Videodb

VideoDB Skill

Perception + memory + actions for video, live streams, and desktop sessions.

Use this skill when you need to:

1) Desktop Perception

• Start/stop a desktop session capturing screen, mic, and system audio
• Stream live context and store episodic session memory
• Run real-time alerts/triggers on what’s spoken and what's happening on screen
• Produce session summaries, a searchable timeline, and playable evidence links

2) Video ingest + stream

• Ingest a file or URL and return a playable web stream link
• Transcode/normalize: codec, bitrate, fps, resolution, aspect ratio

3) Understand + index + retrieve (timestamps + evidence)

• Understand: run analyzers over speech, scenes, objects, OCR, brands, activity
• Index: turn analyzer artifacts into semantic, filterable, and aggregatable indexes
• Retrieve: search moments, ask questions, filter exactly, count and group — with timestamps and playable evidence
• Auto-create clips from results

4) Timeline editing + generation

• Subtitles: generate, translate, burn-in
• Overlays: text/image/branding, motion captions
• Audio: background music, voiceover, dubbing
• Programmatic composition and exports via timeline operations

5) Live streams (RTSP) + monitoring

• Connect RTSP/live feeds
• Run real-time visual and spoken understanding and emit events/alerts for monitoring workflows

Common inputs

• Local file path, public URL, or RTSP URL
• Desktop capture request: start / stop / summarize session
• Desired operations: get context for understanding, transcode spec, index spec, search query, clip ranges, timeline edits, alert rules

Common outputs

• Stream URL — make it playable: https://console.videodb.io/player?url={STREAM_URL}
• Search results with timestamps and evidence links
• Generated assets: subtitles, audio, images, clips
• Event/alert payloads for live streams
• Desktop session summaries and memory entries

Canonical prompts (examples)

• “Start desktop capture and alert when a password field appears.”
• “Record my session and produce an actionable summary when it ends.”
• “Ingest this file and return a playable stream link.”
• “Index this folder and find every scene with people, return timestamps.”
• “Generate subtitles, burn them in, and add light background music.”
• “Connect this RTSP URL and alert when a person enters the zone.”

Running Python code

CRITICAL: Always cd to the user's project directory before running Python code. This ensures load_dotenv(".env") finds the correct .env file.

from dotenv import load_dotenv
load_dotenv(".env")

import videodb
conn = videodb.connect()

This reads VIDEO_DB_API_KEY from:

1. Environment (if already exported)
2. Project's .env file in current directory

If the key is missing, videodb.connect() raises AuthenticationError automatically.

Do NOT write a script file when a short inline command works.

When writing inline Python (python -c "..."), always use properly formatted code — use semicolons to separate statements and keep it readable. For anything longer than ~3 statements, use a heredoc instead:

python << 'EOF'
from dotenv import load_dotenv
load_dotenv(".env")

import videodb
conn = videodb.connect()
coll = conn.get_collection()
print(f"Videos: {len(coll.get_videos())}")
EOF

Setup

When the user asks to "setup videodb" or similar:

1. Install SDK

pip install "videodb[capture]>=0.5.0" python-dotenv

If videodb[capture] fails on Linux, install without the capture extra:

pip install "videodb>=0.5.0" python-dotenv

The >=0.5.0 pin matters — the understand/index/ask/aggregate APIs do not exist in earlier versions.

2. Configure API key

The user must set VIDEO_DB_API_KEY using either method:

• Export in terminal (recommended): export VIDEO_DB_API_KEY=your-key
• Project .env file: Save VIDEO_DB_API_KEY=your-key in the project's .env file

Get a free API key at https://console.videodb.io (50 free uploads, no credit card).

Do NOT read, write, or handle the API key yourself. Always let the user set it.

Quick Reference

Upload media

# URL
video = coll.upload(url="https://example.com/video.mp4")

# YouTube
video = coll.upload(url="https://www.youtube.com/watch?v=VIDEO_ID")

# Local file
video = coll.upload(file_path="/path/to/video.mp4")

Understand → index → retrieve (default path)

Three stages. Run analyzers to produce artifacts, index each artifact, then retrieve.

import time

# 1. Understand. Naming each analyzer keeps `analyzer.name` meaningful downstream.
understanding = video.understand(
    analyzers=[
        {"type": "spoken_words", "name": "transcript"},
        {"type": "vlm", "name": "scene",
         "config": {"prompt": "Describe the scene and any on-screen text."}},
    ],
    segmentation={"type": "shot", "threshold": 30},
)

# A run with a failed or skipped analyzer ends `partial`, which the SDK does not
# treat as terminal — wait_until_complete() would poll to TimeoutError. Poll the
# analyzers instead. The `analyzers and` guard is load-bearing: a refresh can
# transiently return an empty list, and all([]) is True, which would exit the
# loop while the run is still going.
deadline = time.time() + 3600
while time.time() < deadline:
    analyzers = understanding.refresh().list_analyzers()
    if analyzers and all(a.is_complete for a in analyzers):
        break
    time.sleep(15)

# 2. Index each artifact that succeeded.
for analyzer in understanding.list_analyzers():
    if analyzer.is_successful:
        video.index(source=analyzer, name=analyzer.name).wait_until_complete()

# 3. Retrieve.
response = video.search("discussion about pricing")
for shot in response.shots:
    print(f"[{shot.start:.1f}s - {shot.end:.1f}s] {shot.text}")
if response.response_type in ("shots", "deepsearch") and len(response):
    stream_url = response.compile()   # raises SearchError otherwise

Analyzer types: spoken_words (→ artifact transcript), vlm (→ scene), object_detection (→ objects), ocr, brand_detection (→ brands), activity_recognition (→ activity), location_detection (→ location), faces, audio_event_detection. They are plain strings — there is no SDK enum.

See reference/indexing.md for segmentation, sampling, field configuration, and cost tuning.

Retrieval

search(query) is the default — it plans the retrieval and picks the indexes itself. Reach past it when you need something specific:

# Target a specific index, with a relevance floor
video.semantic_search("a customer holding the product", index_names=["scene"], score_threshold=0.7)

# Exact filtering, no natural-language interpretation
video.query(index_name="objects",
            filter=[{"field": "frames.detections.label", "op": "contains", "value": "car"}])

# Counts and facets — returns the raw server payload, not a SearchResult
video.aggregate(index_name="objects", group_by="frames.detections.label", metric="count")

# A written answer plus the moments it came from
answer = video.ask("What did they say about pricing?", include_sources=True)

All five exist on Collection too, fanning out across every indexed video. See reference/search.md.

search() now returns SearchResponse, not SearchResult. get_shots(), compile(), play(), and iteration all work, but there is no .stream_url on it — use .compile().

Transcript + subtitle

# force=True skips the error if the video is already indexed
video.index_spoken_words(force=True)
text = video.get_transcript_text()
stream_url = video.add_subtitle()

index_spoken_words() is the correct call here even on 0.5.0 — add_subtitle() and CaptionAsset(src="auto") read the v1 spoken-word index. A v2 spoken_words artifact does not substitute for it. This is the one place v1 indexing is still the right answer.

Legacy indexing (existing codebases)

# v1 API — still supported in 0.5.0, not deprecated. New code should use the v2 path above.
from videodb import IndexType

video.index_spoken_words(force=True)
scene_index_id = video.index_scenes(prompt="Describe the visual content.")
results = video.legacy_search(
    "person writing on a whiteboard",
    index_type=IndexType.scene,
    scene_index_id=scene_index_id,
)

Recognise this pattern in existing repos and leave it alone unless asked to migrate — it still works. See reference/migration.md to port it, or reference/legacy/search.md to maintain it.

Timeline editing

Use the Editor API to compose videos, images, audio, and text. See reference/editor.md for full workflow.

from videodb.editor import Timeline, Track, Clip, VideoAsset, ImageAsset, AudioAsset, Fit

timeline = Timeline(conn)
timeline.resolution = "1280x720"

video_track = Track()
video_track.add_clip(0, Clip(asset=VideoAsset(id=video.id, start=10), duration=20))

audio_track = Track()
audio_track.add_clip(0, Clip(asset=AudioAsset(id=music.id, volume=0.2), duration=20))

timeline.add_track(video_track)
timeline.add_track(audio_track)
stream_url = timeline.generate_stream()

Transcode video (resolution / quality change)

from videodb import TranscodeMode, VideoConfig, AudioConfig

# Change resolution, quality, or aspect ratio server-side
job_id = conn.transcode(
    source="https://example.com/video.mp4",
    callback_url="https://example.com/webhook",
    mode=TranscodeMode.economy,
    video_config=VideoConfig(resolution=720, quality=23, aspect_ratio="16:9"),
    audio_config=AudioConfig(mute=False),
)

Reframe aspect ratio (for social platforms)

Warning: reframe() is a slow server-side operation. For long videos it can take several minutes and may time out. Best practices:

• Always limit to a short segment using start/end when possible
• For full-length videos, use callback_url for async processing
• Trim the video on a Timeline first, then reframe the shorter result

from videodb import ReframeMode

# Always prefer reframing a short segment:
reframed = video.reframe(start=0, end=60, target="vertical", mode=ReframeMode.smart)

# Async reframe for full-length videos (returns None, result via webhook):
video.reframe(target="vertical", callback_url="https://example.com/webhook")

# Presets: "vertical" (9:16), "square" (1:1), "landscape" (16:9)
reframed = video.reframe(start=0, end=60, target="square")

# Custom dimensions
reframed = video.reframe(start=0, end=60, target={"width": 1280, "height": 720})

Generative media

image = coll.generate_image(
    prompt="a sunset over mountains",
    aspect_ratio="16:9",
)

Sandbox Compute (self-hosted / open-weight models)

Run open-weight models (Gemma, Qwen, Whisper, OmniVoice, FLUX, RT-DETR) by creating a sandbox and passing sandbox_id to a supported job. Requires videodb>=0.5.1.

from videodb import SandboxTier, SandboxModel

# 1. Create a sandbox sized for the largest model, then wait until active.
sandbox = conn.create_sandbox(
    tier=SandboxTier.medium,
    models=[SandboxModel.GEMMA_4_31B.value],   # exact ID, NO -FP8 suffix
)
sandbox.wait_for_ready(timeout=300, interval=5)

# 2. Understanding: set config.model + config.sandbox_id on the analyzer.
understanding = video.understand(analyzers=[{
    "type": "vlm", "name": "scene",
    "config": {"model": "google/gemma-4-31B-it", "sandbox_id": sandbox.id,
               "prompt": "Describe the scene."},
}])

# 2b. Generation: pass model_name + sandbox_id (jobs return GenerationJob → .wait()).
response = coll.generate_text(prompt="Summarize this.", model_name="Qwen/Qwen3.5-9B",
                             sandbox_id=sandbox.id, max_tokens=300)
job = coll.generate_image(prompt="a city at sunset", model_name="black-forest-labs/FLUX.1-dev",
                          sandbox_id=sandbox.id)
image = job.wait(timeout=900, interval=5)

# 3. Stop when done — provisioning/active/alert all count toward the tier limit.
sandbox.stop(); sandbox.wait_for_stop()

Model IDs must match the catalog exactly (no -FP8 suffix) or create_sandbox raises Unsupported sandbox model. See reference/sandbox.md for the full model catalog, tiers, categories, pricing, and pitfalls.

Error handling

from videodb.exceptions import AuthenticationError, InvalidRequestError

try:
    conn = videodb.connect()
except AuthenticationError:
    print("Check your VIDEO_DB_API_KEY")

try:
    video = coll.upload(url="https://example.com/video.mp4")
except InvalidRequestError as e:
    print(f"Upload failed: {e}")

Common pitfalls

Additional docs

Reference documentation is in the reference/ directory adjacent to this SKILL.md file. Use the Glob tool to locate it if needed.

• reference/api-reference.md - Complete VideoDB Python SDK API reference
• reference/indexing.md - Understand → index pipeline: analyzers, artifacts, segmentation, field configuration
• reference/indexing-reference.md - Analyzer catalog and Understanding/Index class reference
• reference/search.md - Retrieval guide: search, ask, semantic_search, query, aggregate
• reference/search-reference.md - Retrieval signatures, filter syntax, response objects
• reference/migration.md - v1 → v2 mapping and SDK 0.5.0 breaking changes. Read when you find v1 code
• reference/editor.md - Timeline editing workflow guide (4-layer model, use cases, examples)
• reference/editor-reference.md - Editor code reference (constructors, parameters, enums)
• reference/streaming.md - HLS streaming and instant playback
• reference/generative.md - AI-powered media generation (images, video, audio)
• reference/sandbox.md - Sandbox Compute workflow (run open-weight models: Gemma, Qwen, Whisper, OmniVoice, FLUX, RT-DETR)
• reference/sandbox-reference.md - Sandbox code reference (create/get/list/stop, tiers, model catalog, sandbox-aware generation)
• reference/rtstream.md - Live stream ingestion workflow (RTSP/RTMP)
• reference/rtstream-reference.md - RTStream SDK methods and AI pipelines
• reference/capture.md - Desktop capture workflow
• reference/capture-reference.md - Capture SDK and WebSocket events
• reference/use-cases.md - Common video processing patterns and examples

Legacy v1 indexing and search. These APIs still work and are not deprecated, but read these only when maintaining existing v1 code:

• reference/legacy/index.md - v1 scene indexing and frame extraction workflow
• reference/legacy/index-reference.md - v1 scene index code reference (SceneCollection/Scene/Frame)
• reference/legacy/search.md - v1 spoken-word and scene search

Screen Recording (Desktop Capture)

Use ws_listener.py to capture WebSocket events during recording sessions. Desktop capture supports macOS only.

Quick Start

1. Start listener: python scripts/ws_listener.py --cwd=<PROJECT_ROOT> &
2. Get WebSocket ID: cat /tmp/videodb_ws_id
3. Run capture code (see reference/capture.md for full workflow)
4. Events written to: /tmp/videodb_events.jsonl

Query Events

import json
events = [json.loads(l) for l in open("/tmp/videodb_events.jsonl")]

# Get all transcripts
transcripts = [e["data"]["text"] for e in events if e.get("channel") == "transcript"]

# Get visual descriptions from last 5 minutes
import time
cutoff = time.time() - 300
recent_visual = [e for e in events 
                 if e.get("channel") == "visual_index" and e["unix_ts"] > cutoff]

Utility Scripts

• scripts/ws_listener.py - WebSocket event listener (dumps to JSONL)

For complete capture workflow, see reference/capture.md.

Do not use ffmpeg, moviepy, or local encoding tools when VideoDB supports the operation. The following are all handled server-side by VideoDB — trimming, combining clips, overlaying audio or music, adding subtitles, text/image overlays, transcoding, resolution changes, aspect-ratio conversion, resizing for platform requirements, transcription, volume control, fade transitions, and media generation. Only fall back to local tools for operations listed under Limitations in reference/editor.md (speed changes, crop/zoom, colour grading, keyframe animation).

When to use what