everything-claude-code/skills/videodb/reference/rtstream-reference.md

RTStream Reference

Code-level details for RTStream operations. For workflow guide, see rtstream.md.

Based on docs.videodb.io.

---

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

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

rtstream = coll.get_rtstream("rts-xxx")

List RTStreams

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:

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:

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

# 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:

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:

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:

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:

{"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:

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:

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:

# 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:

# 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.

# 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:

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

# 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

event_id = conn.create_event(
    event_prompt="User opened Slack application",
    label="slack_opened",
)

Listing Events

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

# 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

# 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

{
  "channel": "alert",
  "rtstream_id": "rts-xxx",
  "data": {
    "event_label": "slack_opened",
    "timestamp": 1710000012340,
    "text": "User opened Slack application"
  }
}

Webhook Payload

{
  "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.

---

Complete Workflow

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}")