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16 Commits

Author SHA1 Message Date
Igor Loskutov
6a57388723 format 2026-01-26 17:38:38 -05:00
ddef1d4a4a Merge branch 'main' into feature/split-padding-transcription 2026-01-26 13:22:16 -05:00
88e0d11ccd Update server/reflector/hatchet/workflows/padding_workflow.py
Co-authored-by: pr-agent-monadical[bot] <198624643+pr-agent-monadical[bot]@users.noreply.github.com>
2026-01-23 19:59:32 -05:00
Igor Loskutov
9f6e7b515b Revert transcript text broadcast to empty string
Empty string was intentional - reverting my incorrect fix
2026-01-23 17:00:24 -05:00
Igor Loskutov
d0110f4dd4 Fix: Remove redundant checks and clarify variable scope
- Remove redundant padded_key None check (NonEmptyString cannot be None)
- Move storage_path definition before try block for clarity
- All padded tracks added to list (original or new)
2026-01-23 16:55:34 -05:00
Igor Loskutov
7dfb37154d Fix critical data flow and concurrency bugs
- Add empty padded_tracks guard in process_transcriptions
- Fix created_padded_files: use list instead of set to preserve order for zip cleanup
- Document size=0 contract in PadTrackResult (size=0 means original key, not padded)
- Remove redundant ctx.log in padding_workflow
2026-01-23 16:47:11 -05:00
Igor Loskutov
67679e90b2 Revert waveform dependency - allow background completion
Waveform generation can complete after transcript marked "ended".
User can see transcript immediately while waveform finishes in background.
2026-01-23 16:42:36 -05:00
Igor Loskutov
aa4c368479 Fix critical bugs from refactoring
- Fix empty transcript broadcast (was sending text="", should send merged_transcript.text)
- Restore generate_waveform to finalize parents (finalize must wait for waveform)
2026-01-23 16:40:57 -05:00
Igor Loskutov
deb5ed6010 Fix: Preserve track_index explicitly in PaddedTrackInfo
- Add track_index to PaddedTrackInfo model
- Preserve track_index from PadTrackResult when building padded_tracks list
- Use explicit track_index instead of enumerate in process_transcriptions
- Removes fragile ordering assumption
2026-01-23 16:36:16 -05:00
Igor Loskutov
30b28eed3b Merge main into feature/split-padding-transcription 2026-01-23 16:20:39 -05:00
Igor Loskutov
1b33fba3ba Fix: Move padding_workflow to LLM worker for parallel execution
Critical bug fix: padding_workflow was registered on CPU worker (slots=1),
causing all padding tasks to run serially instead of in parallel.

Changes:
- Moved padding_workflow from run_workers_cpu.py to run_workers_llm.py
- LLM worker has slots=10, allowing up to 10 parallel padding operations
- Padding is I/O-bound (S3 download/upload), not CPU-intensive
- CPU worker now handles only mixdown_tracks (compute-heavy, serialized)

Impact:
- Before: 4 tracks × 5s padding = 20s serial execution
- After: 4 tracks × 5s padding = ~5s parallel execution (4 concurrent)
- Restores intended performance benefit of the refactoring
2026-01-23 16:05:43 -05:00
Igor Loskutov
3ce279daa4 Split padding and transcription into separate workflow steps
- Split process_tracks into process_paddings + process_transcriptions
- Create PaddingWorkflow and TranscriptionWorkflow as separate child workflows
- Update dependency: mixdown_tracks now depends on process_paddings (not process_transcriptions)
- Performance: mixdown starts ~295s earlier (after padding completes, not after transcription)

Changes:
- New: padding_workflow.py, transcription_workflow.py
- Modified: daily_multitrack_pipeline.py (new tasks, updated dependencies)
- Modified: models.py (new ProcessPaddingsResult, ProcessTranscriptionsResult, deleted dead ProcessTracksResult)
- Modified: constants.py (new task names)
- Modified: run_workers_cpu.py, run_workers_llm.py (workflow registration)
- Deleted: track_processing.py

Code quality fixes:
- Removed redundant comments and verbose docstrings
- Added language validation in process_transcriptions
- Improved error logging with full context (transcript_id, track_index)
- Fixed log accuracy bugs (use correct counts)
- Updated worker pool documentation
2026-01-21 16:53:06 -05:00
Igor Loskutov
01650be787 fix tests 2026-01-21 15:04:05 -05:00
f00c16a41c Merge branch 'main' into fix/ics-window-bug 2026-01-21 14:38:36 -05:00
859df5513e Merge branch 'main' into fix/ics-window-bug 2026-01-21 08:47:34 -05:00
Igor Loskutov
2af9918979 ics non-sync bugfix 2026-01-20 16:56:06 -05:00
37 changed files with 1078 additions and 2097 deletions

1
.gitignore vendored
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@@ -1,6 +1,5 @@
.DS_Store
server/.env
server/.env.production
.env
Caddyfile
server/exportdanswer

View File

@@ -4,4 +4,4 @@ docs/docs/installation/daily-setup.md:curl-auth-header:277
gpu/self_hosted/DEV_SETUP.md:curl-auth-header:74
gpu/self_hosted/DEV_SETUP.md:curl-auth-header:83
server/reflector/worker/process.py:generic-api-key:465
server/tests/test_recording_request_flow.py:generic-api-key:121
server/reflector/worker/process.py:generic-api-key:594

View File

@@ -1,20 +1,5 @@
# Changelog
## [0.32.1](https://github.com/Monadical-SAS/reflector/compare/v0.32.0...v0.32.1) (2026-01-30)
### Bug Fixes
* daily multitrack pipeline finalze dependency fix ([23eb137](https://github.com/Monadical-SAS/reflector/commit/23eb1371cb9348c4b81eb12ad506b582f8a4799e))
* match httpx pad with hatchet audio timeout ([c05d1f0](https://github.com/Monadical-SAS/reflector/commit/c05d1f03cd8369fc06efd455527e50246887efd0))
## [0.32.0](https://github.com/Monadical-SAS/reflector/compare/v0.31.0...v0.32.0) (2026-01-30)
### Features
* modal padding ([#837](https://github.com/Monadical-SAS/reflector/issues/837)) ([7fde64e](https://github.com/Monadical-SAS/reflector/commit/7fde64e2529a1d37b0f7507c62d983a7bd0b5b89))
## [0.31.0](https://github.com/Monadical-SAS/reflector/compare/v0.30.0...v0.31.0) (2026-01-23)

View File

@@ -1,8 +1,6 @@
# Reflector Caddyfile (optional reverse proxy)
# Use this only when you run Caddy via: docker compose -f docker-compose.prod.yml --profile caddy up -d
# If Coolify, Traefik, or nginx already use ports 80/443, do NOT start Caddy; point your proxy at web:3000 and server:1250.
#
# Replace example.com with your actual domains. CORS is handled by the backend - Caddy just proxies.
# Reflector Caddyfile
# Replace example.com with your actual domains
# CORS is handled by the backend - Caddy just proxies
#
# For environment variable substitution, set:
# FRONTEND_DOMAIN=app.example.com

View File

@@ -1,14 +1,9 @@
# Production Docker Compose configuration
# Usage: docker compose -f docker-compose.prod.yml up -d
#
# Caddy (reverse proxy on ports 80/443) is OPTIONAL and behind the "caddy" profile:
# - With Caddy (self-hosted, you manage SSL): docker compose -f docker-compose.prod.yml --profile caddy up -d
# - Without Caddy (Coolify/Traefik/nginx already on 80/443): docker compose -f docker-compose.prod.yml up -d
# Then point your proxy at web:3000 (frontend) and server:1250 (API).
#
# Prerequisites:
# 1. Copy .env.example to .env and configure for both server/ and www/
# 2. If using Caddy: copy Caddyfile.example to Caddyfile and edit your domains
# 2. Copy Caddyfile.example to Caddyfile and edit with your domains
# 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
services:
@@ -89,8 +84,6 @@ services:
retries: 3
caddy:
profiles:
- caddy
image: caddy:2-alpine
restart: unless-stopped
ports:

View File

@@ -12,14 +12,14 @@ This page documents the Docker Compose configuration for Reflector. For the comp
The `docker-compose.prod.yml` includes these services:
| Service | Image | Purpose |
| ---------- | --------------------------------- | --------------------------------------------------------------------------- |
|---------|-------|---------|
| `web` | `monadicalsas/reflector-frontend` | Next.js frontend |
| `server` | `monadicalsas/reflector-backend` | FastAPI backend |
| `worker` | `monadicalsas/reflector-backend` | Celery worker for background tasks |
| `beat` | `monadicalsas/reflector-backend` | Celery beat scheduler |
| `redis` | `redis:7.2-alpine` | Message broker and cache |
| `postgres` | `postgres:17-alpine` | Primary database |
| `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL (optional; see [Caddy profile](#caddy-profile)) |
| `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL |
## Environment Files
@@ -30,7 +30,6 @@ Reflector uses two separate environment files:
Used by: `server`, `worker`, `beat`
Key variables:
```env
# Database connection
DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
@@ -55,7 +54,6 @@ TRANSCRIPT_MODAL_API_KEY=...
Used by: `web`
Key variables:
```env
# Domain configuration
SITE_URL=https://app.example.com
@@ -73,7 +71,7 @@ Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-s
## Volumes
| Volume | Purpose |
| --------------- | ----------------------------- |
|--------|---------|
| `redis_data` | Redis persistence |
| `postgres_data` | PostgreSQL data |
| `server_data` | Uploaded files, local storage |
@@ -84,30 +82,14 @@ Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-s
All services share the default network. The network is marked `attachable: true` to allow external containers (like Authentik) to join.
## Caddy profile
Caddy (ports 80 and 443) is **optional** and behind the `caddy` profile so it does not conflict with an existing reverse proxy (e.g. Coolify, Traefik, nginx).
- **With Caddy** (you want Reflector to handle SSL):
`docker compose -f docker-compose.prod.yml --profile caddy up -d`
- **Without Caddy** (Coolify or another proxy already on 80/443):
`docker compose -f docker-compose.prod.yml up -d`
Then configure your proxy to send traffic to `web:3000` (frontend) and `server:1250` (API).
## Common Commands
### Start all services
```bash
# Without Caddy (e.g. when using Coolify)
docker compose -f docker-compose.prod.yml up -d
# With Caddy as reverse proxy
docker compose -f docker-compose.prod.yml --profile caddy up -d
```
### View logs
```bash
# All services
docker compose -f docker-compose.prod.yml logs -f
@@ -117,7 +99,6 @@ docker compose -f docker-compose.prod.yml logs server --tail 50
```
### Restart a service
```bash
# Quick restart (doesn't reload .env changes)
docker compose -f docker-compose.prod.yml restart server
@@ -127,32 +108,27 @@ docker compose -f docker-compose.prod.yml up -d server
```
### Run database migrations
```bash
docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
```
### Access database
```bash
docker compose -f docker-compose.prod.yml exec postgres psql -U reflector
```
### Pull latest images
```bash
docker compose -f docker-compose.prod.yml pull
docker compose -f docker-compose.prod.yml up -d
```
### Stop all services
```bash
docker compose -f docker-compose.prod.yml down
```
### Full reset (WARNING: deletes data)
```bash
docker compose -f docker-compose.prod.yml down -v
```
@@ -211,7 +187,6 @@ The Caddyfile supports environment variable substitution:
Set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables, or edit the file directly.
### Reload Caddy after changes
```bash
docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
```

View File

@@ -61,7 +61,7 @@ Type: A Name: api Value: <your-server-ip>
Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
| | **Modal.com (Cloud)** | **Self-Hosted GPU** |
| ------------ | --------------------------------- | ---------------------------- |
|---|---|---|
| **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
| **Pricing** | Pay-per-use | Fixed infrastructure cost |
@@ -70,7 +70,6 @@ Reflector requires GPU processing for transcription and speaker diarization. Cho
#### Accept HuggingFace Licenses
Visit both pages and click "Accept":
- https://huggingface.co/pyannote/speaker-diarization-3.1
- https://huggingface.co/pyannote/segmentation-3.0
@@ -180,7 +179,6 @@ Save these credentials - you'll need them in the next step.
## Configure Environment
Reflector has two env files:
- `server/.env` - Backend configuration
- `www/.env` - Frontend configuration
@@ -192,7 +190,6 @@ nano server/.env
```
**Required settings:**
```env
# Database (defaults work with docker-compose.prod.yml)
DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
@@ -252,7 +249,6 @@ nano www/.env
```
**Required settings:**
```env
# Your domains
SITE_URL=https://app.example.com
@@ -270,11 +266,7 @@ FEATURE_REQUIRE_LOGIN=false
---
## Reverse proxy (Caddy or existing)
**If Coolify, Traefik, or nginx already use ports 80/443** (e.g. Coolify on your host): skip Caddy. Start the stack without the Caddy profile (see [Start Services](#start-services) below), then point your proxy at `web:3000` (frontend) and `server:1250` (API).
**If you want Reflector to provide the reverse proxy and SSL:**
## Configure Caddy
```bash
cp Caddyfile.example Caddyfile
@@ -297,18 +289,10 @@ Replace `example.com` with your domains. The `{$VAR:default}` syntax uses Caddy'
## Start Services
**Without Caddy** (e.g. Coolify already on 80/443):
```bash
docker compose -f docker-compose.prod.yml up -d
```
**With Caddy** (Reflector handles SSL):
```bash
docker compose -f docker-compose.prod.yml --profile caddy up -d
```
Wait for containers to start (first run may take 1-2 minutes to pull images and initialize).
---
@@ -316,21 +300,18 @@ Wait for containers to start (first run may take 1-2 minutes to pull images and
## Verify Deployment
### Check services
```bash
docker compose -f docker-compose.prod.yml ps
# All should show "Up"
```
### Test API
```bash
curl https://api.example.com/health
# Should return: {"status":"healthy"}
```
### Test Frontend
- Visit https://app.example.com
- You should see the Reflector interface
- Try uploading an audio file to test transcription
@@ -346,7 +327,6 @@ By default, Reflector is open (no login required). **Authentication is required
See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
Quick summary:
1. Deploy Authentik on your server
2. Create OAuth provider in Authentik
3. Extract public key for JWT verification
@@ -378,7 +358,6 @@ DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
```
Reload env and restart:
```bash
docker compose -f docker-compose.prod.yml up -d server worker
```
@@ -388,43 +367,35 @@ docker compose -f docker-compose.prod.yml up -d server worker
## Troubleshooting
### Check logs for errors
```bash
docker compose -f docker-compose.prod.yml logs server --tail 20
docker compose -f docker-compose.prod.yml logs worker --tail 20
```
### Services won't start
```bash
docker compose -f docker-compose.prod.yml logs
```
### CORS errors in browser
- Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
- Reload env: `docker compose -f docker-compose.prod.yml up -d server`
### SSL certificate errors (when using Caddy)
### SSL certificate errors
- Caddy auto-provisions Let's Encrypt certificates
- Ensure ports 80 and 443 are open and not used by another proxy
- Ensure ports 80 and 443 are open
- Check: `docker compose -f docker-compose.prod.yml logs caddy`
- If port 80 is already in use (e.g. by Coolify), run without Caddy: `docker compose -f docker-compose.prod.yml up -d` and use your existing proxy
### Transcription not working
- Check Modal dashboard: https://modal.com/apps
- Verify URLs in `server/.env` match deployed functions
- Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
### "Login required" but auth not configured
- Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
- Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
### Database migrations or connectivity issues
Migrations run automatically on server startup. To check database connectivity or debug migration failures:
```bash
@@ -437,3 +408,4 @@ docker compose -f docker-compose.prod.yml exec server uv run python -c "from ref
# Manually run migrations (if needed)
docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
```

View File

@@ -131,15 +131,6 @@ if [ -z "$DIARIZER_URL" ]; then
fi
echo " -> $DIARIZER_URL"
echo ""
echo "Deploying padding (CPU audio processing via Modal SDK)..."
modal deploy reflector_padding.py
if [ $? -ne 0 ]; then
echo "Error: Failed to deploy padding. Check Modal dashboard for details."
exit 1
fi
echo " -> reflector-padding.pad_track (Modal SDK function)"
# --- Output Configuration ---
echo ""
echo "=========================================="
@@ -156,6 +147,4 @@ echo ""
echo "DIARIZATION_BACKEND=modal"
echo "DIARIZATION_URL=$DIARIZER_URL"
echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
echo ""
echo "# Padding uses Modal SDK (requires MODAL_TOKEN_ID/SECRET in worker containers)"
echo "# --- End Modal Configuration ---"

View File

@@ -1,277 +0,0 @@
"""
Reflector GPU backend - audio padding
======================================
CPU-intensive audio padding service for adding silence to audio tracks.
Uses PyAV filter graph (adelay) for precise track synchronization.
IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
for Modal deployment isolation (Modal can't import from server/reflector/). If you modify
the PyAV filter graph or padding algorithm, you MUST update both:
- gpu/modal_deployments/reflector_padding.py (this file)
- server/reflector/utils/audio_padding.py
Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
"""
import os
import tempfile
from fractions import Fraction
import math
import asyncio
import modal
S3_TIMEOUT = 60 # happens 2 times
PADDING_TIMEOUT = 600 + (S3_TIMEOUT * 2)
SCALEDOWN_WINDOW = 60 # The maximum duration (in seconds) that individual containers can remain idle when scaling down.
DISCONNECT_CHECK_INTERVAL = 2 # Check for client disconnect
app = modal.App("reflector-padding")
# CPU-based image
image = (
modal.Image.debian_slim(python_version="3.12")
.apt_install("ffmpeg") # Required by PyAV
.pip_install(
"av==13.1.0", # PyAV for audio processing
"requests==2.32.3", # HTTP for presigned URL downloads/uploads
"fastapi==0.115.12", # API framework
)
)
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_STANDARD_SAMPLE_RATE = 48000
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_DEFAULT_BIT_RATE = 128000
@app.function(
cpu=2.0,
timeout=PADDING_TIMEOUT,
scaledown_window=SCALEDOWN_WINDOW,
image=image,
)
@modal.asgi_app()
def web():
from fastapi import FastAPI, Request, HTTPException
from pydantic import BaseModel
class PaddingRequest(BaseModel):
track_url: str
output_url: str
start_time_seconds: float
track_index: int
class PaddingResponse(BaseModel):
size: int
cancelled: bool = False
web_app = FastAPI()
@web_app.post("/pad")
async def pad_track_endpoint(request: Request, req: PaddingRequest) -> PaddingResponse:
"""Modal web endpoint for padding audio tracks with disconnect detection.
"""
import logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
if not req.track_url:
raise HTTPException(status_code=400, detail="track_url cannot be empty")
if not req.output_url:
raise HTTPException(status_code=400, detail="output_url cannot be empty")
if req.start_time_seconds <= 0:
raise HTTPException(status_code=400, detail=f"start_time_seconds must be positive, got {req.start_time_seconds}")
if req.start_time_seconds > 18000:
raise HTTPException(status_code=400, detail=f"start_time_seconds exceeds maximum 18000s (5 hours)")
logger.info(f"Padding request: track {req.track_index}, delay={req.start_time_seconds}s")
# Thread-safe cancellation flag shared between async disconnect checker and blocking thread
import threading
cancelled = threading.Event()
async def check_disconnect():
"""Background task to check for client disconnect every 2 seconds."""
while not cancelled.is_set():
await asyncio.sleep(DISCONNECT_CHECK_INTERVAL)
if await request.is_disconnected():
logger.warning("Client disconnected, setting cancellation flag")
cancelled.set()
break
# Start disconnect checker in background
disconnect_task = asyncio.create_task(check_disconnect())
try:
result = await asyncio.get_event_loop().run_in_executor(
None, _pad_track_blocking, req, cancelled, logger
)
return PaddingResponse(**result)
finally:
cancelled.set()
disconnect_task.cancel()
try:
await disconnect_task
except asyncio.CancelledError:
pass
def _pad_track_blocking(req, cancelled, logger) -> dict:
"""Blocking CPU-bound padding work with periodic cancellation checks.
Args:
cancelled: threading.Event for thread-safe cancellation signaling
"""
import av
import requests
from av.audio.resampler import AudioResampler
import time
temp_dir = tempfile.mkdtemp()
input_path = None
output_path = None
last_check = time.time()
try:
logger.info("Downloading track for padding")
response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
response.raise_for_status()
input_path = os.path.join(temp_dir, "track.webm")
total_bytes = 0
chunk_count = 0
with open(input_path, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
total_bytes += len(chunk)
chunk_count += 1
# Check for cancellation every arbitrary amount of chunks
if chunk_count % 12 == 0:
now = time.time()
if now - last_check >= DISCONNECT_CHECK_INTERVAL:
if cancelled.is_set():
logger.info("Cancelled during download, exiting early")
return {"size": 0, "cancelled": True}
last_check = now
logger.info(f"Track downloaded: {total_bytes} bytes")
if cancelled.is_set():
logger.info("Cancelled after download, exiting early")
return {"size": 0, "cancelled": True}
# Apply padding using PyAV
output_path = os.path.join(temp_dir, "padded.webm")
delay_ms = math.floor(req.start_time_seconds * 1000)
logger.info(f"Padding track {req.track_index} with {delay_ms}ms delay using PyAV")
in_container = av.open(input_path)
in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
if in_stream is None:
raise ValueError("No audio stream in input")
with av.open(output_path, "w", format="webm") as out_container:
out_stream = out_container.add_stream("libopus", rate=OPUS_STANDARD_SAMPLE_RATE)
out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
graph = av.filter.Graph()
abuf_args = (
f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
f"sample_fmt=s16:"
f"channel_layout=stereo"
)
src = graph.add("abuffer", args=abuf_args, name="src")
aresample_f = graph.add("aresample", args="async=1", name="ares")
delays_arg = f"{delay_ms}|{delay_ms}"
adelay_f = graph.add("adelay", args=f"delays={delays_arg}:all=1", name="delay")
sink = graph.add("abuffersink", name="sink")
src.link_to(aresample_f)
aresample_f.link_to(adelay_f)
adelay_f.link_to(sink)
graph.configure()
resampler = AudioResampler(
format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
)
for frame in in_container.decode(in_stream):
# Check for cancellation periodically
now = time.time()
if now - last_check >= DISCONNECT_CHECK_INTERVAL:
if cancelled.is_set():
logger.info("Cancelled during processing, exiting early")
in_container.close()
return {"size": 0, "cancelled": True}
last_check = now
out_frames = resampler.resample(frame) or []
for rframe in out_frames:
rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
src.push(rframe)
while True:
try:
f_out = sink.pull()
except Exception:
break
f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
for packet in out_stream.encode(f_out):
out_container.mux(packet)
# Flush filter graph
src.push(None)
while True:
try:
f_out = sink.pull()
except Exception:
break
f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
for packet in out_stream.encode(f_out):
out_container.mux(packet)
# Flush encoder
for packet in out_stream.encode(None):
out_container.mux(packet)
in_container.close()
file_size = os.path.getsize(output_path)
logger.info(f"Padding complete: {file_size} bytes")
logger.info("Uploading padded track to S3")
with open(output_path, "rb") as f:
upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
upload_response.raise_for_status()
logger.info(f"Upload complete: {file_size} bytes")
return {"size": file_size}
finally:
if input_path and os.path.exists(input_path):
try:
os.unlink(input_path)
except Exception as e:
logger.warning(f"Failed to cleanup input file: {e}")
if output_path and os.path.exists(output_path):
try:
os.unlink(output_path)
except Exception as e:
logger.warning(f"Failed to cleanup output file: {e}")
try:
os.rmdir(temp_dir)
except Exception as e:
logger.warning(f"Failed to cleanup temp directory: {e}")
return web_app

View File

@@ -86,7 +86,7 @@ Daily.co Room: "daily-private-igor-20260110042117"
| **Purpose** | Tracks active session state | Links recordings, transcripts, participants |
| **Scope** | Per room instance | Per Reflector room + timestamp |
**Critical Limitation:** Daily.co's recordings API often does NOT return `mtgSessionId` (can be null), requiring time-based matching (see [Time-Based Matching](#time-based-matching)).
**Critical Limitation:** Daily.co's recordings API often does NOT return `mtgSessionId`, requiring time-based matching (see [Time-Based Matching](#time-based-matching)).
### Recording
@@ -101,30 +101,6 @@ Daily.co Room: "daily-private-igor-20260110042117"
**Critical Behavior:** Recording **stops/restarts** create **separate recording objects** with unique IDs.
### instanceId (Reflector-Generated)
**Definition:** UUID we generate and send when starting recording via REST API.
**Generation:** Deterministic from meeting_id
- Cloud: `instanceId = meeting_id` directly
- Raw-tracks: `instanceId = UUIDv5(meeting_id, namespace)`
**Key behaviors:**
-**Reuse allowed:** Same instanceId can be used after stop (validated 2026-01-20)
-**Not returned:** Daily.co does NOT echo instanceId back in GET /recordings response
-**Present in error webhooks:** `recording.error` webhook includes instanceId
- **Purpose:** Allows multiple concurrent recordings (cloud + raw-tracks) in same room
**Stop/restart example:**
```
Recording 1: POST /start with instanceId="779e6376..." → recording_id="ee00c4e8..."
Stop recording
Recording 2: POST /start with instanceId="779e6376..." (SAME) → recording_id="b702f509..." (DIFFERENT)
✅ Both succeed, different recording_ids returned
```
**Implication:** Cannot match recordings by instanceId (not in response) - must use recording_id.
---
## Entity Relationships
@@ -220,19 +196,6 @@ Daily.co Room: "daily-private-igor-20260110042117"
`mtgSessionId` identifies a **Daily.co meeting session** (not individual participants, not a room).
**Reliability:** Can be null or present in GET /recordings response (unreliable).
**When present:** Multiple recordings from same session (stop/restart with participants connected) share same mtgSessionId.
**Example (validated 2026-01-20):**
```json
Recording 1: {"id": "ee00c4e8...", "mtgSessionId": "92c4136a-a8da-41c5-9c45-e9a2baae6bd6"}
Recording 2: {"id": "b702f509...", "mtgSessionId": "92c4136a-a8da-41c5-9c45-e9a2baae6bd6"}
// Same mtgSessionId (stop/restart in same session)
```
**When null:** Common - Daily.co API does not reliably populate this field.
### session_id (Per-Participant)
**Different concept:** Per-participant connection identifier from webhooks.
@@ -257,24 +220,16 @@ TABLE daily_participant_session (
Daily.co's recordings API does not reliably return `mtgSessionId`, making it impossible to directly link recordings to meetings via Daily.co's identifiers.
**Example API response (mtgSessionId can be null OR present):**
**Example API response:**
```json
{
"id": "recording-uuid",
"room_name": "daily-private-igor-20260110042117",
"start_ts": 1768018896,
"mtgSessionId": null // ← Often null (unreliable)
}
// OR (when present):
{
"id": "recording-uuid",
"mtgSessionId": "92c4136a-a8da-41c5-9c45-e9a2baae6bd6" // ← Sometimes present
"mtgSessionId": null Missing!
}
```
**Key insight:** Cannot rely on mtgSessionId for matching (unreliable). instanceId also not returned. Only reliable identifier is recording.id.
### Solution: Time-Based Matching
**Implementation:** `reflector/db/meetings.py:get_by_room_name_and_time()`
@@ -536,10 +491,6 @@ UI: User sees 3 separate transcripts
---
**Document Version:** 1.1
**Last Updated:** 2026-01-20
**Data Source:** Production database + Daily.co API inspection + empirical testing
**Changes in 1.1:**
- Added instanceId behavior documentation (reuse allowed, not returned in API)
- Clarified mtgSessionId reliability (can be null or present)
- Added empirical validation of stop/restart behavior
**Document Version:** 1.0
**Last Verified:** 2026-01-15
**Data Source:** Production database + Daily.co API inspection

View File

@@ -1,67 +0,0 @@
"""add_daily_recording_requests
Revision ID: f5b008fa8a14
Revises: 1b1e6a6fc465
Create Date: 2026-01-20 22:32:06.697144
"""
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "f5b008fa8a14"
down_revision: Union[str, None] = "1b1e6a6fc465"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
op.create_table(
"daily_recording_request",
sa.Column("recording_id", sa.String(), nullable=False),
sa.Column("meeting_id", sa.String(), nullable=False),
sa.Column("instance_id", sa.String(), nullable=False),
sa.Column("type", sa.String(), nullable=False),
sa.Column("requested_at", sa.DateTime(timezone=True), nullable=False),
sa.ForeignKeyConstraint(["meeting_id"], ["meeting.id"], ondelete="CASCADE"),
sa.PrimaryKeyConstraint("recording_id"),
)
op.create_index("idx_meeting_id", "daily_recording_request", ["meeting_id"])
op.create_index("idx_instance_id", "daily_recording_request", ["instance_id"])
# Clean up orphaned recordings before adding FK constraint
op.execute("""
UPDATE recording SET status = 'orphan', meeting_id = NULL
WHERE meeting_id IS NOT NULL
AND meeting_id NOT IN (SELECT id FROM meeting)
""")
# Add FK constraint to recording table (cascade delete recordings when meeting deleted)
op.execute("""
ALTER TABLE recording ADD CONSTRAINT fk_recording_meeting
FOREIGN KEY (meeting_id) REFERENCES meeting(id) ON DELETE CASCADE
""")
# Add CHECK constraints to enforce orphan invariants
op.execute("""
ALTER TABLE recording ADD CONSTRAINT chk_orphan_no_meeting
CHECK (status != 'orphan' OR meeting_id IS NULL)
""")
op.execute("""
ALTER TABLE recording ADD CONSTRAINT chk_non_orphan_has_meeting
CHECK (status = 'orphan' OR meeting_id IS NOT NULL)
""")
def downgrade() -> None:
op.execute("ALTER TABLE recording DROP CONSTRAINT IF EXISTS chk_orphan_no_meeting")
op.execute(
"ALTER TABLE recording DROP CONSTRAINT IF EXISTS chk_non_orphan_has_meeting"
)
op.execute("ALTER TABLE recording DROP CONSTRAINT IF EXISTS fk_recording_meeting")
op.drop_index("idx_instance_id", table_name="daily_recording_request")
op.drop_index("idx_meeting_id", table_name="daily_recording_request")
op.drop_table("daily_recording_request")

View File

@@ -8,7 +8,7 @@ readme = "README.md"
dependencies = [
"aiohttp>=3.9.0",
"aiohttp-cors>=0.7.0",
"av>=15.0.0",
"av>=10.0.0",
"requests>=2.31.0",
"aiortc>=1.5.0",
"sortedcontainers>=2.4.0",

View File

@@ -1,56 +0,0 @@
"""Utility for creating orphan recordings."""
import os
from datetime import datetime, timezone
from reflector.db.recordings import Recording, recordings_controller
from reflector.logger import logger
from reflector.utils.string import NonEmptyString
async def create_and_log_orphan(
recording_id: NonEmptyString,
bucket_name: str,
room_name: str,
start_ts: int,
track_keys: list[str] | None,
source: str,
) -> bool:
"""Create orphan recording and log if first occurrence.
Args:
recording_id: Daily.co recording ID
bucket_name: S3 bucket (empty string for cloud recordings)
room_name: Daily.co room name
start_ts: Unix timestamp
track_keys: Track keys for raw-tracks, None for cloud
source: "webhook" or "polling" for logging
Returns:
True if created (first poller), False if already exists
"""
if track_keys:
object_key = os.path.dirname(track_keys[0]) if track_keys else room_name
else:
object_key = room_name
created = await recordings_controller.create_orphan(
Recording(
id=recording_id,
bucket_name=bucket_name,
object_key=object_key,
recorded_at=datetime.fromtimestamp(start_ts, tz=timezone.utc),
track_keys=track_keys,
meeting_id=None,
status="orphan",
)
)
if created:
logger.error(
f"Orphan recording ({source})",
recording_id=recording_id,
room_name=room_name,
)
return created

View File

@@ -26,7 +26,6 @@ def get_database() -> databases.Database:
# import models
import reflector.db.calendar_events # noqa
import reflector.db.daily_participant_sessions # noqa
import reflector.db.daily_recording_requests # noqa
import reflector.db.meetings # noqa
import reflector.db.recordings # noqa
import reflector.db.rooms # noqa

View File

@@ -1,111 +0,0 @@
from datetime import datetime
from typing import Literal
from uuid import UUID
import sqlalchemy as sa
from pydantic import BaseModel
from sqlalchemy.dialects.postgresql import insert
from reflector.db import get_database, metadata
from reflector.utils.string import NonEmptyString
daily_recording_requests = sa.Table(
"daily_recording_request",
metadata,
sa.Column("recording_id", sa.String, primary_key=True),
sa.Column(
"meeting_id",
sa.String,
sa.ForeignKey("meeting.id", ondelete="CASCADE"),
nullable=False,
),
sa.Column("instance_id", sa.String, nullable=False),
sa.Column("type", sa.String, nullable=False),
sa.Column("requested_at", sa.DateTime(timezone=True), nullable=False),
sa.Index("idx_meeting_id", "meeting_id"),
sa.Index("idx_instance_id", "instance_id"),
)
class DailyRecordingRequest(BaseModel):
recording_id: NonEmptyString
meeting_id: NonEmptyString
instance_id: UUID
type: Literal["cloud", "raw-tracks"]
requested_at: datetime
class DailyRecordingRequestsController:
async def create(self, request: DailyRecordingRequest) -> None:
stmt = insert(daily_recording_requests).values(
recording_id=request.recording_id,
meeting_id=request.meeting_id,
instance_id=str(request.instance_id),
type=request.type,
requested_at=request.requested_at,
)
stmt = stmt.on_conflict_do_nothing(index_elements=["recording_id"])
await get_database().execute(stmt)
async def find_by_recording_id(
self,
recording_id: NonEmptyString,
) -> tuple[NonEmptyString, Literal["cloud", "raw-tracks"]] | None:
query = daily_recording_requests.select().where(
daily_recording_requests.c.recording_id == recording_id
)
result = await get_database().fetch_one(query)
if not result:
return None
req = DailyRecordingRequest(
recording_id=result["recording_id"],
meeting_id=result["meeting_id"],
instance_id=UUID(result["instance_id"]),
type=result["type"],
requested_at=result["requested_at"],
)
return (req.meeting_id, req.type)
async def find_by_instance_id(
self,
instance_id: UUID,
) -> list[DailyRecordingRequest]:
"""Multiple recordings can have same instance_id (stop/restart)."""
query = daily_recording_requests.select().where(
daily_recording_requests.c.instance_id == str(instance_id)
)
results = await get_database().fetch_all(query)
return [
DailyRecordingRequest(
recording_id=r["recording_id"],
meeting_id=r["meeting_id"],
instance_id=UUID(r["instance_id"]),
type=r["type"],
requested_at=r["requested_at"],
)
for r in results
]
async def get_by_meeting_id(
self,
meeting_id: NonEmptyString,
) -> list[DailyRecordingRequest]:
query = daily_recording_requests.select().where(
daily_recording_requests.c.meeting_id == meeting_id
)
results = await get_database().fetch_all(query)
return [
DailyRecordingRequest(
recording_id=r["recording_id"],
meeting_id=r["meeting_id"],
instance_id=UUID(r["instance_id"]),
type=r["type"],
requested_at=r["requested_at"],
)
for r in results
]
daily_recording_requests_controller = DailyRecordingRequestsController()

View File

@@ -1,4 +1,4 @@
from datetime import datetime
from datetime import datetime, timedelta
from typing import Any, Literal
import sqlalchemy as sa
@@ -183,6 +183,84 @@ class MeetingController:
results = await get_database().fetch_all(query)
return [Meeting(**r) for r in results]
async def get_by_room_name_and_time(
self,
room_name: NonEmptyString,
recording_start: datetime,
time_window_hours: int = 168,
) -> Meeting | None:
"""
Get meeting by room name closest to recording timestamp.
HACK ALERT: Daily.co doesn't return instanceId in recordings API response,
and mtgSessionId is separate from our instanceId. Time-based matching is
the least-bad workaround.
This handles edge case of duplicate room_name values in DB (race conditions,
double-clicks, etc.) by matching based on temporal proximity.
Algorithm:
1. Find meetings within time_window_hours of recording_start
2. Return meeting with start_date closest to recording_start
3. If tie, return first by meeting.id (deterministic)
Args:
room_name: Daily.co room name from recording
recording_start: Timezone-aware datetime from recording.start_ts
time_window_hours: Search window (default 168 = 1 week)
Returns:
Meeting closest to recording timestamp, or None if no matches
Failure modes:
- Multiple meetings in same room within ~5 minutes: picks closest
- All meetings outside time window: returns None
- Clock skew between Daily.co and DB: 1-week window tolerates this
Why 1 week window:
- Handles webhook failures (recording discovered days later)
- Tolerates clock skew
- Rejects unrelated meetings from weeks ago
"""
# Validate timezone-aware datetime
if recording_start.tzinfo is None:
raise ValueError(
f"recording_start must be timezone-aware, got naive datetime: {recording_start}"
)
window_start = recording_start - timedelta(hours=time_window_hours)
window_end = recording_start + timedelta(hours=time_window_hours)
query = (
meetings.select()
.where(
sa.and_(
meetings.c.room_name == room_name,
meetings.c.start_date >= window_start,
meetings.c.start_date <= window_end,
)
)
.order_by(meetings.c.start_date)
)
results = await get_database().fetch_all(query)
if not results:
return None
candidates = [Meeting(**r) for r in results]
# Find meeting with start_date closest to recording_start
closest = min(
candidates,
key=lambda m: (
abs((m.start_date - recording_start).total_seconds()),
m.id, # Tie-breaker: deterministic by UUID
),
)
return closest
async def get_active(self, room: Room, current_time: datetime) -> Meeting | None:
"""
Get latest active meeting for a room.
@@ -272,6 +350,44 @@ class MeetingController:
query = meetings.update().where(meetings.c.id == meeting_id).values(**kwargs)
await get_database().execute(query)
async def set_cloud_recording_if_missing(
self,
meeting_id: NonEmptyString,
s3_key: NonEmptyString,
duration: int,
) -> bool:
"""
Set cloud recording only if not already set.
Returns True if updated, False if already set.
Prevents webhook/polling race condition via atomic WHERE clause.
"""
# Check current value before update to detect actual change
meeting_before = await self.get_by_id(meeting_id)
if not meeting_before:
return False
was_null = meeting_before.daily_composed_video_s3_key is None
query = (
meetings.update()
.where(
sa.and_(
meetings.c.id == meeting_id,
meetings.c.daily_composed_video_s3_key.is_(None),
)
)
.values(
daily_composed_video_s3_key=s3_key,
daily_composed_video_duration=duration,
)
)
await get_database().execute(query)
# Return True only if value was NULL before (actual update occurred)
# If was_null=False, the WHERE clause prevented the update
return was_null
async def increment_num_clients(self, meeting_id: str) -> None:
"""Atomically increment participant count."""
query = (
@@ -351,27 +467,6 @@ class MeetingConsentController:
result = await get_database().fetch_one(query)
return result is not None
async def set_cloud_recording_if_missing(
self,
meeting_id: NonEmptyString,
s3_key: NonEmptyString,
duration: int,
) -> bool:
"""Returns True if updated, False if already set."""
query = (
meetings.update()
.where(
meetings.c.id == meeting_id,
meetings.c.daily_composed_video_s3_key.is_(None),
)
.values(
daily_composed_video_s3_key=s3_key,
daily_composed_video_duration=duration,
)
)
result = await get_database().execute(query)
return result.rowcount > 0
meetings_controller = MeetingController()
meeting_consent_controller = MeetingConsentController()

View File

@@ -4,10 +4,10 @@ from typing import Literal
import sqlalchemy as sa
from pydantic import BaseModel, Field
from sqlalchemy import or_
from sqlalchemy.dialects.postgresql import insert
from reflector.db import get_database, metadata
from reflector.utils import generate_uuid4
from reflector.utils.string import NonEmptyString
recordings = sa.Table(
"recording",
@@ -31,13 +31,14 @@ recordings = sa.Table(
class Recording(BaseModel):
id: str = Field(default_factory=generate_uuid4)
bucket_name: str
# for single-track
object_key: str
recorded_at: datetime
status: Literal["pending", "processing", "completed", "failed", "orphan"] = (
"pending"
)
status: Literal["pending", "processing", "completed", "failed"] = "pending"
meeting_id: str | None = None
# None = single-track, [] = multitrack with no audio, [keys...] = multitrack with audio
# for multitrack reprocessing
# track_keys can be empty list [] if recording finished but no audio was captured (silence/muted)
# None means not a multitrack recording, [] means multitrack with no tracks
track_keys: list[str] | None = None
@property
@@ -71,6 +72,20 @@ class RecordingController:
query = recordings.delete().where(recordings.c.id == id)
await get_database().execute(query)
async def set_meeting_id(
self,
recording_id: NonEmptyString,
meeting_id: NonEmptyString,
) -> None:
"""Link recording to meeting."""
query = (
recordings.update()
.where(recordings.c.id == recording_id)
.values(meeting_id=meeting_id)
)
await get_database().execute(query)
# no check for existence
async def get_by_ids(self, recording_ids: list[str]) -> list[Recording]:
if not recording_ids:
return []
@@ -89,12 +104,9 @@ class RecordingController:
This is more efficient than fetching all recordings and filtering in Python.
"""
# INLINE IMPORT REQUIRED: Circular dependency
# - recordings.py needs transcripts table for JOIN query
# - transcripts.py imports recordings_controller
# - db/__init__.py loads recordings before transcripts (line 31 vs 33)
# - Top-level import would fail during module initialization
from reflector.db.transcripts import transcripts
from reflector.db.transcripts import (
transcripts, # noqa: PLC0415 cyclic import
)
query = (
recordings.select()
@@ -112,27 +124,5 @@ class RecordingController:
recordings_list = [Recording(**row) for row in results]
return [r for r in recordings_list if r.is_multitrack]
async def try_create_with_meeting(self, recording: Recording) -> bool:
"""Returns True if created, False if already exists."""
assert recording.meeting_id is not None, "meeting_id required for non-orphan"
assert recording.status != "orphan", "use create_orphan for orphans"
stmt = insert(recordings).values(**recording.model_dump())
stmt = stmt.on_conflict_do_nothing(index_elements=["id"])
result = await get_database().execute(stmt)
return result.rowcount > 0
async def create_orphan(self, recording: Recording) -> bool:
"""Returns True if created, False if already exists."""
assert recording.status == "orphan", "status must be 'orphan'"
assert recording.meeting_id is None, "meeting_id must be NULL for orphan"
stmt = insert(recordings).values(**recording.model_dump())
stmt = stmt.on_conflict_do_nothing(index_elements=["id"])
result = await get_database().execute(stmt)
return result.rowcount > 0
recordings_controller = RecordingController()

View File

@@ -8,7 +8,8 @@ from enum import StrEnum
class TaskName(StrEnum):
GET_RECORDING = "get_recording"
GET_PARTICIPANTS = "get_participants"
PROCESS_TRACKS = "process_tracks"
PROCESS_PADDINGS = "process_paddings"
PROCESS_TRANSCRIPTIONS = "process_transcriptions"
MIXDOWN_TRACKS = "mixdown_tracks"
GENERATE_WAVEFORM = "generate_waveform"
DETECT_TOPICS = "detect_topics"
@@ -35,9 +36,7 @@ LLM_RATE_LIMIT_PER_SECOND = 10
# Task execution timeouts (seconds)
TIMEOUT_SHORT = 60 # Quick operations: API calls, DB updates
TIMEOUT_MEDIUM = (
300 # Single LLM calls, waveform generation (5m for slow LLM responses)
)
TIMEOUT_MEDIUM = 120 # Single LLM calls, waveform generation
TIMEOUT_LONG = 180 # Action items (larger context LLM)
TIMEOUT_AUDIO = 720 # Audio processing: padding, mixdown
TIMEOUT_AUDIO = 300 # Audio processing: padding, mixdown
TIMEOUT_HEAVY = 600 # Transcription, fan-out LLM tasks

View File

@@ -1,9 +1,9 @@
"""
CPU-heavy worker pool for audio processing tasks.
Handles ONLY: mixdown_tracks
Handles: mixdown_tracks only (serialized with max_runs=1)
Configuration:
- slots=1: Only mixdown (already serialized globally with max_runs=1)
- slots=1: Only one mixdown at a time
- Worker affinity: pool=cpu-heavy
"""
@@ -26,7 +26,7 @@ def main():
cpu_worker = hatchet.worker(
"cpu-worker-pool",
slots=1, # Only 1 mixdown at a time (already serialized globally)
slots=1,
labels={
"pool": "cpu-heavy",
},

View File

@@ -1,15 +1,16 @@
"""
LLM/I/O worker pool for all non-CPU tasks.
Handles: all tasks except mixdown_tracks (transcription, LLM inference, orchestration)
Handles: all tasks except mixdown_tracks (padding, transcription, LLM inference, orchestration)
"""
from reflector.hatchet.client import HatchetClientManager
from reflector.hatchet.workflows.daily_multitrack_pipeline import (
daily_multitrack_pipeline,
)
from reflector.hatchet.workflows.padding_workflow import padding_workflow
from reflector.hatchet.workflows.subject_processing import subject_workflow
from reflector.hatchet.workflows.topic_chunk_processing import topic_chunk_workflow
from reflector.hatchet.workflows.track_processing import track_workflow
from reflector.hatchet.workflows.transcription_workflow import transcription_workflow
from reflector.logger import logger
SLOTS = 10
@@ -29,7 +30,7 @@ def main():
llm_worker = hatchet.worker(
WORKER_NAME,
slots=SLOTS, # not all slots are probably used
slots=SLOTS,
labels={
"pool": POOL,
},
@@ -37,7 +38,8 @@ def main():
daily_multitrack_pipeline,
topic_chunk_workflow,
subject_workflow,
track_workflow,
padding_workflow,
transcription_workflow,
],
)

View File

@@ -4,6 +4,10 @@ from reflector.hatchet.workflows.daily_multitrack_pipeline import (
PipelineInput,
daily_multitrack_pipeline,
)
from reflector.hatchet.workflows.padding_workflow import (
PaddingInput,
padding_workflow,
)
from reflector.hatchet.workflows.subject_processing import (
SubjectInput,
subject_workflow,
@@ -12,15 +16,20 @@ from reflector.hatchet.workflows.topic_chunk_processing import (
TopicChunkInput,
topic_chunk_workflow,
)
from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
from reflector.hatchet.workflows.transcription_workflow import (
TranscriptionInput,
transcription_workflow,
)
__all__ = [
"daily_multitrack_pipeline",
"subject_workflow",
"topic_chunk_workflow",
"track_workflow",
"padding_workflow",
"transcription_workflow",
"PipelineInput",
"SubjectInput",
"TopicChunkInput",
"TrackInput",
"PaddingInput",
"TranscriptionInput",
]

View File

@@ -54,8 +54,9 @@ from reflector.hatchet.workflows.models import (
PadTrackResult,
ParticipantInfo,
ParticipantsResult,
ProcessPaddingsResult,
ProcessSubjectsResult,
ProcessTracksResult,
ProcessTranscriptionsResult,
RecapResult,
RecordingResult,
SubjectsResult,
@@ -68,6 +69,7 @@ from reflector.hatchet.workflows.models import (
WebhookResult,
ZulipResult,
)
from reflector.hatchet.workflows.padding_workflow import PaddingInput, padding_workflow
from reflector.hatchet.workflows.subject_processing import (
SubjectInput,
subject_workflow,
@@ -76,7 +78,10 @@ from reflector.hatchet.workflows.topic_chunk_processing import (
TopicChunkInput,
topic_chunk_workflow,
)
from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
from reflector.hatchet.workflows.transcription_workflow import (
TranscriptionInput,
transcription_workflow,
)
from reflector.logger import logger
from reflector.pipelines import topic_processing
from reflector.processors import AudioFileWriterProcessor
@@ -322,7 +327,6 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
mtg_session_id = recording.mtg_session_id
async with fresh_db_connection():
from reflector.db.transcripts import ( # noqa: PLC0415
TranscriptDuration,
TranscriptParticipant,
transcripts_controller,
)
@@ -331,26 +335,15 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
if not transcript:
raise ValueError(f"Transcript {input.transcript_id} not found")
# Note: title NOT cleared - preserves existing titles
# Duration from Daily API (seconds -> milliseconds) - master source
duration_ms = recording.duration * 1000 if recording.duration else 0
await transcripts_controller.update(
transcript,
{
"events": [],
"topics": [],
"participants": [],
"duration": duration_ms,
},
)
await append_event_and_broadcast(
input.transcript_id,
transcript,
"DURATION",
TranscriptDuration(duration=duration_ms),
logger=logger,
)
mtg_session_id = assert_non_none_and_non_empty(
mtg_session_id, "mtg_session_id is required"
)
@@ -416,72 +409,115 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
retries=3,
)
@with_error_handling(TaskName.PROCESS_TRACKS)
async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksResult:
"""Spawn child workflows for each track (dynamic fan-out)."""
ctx.log(f"process_tracks: spawning {len(input.tracks)} track workflows")
participants_result = ctx.task_output(get_participants)
source_language = participants_result.source_language
@with_error_handling(TaskName.PROCESS_PADDINGS)
async def process_paddings(input: PipelineInput, ctx: Context) -> ProcessPaddingsResult:
"""Spawn child workflows for each track to apply padding (dynamic fan-out)."""
ctx.log(f"process_paddings: spawning {len(input.tracks)} padding workflows")
bulk_runs = [
track_workflow.create_bulk_run_item(
input=TrackInput(
padding_workflow.create_bulk_run_item(
input=PaddingInput(
track_index=i,
s3_key=track["s3_key"],
bucket_name=input.bucket_name,
transcript_id=input.transcript_id,
language=source_language,
)
)
for i, track in enumerate(input.tracks)
]
results = await track_workflow.aio_run_many(bulk_runs)
results = await padding_workflow.aio_run_many(bulk_runs)
target_language = participants_result.target_language
track_words: list[list[Word]] = []
padded_tracks = []
created_padded_files = set()
created_padded_files = []
for result in results:
transcribe_result = TranscribeTrackResult(**result[TaskName.TRANSCRIBE_TRACK])
track_words.append(transcribe_result.words)
pad_result = PadTrackResult(**result[TaskName.PAD_TRACK])
# Store S3 key info (not presigned URL) - consumer tasks presign on demand
if pad_result.padded_key:
padded_tracks.append(
PaddedTrackInfo(
key=pad_result.padded_key, bucket_name=pad_result.bucket_name
key=pad_result.padded_key,
bucket_name=pad_result.bucket_name,
track_index=pad_result.track_index,
)
)
if pad_result.size > 0:
storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{pad_result.track_index}.webm"
created_padded_files.add(storage_path)
created_padded_files.append(storage_path)
all_words = [word for words in track_words for word in words]
all_words.sort(key=lambda w: w.start)
ctx.log(f"process_paddings complete: {len(padded_tracks)} padded tracks")
ctx.log(
f"process_tracks complete: {len(all_words)} words from {len(input.tracks)} tracks"
)
return ProcessTracksResult(
all_words=all_words,
return ProcessPaddingsResult(
padded_tracks=padded_tracks,
word_count=len(all_words),
num_tracks=len(input.tracks),
target_language=target_language,
created_padded_files=list(created_padded_files),
)
@daily_multitrack_pipeline.task(
parents=[process_tracks],
parents=[process_paddings],
execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
retries=3,
)
@with_error_handling(TaskName.PROCESS_TRANSCRIPTIONS)
async def process_transcriptions(
input: PipelineInput, ctx: Context
) -> ProcessTranscriptionsResult:
"""Spawn child workflows for each padded track to transcribe (dynamic fan-out)."""
participants_result = ctx.task_output(get_participants)
paddings_result = ctx.task_output(process_paddings)
source_language = participants_result.source_language
if not source_language:
raise ValueError("source_language is required for transcription")
target_language = participants_result.target_language
padded_tracks = paddings_result.padded_tracks
if not padded_tracks:
raise ValueError("No padded tracks available for transcription")
ctx.log(
f"process_transcriptions: spawning {len(padded_tracks)} transcription workflows"
)
bulk_runs = [
transcription_workflow.create_bulk_run_item(
input=TranscriptionInput(
track_index=padded_track.track_index,
padded_key=padded_track.key,
bucket_name=padded_track.bucket_name,
language=source_language,
)
)
for padded_track in padded_tracks
]
results = await transcription_workflow.aio_run_many(bulk_runs)
track_words: list[list[Word]] = []
for result in results:
transcribe_result = TranscribeTrackResult(**result[TaskName.TRANSCRIBE_TRACK])
track_words.append(transcribe_result.words)
all_words = [word for words in track_words for word in words]
all_words.sort(key=lambda w: w.start)
ctx.log(
f"process_transcriptions complete: {len(all_words)} words from {len(padded_tracks)} tracks"
)
return ProcessTranscriptionsResult(
all_words=all_words,
word_count=len(all_words),
num_tracks=len(input.tracks),
target_language=target_language,
)
@daily_multitrack_pipeline.task(
parents=[process_paddings],
execution_timeout=timedelta(seconds=TIMEOUT_AUDIO),
retries=3,
desired_worker_labels={
@@ -501,12 +537,12 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
)
@with_error_handling(TaskName.MIXDOWN_TRACKS)
async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
"""Mix all padded tracks into single audio file using PyAV (same as Celery)."""
"""Mix all padded tracks into single audio file using PyAV."""
ctx.log("mixdown_tracks: mixing padded tracks into single audio file")
track_result = ctx.task_output(process_tracks)
paddings_result = ctx.task_output(process_paddings)
recording_result = ctx.task_output(get_recording)
padded_tracks = track_result.padded_tracks
padded_tracks = paddings_result.padded_tracks
# Dynamic timeout: scales with track count and recording duration
# Base 300s + 60s per track + 1s per 10s of recording
@@ -660,7 +696,7 @@ async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResul
@daily_multitrack_pipeline.task(
parents=[process_tracks],
parents=[process_transcriptions],
execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
retries=3,
)
@@ -669,8 +705,8 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
"""Detect topics using parallel child workflows (one per chunk)."""
ctx.log("detect_topics: analyzing transcript for topics")
track_result = ctx.task_output(process_tracks)
words = track_result.all_words
transcriptions_result = ctx.task_output(process_transcriptions)
words = transcriptions_result.all_words
if not words:
ctx.log("detect_topics: no words, returning empty topics")
@@ -1107,7 +1143,7 @@ async def identify_action_items(
@daily_multitrack_pipeline.task(
parents=[process_tracks, generate_title, generate_recap, identify_action_items],
parents=[generate_title, generate_recap, identify_action_items],
execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
retries=3,
)
@@ -1120,10 +1156,15 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
"""
ctx.log("finalize: saving transcript and setting status to 'ended'")
track_result = ctx.task_output(process_tracks)
mixdown_result = ctx.task_output(mixdown_tracks)
transcriptions_result = ctx.task_output(process_transcriptions)
paddings_result = ctx.task_output(process_paddings)
duration = mixdown_result.duration
all_words = transcriptions_result.all_words
# Cleanup temporary padded S3 files (deferred until finalize for semantic parity with Celery)
created_padded_files = track_result.created_padded_files
created_padded_files = paddings_result.created_padded_files
if created_padded_files:
ctx.log(f"Cleaning up {len(created_padded_files)} temporary S3 files")
storage = _spawn_storage()
@@ -1141,6 +1182,7 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
async with fresh_db_connection():
from reflector.db.transcripts import ( # noqa: PLC0415
TranscriptDuration,
TranscriptText,
transcripts_controller,
)
@@ -1149,6 +1191,8 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
if transcript is None:
raise ValueError(f"Transcript {input.transcript_id} not found in database")
merged_transcript = TranscriptType(words=all_words, translation=None)
await append_event_and_broadcast(
input.transcript_id,
transcript,
@@ -1160,15 +1204,21 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
logger=logger,
)
# Clear workflow_run_id (workflow completed successfully)
# Note: title/long_summary/short_summary/duration already saved by their callbacks
# Save duration and clear workflow_run_id (workflow completed successfully)
# Note: title/long_summary/short_summary already saved by their callbacks
await transcripts_controller.update(
transcript,
{
"duration": duration,
"workflow_run_id": None, # Clear on success - no need to resume
},
)
duration_data = TranscriptDuration(duration=duration)
await append_event_and_broadcast(
input.transcript_id, transcript, "DURATION", duration_data, logger=logger
)
await set_status_and_broadcast(input.transcript_id, "ended", logger=logger)
ctx.log(

View File

@@ -21,12 +21,14 @@ class ParticipantInfo(BaseModel):
class PadTrackResult(BaseModel):
"""Result from pad_track task."""
"""Result from pad_track task.
padded_key: NonEmptyString # S3 key (not presigned URL) - presign on demand to avoid stale URLs on replay
bucket_name: (
NonEmptyString | None
) # None means use default transcript storage bucket
If size=0, track required no padding and padded_key contains original S3 key.
If size>0, track was padded and padded_key contains new padded file S3 key.
"""
padded_key: NonEmptyString
bucket_name: NonEmptyString | None
size: int
track_index: int
@@ -59,18 +61,25 @@ class PaddedTrackInfo(BaseModel):
"""Info for a padded track - S3 key + bucket for on-demand presigning."""
key: NonEmptyString
bucket_name: NonEmptyString | None # None = use default storage bucket
bucket_name: NonEmptyString | None
track_index: int
class ProcessTracksResult(BaseModel):
"""Result from process_tracks task."""
class ProcessPaddingsResult(BaseModel):
"""Result from process_paddings task."""
padded_tracks: list[PaddedTrackInfo]
num_tracks: int
created_padded_files: list[NonEmptyString]
class ProcessTranscriptionsResult(BaseModel):
"""Result from process_transcriptions task."""
all_words: list[Word]
padded_tracks: list[PaddedTrackInfo] # S3 keys, not presigned URLs
word_count: int
num_tracks: int
target_language: NonEmptyString
created_padded_files: list[NonEmptyString]
class MixdownResult(BaseModel):

View File

@@ -1,9 +1,11 @@
"""
Hatchet child workflow: PaddingWorkflow
Handles individual audio track padding via Modal.com backend.
Handles individual audio track padding only.
"""
import tempfile
from datetime import timedelta
from pathlib import Path
import av
from hatchet_sdk import Context
@@ -14,7 +16,10 @@ from reflector.hatchet.constants import TIMEOUT_AUDIO
from reflector.hatchet.workflows.models import PadTrackResult
from reflector.logger import logger
from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
from reflector.utils.audio_padding import extract_stream_start_time_from_container
from reflector.utils.audio_padding import (
apply_audio_padding_to_file,
extract_stream_start_time_from_container,
)
class PaddingInput(BaseModel):
@@ -63,7 +68,7 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
bucket=input.bucket_name,
)
# Extract start_time to determine if padding needed
with av.open(source_url) as in_container:
with av.open(source_url) as in_container:
if in_container.duration:
try:
@@ -94,52 +99,30 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
# Presign PUT URL for output (Modal will upload directly)
output_url = await storage.get_file_url(
storage_path,
operation="put_object",
expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
)
import httpx # noqa: PLC0415
from reflector.processors.audio_padding_modal import ( # noqa: PLC0415
AudioPaddingModalProcessor,
)
with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as temp_file:
temp_path = temp_file.name
try:
processor = AudioPaddingModalProcessor()
result = await processor.pad_track(
track_url=source_url,
output_url=output_url,
start_time_seconds=start_time_seconds,
track_index=input.track_index,
apply_audio_padding_to_file(
in_container,
temp_path,
start_time_seconds,
input.track_index,
logger=logger,
)
file_size = result.size
ctx.log(f"pad_track: Modal returned size={file_size}")
except httpx.HTTPStatusError as e:
error_detail = e.response.text if hasattr(e.response, "text") else str(e)
logger.error(
"[Hatchet] Modal padding HTTP error",
transcript_id=input.transcript_id,
track_index=input.track_index,
status_code=e.response.status_code if hasattr(e, "response") else None,
error=error_detail,
exc_info=True,
file_size = Path(temp_path).stat().st_size
with open(temp_path, "rb") as padded_file:
await storage.put_file(storage_path, padded_file)
logger.info(
f"Uploaded padded track to S3",
key=storage_path,
size=file_size,
)
raise Exception(
f"Modal padding failed: HTTP {e.response.status_code}"
) from e
except httpx.TimeoutException as e:
logger.error(
"[Hatchet] Modal padding timeout",
transcript_id=input.transcript_id,
track_index=input.track_index,
error=str(e),
exc_info=True,
)
raise Exception("Modal padding timeout") from e
finally:
Path(temp_path).unlink(missing_ok=True)
logger.info(
"[Hatchet] pad_track complete",

View File

@@ -1,205 +0,0 @@
"""
Hatchet child workflow: TrackProcessing
Handles individual audio track processing: padding and transcription.
Spawned dynamically by the main diarization pipeline for each track.
Architecture note: This is a separate workflow (not inline tasks in DailyMultitrackPipeline)
because Hatchet workflow DAGs are defined statically, but the number of tracks varies
at runtime. Child workflow spawning via `aio_run()` + `asyncio.gather()` is the
standard pattern for dynamic fan-out. See `process_tracks` in daily_multitrack_pipeline.py.
Note: This file uses deferred imports (inside tasks) intentionally.
Hatchet workers run in forked processes; fresh imports per task ensure
storage/DB connections are not shared across forks.
"""
from datetime import timedelta
import av
from hatchet_sdk import Context
from pydantic import BaseModel
from reflector.hatchet.client import HatchetClientManager
from reflector.hatchet.constants import TIMEOUT_AUDIO, TIMEOUT_HEAVY
from reflector.hatchet.workflows.models import PadTrackResult, TranscribeTrackResult
from reflector.logger import logger
from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
from reflector.utils.audio_padding import extract_stream_start_time_from_container
class TrackInput(BaseModel):
"""Input for individual track processing."""
track_index: int
s3_key: str
bucket_name: str
transcript_id: str
language: str = "en"
hatchet = HatchetClientManager.get_client()
track_workflow = hatchet.workflow(name="TrackProcessing", input_validator=TrackInput)
@track_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
"""Pad single audio track with silence for alignment.
Extracts stream.start_time from WebM container metadata and applies
silence padding using PyAV filter graph (adelay).
"""
ctx.log(f"pad_track: track {input.track_index}, s3_key={input.s3_key}")
logger.info(
"[Hatchet] pad_track",
track_index=input.track_index,
s3_key=input.s3_key,
transcript_id=input.transcript_id,
)
try:
# Create fresh storage instance to avoid aioboto3 fork issues
from reflector.settings import settings # noqa: PLC0415
from reflector.storage.storage_aws import AwsStorage # noqa: PLC0415
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,
)
source_url = await storage.get_file_url(
input.s3_key,
operation="get_object",
expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
bucket=input.bucket_name,
)
with av.open(source_url) as in_container:
start_time_seconds = extract_stream_start_time_from_container(
in_container, input.track_index, logger=logger
)
# If no padding needed, return original S3 key
if start_time_seconds <= 0:
logger.info(
f"Track {input.track_index} requires no padding",
track_index=input.track_index,
)
return PadTrackResult(
padded_key=input.s3_key,
bucket_name=input.bucket_name,
size=0,
track_index=input.track_index,
)
storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
# Presign PUT URL for output (Modal uploads directly)
output_url = await storage.get_file_url(
storage_path,
operation="put_object",
expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
)
from reflector.processors.audio_padding_modal import ( # noqa: PLC0415
AudioPaddingModalProcessor,
)
processor = AudioPaddingModalProcessor()
result = await processor.pad_track(
track_url=source_url,
output_url=output_url,
start_time_seconds=start_time_seconds,
track_index=input.track_index,
)
file_size = result.size
ctx.log(f"pad_track complete: track {input.track_index} -> {storage_path}")
logger.info(
"[Hatchet] pad_track complete",
track_index=input.track_index,
padded_key=storage_path,
)
# Return S3 key (not presigned URL) - consumer tasks presign on demand
# This avoids stale URLs when workflow is replayed
return PadTrackResult(
padded_key=storage_path,
bucket_name=None, # None = use default transcript storage bucket
size=file_size,
track_index=input.track_index,
)
except Exception as e:
logger.error("[Hatchet] pad_track failed", error=str(e), exc_info=True)
raise
@track_workflow.task(
parents=[pad_track], execution_timeout=timedelta(seconds=TIMEOUT_HEAVY), retries=3
)
async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackResult:
"""Transcribe audio track using GPU (Modal.com) or local Whisper."""
ctx.log(f"transcribe_track: track {input.track_index}, language={input.language}")
logger.info(
"[Hatchet] transcribe_track",
track_index=input.track_index,
language=input.language,
)
try:
pad_result = ctx.task_output(pad_track)
padded_key = pad_result.padded_key
bucket_name = pad_result.bucket_name
if not padded_key:
raise ValueError("Missing padded_key from pad_track")
# Presign URL on demand (avoids stale URLs on workflow replay)
from reflector.settings import settings # noqa: PLC0415
from reflector.storage.storage_aws import AwsStorage # noqa: PLC0415
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,
)
audio_url = await storage.get_file_url(
padded_key,
operation="get_object",
expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
bucket=bucket_name,
)
from reflector.pipelines.transcription_helpers import ( # noqa: PLC0415
transcribe_file_with_processor,
)
transcript = await transcribe_file_with_processor(audio_url, input.language)
# Tag all words with speaker index
for word in transcript.words:
word.speaker = input.track_index
ctx.log(
f"transcribe_track complete: track {input.track_index}, {len(transcript.words)} words"
)
logger.info(
"[Hatchet] transcribe_track complete",
track_index=input.track_index,
word_count=len(transcript.words),
)
return TranscribeTrackResult(
words=transcript.words,
track_index=input.track_index,
)
except Exception as e:
logger.error("[Hatchet] transcribe_track failed", error=str(e), exc_info=True)
raise

View File

@@ -0,0 +1,98 @@
"""
Hatchet child workflow: TranscriptionWorkflow
Handles individual audio track transcription only.
"""
from datetime import timedelta
from hatchet_sdk import Context
from pydantic import BaseModel
from reflector.hatchet.client import HatchetClientManager
from reflector.hatchet.constants import TIMEOUT_HEAVY
from reflector.hatchet.workflows.models import TranscribeTrackResult
from reflector.logger import logger
from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
class TranscriptionInput(BaseModel):
"""Input for individual track transcription."""
track_index: int
padded_key: str # S3 key from padding step
bucket_name: str | None # None = use default bucket
language: str = "en"
hatchet = HatchetClientManager.get_client()
transcription_workflow = hatchet.workflow(
name="TranscriptionWorkflow", input_validator=TranscriptionInput
)
@transcription_workflow.task(
execution_timeout=timedelta(seconds=TIMEOUT_HEAVY), retries=3
)
async def transcribe_track(
input: TranscriptionInput, ctx: Context
) -> TranscribeTrackResult:
"""Transcribe audio track using GPU (Modal.com) or local Whisper."""
ctx.log(f"transcribe_track: track {input.track_index}, language={input.language}")
logger.info(
"[Hatchet] transcribe_track",
track_index=input.track_index,
language=input.language,
)
try:
from reflector.settings import settings # noqa: PLC0415
from reflector.storage.storage_aws import AwsStorage # noqa: PLC0415
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,
)
audio_url = await storage.get_file_url(
input.padded_key,
operation="get_object",
expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
bucket=input.bucket_name,
)
from reflector.pipelines.transcription_helpers import ( # noqa: PLC0415
transcribe_file_with_processor,
)
transcript = await transcribe_file_with_processor(audio_url, input.language)
for word in transcript.words:
word.speaker = input.track_index
ctx.log(
f"transcribe_track complete: track {input.track_index}, {len(transcript.words)} words"
)
logger.info(
"[Hatchet] transcribe_track complete",
track_index=input.track_index,
word_count=len(transcript.words),
)
return TranscribeTrackResult(
words=transcript.words,
track_index=input.track_index,
)
except Exception as e:
logger.error(
"[Hatchet] transcribe_track failed",
track_index=input.track_index,
padded_key=input.padded_key,
language=input.language,
error=str(e),
exc_info=True,
)
raise

View File

@@ -1,113 +0,0 @@
"""
Modal.com backend for audio padding.
"""
import asyncio
import os
import httpx
from pydantic import BaseModel
from reflector.hatchet.constants import TIMEOUT_AUDIO
from reflector.logger import logger
class PaddingResponse(BaseModel):
size: int
cancelled: bool = False
class AudioPaddingModalProcessor:
"""Audio padding processor using Modal.com CPU backend via HTTP."""
def __init__(
self, padding_url: str | None = None, modal_api_key: str | None = None
):
self.padding_url = padding_url or os.getenv("PADDING_URL")
if not self.padding_url:
raise ValueError(
"PADDING_URL required to use AudioPaddingModalProcessor. "
"Set PADDING_URL environment variable or pass padding_url parameter."
)
self.modal_api_key = modal_api_key or os.getenv("MODAL_API_KEY")
async def pad_track(
self,
track_url: str,
output_url: str,
start_time_seconds: float,
track_index: int,
) -> PaddingResponse:
"""Pad audio track with silence via Modal backend.
Args:
track_url: Presigned GET URL for source audio track
output_url: Presigned PUT URL for output WebM
start_time_seconds: Amount of silence to prepend
track_index: Track index for logging
"""
if not track_url:
raise ValueError("track_url cannot be empty")
if start_time_seconds <= 0:
raise ValueError(
f"start_time_seconds must be positive, got {start_time_seconds}"
)
log = logger.bind(track_index=track_index, padding_seconds=start_time_seconds)
log.info("Sending Modal padding HTTP request")
url = f"{self.padding_url}/pad"
headers = {}
if self.modal_api_key:
headers["Authorization"] = f"Bearer {self.modal_api_key}"
try:
async with httpx.AsyncClient(timeout=TIMEOUT_AUDIO) as client:
response = await client.post(
url,
headers=headers,
json={
"track_url": track_url,
"output_url": output_url,
"start_time_seconds": start_time_seconds,
"track_index": track_index,
},
follow_redirects=True,
)
if response.status_code != 200:
error_body = response.text
log.error(
"Modal padding API error",
status_code=response.status_code,
error_body=error_body,
)
response.raise_for_status()
result = response.json()
# Check if work was cancelled
if result.get("cancelled"):
log.warning("Modal padding was cancelled by disconnect detection")
raise asyncio.CancelledError(
"Padding cancelled due to client disconnect"
)
log.info("Modal padding complete", size=result["size"])
return PaddingResponse(**result)
except asyncio.CancelledError:
log.warning(
"Modal padding cancelled (Hatchet timeout, disconnect detected on Modal side)"
)
raise
except httpx.TimeoutException as e:
log.error("Modal padding timeout", error=str(e), exc_info=True)
raise Exception(f"Modal padding timeout: {e}") from e
except httpx.HTTPStatusError as e:
log.error("Modal padding HTTP error", error=str(e), exc_info=True)
raise Exception(f"Modal padding HTTP error: {e}") from e
except Exception as e:
log.error("Modal padding unexpected error", error=str(e), exc_info=True)
raise

View File

@@ -98,10 +98,6 @@ class Settings(BaseSettings):
# 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
# Sentry
SENTRY_DSN: str | None = None

View File

@@ -5,9 +5,7 @@ Used by both Hatchet workflows and Celery pipelines for consistent audio encodin
"""
# Opus codec settings
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_STANDARD_SAMPLE_RATE = 48000
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_DEFAULT_BIT_RATE = 128000 # 128kbps for good speech quality
# S3 presigned URL expiration

View File

@@ -1,6 +1,4 @@
import json
import os
from datetime import datetime, timezone
from typing import assert_never
from fastapi import APIRouter, HTTPException, Request
@@ -14,10 +12,7 @@ from reflector.dailyco_api import (
RecordingReadyEvent,
RecordingStartedEvent,
)
from reflector.dailyco_api.recording_orphans import create_and_log_orphan
from reflector.db.daily_recording_requests import daily_recording_requests_controller
from reflector.db.meetings import meetings_controller
from reflector.db.recordings import Recording, recordings_controller
from reflector.logger import logger as _logger
from reflector.settings import settings
from reflector.video_platforms.factory import create_platform_client
@@ -217,73 +212,10 @@ async def _handle_recording_ready(event: RecordingReadyEvent):
track_keys = [t.s3Key for t in tracks if t.type == "audio"]
# Lookup request
match = await daily_recording_requests_controller.find_by_recording_id(
recording_id
)
if not match:
await create_and_log_orphan(
recording_id=recording_id,
bucket_name=bucket_name,
room_name=room_name,
start_ts=event.payload.start_ts,
track_keys=track_keys,
source="webhook",
)
return
meeting_id, _ = match
# Verify meeting exists
meeting = await meetings_controller.get_by_id(meeting_id)
if not meeting:
logger.error(
"Meeting not found (webhook)",
recording_id=recording_id,
meeting_id=meeting_id,
)
await create_and_log_orphan(
recording_id=recording_id,
bucket_name=bucket_name,
room_name=room_name,
start_ts=event.payload.start_ts,
track_keys=track_keys,
source="webhook",
)
return
# Create recording atomically
created = await recordings_controller.try_create_with_meeting(
Recording(
id=recording_id,
bucket_name=bucket_name,
object_key=(
os.path.dirname(track_keys[0]) if track_keys else room_name
),
recorded_at=datetime.fromtimestamp(
event.payload.start_ts, tz=timezone.utc
),
track_keys=track_keys,
meeting_id=meeting_id,
status="pending",
)
)
if not created:
# Already created (polling got it first)
logger.debug(
"Recording already exists (webhook late)",
recording_id=recording_id,
meeting_id=meeting_id,
)
return
logger.info(
"Raw-tracks recording queuing processing (webhook)",
"Raw-tracks recording queuing processing",
recording_id=recording_id,
room_name=room_name,
meeting_id=meeting_id,
num_tracks=len(track_keys),
)

View File

@@ -1,5 +1,4 @@
import json
import logging
from datetime import datetime, timezone
from typing import Annotated, Any, Optional
from uuid import UUID
@@ -10,21 +9,16 @@ from pydantic import BaseModel
import reflector.auth as auth
from reflector.dailyco_api import RecordingType
from reflector.dailyco_api.client import DailyApiError
from reflector.db.daily_recording_requests import (
DailyRecordingRequest,
daily_recording_requests_controller,
)
from reflector.db.meetings import (
MeetingConsent,
meeting_consent_controller,
meetings_controller,
)
from reflector.db.rooms import rooms_controller
from reflector.logger import logger
from reflector.utils.string import NonEmptyString
from reflector.video_platforms.factory import create_platform_client
logger = logging.getLogger(__name__)
router = APIRouter()
@@ -108,6 +102,13 @@ async def start_recording(
if not meeting:
raise HTTPException(status_code=404, detail="Meeting not found")
log = logger.bind(
meeting_id=meeting_id,
room_name=meeting.room_name,
recording_type=body.type,
instance_id=body.instanceId,
)
try:
client = create_platform_client("daily")
result = await client.start_recording(
@@ -116,30 +117,9 @@ async def start_recording(
instance_id=body.instanceId,
)
recording_id = result["id"]
log.info(f"Started {body.type} recording via REST API")
await daily_recording_requests_controller.create(
DailyRecordingRequest(
recording_id=recording_id,
meeting_id=meeting_id,
instance_id=body.instanceId,
type=body.type,
requested_at=datetime.now(timezone.utc),
)
)
logger.info(
f"Started {body.type} recording via REST API",
extra={
"meeting_id": meeting_id,
"room_name": meeting.room_name,
"recording_type": body.type,
"instance_id": body.instanceId,
"recording_id": recording_id,
},
)
return {"status": "ok", "recording_id": recording_id}
return {"status": "ok", "result": result}
except DailyApiError as e:
# Parse Daily.co error response to detect "has an active stream"
@@ -150,42 +130,22 @@ async def start_recording(
# "has an active stream" means recording already started by another participant
# This is SUCCESS from business logic perspective - return 200
if "has an active stream" in error_info:
logger.info(
f"{body.type} recording already active (started by another participant)",
extra={
"meeting_id": meeting_id,
"room_name": meeting.room_name,
"recording_type": body.type,
"instance_id": body.instanceId,
},
log.info(
f"{body.type} recording already active (started by another participant)"
)
return {"status": "already_active", "instanceId": str(body.instanceId)}
except (json.JSONDecodeError, KeyError):
pass # Fall through to error handling
# All other Daily.co API errors
logger.error(
f"Failed to start {body.type} recording",
extra={
"meeting_id": meeting_id,
"recording_type": body.type,
"error": str(e),
},
)
log.error(f"Failed to start {body.type} recording", error=str(e))
raise HTTPException(
status_code=500, detail=f"Failed to start recording: {str(e)}"
)
except Exception as e:
# Non-Daily.co errors
logger.error(
f"Failed to start {body.type} recording",
extra={
"meeting_id": meeting_id,
"recording_type": body.type,
"error": str(e),
},
)
log.error(f"Failed to start {body.type} recording", error=str(e))
raise HTTPException(
status_code=500, detail=f"Failed to start recording: {str(e)}"
)

View File

@@ -1,5 +1,6 @@
import json
import os
import re
from datetime import datetime, timezone
from typing import List, Literal
from urllib.parse import unquote
@@ -12,12 +13,10 @@ from celery.utils.log import get_task_logger
from pydantic import ValidationError
from reflector.dailyco_api import FinishedRecordingResponse, RecordingResponse
from reflector.dailyco_api.recording_orphans import create_and_log_orphan
from reflector.db.daily_participant_sessions import (
DailyParticipantSession,
daily_participant_sessions_controller,
)
from reflector.db.daily_recording_requests import daily_recording_requests_controller
from reflector.db.meetings import meetings_controller
from reflector.db.recordings import Recording, recordings_controller
from reflector.db.rooms import rooms_controller
@@ -231,44 +230,79 @@ async def _process_multitrack_recording_inner(
recording_start_ts: int,
):
"""
Process multitrack recording.
Process multitrack recording (first time or reprocessing).
Recording must already exist with meeting_id set (created by webhook/polling before queueing).
For first processing (webhook/polling):
- Uses recording_start_ts for time-based meeting matching (no instanceId available)
For reprocessing:
- Uses recording.meeting_id directly (already linked during first processing)
- recording_start_ts is ignored
"""
# Get recording (must exist - created by webhook/polling)
tz = timezone.utc
recorded_at = datetime.now(tz)
try:
if track_keys:
folder = os.path.basename(os.path.dirname(track_keys[0]))
ts_match = re.search(r"(\d{14})$", folder)
if ts_match:
ts = ts_match.group(1)
recorded_at = datetime.strptime(ts, "%Y%m%d%H%M%S").replace(tzinfo=tz)
except Exception as e:
logger.warning(
f"Could not parse recorded_at from keys, using now() {recorded_at}",
e,
exc_info=True,
)
# Check if recording already exists (reprocessing path)
recording = await recordings_controller.get_by_id(recording_id)
if not recording:
logger.error(
"Recording not found - should have been created by webhook/polling",
recording_id=recording_id,
)
return
if not recording.meeting_id:
logger.error(
"Recording has no meeting_id - orphan should not be queued",
recording_id=recording_id,
)
return
# Get meeting
if recording and recording.meeting_id:
# Reprocessing: recording exists with meeting already linked
meeting = await meetings_controller.get_by_id(recording.meeting_id)
if not meeting:
logger.error(
"Meeting not found for recording",
"Reprocessing: meeting not found for recording - skipping",
meeting_id=recording.meeting_id,
recording_id=recording_id,
)
return
logger.info(
"Processing multitrack recording",
"Reprocessing: using existing recording.meeting_id",
recording_id=recording_id,
meeting_id=meeting.id,
room_name=daily_room_name,
)
else:
# First processing: recording doesn't exist, need time-based matching
# (Daily.co doesn't return instanceId in API, must match by timestamp)
recording_start = datetime.fromtimestamp(recording_start_ts, tz=timezone.utc)
meeting = await meetings_controller.get_by_room_name_and_time(
room_name=daily_room_name,
recording_start=recording_start,
time_window_hours=168, # 1 week
)
if not meeting:
logger.error(
"Raw-tracks: no meeting found within 1-week window (time-based match) - skipping",
recording_id=recording_id,
room_name=daily_room_name,
recording_start_ts=recording_start_ts,
recording_start=recording_start.isoformat(),
)
return # Skip processing, will retry on next poll
logger.info(
"First processing: found meeting via time-based matching",
meeting_id=meeting.id,
room_name=daily_room_name,
recording_id=recording_id,
time_delta_seconds=abs(
(meeting.start_date - recording_start).total_seconds()
),
)
room_name_base = extract_base_room_name(daily_room_name)
@@ -276,6 +310,33 @@ async def _process_multitrack_recording_inner(
if not room:
raise Exception(f"Room not found: {room_name_base}")
if not recording:
# Create recording (only happens during first processing)
object_key_dir = os.path.dirname(track_keys[0]) if track_keys else ""
recording = await recordings_controller.create(
Recording(
id=recording_id,
bucket_name=bucket_name,
object_key=object_key_dir,
recorded_at=recorded_at,
meeting_id=meeting.id,
track_keys=track_keys,
)
)
elif not recording.meeting_id:
# Recording exists but meeting_id is null (failed first processing)
# Update with meeting from time-based matching
await recordings_controller.set_meeting_id(
recording_id=recording.id,
meeting_id=meeting.id,
)
recording.meeting_id = meeting.id
logger.info(
"Updated existing recording with meeting_id",
recording_id=recording.id,
meeting_id=meeting.id,
)
transcript = await transcripts_controller.get_by_recording_id(recording.id)
if not transcript:
transcript = await transcripts_controller.add(
@@ -461,7 +522,7 @@ async def store_cloud_recording(
Store cloud recording reference in meeting table.
Common function for both webhook and polling code paths.
Uses direct recording_id lookup via daily_recording_requests table.
Uses time-based matching to handle duplicate room_name values.
Args:
recording_id: Daily.co recording ID
@@ -474,170 +535,155 @@ async def store_cloud_recording(
Returns:
True if stored, False if skipped/failed
"""
# Lookup request
match = await daily_recording_requests_controller.find_by_recording_id(recording_id)
recording_start = datetime.fromtimestamp(start_ts, tz=timezone.utc)
if not match:
# ORPHAN: No request found (pre-migration recording or failed request creation)
await create_and_log_orphan(
recording_id=recording_id,
bucket_name="",
meeting = await meetings_controller.get_by_room_name_and_time(
room_name=room_name,
start_ts=start_ts,
track_keys=None,
source=source,
recording_start=recording_start,
time_window_hours=168, # 1 week
)
if not meeting:
logger.warning(
f"Cloud recording ({source}): no meeting found within 1-week window",
recording_id=recording_id,
room_name=room_name,
recording_start_ts=start_ts,
recording_start=recording_start.isoformat(),
)
return False
meeting_id, _ = match
success = await meetings_controller.set_cloud_recording_if_missing(
meeting_id=meeting_id,
meeting_id=meeting.id,
s3_key=s3_key,
duration=duration,
)
if not success:
logger.debug(
f"Cloud recording ({source}): already set (stop/restart?)",
f"Cloud recording ({source}): already set (race lost)",
recording_id=recording_id,
room_name=room_name,
meeting_id=meeting_id,
meeting_id=meeting.id,
)
return False
logger.info(
f"Cloud recording stored via {source}",
meeting_id=meeting_id,
f"Cloud recording stored via {source} (time-based match)",
meeting_id=meeting.id,
recording_id=recording_id,
s3_key=s3_key,
duration=duration,
time_delta_seconds=abs((meeting.start_date - recording_start).total_seconds()),
)
return True
async def _poll_cloud_recordings(cloud_recordings: List[FinishedRecordingResponse]):
"""Process cloud recordings (database deduplication, worker-agnostic).
"""
Store cloud recordings missing from meeting table via polling.
Cloud recordings stored in meeting.daily_composed_video_s3_key, not recording table.
Only first cloud recording per meeting is kept (existing behavior).
Uses time-based matching via store_cloud_recording().
"""
if not cloud_recordings:
return
for rec in cloud_recordings:
# Lookup request
match = await daily_recording_requests_controller.find_by_recording_id(rec.id)
stored_count = 0
for recording in cloud_recordings:
# Extract S3 key from recording (cloud recordings use s3key field)
s3_key = recording.s3key or (recording.s3.key if recording.s3 else None)
if not s3_key:
logger.warning(
"Cloud recording: missing S3 key",
recording_id=recording.id,
room_name=recording.room_name,
)
continue
if not match:
await create_and_log_orphan(
recording_id=rec.id,
bucket_name="",
room_name=rec.room_name,
start_ts=rec.start_ts,
track_keys=None,
stored = await store_cloud_recording(
recording_id=recording.id,
room_name=recording.room_name,
s3_key=s3_key,
duration=recording.duration,
start_ts=recording.start_ts,
source="polling",
)
continue
if stored:
stored_count += 1
meeting_id, _ = match
if not rec.s3key:
logger.error("Cloud recording missing s3_key", recording_id=rec.id)
continue
# Store in meeting table (atomic, only if not already set)
success = await meetings_controller.set_cloud_recording_if_missing(
meeting_id=meeting_id,
s3_key=rec.s3key,
duration=rec.duration,
)
if success:
logger.info(
"Stored cloud recording", recording_id=rec.id, meeting_id=meeting_id
)
else:
logger.warning(
"Cloud recording already exists for meeting (stop/restart?)",
recording_id=rec.id,
meeting_id=meeting_id,
"Cloud recording polling complete",
total=len(cloud_recordings),
stored=stored_count,
)
async def _poll_raw_tracks_recordings(
raw_tracks_recordings: List[FinishedRecordingResponse],
bucket_name: NonEmptyString,
) -> None:
"""Process raw-tracks (database deduplication, worker-agnostic)."""
bucket_name: str,
):
"""Queue raw-tracks recordings missing from DB (existing logic)."""
if not raw_tracks_recordings:
return
for rec in raw_tracks_recordings:
# Lookup request FIRST (before any DB writes)
match = await daily_recording_requests_controller.find_by_recording_id(rec.id)
recording_ids = [rec.id for rec in raw_tracks_recordings]
existing_recordings = await recordings_controller.get_by_ids(recording_ids)
existing_ids = {rec.id for rec in existing_recordings}
if not match:
await create_and_log_orphan(
recording_id=rec.id,
bucket_name=bucket_name,
room_name=rec.room_name,
start_ts=rec.start_ts,
track_keys=[t.s3Key for t in rec.tracks if t.type == "audio"],
source="polling",
missing_recordings = [
rec for rec in raw_tracks_recordings if rec.id not in existing_ids
]
if not missing_recordings:
logger.debug(
"All raw-tracks recordings already in DB",
api_count=len(raw_tracks_recordings),
existing_count=len(existing_recordings),
)
return
logger.info(
"Found raw-tracks recordings missing from DB",
missing_count=len(missing_recordings),
total_api_count=len(raw_tracks_recordings),
existing_count=len(existing_recordings),
)
for recording in missing_recordings:
if not recording.tracks:
logger.warning(
"Finished raw-tracks recording has no tracks (no audio captured)",
recording_id=recording.id,
room_name=recording.room_name,
)
continue
meeting_id, _ = match
track_keys = [t.s3Key for t in recording.tracks if t.type == "audio"]
# Verify meeting exists
meeting = await meetings_controller.get_by_id(meeting_id)
if not meeting:
logger.error(
"Meeting not found", recording_id=rec.id, meeting_id=meeting_id
)
await create_and_log_orphan(
recording_id=rec.id,
bucket_name=bucket_name,
room_name=rec.room_name,
start_ts=rec.start_ts,
track_keys=[t.s3Key for t in rec.tracks if t.type == "audio"],
source="polling",
if not track_keys:
logger.warning(
"No audio tracks found in raw-tracks recording",
recording_id=recording.id,
room_name=recording.room_name,
total_tracks=len(recording.tracks),
)
continue
# DEDUPLICATION: Atomically create recording (single operation, no race window)
# ON CONFLICT → concurrent poller already got it, skip entire logic
track_keys = [t.s3Key for t in rec.tracks if t.type == "audio"]
created = await recordings_controller.try_create_with_meeting(
Recording(
id=rec.id,
bucket_name=bucket_name,
object_key=os.path.dirname(track_keys[0]) if track_keys else "",
recorded_at=datetime.fromtimestamp(rec.start_ts, tz=timezone.utc),
track_keys=track_keys,
meeting_id=meeting_id, # Set at creation (constraint-safe)
status="pending",
)
logger.info(
"Queueing missing raw-tracks recording for processing",
recording_id=recording.id,
room_name=recording.room_name,
track_count=len(track_keys),
)
if not created:
# Conflict: another poller already created/queued this
# Skip all remaining logic (match already done by winner)
continue
# Only winner reaches here - queue processing (works with Celery or Hatchet)
process_multitrack_recording.delay(
recording_id=rec.id,
daily_room_name=rec.room_name,
recording_start_ts=rec.start_ts,
bucket_name=bucket_name,
daily_room_name=recording.room_name,
recording_id=recording.id,
track_keys=track_keys,
recording_start_ts=recording.start_ts,
)
logger.info("Queued recording", recording_id=rec.id, meeting_id=meeting_id)
async def poll_daily_room_presence(meeting_id: str) -> None:
"""Poll Daily.co room presence and reconcile with DB sessions. New presence is added, old presence is marked as closed.

View File

@@ -1,39 +0,0 @@
#!/usr/bin/env python3
"""Test script to fetch Daily.co recordings for a specific room and show raw API response."""
import asyncio
import json
from reflector.video_platforms.factory import create_platform_client
async def main():
room_name = "daily-private-igor-20260110042117"
print(f"\n=== Fetching recordings for room: {room_name} ===\n")
async with create_platform_client("daily") as client:
recordings = await client.list_recordings(room_name=room_name)
print(f"Found {len(recordings)} recording objects from Daily.co API\n")
for i, rec in enumerate(recordings, 1):
print(f"--- Recording #{i} ---")
print(f"ID: {rec.id}")
print(f"Room: {rec.room_name}")
print(f"Start TS: {rec.start_ts}")
print(f"Status: {rec.status}")
print(f"Duration: {rec.duration}")
print(f"Type: {rec.type}")
print(f"Tracks count: {len(rec.tracks)}")
if rec.tracks:
print(f"Tracks:")
for j, track in enumerate(rec.tracks, 1):
print(f" Track {j}: {track.s3Key}")
print(f"\nRaw JSON:\n{json.dumps(rec.model_dump(), indent=2, default=str)}\n")
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -1,258 +0,0 @@
from datetime import datetime, timezone
from uuid import UUID
import pytest
from reflector.db.daily_recording_requests import (
DailyRecordingRequest,
daily_recording_requests_controller,
)
from reflector.db.meetings import Meeting, meetings_controller
from reflector.db.recordings import Recording, recordings_controller
from reflector.db.rooms import Room, rooms_controller
@pytest.mark.asyncio
async def test_create_request():
"""Test creating a recording request."""
# Create meeting first
room = Room(id="test-room", name="Test Room", slug="test-room", user_id="test-user")
await rooms_controller.create(room)
meeting = Meeting(
id="meeting-123",
room_name="test-room",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="cloud",
)
await meetings_controller.create(meeting)
request = DailyRecordingRequest(
recording_id="rec-1",
meeting_id="meeting-123",
instance_id=UUID("a1b2c3d4-e5f6-7890-abcd-ef1234567890"),
type="cloud",
requested_at=datetime.now(timezone.utc),
)
await daily_recording_requests_controller.create(request)
result = await daily_recording_requests_controller.find_by_recording_id("rec-1")
assert result is not None
assert result[0] == "meeting-123"
assert result[1] == "cloud"
@pytest.mark.asyncio
async def test_multiple_recordings_same_meeting():
"""Test stop/restart creates multiple request rows."""
# Create room and meeting
room = Room(
id="test-room-2", name="Test Room 2", slug="test-room-2", user_id="test-user"
)
await rooms_controller.create(room)
meeting_id = "meeting-456"
meeting = Meeting(
id=meeting_id,
room_name="test-room-2",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="cloud",
)
await meetings_controller.create(meeting)
instance_id = UUID("b1c2d3e4-f5a6-7890-abcd-ef1234567890")
# First recording
await daily_recording_requests_controller.create(
DailyRecordingRequest(
recording_id="rec-1",
meeting_id=meeting_id,
instance_id=instance_id,
type="cloud",
requested_at=datetime.now(timezone.utc),
)
)
# Stop, then restart (new recording_id, same instance_id)
await daily_recording_requests_controller.create(
DailyRecordingRequest(
recording_id="rec-2", # DIFFERENT
meeting_id=meeting_id,
instance_id=instance_id, # SAME
type="cloud",
requested_at=datetime.now(timezone.utc),
)
)
# Both exist
requests = await daily_recording_requests_controller.get_by_meeting_id(meeting_id)
assert len(requests) == 2
assert {r.recording_id for r in requests} == {"rec-1", "rec-2"}
@pytest.mark.asyncio
async def test_deduplication_via_database():
"""Test concurrent pollers use database for deduplication."""
# Create room and meeting
room = Room(
id="test-room-3", name="Test Room 3", slug="test-room-3", user_id="test-user"
)
await rooms_controller.create(room)
meeting = Meeting(
id="meeting-789",
room_name="test-room-3",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="raw-tracks",
)
await meetings_controller.create(meeting)
recording_id = "rec-123"
# Poller 1
created1 = await recordings_controller.try_create_with_meeting(
Recording(
id=recording_id,
bucket_name="test-bucket",
object_key="test-key",
recorded_at=datetime.now(timezone.utc),
meeting_id="meeting-789",
status="pending",
track_keys=["track1.webm", "track2.webm"],
)
)
assert created1 is True # First wins
# Poller 2 (concurrent)
created2 = await recordings_controller.try_create_with_meeting(
Recording(
id=recording_id,
bucket_name="test-bucket",
object_key="test-key",
recorded_at=datetime.now(timezone.utc),
meeting_id="meeting-789",
status="pending",
track_keys=["track1.webm", "track2.webm"],
)
)
assert created2 is False # Conflict, skip
@pytest.mark.asyncio
async def test_orphan_logged_once():
"""Test orphan marked once, skipped on re-poll."""
# First poll
created1 = await recordings_controller.create_orphan(
Recording(
id="orphan-123",
bucket_name="test-bucket",
object_key="orphan-key",
recorded_at=datetime.now(timezone.utc),
meeting_id=None,
status="orphan",
track_keys=None,
)
)
assert created1 is True
# Second poll (same orphan discovered again)
created2 = await recordings_controller.create_orphan(
Recording(
id="orphan-123",
bucket_name="test-bucket",
object_key="orphan-key",
recorded_at=datetime.now(timezone.utc),
meeting_id=None,
status="orphan",
track_keys=None,
)
)
assert created2 is False # Already exists
# Verify it exists
existing = await recordings_controller.get_by_id("orphan-123")
assert existing is not None
assert existing.status == "orphan"
@pytest.mark.asyncio
async def test_orphan_constraints():
"""Test orphan invariants are enforced."""
# Can't create orphan with meeting_id
with pytest.raises(AssertionError, match="meeting_id must be NULL"):
await recordings_controller.create_orphan(
Recording(
id="bad-orphan-1",
bucket_name="test",
object_key="test",
recorded_at=datetime.now(timezone.utc),
meeting_id="meeting-123", # Should be None
status="orphan",
track_keys=None,
)
)
# Can't create orphan with wrong status
with pytest.raises(AssertionError, match="status must be 'orphan'"):
await recordings_controller.create_orphan(
Recording(
id="bad-orphan-2",
bucket_name="test",
object_key="test",
recorded_at=datetime.now(timezone.utc),
meeting_id=None,
status="pending", # Should be "orphan"
track_keys=None,
)
)
@pytest.mark.asyncio
async def test_try_create_with_meeting_constraints():
"""Test try_create_with_meeting enforces constraints."""
# Create room and meeting
room = Room(
id="test-room-4", name="Test Room 4", slug="test-room-4", user_id="test-user"
)
await rooms_controller.create(room)
meeting = Meeting(
id="meeting-999",
room_name="test-room-4",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="cloud",
)
await meetings_controller.create(meeting)
# Can't create with orphan status
with pytest.raises(AssertionError, match="use create_orphan"):
await recordings_controller.try_create_with_meeting(
Recording(
id="bad-rec-1",
bucket_name="test",
object_key="test",
recorded_at=datetime.now(timezone.utc),
meeting_id="meeting-999",
status="orphan", # Should not be orphan
track_keys=None,
)
)
# Can't create without meeting_id
with pytest.raises(AssertionError, match="meeting_id required"):
await recordings_controller.try_create_with_meeting(
Recording(
id="bad-rec-2",
bucket_name="test",
object_key="test",
recorded_at=datetime.now(timezone.utc),
meeting_id=None, # Should have meeting_id
status="pending",
track_keys=None,
)
)

View File

@@ -1,300 +0,0 @@
"""
Integration tests for recording request flow.
These tests verify the end-to-end flow of:
1. Starting a recording (creates request)
2. Webhook/polling discovering recording (matches via request)
3. Recording processing (uses existing meeting_id)
"""
from datetime import datetime, timezone
from uuid import UUID, uuid4
import pytest
from reflector.db.daily_recording_requests import (
DailyRecordingRequest,
daily_recording_requests_controller,
)
from reflector.db.meetings import Meeting, meetings_controller
from reflector.db.recordings import Recording, recordings_controller
from reflector.db.rooms import Room, rooms_controller
@pytest.mark.asyncio
async def test_recording_request_flow_cloud(client):
"""Test full cloud recording flow: start -> webhook -> match"""
# Create room and meeting
room = Room(id="test-room", name="Test Room", slug="test-room", user_id="test-user")
await rooms_controller.create(room)
meeting_id = f"meeting-{uuid4()}"
meeting = Meeting(
id=meeting_id,
room_name="test-room",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="cloud",
)
await meetings_controller.create(meeting)
# Simulate recording start (what endpoint does)
recording_id = "rec-cloud-123"
instance_id = UUID("a1b2c3d4-e5f6-7890-abcd-ef1234567890")
request = DailyRecordingRequest(
recording_id=recording_id,
meeting_id=meeting_id,
instance_id=instance_id,
type="cloud",
requested_at=datetime.now(timezone.utc),
)
await daily_recording_requests_controller.create(request)
# Verify request exists
match = await daily_recording_requests_controller.find_by_recording_id(recording_id)
assert match is not None
assert match[0] == meeting_id
assert match[1] == "cloud"
# Simulate webhook/polling storing cloud recording
success = await meetings_controller.set_cloud_recording_if_missing(
meeting_id=meeting_id,
s3_key="s3://bucket/recording.mp4",
duration=120,
)
assert success is True
# Verify meeting updated
updated_meeting = await meetings_controller.get_by_id(meeting_id)
assert updated_meeting.daily_composed_video_s3_key == "s3://bucket/recording.mp4"
assert updated_meeting.daily_composed_video_duration == 120
@pytest.mark.asyncio
async def test_recording_request_flow_raw_tracks(client):
"""Test full raw-tracks recording flow: start -> webhook/polling -> process"""
# Create room and meeting
room = Room(
id="test-room-2",
name="Test Room 2",
slug="test-room-2",
user_id="test-user",
)
await rooms_controller.create(room)
meeting_id = f"meeting-{uuid4()}"
meeting = Meeting(
id=meeting_id,
room_name="test-room-2",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="raw-tracks",
)
await meetings_controller.create(meeting)
# Simulate recording start
recording_id = "rec-raw-456"
instance_id = UUID("b1c2d3e4-f5a6-7890-abcd-ef1234567890")
request = DailyRecordingRequest(
recording_id=recording_id,
meeting_id=meeting_id,
instance_id=instance_id,
type="raw-tracks",
requested_at=datetime.now(timezone.utc),
)
await daily_recording_requests_controller.create(request)
# Simulate webhook/polling discovering recording
match = await daily_recording_requests_controller.find_by_recording_id(recording_id)
assert match is not None
found_meeting_id, recording_type = match
assert found_meeting_id == meeting_id
assert recording_type == "raw-tracks"
# Create recording (what webhook/polling does)
created = await recordings_controller.try_create_with_meeting(
Recording(
id=recording_id,
bucket_name="test-bucket",
object_key="recordings/20260120/",
recorded_at=datetime.now(timezone.utc),
track_keys=["track1.webm", "track2.webm"],
meeting_id=meeting_id,
status="pending",
)
)
assert created is True
# Verify recording exists with meeting_id
recording = await recordings_controller.get_by_id(recording_id)
assert recording is not None
assert recording.meeting_id == meeting_id
assert recording.status == "pending"
assert len(recording.track_keys) == 2
@pytest.mark.asyncio
async def test_stop_restart_creates_multiple_requests(client):
"""Test stop/restart creates multiple request rows with same instance_id"""
# Create room and meeting
room = Room(
id="test-room-3",
name="Test Room 3",
slug="test-room-3",
user_id="test-user",
)
await rooms_controller.create(room)
meeting_id = f"meeting-{uuid4()}"
meeting = Meeting(
id=meeting_id,
room_name="test-room-3",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="cloud",
)
await meetings_controller.create(meeting)
instance_id = UUID("c1d2e3f4-a5b6-7890-abcd-ef1234567890")
# First recording
await daily_recording_requests_controller.create(
DailyRecordingRequest(
recording_id="rec-first",
meeting_id=meeting_id,
instance_id=instance_id,
type="cloud",
requested_at=datetime.now(timezone.utc),
)
)
# Stop, then restart (new recording_id, same instance_id)
await daily_recording_requests_controller.create(
DailyRecordingRequest(
recording_id="rec-second", # DIFFERENT
meeting_id=meeting_id,
instance_id=instance_id, # SAME
type="cloud",
requested_at=datetime.now(timezone.utc),
)
)
# Both exist
requests = await daily_recording_requests_controller.get_by_meeting_id(meeting_id)
assert len(requests) == 2
assert {r.recording_id for r in requests} == {"rec-first", "rec-second"}
assert all(r.instance_id == instance_id for r in requests)
@pytest.mark.asyncio
async def test_orphan_recording_no_request(client):
"""Test orphan recording (no request found)"""
# Simulate polling discovering recording with no request
recording_id = "rec-orphan"
match = await daily_recording_requests_controller.find_by_recording_id(recording_id)
assert match is None # No request
# Mark as orphan
created = await recordings_controller.create_orphan(
Recording(
id=recording_id,
bucket_name="test-bucket",
object_key="orphan-key",
recorded_at=datetime.now(timezone.utc),
meeting_id=None,
status="orphan",
track_keys=None,
)
)
assert created is True
# Verify orphan exists
recording = await recordings_controller.get_by_id(recording_id)
assert recording is not None
assert recording.status == "orphan"
assert recording.meeting_id is None
# Second poll - already exists
created_again = await recordings_controller.create_orphan(
Recording(
id=recording_id,
bucket_name="test-bucket",
object_key="orphan-key",
recorded_at=datetime.now(timezone.utc),
meeting_id=None,
status="orphan",
track_keys=None,
)
)
assert created_again is False # Already exists
@pytest.mark.asyncio
async def test_concurrent_polling_deduplication(client):
"""Test concurrent pollers only queue once"""
# Create room and meeting
room = Room(
id="test-room-4",
name="Test Room 4",
slug="test-room-4",
user_id="test-user",
)
await rooms_controller.create(room)
meeting_id = f"meeting-{uuid4()}"
meeting = Meeting(
id=meeting_id,
room_name="test-room-4",
start_date=datetime.now(timezone.utc),
end_date=None,
recording_type="raw-tracks",
)
await meetings_controller.create(meeting)
# Create request
recording_id = "rec-concurrent"
await daily_recording_requests_controller.create(
DailyRecordingRequest(
recording_id=recording_id,
meeting_id=meeting_id,
instance_id=UUID("d1e2f3a4-b5c6-7890-abcd-ef1234567890"),
type="raw-tracks",
requested_at=datetime.now(timezone.utc),
)
)
# Poller 1
created1 = await recordings_controller.try_create_with_meeting(
Recording(
id=recording_id,
bucket_name="test-bucket",
object_key="test-key",
recorded_at=datetime.now(timezone.utc),
meeting_id=meeting_id,
status="pending",
track_keys=["track1.webm"],
)
)
assert created1 is True # First wins
# Poller 2 (concurrent)
created2 = await recordings_controller.try_create_with_meeting(
Recording(
id=recording_id,
bucket_name="test-bucket",
object_key="test-key",
recorded_at=datetime.now(timezone.utc),
meeting_id=meeting_id,
status="pending",
track_keys=["track1.webm"],
)
)
assert created2 is False # Conflict, skip
# Only one recording exists
recording = await recordings_controller.get_by_id(recording_id)
assert recording is not None
assert recording.meeting_id == meeting_id

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@@ -0,0 +1,374 @@
"""
Integration tests for time-based meeting-to-recording matching.
Tests the critical path for matching Daily.co recordings to meetings when
API doesn't return instanceId.
"""
from datetime import datetime, timedelta, timezone
import pytest
from reflector.db.meetings import meetings_controller
from reflector.db.rooms import rooms_controller
@pytest.fixture
async def test_room():
"""Create a test room for meetings."""
room = await rooms_controller.add(
name="test-room-time",
user_id="test-user-id",
zulip_auto_post=False,
zulip_stream="",
zulip_topic="",
is_locked=False,
room_mode="normal",
recording_type="cloud",
recording_trigger="automatic",
is_shared=False,
platform="daily",
)
return room
@pytest.fixture
def base_time():
"""Fixed timestamp for deterministic tests."""
return datetime(2026, 1, 14, 9, 0, 0, tzinfo=timezone.utc)
class TestTimeBasedMatching:
"""Test get_by_room_name_and_time() matching logic."""
async def test_exact_time_match(self, test_room, base_time):
"""Recording timestamp exactly matches meeting start_date."""
meeting = await meetings_controller.create(
id="meeting-exact",
room_name="daily-test-20260114090000",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-20260114090000",
recording_start=base_time,
time_window_hours=168,
)
assert result is not None
assert result.id == meeting.id
async def test_recording_slightly_after_meeting_start(self, test_room, base_time):
"""Recording started 1 minute after meeting (participants joined late)."""
meeting = await meetings_controller.create(
id="meeting-late",
room_name="daily-test-20260114090100",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
recording_start = base_time + timedelta(minutes=1)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-20260114090100",
recording_start=recording_start,
time_window_hours=168,
)
assert result is not None
assert result.id == meeting.id
async def test_duplicate_room_names_picks_closest(self, test_room, base_time):
"""
Two meetings with same room_name (duplicate/race condition).
Should pick closest by timestamp.
"""
meeting1 = await meetings_controller.create(
id="meeting-1-first",
room_name="daily-duplicate-room",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
meeting2 = await meetings_controller.create(
id="meeting-2-second",
room_name="daily-duplicate-room", # Same room_name!
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time + timedelta(seconds=0.99), # 0.99s later
end_date=base_time + timedelta(hours=1),
room=test_room,
)
# Recording started 0.5s after meeting1
# Distance: meeting1 = 0.5s, meeting2 = 0.49s → meeting2 is closer
recording_start = base_time + timedelta(seconds=0.5)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-duplicate-room",
recording_start=recording_start,
time_window_hours=168,
)
assert result is not None
assert result.id == meeting2.id # meeting2 is closer (0.49s vs 0.5s)
async def test_outside_time_window_returns_none(self, test_room, base_time):
"""Recording outside 1-week window returns None."""
await meetings_controller.create(
id="meeting-old",
room_name="daily-test-old",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
# Recording 8 days later (outside 7-day window)
recording_start = base_time + timedelta(days=8)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-old",
recording_start=recording_start,
time_window_hours=168,
)
assert result is None
async def test_tie_breaker_deterministic(self, test_room, base_time):
"""When time delta identical, tie-breaker by meeting.id is deterministic."""
meeting_z = await meetings_controller.create(
id="zzz-last-uuid",
room_name="daily-test-tie",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
meeting_a = await meetings_controller.create(
id="aaa-first-uuid",
room_name="daily-test-tie",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time, # Exact same start_date
end_date=base_time + timedelta(hours=1),
room=test_room,
)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-tie",
recording_start=base_time,
time_window_hours=168,
)
assert result is not None
# Tie-breaker: lexicographically first UUID
assert result.id == "aaa-first-uuid"
async def test_timezone_naive_datetime_raises(self, test_room, base_time):
"""Timezone-naive datetime raises ValueError."""
await meetings_controller.create(
id="meeting-tz",
room_name="daily-test-tz",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
# Naive datetime (no timezone)
naive_dt = datetime(2026, 1, 14, 9, 0, 0)
with pytest.raises(ValueError, match="timezone-aware"):
await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-tz",
recording_start=naive_dt,
time_window_hours=168,
)
async def test_one_week_boundary_after_included(self, test_room, base_time):
"""Meeting 1-week AFTER recording is included (window_end boundary)."""
meeting_time = base_time + timedelta(hours=168)
await meetings_controller.create(
id="meeting-boundary-after",
room_name="daily-test-boundary-after",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=meeting_time,
end_date=meeting_time + timedelta(hours=1),
room=test_room,
)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-boundary-after",
recording_start=base_time,
time_window_hours=168,
)
assert result is not None
assert result.id == "meeting-boundary-after"
async def test_one_week_boundary_before_included(self, test_room, base_time):
"""Meeting 1-week BEFORE recording is included (window_start boundary)."""
meeting_time = base_time - timedelta(hours=168)
await meetings_controller.create(
id="meeting-boundary-before",
room_name="daily-test-boundary-before",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=meeting_time,
end_date=meeting_time + timedelta(hours=1),
room=test_room,
)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-boundary-before",
recording_start=base_time,
time_window_hours=168,
)
assert result is not None
assert result.id == "meeting-boundary-before"
async def test_recording_before_meeting_start(self, test_room, base_time):
"""Recording started before meeting (clock skew or early join)."""
await meetings_controller.create(
id="meeting-early",
room_name="daily-test-early",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
recording_start = base_time - timedelta(minutes=2)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-early",
recording_start=recording_start,
time_window_hours=168,
)
assert result is not None
assert result.id == "meeting-early"
async def test_mixed_inside_outside_window(self, test_room, base_time):
"""Multiple meetings, only one inside window - returns the inside one."""
await meetings_controller.create(
id="meeting-old",
room_name="daily-test-mixed",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time - timedelta(days=10),
end_date=base_time - timedelta(days=10, hours=-1),
room=test_room,
)
await meetings_controller.create(
id="meeting-inside",
room_name="daily-test-mixed",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time - timedelta(days=2),
end_date=base_time - timedelta(days=2, hours=-1),
room=test_room,
)
await meetings_controller.create(
id="meeting-future",
room_name="daily-test-mixed",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time + timedelta(days=10),
end_date=base_time + timedelta(days=10, hours=1),
room=test_room,
)
result = await meetings_controller.get_by_room_name_and_time(
room_name="daily-test-mixed",
recording_start=base_time,
time_window_hours=168,
)
assert result is not None
assert result.id == "meeting-inside"
class TestAtomicCloudRecordingUpdate:
"""Test atomic update prevents race conditions."""
async def test_first_update_succeeds(self, test_room, base_time):
"""First call to set_cloud_recording_if_missing succeeds."""
meeting = await meetings_controller.create(
id="meeting-atomic-1",
room_name="daily-test-atomic",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
success = await meetings_controller.set_cloud_recording_if_missing(
meeting_id=meeting.id,
s3_key="first-s3-key",
duration=100,
)
assert success is True
updated = await meetings_controller.get_by_id(meeting.id)
assert updated.daily_composed_video_s3_key == "first-s3-key"
assert updated.daily_composed_video_duration == 100
async def test_second_update_fails_atomically(self, test_room, base_time):
"""Second call to update same meeting doesn't overwrite (atomic check)."""
meeting = await meetings_controller.create(
id="meeting-atomic-2",
room_name="daily-test-atomic2",
room_url="https://example.daily.co/test",
host_room_url="https://example.daily.co/test?t=host",
start_date=base_time,
end_date=base_time + timedelta(hours=1),
room=test_room,
)
success1 = await meetings_controller.set_cloud_recording_if_missing(
meeting_id=meeting.id,
s3_key="first-s3-key",
duration=100,
)
assert success1 is True
after_first = await meetings_controller.get_by_id(meeting.id)
assert after_first.daily_composed_video_s3_key == "first-s3-key"
success2 = await meetings_controller.set_cloud_recording_if_missing(
meeting_id=meeting.id,
s3_key="bucket/path/should-not-overwrite",
duration=200,
)
assert success2 is False
final = await meetings_controller.get_by_id(meeting.id)
assert final.daily_composed_video_s3_key == "first-s3-key"
assert final.daily_composed_video_duration == 100

45
server/uv.lock generated
View File

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