videodb skills update: add reference files for videodb skills

2026-04-09 19:03:28 +08:00 · 2026-03-03 18:20:30 +05:30
parent cff0308568
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--- a/skills/videodb/reference/rtstream-reference.md
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+# RTStream Reference
+
+Code-level details for RTStream operations. For workflow guide, see [rtstream.md](rtstream.md).
+
+Based on [docs.videodb.io](https://docs.videodb.io/pages/ingest/live-streams/realtime-apis.md).
+
+---
+
+## Collection RTStream Methods
+
+Methods on `Collection` for managing RTStreams:
+
+| Method | Returns | Description |
+|--------|---------|-------------|
+| `coll.connect_rtstream(url, name, ...)` | `RTStream` | Create new RTStream from RTSP/RTMP URL |
+| `coll.get_rtstream(id)` | `RTStream` | Get existing RTStream by ID |
+| `coll.list_rtstreams(limit, offset, status, name, ordering)` | `List[RTStream]` | List all RTStreams in collection |
+| `coll.search(query, namespace="rtstream")` | `RTStreamSearchResult` | Search across all RTStreams |
+
+### Connect RTStream
+
+```python
+import videodb
+
+conn = videodb.connect()
+coll = conn.get_collection()
+
+rtstream = coll.connect_rtstream(
+    url="rtmp://your-stream-server/live/stream-key",
+    name="My Live Stream",
+    media_types=["video"],  # or ["audio", "video"]
+    sample_rate=30,         # optional
+    store=True,             # enable recording storage for export
+    enable_transcript=True, # optional
+    ws_connection_id=ws_id, # optional, for real-time events
+)
+```
+
+### Get Existing RTStream
+
+```python
+rtstream = coll.get_rtstream("rts-xxx")
+```
+
+### List RTStreams
+
+```python
+rtstreams = coll.list_rtstreams(
+    limit=10,
+    offset=0,
+    status="connected",  # optional filter
+    name="meeting",      # optional filter
+    ordering="-created_at",
+)
+
+for rts in rtstreams:
+    print(f"{rts.id}: {rts.name} - {rts.status}")
+```
+
+### From Capture Session
+
+After a capture session is active, retrieve RTStream objects:
+
+```python
+session = conn.get_capture_session(session_id)
+
+mics = session.get_rtstream("mic")
+displays = session.get_rtstream("screen")
+system_audios = session.get_rtstream("system_audio")
+```
+
+Or use the `rtstreams` data from the `capture_session.active` WebSocket event:
+
+```python
+for rts in rtstreams:
+    rtstream = coll.get_rtstream(rts["rtstream_id"])
+```
+
+---
+
+## RTStream Methods
+
+| Method | Returns | Description |
+|--------|---------|-------------|
+| `rtstream.start()` | `None` | Begin ingestion |
+| `rtstream.stop()` | `None` | Stop ingestion |
+| `rtstream.generate_stream(start, end)` | `str` | Stream recorded segment (Unix timestamps) |
+| `rtstream.export(name=None)` | `RTStreamExportResult` | Export to permanent video |
+| `rtstream.index_visuals(prompt, ...)` | `RTStreamSceneIndex` | Create visual index with AI analysis |
+| `rtstream.index_audio(prompt, ...)` | `RTStreamSceneIndex` | Create audio index with LLM summarization |
+| `rtstream.list_scene_indexes()` | `List[RTStreamSceneIndex]` | List all scene indexes on the stream |
+| `rtstream.get_scene_index(index_id)` | `RTStreamSceneIndex` | Get a specific scene index |
+| `rtstream.search(query, ...)` | `RTStreamSearchResult` | Search indexed content |
+| `rtstream.start_transcript(ws_connection_id, engine)` | `dict` | Start live transcription |
+| `rtstream.get_transcript(page, page_size, start, end, since)` | `dict` | Get transcript pages |
+| `rtstream.stop_transcript(engine)` | `dict` | Stop transcription |
+
+---
+
+## Starting and Stopping
+
+```python
+# Begin ingestion
+rtstream.start()
+
+# ... stream is being recorded ...
+
+# Stop ingestion
+rtstream.stop()
+```
+
+---
+
+## Generating Streams
+
+Use Unix timestamps (not seconds offsets) to generate a playback stream from recorded content:
+
+```python
+import time
+
+start_ts = time.time()
+rtstream.start()
+
+# Let it record for a while...
+time.sleep(60)
+
+end_ts = time.time()
+rtstream.stop()
+
+# Generate a stream URL for the recorded segment
+stream_url = rtstream.generate_stream(start=start_ts, end=end_ts)
+print(f"Recorded stream: {stream_url}")
+```
+
+---
+
+## Exporting to Video
+
+Export the recorded stream to a permanent video in the collection:
+
+```python
+export_result = rtstream.export(name="Meeting Recording 2024-01-15")
+
+print(f"Video ID: {export_result.video_id}")
+print(f"Stream URL: {export_result.stream_url}")
+print(f"Player URL: {export_result.player_url}")
+print(f"Duration: {export_result.duration}s")
+```
+
+### RTStreamExportResult Properties
+
+| Property | Type | Description |
+|----------|------|-------------|
+| `video_id` | `str` | ID of the exported video |
+| `stream_url` | `str` | HLS stream URL |
+| `player_url` | `str` | Web player URL |
+| `name` | `str` | Video name |
+| `duration` | `float` | Duration in seconds |
+
+---
+
+## AI Pipelines
+
+AI pipelines process live streams and send results via WebSocket.
+
+### RTStream AI Pipeline Methods
+
+| Method | Returns | Description |
+|--------|---------|-------------|
+| `rtstream.index_audio(prompt, batch_config, ...)` | `RTStreamSceneIndex` | Start audio indexing with LLM summarization |
+| `rtstream.index_visuals(prompt, batch_config, ...)` | `RTStreamSceneIndex` | Start visual indexing of screen content |
+
+### Audio Indexing
+
+Generate LLM summaries of audio content at intervals:
+
+```python
+audio_index = rtstream.index_audio(
+    prompt="Summarize what is being discussed",
+    batch_config={"type": "word", "value": 50},
+    model_name=None,       # optional
+    name="meeting_audio",  # optional
+    ws_connection_id=ws_id,
+)
+```
+
+**Audio batch_config options:**
+
+| Type | Value | Description |
+|------|-------|-------------|
+| `"word"` | count | Segment every N words |
+| `"sentence"` | count | Segment every N sentences |
+| `"time"` | seconds | Segment every N seconds |
+
+Examples:
+```python
+{"type": "word", "value": 50}      # every 50 words
+{"type": "sentence", "value": 5}   # every 5 sentences
+{"type": "time", "value": 30}      # every 30 seconds
+```
+
+Results arrive on the `audio_index` WebSocket channel.
+
+### Visual Indexing
+
+Generate AI descriptions of visual content:
+
+```python
+scene_index = rtstream.index_visuals(
+    prompt="Describe what is happening on screen",
+    batch_config={"type": "time", "value": 2, "frame_count": 5},
+    model_name="basic",
+    name="screen_monitor",  # optional
+    ws_connection_id=ws_id,
+)
+```
+
+**Parameters:**
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `prompt` | `str` | Instructions for the AI model (supports structured JSON output) |
+| `batch_config` | `dict` | Controls frame sampling (see below) |
+| `model_name` | `str` | Model tier: `"mini"`, `"basic"`, `"pro"`, `"ultra"` |
+| `name` | `str` | Name for the index (optional) |
+| `ws_connection_id` | `str` | WebSocket connection ID for receiving results |
+
+**Visual batch_config:**
+
+| Key | Type | Description |
+|-----|------|-------------|
+| `type` | `str` | Only `"time"` is supported for visuals |
+| `value` | `int` | Window size in seconds |
+| `frame_count` | `int` | Number of frames to extract per window |
+
+Example: `{"type": "time", "value": 2, "frame_count": 5}` samples 5 frames every 2 seconds and sends them to the model.
+
+**Structured JSON output:**
+
+Use a prompt that requests JSON format for structured responses:
+
+```python
+scene_index = rtstream.index_visuals(
+    prompt="""Analyze the screen and return a JSON object with:
+{
+  "app_name": "name of the active application",
+  "activity": "what the user is doing",
+  "ui_elements": ["list of visible UI elements"],
+  "contains_text": true/false,
+  "dominant_colors": ["list of main colors"]
+}
+Return only valid JSON.""",
+    batch_config={"type": "time", "value": 3, "frame_count": 3},
+    model_name="pro",
+    ws_connection_id=ws_id,
+)
+```
+
+Results arrive on the `scene_index` WebSocket channel.
+
+---
+
+## Batch Config Summary
+
+| Indexing Type | `type` Options | `value` | Extra Keys |
+|---------------|----------------|---------|------------|
+| **Audio** | `"word"`, `"sentence"`, `"time"` | words/sentences/seconds | - |
+| **Visual** | `"time"` only | seconds | `frame_count` |
+
+Examples:
+```python
+# Audio: every 50 words
+{"type": "word", "value": 50}
+
+# Audio: every 30 seconds  
+{"type": "time", "value": 30}
+
+# Visual: 5 frames every 2 seconds
+{"type": "time", "value": 2, "frame_count": 5}
+```
+
+---
+
+## Transcription
+
+Real-time transcription via WebSocket:
+
+```python
+# Start live transcription
+rtstream.start_transcript(
+    ws_connection_id=ws_id,
+    engine=None,  # optional, defaults to "assemblyai"
+)
+
+# Get transcript pages (with optional filters)
+transcript = rtstream.get_transcript(
+    page=1,
+    page_size=100,
+    start=None,   # optional: start timestamp filter
+    end=None,     # optional: end timestamp filter
+    since=None,   # optional: for polling, get transcripts after this timestamp
+    engine=None,
+)
+
+# Stop transcription
+rtstream.stop_transcript(engine=None)
+```
+
+Transcript results arrive on the `transcript` WebSocket channel.
+
+---
+
+## RTStreamSceneIndex
+
+When you call `index_audio()` or `index_visuals()`, the method returns an `RTStreamSceneIndex` object. This object represents the running index and provides methods for managing scenes and alerts.
+
+```python
+# index_visuals returns an RTStreamSceneIndex
+scene_index = rtstream.index_visuals(
+    prompt="Describe what is on screen",
+    ws_connection_id=ws_id,
+)
+
+# index_audio also returns an RTStreamSceneIndex
+audio_index = rtstream.index_audio(
+    prompt="Summarize the discussion",
+    ws_connection_id=ws_id,
+)
+```
+
+### RTStreamSceneIndex Properties
+
+| Property | Type | Description |
+|----------|------|-------------|
+| `rtstream_index_id` | `str` | Unique ID of the index |
+| `rtstream_id` | `str` | ID of the parent RTStream |
+| `extraction_type` | `str` | Type of extraction (`time` or `transcript`) |
+| `extraction_config` | `dict` | Extraction configuration |
+| `prompt` | `str` | The prompt used for analysis |
+| `name` | `str` | Name of the index |
+| `status` | `str` | Status (`connected`, `stopped`) |
+
+### RTStreamSceneIndex Methods
+
+| Method | Returns | Description |
+|--------|---------|-------------|
+| `index.get_scenes(start, end, page, page_size)` | `dict` | Get indexed scenes |
+| `index.start()` | `None` | Start/resume the index |
+| `index.stop()` | `None` | Stop the index |
+| `index.create_alert(event_id, callback_url, ws_connection_id)` | `str` | Create alert for event detection |
+| `index.list_alerts()` | `list` | List all alerts on this index |
+| `index.enable_alert(alert_id)` | `None` | Enable an alert |
+| `index.disable_alert(alert_id)` | `None` | Disable an alert |
+
+### Getting Scenes
+
+Poll indexed scenes from the index:
+
+```python
+result = scene_index.get_scenes(
+    start=None,      # optional: start timestamp
+    end=None,        # optional: end timestamp
+    page=1,
+    page_size=100,
+)
+
+for scene in result["scenes"]:
+    print(f"[{scene['start']}-{scene['end']}] {scene['text']}")
+
+if result["next_page"]:
+    # fetch next page
+    pass
+```
+
+### Managing Scene Indexes
+
+```python
+# List all indexes on the stream
+indexes = rtstream.list_scene_indexes()
+
+# Get a specific index by ID
+scene_index = rtstream.get_scene_index(index_id)
+
+# Stop an index
+scene_index.stop()
+
+# Restart an index
+scene_index.start()
+```
+
+---
+
+## Events
+
+Events are reusable detection rules. Create them once, attach to any index via alerts.
+
+### Connection Event Methods
+
+| Method | Returns | Description |
+|--------|---------|-------------|
+| `conn.create_event(event_prompt, label)` | `str` (event_id) | Create detection event |
+| `conn.list_events()` | `list` | List all events |
+
+### Creating an Event
+
+```python
+event_id = conn.create_event(
+    event_prompt="User opened Slack application",
+    label="slack_opened",
+)
+```
+
+### Listing Events
+
+```python
+events = conn.list_events()
+for event in events:
+    print(f"{event['event_id']}: {event['label']}")
+```
+
+---
+
+## Alerts
+
+Alerts wire events to indexes for real-time notifications. When the AI detects content matching the event description, an alert is sent.
+
+### Creating an Alert
+
+```python
+# Get the RTStreamSceneIndex from index_visuals
+scene_index = rtstream.index_visuals(
+    prompt="Describe what application is open on screen",
+    ws_connection_id=ws_id,
+)
+
+# Create an alert on the index
+alert_id = scene_index.create_alert(
+    event_id=event_id,
+    callback_url="https://your-backend.com/alerts",  # for webhook delivery
+    ws_connection_id=ws_id,  # for WebSocket delivery (optional)
+)
+```
+
+**Note:** `callback_url` is required. Pass an empty string `""` if only using WebSocket delivery.
+
+### Managing Alerts
+
+```python
+# List all alerts on an index
+alerts = scene_index.list_alerts()
+
+# Enable/disable alerts
+scene_index.disable_alert(alert_id)
+scene_index.enable_alert(alert_id)
+```
+
+### Alert Delivery
+
+| Method | Latency | Use Case |
+|--------|---------|----------|
+| WebSocket | Real-time | Dashboards, live UI |
+| Webhook | < 1 second | Server-to-server, automation |
+
+### WebSocket Alert Event
+
+```json
+{
+  "channel": "alert",
+  "rtstream_id": "rts-xxx",
+  "data": {
+    "event_label": "slack_opened",
+    "timestamp": 1710000012340,
+    "text": "User opened Slack application"
+  }
+}
+```
+
+### Webhook Payload
+
+```json
+{
+  "event_id": "event-xxx",
+  "label": "slack_opened",
+  "confidence": 0.95,
+  "explanation": "User opened the Slack application",
+  "timestamp": "2024-01-15T10:30:45Z",
+  "start_time": 1234.5,
+  "end_time": 1238.0,
+  "stream_url": "https://stream.videodb.io/v3/...",
+  "player_url": "https://console.videodb.io/player?url=..."
+}
+```
+
+---
+
+## WebSocket Integration
+
+All real-time AI results are delivered via WebSocket. Pass `ws_connection_id` to:
+- `rtstream.start_transcript()`
+- `rtstream.index_audio()`
+- `rtstream.index_visuals()`
+- `scene_index.create_alert()`
+
+### WebSocket Channels
+
+| Channel | Source | Content |
+|---------|--------|---------|
+| `transcript` | `start_transcript()` | Real-time speech-to-text |
+| `scene_index` | `index_visuals()` | Visual analysis results |
+| `audio_index` | `index_audio()` | Audio analysis results |
+| `alert` | `create_alert()` | Alert notifications |
+
+For WebSocket event structures and ws_listener usage, see [capture-reference.md](capture-reference.md).
+
+---
+
+## Complete Workflow
+
+```python
+import time
+import videodb
+
+conn = videodb.connect()
+coll = conn.get_collection()
+
+# 1. Connect and start recording
+rtstream = coll.connect_rtstream(
+    url="rtmp://your-stream-server/live/stream-key",
+    name="Weekly Standup",
+)
+rtstream.start()
+
+# 2. Record for the duration of the meeting
+start_ts = time.time()
+time.sleep(1800)  # 30 minutes
+end_ts = time.time()
+rtstream.stop()
+
+# 3. Export to a permanent video
+export_result = rtstream.export(name="Weekly Standup Recording")
+print(f"Exported video: {export_result.video_id}")
+
+# 4. Index the exported video for search
+video = coll.get_video(export_result.video_id)
+video.index_spoken_words(force=True)
+
+# 5. Search for action items
+results = video.search("action items and next steps")
+stream_url = results.compile()
+print(f"Action items clip: {stream_url}")
+```