SDK Usage Examples

Quick Start Notebook

For programmatic access to Nomadic, you can use our Python SDK. Quick Start Notebook below.

1. Install the SDK

pip install nomadic

2. Initialize the Client

from nomadic import NomadicAI
import os

# Initialize with your API key
client = NomadicAI(
    api_key=os.environ.get("NOMADICAI_API_KEY")
)

To get your API key, log in to the web platform, go to Profile > API Key, and generate a new key. We recommend storing your API key in an environment variable for security.

3. Upload and Analyze Videos

The standard workflow involves uploading your videos first, then running analysis on them. Uploads accept local paths or remote URLs that end with a common video extension (.mp4, .mov, .avi, .webm):

response = client.upload('https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Mayhem-on-Road-Compilation.mp4')

# Extract video ID
video_id = response["video_id"]

# Add scope="org" when uploading to shared organization folders.

# Then analyze it
analysis = client.analyze(video_id, prompt="Find outlier events")
print(analysis)

You can also pass a list of paths/URLs to upload and a list of ids to analyze for batch operations.

paths = [
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Driving-a-bus-in-Switzerland-on-Snowy-Roads.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/LIDAR-RBG-Waymo-YouTube-Public-Sample.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Mayhem-on-Road-Compilation.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Oakland-to-SF-on-Bridge.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Zoox_San%20Francisco-Bike-To-Wherever-Day.mp4'
]
response = client.upload(paths)
video_ids = [v['video_id'] for v in response]

batch = client.analyze(video_ids, prompt="Find outlier events")
print(batch["batch_metadata"])  # Contains batch_id, batch_viewer_url, batch_type
for result in batch["results"]:
    print(result["video_id"], result["analysis_id"], len(result.get("events", [])))

4. Semantic Search with Chain-of-Thought

Search can be used are open in the natural language queries. Nomadic will reason about what fits best. Search response includes a chain-of-thought summary plus the reasoning behind each matched video. Supply the natural language query, the folder name, and the scope ("user", "org", or "sample"), and the call returns the complete set of results in one payload.

results = client.search(
    query="Find near-misses with pedestrians on crosswalks",
    folder_name="fleet_uploads_march",
    scope="org",          # optional, defaults to "user"
)

print(results["summary"])       # overall overview
print(results["thoughts"])      # list of reasoning steps
for match in results["matches"]:
    print(match["video_id"], match["reason"], match["similarity"])

# Advanced: reuse the session ID if you want to reference the same results later
print(results["session_id"])  # Unique identifier for this search session

5. Analysis

Nomadic prompt analysis can be run on a single video, a list of videos, or a folder.

Prompt Analysis

Extracts custom events based on your specific requirements. The default analyzer uses Thinking mode. Use Fast mode when you want speed-preferring router behavior.

analysis = client.analyze("video_id_1", prompt="green crosswalk")
fast_analysis = client.analyze("video_id_1", prompt="green crosswalk", mode="fast")
print(analysis)

6. Project-Based File Management & Composite Workflows

For larger projects, you can organize videos into folders and run batch analysis on entire folders at once. This is especially useful for processing datasets or running systematic reviews.

Example: Deleting videos

client.delete_video(video_id)

# OR
for v in video_ids:
    client.delete_video(v)

Example: Creating and looking up folders

# Create a new personal folder
folder = client.create_folder("marketing", description="Q1 campaign")
print(folder["id"], folder["created_at"])

# Lookup by name (personal scope by default)
existing = client.get_folder("marketing")
print(existing["id"], existing["video_count"])

# Organization-scoped folders
org_folder = client.create_folder("fleet_uploads", scope="org")
org_existing = client.get_folder("fleet_uploads", scope="org")

Example: Analysis + Search Workflow

This example demonstrates a common workflow: first, run a broad analysis to cast a wide net, then use search across analysis results to hone in on specific events, and finally, run a detailed analysis on the resulting subset of videos.

paths = [
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Driving-a-bus-in-Switzerland-on-Snowy-Roads.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/LIDAR-RBG-Waymo-YouTube-Public-Sample.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Mayhem-on-Road-Compilation.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Oakland-to-SF-on-Bridge.mp4',
    'https://storage.googleapis.com/videolm-bc319.firebasestorage.app/example-videos/Zoox_San%20Francisco-Bike-To-Wherever-Day.mp4'
]

# Define a folder for the project
folder_name = "prompt-analysis-videos"
project_folder = client.create_folder(folder_name, scope="org")
# If you're reusing an existing folder, use:
# project_folder = client.get_folder(folder_name, scope="org")
print(f"Using folder '{project_folder['name']}' scoped to {project_folder['scope']} (id={project_folder['id']})")

print("📁 Step 1: Uploading videos to project folder...")
response = client.upload(paths, folder=folder_name, scope="org")
print(f"✅ Successfully uploaded {len(response)} videos to '{folder_name}' folder")

print("\n🔍 Step 2: Running broad prompt analysis on all videos...")
analyses = client.analyze(
    folder=folder_name,
    scope="org",
    prompt="Find risky road-user interactions and traffic safety events",
)
print(f"✅ Completed prompt analysis on {len(analyses)} videos")

print("\n🔎 Step 3: Searching for pedestrian-related incidents...")
# Use natural-language search over the analysis results
search_results = client.search(
    query="Find risky incidents involving pedestrians",
    folder_name=folder_name,
    scope="org",
)

matching_video_ids = list(set([match['video_id'] for match in search_results['matches']]))
print(f"✅ Found {len(matching_video_ids)} videos with pedestrian incidents")

print(f"\n🎯 Step 4: Re-analyzing {len(matching_video_ids)} videos for pedestrian fault analysis...")
analyses = client.analyze(
    matching_video_ids,
    prompt="Mark incidents involving pedestrians where pedestrians are at fault",
)
print(f"✅ Completed detailed analysis on {len(analyses)} videos")

for analysis in analyses:
    if analysis['events']:
        print(f"\n🎬 Events found in video {analysis['video_id']}:")
        for e in analysis['events']:
            print(f"  • {e}")
        print("-" * 80)

print("\n🧹 Step 5: Cleaning up - deleting project videos...")
for response in client.my_videos(folder_name):
    result = client.delete_video(response['video_id'])

7. Re-analyzing Videos

You don’t need to re-upload videos to run new analyses. You can efficiently query already uploaded videos using either their specific video_ids or by organizing them into folders.

Re-analyzing Specific Videos by ID

This is the most direct way to re-run analysis on a few specific videos. After you upload a video, the API returns a video_id. Store this ID to reference the video in future calls.

# Replace these strings with the video IDs returned by upload
pedestrian_analysis = client.analyze(
    ["video_id_1", "video_id_2"],
    prompt="pedestrians close to vehicle",
)
print(f"Found {len(pedestrian_analysis)} videos with pedestrian interactions.")

Using Folders for Batch Re-analysis

For larger-scale projects, organizing videos into folders is the best practice. This allows you to run analysis on an entire dataset with a single command.

folder_name = "2024_urban_driving_set"

# Step 1: Upload and organize your videos into a folder (only needs to be done once)
client.upload(
    ['/path/to/city_drive_1.mp4', '/path/to/city_drive_2.mp4'],
    folder=folder_name
)

# Step 2: Run an initial analysis to find all road signs and their MUTCD codes
print(f"\nRunning initial analysis for 'road signs & MUTCD codes' in folder '{folder_name}'...")
pedestrian_analysis = client.analyze(
    folder=folder_name,
    prompt="Find all road signs and note their corresponding MUTCD codes?",
)
print(f"Found {len(pedestrian_analysis)} videos with road signs.")
# Step 3: Later, run a different analysis on the same set of videos
print(f"\nRunning second analysis for 'potholes' in folder '{folder_name}'...")
pothole_analysis = client.analyze(
    folder=folder_name,
    prompt="potholes or major road cracks",
)
print(f"Found {len(pothole_analysis)} videos with potholes.")

# Read-only demo/sample folders can be analyzed by opting into sample scope
sample_analysis = client.analyze(
    folder="Construction Samples",
    scope="sample",
    prompt="detect workers near active machinery",
)

8. Visualizing Results

Use the SDK visualizer to inspect detected events against the source video.

Creating a Video/Event Viewer

The visualizer returns standalone HTML and displays inline in notebooks when possible.

result = client.analyze(
    "video_id_1",
    prompt="green crosswalk",
)

html = client.visualize(result)

For batch analysis, pass either the batch result or a saved batch_id:

batch = client.analyze(
    ["video_id_1", "video_id_2"],
    prompt="green crosswalk",
)

html = client.visualize(batch)
html = client.visualize(batch, only_with_events=True)  # Hide videos with zero events

# Later, hydrate the batch and render it by ID.
html = client.visualize(batch["batch_metadata"]["batch_id"])

9. Livestream Analysis

Use client.livestream to start a live HLS session, run a continuous rapid-review query, and poll newly detected events.

from getpass import getpass
import os

from nomadic import NomadicAI

client = NomadicAI(
    api_key=os.environ.get("NOMADICAI_API_KEY") or getpass("Nomadic API key: ")
)

result = client.livestream.start_session(
    source_url="https://stream.nomadicml.com/stream2.m3u8",
    name="Robot pick demo",
    rapid_review_query="detect robot picking up an apple",
)

stream_id = result["stream_id"]
session_id = result["session_id"]

for event in client.livestream.iter_events(
    stream_id,
    session_id,
    poll_interval=10,
    timeout=180,
):
    print(event.get("stream_time"), event.get("description"))

client.livestream.end_session(stream_id=stream_id, session_id=session_id)
final = client.livestream.get_session(stream_id, session_id)

print(final["status"], final["chunk_count"], len(final.get("events", [])))

Livestream event timing fields include stream_time, capture_time, chunk_relative_time, t_start, t_end, and backend creation timestamp created_at. See Livestreams for the full event schema.

10. Working with Overlay Metadata

Nomadic can extract telemetry data from on-screen overlays in videos. This is useful for videos with embedded metadata like timestamps, GPS coordinates, speed, altitude, or custom telemetry values.

Important: Metadata describing overlay fields must be provided at upload time. During analysis, request the telemetry you need directly in the prompt.

Uploading Videos with Metadata

You can provide metadata files that describe the overlay fields in your videos. Metadata must be a properly formatted JSON file according to the Metadata Ingestion Spec, and the .json file must share the same base filename as the video (for example, drone_footage.mp4 pairs with drone_footage.json).

# Single video with metadata file (names must match)
result = client.upload(("dashcam_video.mp4", "dashcam_video.json"))

# Multiple videos with mixed metadata
uploads = client.upload([
    ("video1.mp4", "video1.json"),           # Video with metadata
    "video2.mp4",                             # Video without metadata
    ("video3.mp4", "video3.json"),           # Another video with metadata
])

print(f"Uploaded {len(uploads)} videos")
for upload in uploads:
    print(f"Video ID: {upload['video_id']}, Status: {upload['status']}")

Overlay-Aware Queries

For videos uploaded with metadata or visible overlays, request the telemetry you need in the prompt. The router selects the appropriate extraction path.

analysis = client.analyze(
    video_id,
    prompt="Find speed limit violations and include available timestamp and GPS evidence",
)

# Access extracted overlay data in events
for event in analysis["events"]:
    print(f"Event: {event['label']} at {event['t_start']}-{event['t_end']}")

    overlay_values = event.get("overlay", {})
    for field, values in overlay_values.items():
        start = values.get("start")
        end = values.get("end")
        print(f"  {field}: {start} -> {end}")

Batch Analysis with Overlay Extraction

For batch processing of videos with overlays:

# Step 1: Upload batch of videos with metadata
videos_with_metadata = [
    ("fleet_cam_001.mp4", "fleet_cam_001.json"),
    ("fleet_cam_002.mp4", "fleet_cam_002.json"),
    ("fleet_cam_003.mp4", "fleet_cam_003.json"),
]

upload_results = client.upload(videos_with_metadata, folder="fleet_telemetry")
video_ids = [r['video_id'] for r in upload_results]

batch_analysis = client.analyze(
    video_ids,
    prompt="harsh braking events where speed drops rapidly; include available speed telemetry",
)

# Process results with overlay data
for result in batch_analysis["results"]:
    video_id = result["video_id"]
    for event in result["events"]:
        # Speed (and other custom telemetry) is exposed through the overlay map
        speed_overlay = event.get("overlay", {}).get("frame_speed")
        if speed_overlay:
            print(
                f"Video {video_id}: Speed changed from "
                f"{speed_overlay.get('start')} to {speed_overlay.get('end')}"
            )

Metadata File Format

The metadata JSON file should describe the fields that appear as overlays in your video. For the complete metadata ingestion specification and detailed schema documentation, see the Metadata Ingestion Spec. Example metadata file:

{
  "fields": [
    {
      "name": "speed",
      "type": "number",
      "unit": "mph",
      "position": "top-left"
    },
    {
      "name": "gps_lat",
      "type": "number",
      "unit": "degrees"
    },
    {
      "name": "gps_lon",
      "type": "number",
      "unit": "degrees"
    },
    {
      "name": "timestamp",
      "type": "timestamp",
      "format": "ISO8601"
    },
    {
      "name": "altitude",
      "type": "number",
      "unit": "meters"
    }
  ]
}

Metadata files must have the same base filename as their corresponding video file. For example, dashcam_recording.mp4 should have metadata named dashcam_recording.json.

11. Storing Results in a Document Database

All SDK methods return serializable Python dictionaries, which can be easily processed and stored in any document database.

Example: Storing in MongoDB

from pymongo import MongoClient

# Assume 'analysis_results' is the list of dicts from a client.analyze() call
results_to_store = []
for analysis in analysis_results:
    # ... (processing logic from previous examples) ...
    results_to_store.append(processed_event)

# Connect to MongoDB and insert the documents
try:
    db_client = MongoClient('mongodb://localhost:27017/')
    db = db_client['nomadicml_results']
    collection = db['driving_events']
    if results_to_store:
      collection.insert_many(results_to_store)
      print("Successfully saved results to MongoDB.")
except Exception as e:
    print(f"An error occurred with MongoDB: {e}")

Example: Storing in Supabase

Supabase provides a Postgres database with a Python client that’s simple to use.

from supabase import create_client, Client
import os

# Assume 'analysis_results' is the list of dicts from a client.analyze() call
results_to_store = []
for analysis in analysis_results:
    # ... (processing logic from previous examples) ...
    # Ensure your dict keys match your Supabase table columns
    processed_event_for_supabase = {
        'source_video_id': video_id,
        'event_type': event.get('type'),
        'timestamp_sec': event.get('time'),
        'description': event.get('description'),
        'severity': event.get('severity'),
        'dmv_rule': event.get('dmvRule'),
        'raw_ai_analysis': event.get('aiAnalysis')
    }
    results_to_store.append(processed_event_for_supabase)

# Initialize Supabase client
try:
    url: str = os.environ.get("SUPABASE_URL")
    key: str = os.environ.get("SUPABASE_KEY")
    supabase: Client = create_client(url, key)

    # Insert data into your 'events' table
    if results_to_store:
        data, count = supabase.table('events').insert(results_to_store).execute()
        print(f"Successfully saved {len(data[1])} results to Supabase.")
except Exception as e:
    print(f"An error occurred with Supabase: {e}")

SDK

SDK Usage Examples

Quick Start Notebook

1. Install the SDK

2. Initialize the Client

3. Upload and Analyze Videos

4. Semantic Search with Chain-of-Thought

5. Analysis

Prompt Analysis

6. Project-Based File Management & Composite Workflows

Example: Deleting videos

Example: Creating and looking up folders

Example: Analysis + Search Workflow

7. Re-analyzing Videos

Re-analyzing Specific Videos by ID

Using Folders for Batch Re-analysis

8. Visualizing Results

Creating a Video/Event Viewer

9. Livestream Analysis

10. Working with Overlay Metadata

Uploading Videos with Metadata

Overlay-Aware Queries

Batch Analysis with Overlay Extraction

Metadata File Format

11. Storing Results in a Document Database

Example: Storing in MongoDB

Example: Storing in Supabase

Next Steps

Uploading From Cloud Storage

SDK Documentation

​Quick Start Notebook

​1. Install the SDK

​2. Initialize the Client

​3. Upload and Analyze Videos

​4. Semantic Search with Chain-of-Thought

​5. Analysis

​Prompt Analysis

​6. Project-Based File Management & Composite Workflows

​Example: Deleting videos

​Example: Creating and looking up folders

​Example: Analysis + Search Workflow

​7. Re-analyzing Videos

​Re-analyzing Specific Videos by ID

​Using Folders for Batch Re-analysis

​8. Visualizing Results

​Creating a Video/Event Viewer

​9. Livestream Analysis

​10. Working with Overlay Metadata

​Uploading Videos with Metadata

​Overlay-Aware Queries

​Batch Analysis with Overlay Extraction

​Metadata File Format

​11. Storing Results in a Document Database

​Example: Storing in MongoDB

​Example: Storing in Supabase

​Next Steps

Uploading From Cloud Storage

SDK Documentation

Quick Start Notebook

1. Install the SDK

2. Initialize the Client

3. Upload and Analyze Videos

4. Semantic Search with Chain-of-Thought

5. Analysis

Prompt Analysis

6. Project-Based File Management & Composite Workflows

Example: Deleting videos

Example: Creating and looking up folders

Example: Analysis + Search Workflow

7. Re-analyzing Videos

Re-analyzing Specific Videos by ID

Using Folders for Batch Re-analysis

8. Visualizing Results

Creating a Video/Event Viewer

9. Livestream Analysis

10. Working with Overlay Metadata

Uploading Videos with Metadata

Overlay-Aware Queries

Batch Analysis with Overlay Extraction

Metadata File Format

11. Storing Results in a Document Database

Example: Storing in MongoDB

Example: Storing in Supabase

Next Steps