaskStatus;
-  started_at: string | null;
-  finished_at: string | null;
-  duration_seconds: number | null;
-  parents: string[];
-  error: string | null;
-  children_total: number | null;
-  children_completed: number | null;
-  progress_pct: number | null;
-};