Python CLI Client

Pre-requisites

A reference Python CLI client is provided along with VSS. The client internally calls the REST APIs exposed by VSS.

The Python package dependencies for the CLI client can be installed using:

pip3 install tabulate tqdm sseclient-py requests fastapi uvicorn

Download the CLI client from the VSS GitHub repository.

curl -LO https://raw.githubusercontent.com/NVIDIA-AI-Blueprints/video-search-and-summarization/refs/heads/main/src/vss-engine/src/via_client_cli.py

The CLI client can be executed by running:

python3 via_client_cli.py <command> <args> [--print-curl-command]

By default, the client assumes that the VSS API server is running at http://localhost:8000. This can be configured by exporting the environment variable VIA_BACKEND=<VIA_API_URL> or passing the argument --backend <VIA_API_URL>.

Note

Please refer to Tuning Prompts for more info on tuning prompts

Please refer to Launch VSS UI for info on the backend URL for helm deployments.

Please refer to Step 6) Deploy VSS for info on the backend URL for docker compose deployments.

The CLI client also provides an option to print the curl command for any operation. This can be done by passing the --print-curl-command argument to the client.

To get a list of all supported commands and options supported by each command run:

python3 via_client_cli.py -h
python3 via_client_cli.py <command> -h

File Summarization and Q&A

The following section describes the python CLI commands for file summarization and Q&A.

First, export the VSS API endpoint as an environment variable.

export VIA_BACKEND=http://<VSS_API_HOST>:<VSS_API_PORT>

Next, upload the file to VSS using the add-file command.

python3 via_client_cli.py add-file warehouse.mp4  # Uploads the warehouse.mp4 file from the local file system where the client is running

The above command will upload the warehouse.mp4 file from your local file system to VSS. To instead use a file already inside the VSS container use:

python3 via_client_cli.py add-file /opt/nvidia/via/streams/warehouse.mp4 --add-as-path # /opt/nvidia/via/streams/warehouse.mp4 is a path inside the VSS container

# Output
File added - id: 0d975eca-64f8-4c64-a4d7-a49ea834b3ee, filename warehouse.mp4, bytes 156822870, purpose vision, media_type video

Get the VLM model id from the list-models command.

python3 via_client_cli.py list-models

# Output
┌──────────┬─────────────────────┬────────────┬────────────┐
│ ID       │ Created             │ Owned By   │ API Type   │
├──────────┼─────────────────────┼────────────┼────────────┤
│ vila-1.5 │ 2025-03-16 09:33:43 │ NVIDIA     │ internal   │
└──────────┴─────────────────────┴────────────┴────────────┘

Use the summarize command to summarize the file. id is the file id returned by the add-file command and model is the VLM model id returned by the list-models command.

python3 via_client_cli.py summarize --id 0d975eca-64f8-4c64-a4d7-a49ea834b3ee --model vila-1.5 \
   --chunk-duration 10 --prompt "You are a warehouse monitoring system. Describe the events in this warehouse and look for any anomalies. Start and end each sentence with a time stamp." \
   --caption-summarization-prompt "Summarize similar captions that are sequential to one another, while maintaining the details of each caption, in the format start_time:end_time:caption. The output should be bullet points in the format start_time:end_time: detailed_event_description." \
   --summary-aggregation-prompt "Aggregate captions in the format start_time:end_time:caption based on whether captions are related to one another or create a continuous scene. The output should only be bullet points in the format start_time:end_time: detailed_event_description. " \
   --enable-chat

# Output
Summarization finished
Request ID: 797ceb0a-96ec-4b29-9063-c378b4e63c9e
Request Creation Time: 2025-03-16 09:50:00
Model: vila-1.5
Object: summarization.completion
Media start offset: 00:00
Media end offset: 03:30
Chunks processed: 21
Processing Time: 46 seconds
Response:
Here are the summarized captions in the format start_time:end_time: detailed_event_description:

• 0:00-0:20: A warehouse with tall shelves stacked with boxes and pallets.
• 20.00-30.00: The warehouse is well-lit and organized, with aisles labeled "C", "D", "E", and "F". Cardboard boxes of various sizes are stacked on metal shelving units. A person wearing a high-visibility vest walks down the aisle, and there is a small cardboard box on the floor. The warehouse appears to be empty of any other people or objects.
...

Additional features like audio transcription, Set-of-Marks prompting using CV pipeline, and alerts can also be enabled.

As a next step, Q&A can be performed on the file using the chat command.

python3 via_client_cli.py chat --id 0d975eca-64f8-4c64-a4d7-a49ea834b3ee --model vila-1.5 \
   --prompt "When did the forklift first arrive?"

# Output
Response:
The forklift first entered the warehouse between 0:20-0:40.

Note

The summarization and chat requests will keep running on the server side till the request is completed even if the client is disconnected.

Finally, remove the file using the delete-file command.

python3 via_client_cli.py delete-file 0d975eca-64f8-4c64-a4d7-a49ea834b3ee

# Output
File deleted - id 0d975eca-64f8-4c64-a4d7-a49ea834b3ee, status True

Live Stream Summarization, Alerts and Q&A

The following section describes the python CLI commands for file summarization and Q&A.

First, export the VSS API endpoint as an environment variable.

export VIA_BACKEND=http://<VSS_API_HOST>:<VSS_API_PORT>

Next, start a alert callback test server using the alert-callback-server command.

python3 via_client_cli.py alert-callback-server --host 0.0.0.0 --port 5004

# Output
Server starting. Alert callback handler at path /via-alert-callback
INFO:     Started server process [2282991]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:5004 (Press CTRL+C to quit)

Add a live stream using the add-live-stream command.

python3 via_client_cli.py add-live-stream --description "Some live stream description" \
   rtsp://192.168.1.100:8554/video/media1

# Output
Live stream added - id: de0e04bc-b007-4967-9cad-28f48a02c126

Get the VLM model id from the list-models command.

python3 via_client_cli.py list-models

# Output
┌──────────┬─────────────────────┬────────────┬────────────┐
│ ID       │ Created             │ Owned By   │ API Type   │
├──────────┼─────────────────────┼────────────┼────────────┤
│ vila-1.5 │ 2025-03-16 09:33:43 │ NVIDIA     │ internal   │
└──────────┴─────────────────────┴────────────┴────────────┘

Use the summarize command to summarize the live-stream. id is the live-stream id returned by the add-live-strean command and model is the VLM model id returned by the list-models command. For live-streams, the --stream flag is required. Summaries are periodically generated for every summary-duration seconds and returned to the client using server-sent events. Alerts configured using the --alert flag are also sent to the client via server-sent events.

python3 via_client_cli.py summarize --id de0e04bc-b007-4967-9cad-28f48a02c126 --model vila-1.5 \
   --chunk-duration 10 --summary-duration 60 \
   --prompt "You are a warehouse monitoring system. Describe the events in this warehouse and look for any anomalies. Start and end each sentence with a time stamp." \
   --caption-summarization-prompt "Summarize similar captions that are sequential to one another, while maintaining the details of each caption, in the format start_time:end_time:caption. The output should be bullet points in the format start_time:end_time: detailed_event_description." \
   --summary-aggregation-prompt "Aggregate captions in the format start_time:end_time:caption based on whether captions are related to one another or create a continuous scene. The output should only be bullet points in the format start_time:end_time: detailed_event_description. " \
   --alert "safety-issues:workers not wearing safety equipment" \
   --enable-chat --stream

# Output
Request ID: e35bfb7c-d856-4378-a2d1-dfc21a555faf
Request Creation Time: 2025-03-16 10:30:08
Model: vila-1.5
----------------------------------------
Object: summarization.progressing
Media start timestamp: 2025-03-16T10:30:10.254Z
Media end timestamp: 2025-03-16T10:31:10.301Z
Response:
Here are the aggregated captions in the format start_time:end_time: detailed_event_description:

• 2025-03-16T10:30:10.254Z:2025-03-16T10:30:20.254Z: A woman wearing a neon orange vest, black pants, and a pink cap walks towards the right side of the frame, followed by a woman wearing a pink hoodie, black pants, and a pink beanie walking in the same direction.
• 2025-03-16T10:30:30.298Z:2025-03-16T10:31:10.301Z: The video shows a warehouse with multiple rows of shelving units, workers wearing safety vests and hard hats, and a worker on a ladder, possibly performing maintenance or stocking. The warehouse is filled with workers engaged in various tasks such as moving boxes and organizing the shelves, with multiple workers moving around the warehouse, carrying boxes, and interacting with each other.
----------------------------------------
...

Additional features like audio transcription, Set-of-Marks prompting using CV pipeline, and alerts can also be enabled.

The above summarize command will keep running until the user interrupts the process or the live-stream ends. In case, the client gets disconnected from the server, it can reconnect to the live-stream by re-running the summarize command with the same id as the live-stream.

python3 via_client_cli.py summarize --id de0e04bc-b007-4967-9cad-28f48a02c126 \
    --model vila-1.5 --stream

The server caches the summaries that were generated while the client was disconnected and returns them as soon as the client reconnects.

Note

As mentioned, The summarization requests will keep running on the server side till the live-stream reaches end-of-stream even if the client is disconnected. Delete the live-stream to stop the summarization request.

Alerts can also be configured to be sent to a callback URL using the add-alert command.

python3 via_client_cli.py add-alert --live-stream-id de0e04bc-b007-4967-9cad-28f48a02c126 \
   --events "workers not wearing safety equipment" \
   --callback-url "http://<HOST_IP_FOR_ALERT_SERVER>:5004/via-alert-callback"

# Output
Alert added - id: d60f3420-578b-42bb-8d34-0829d9e4269c

As a next step, Q&A can be performed on the live-stream using the chat command.

python3 via_client_cli.py chat --id de0e04bc-b007-4967-9cad-28f48a02c126 --model vila-1.5 \
   --prompt "How many people are seen?"

# Output
Response:
There are at least 2 people seen in the video. One woman wearing a pink beanie, black pants, and a pink hoodie is seen walking towards the right side of the frame. Another woman wearing a neon orange vest, black pants, and a pink cap is also seen walking towards the right side of the frame.

Finally, remove the live-stream using the delete-live-stream command.

python3 via_client_cli.py delete-live-stream de0e04bc-b007-4967-9cad-28f48a02c126

# Output
Live stream deleted - id de0e04bc-b007-4967-9cad-28f48a02c126

Multi-Image Summarization and Q&A

The following section describes the python CLI commands for file summarization and Q&A.

First, export the VSS API endpoint as an environment variable.

export VIA_BACKEND=http://<VSS_API_HOST>:<VSS_API_PORT>

Add images using the add-file command.

python3 via_client_cli.py add-file its_overlay_1.png --is-image
python3 via_client_cli.py add-file its_overlay_2.png --is-image
python3 via_client_cli.py add-file its_overlay_3.png --is-image
python3 via_client_cli.py add-file its_overlay_4.png --is-image
python3 via_client_cli.py add-file its_overlay_5.png --is-image
python3 via_client_cli.py add-file its_overlay_6.png --is-image

# Output
File added - id: 9fcf2676-6ce8-4fb8-a6b1-c833f054c656, filename its_overlay_1.png, bytes 2619926, purpose vision, media_type image
File added - id: e917b30f-43ae-4f7d-bad1-cab90d2df7c9, filename its_overlay_2.png, bytes 2436534, purpose vision, media_type image
...

Get the VLM model id from the list-models command.

python3 via_client_cli.py list-models

# Output
┌──────────┬─────────────────────┬────────────┬────────────┐
│ ID       │ Created             │ Owned By   │ API Type   │
├──────────┼─────────────────────┼────────────┼────────────┤
│ vila-1.5 │ 2025-03-16 09:33:43 │ NVIDIA     │ internal   │
└──────────┴─────────────────────┴────────────┴────────────┘

Use the summarize command to summarize the live-stream. id is the file id returned by the add-file command, multiple ids can be provided for multi-image summarization. model is the VLM model id returned by the list-models command.

python3 via_client_cli.py summarize --id 9fcf2676-6ce8-4fb8-a6b1-c833f054c656 \
      --id e917b30f-43ae-4f7d-bad1-cab90d2df7c9 \
      --id 6c02d9af-b08f-4cff-9539-63b36f4badfe \
      --id 32fe4823-7659-493c-a97e-44136b3efa84 \
      --id b82684f3-de46-4a81-8f47-abed6c0461af \
      --id 646da2d7-7dc9-4c9e-8a72-ff1727859ee2 \
      --model vila-1.5 \
      --prompt "You are an intelligent traffic system. You will be given a set of images from a traffic intersection. Write a detailed caption for each image to capture all traffic related events and details. For each caption, include the timestamp from the image." \
      --caption-summarization-prompt "Combine the captions if needed. Do not lose any information" \
      --summary-aggregation-prompt "You will be given a set of captions describing several images from a traffic intersection. Write a summary of the events from the captions and include the timestamp information. " \
      --enable-chat

# Output
Summarization finished
Request ID: dbcca47c-0e00-4fc7-a93f-e3eafe1cd26c
Request Creation Time: 2025-03-18 14:30:51
Model: vila-1.5
Object: summarization.completion
Media start offset: 00:00
 Media end offset: 00:00
 Chunks processed: 1
 Processing Time: 39 seconds
 Response:
 **Traffic Report**
 ================

 **00:00:00 - 00:00:11: Intersection Congestion**
 ---------------------------------------------

 Multiple vehicles enter the intersection and wait for the right of way, including:

 * A red car
 * A yellow car
 * A black car
 * A yellow school bus
 * A red fire truck
 * A black and white police car

 All vehicles are waiting for their turn to proceed, causing a brief congestion in the intersection.

As a next step, Q&A can be performed on the live-stream using the chat command.

python3 via_client_cli.py chat --id 9fcf2676-6ce8-4fb8-a6b1-c833f054c656 \
   --id e917b30f-43ae-4f7d-bad1-cab90d2df7c9 \
   --id 6c02d9af-b08f-4cff-9539-63b36f4badfe \
   --id 32fe4823-7659-493c-a97e-44136b3efa84 \
   --id b82684f3-de46-4a81-8f47-abed6c0461af \
   --id 646da2d7-7dc9-4c9e-8a72-ff1727859ee2 \
   --model vila-1.5 \
   --prompt "Is there a police car?"

# Output
Response:
Yes, there is a black and white police car in the intersection, waiting for the right of way.

Finally, remove all the files using the delete-file command.

python3 via_client_cli.py delete-file 9fcf2676-6ce8-4fb8-a6b1-c833f054c656
python3 via_client_cli.py delete-file e917b30f-43ae-4f7d-bad1-cab90d2df7c9
python3 via_client_cli.py delete-file 6c02d9af-b08f-4cff-9539-63b36f4badfe
python3 via_client_cli.py delete-file 32fe4823-7659-493c-a97e-44136b3efa84
python3 via_client_cli.py delete-file b82684f3-de46-4a81-8f47-abed6c0461af
python3 via_client_cli.py delete-file 646da2d7-7dc9-4c9e-8a72-ff1727859ee2

# Output
File deleted - id 9fcf2676-6ce8-4fb8-a6b1-c833f054c656, status True
...

Multi-stream / Concurrent Requests

VSS supports multi-stream / concurrent requests. This can be achieved by running multiple summarization / chat commands in parallel using python CLI client in multiple terminals.

For programmatic API usage, this can be achieved by calling /summarize and /chat APIs in parallel in multiple threads / processes.

All Commands Reference

The following section describes each of the commands in detail.

Files Commands

Add File

Calls POST /files internally. Uploads or adds a file as path. Prints the file id and other details.

Reference:

via_client_cli.py add-file [-h] [--add-as-path] [--is-image]
[--backend BACKEND] [--print-curl-command] file

Example for uploading a file:

Note

File types supported: mp4, mkv - with h264/h265 videos. Images: jpg, png.

python3 via_client_cli.py add-file video.mp4

Example for adding a file as path (This requires the file path to be accessible inside the container):

python3 via_client_cli.py add-file --add-as-path /media/video.mp4

Example for uploading an image file:

python3 via_client_cli.py add-file image.jpg --is-image

List Files

Calls GET /files internally. Prints the list of files added to the server and their details in a tabular format.

Reference:

via_client_cli.py list-files [-h] [--backend BACKEND]
[--print-curl-command]

Example:

python3 via_client_cli.py list-files

Get File Details

Calls GET /files/{id} internally. Prints the details of the file.

Reference:

via_client_cli.py file-info [-h] [--backend BACKEND]
[--print-curl-command] file_id

Example:

python3 via_client_cli.py file-info
7ce1127a-2009-4bfa-bdf8-efa9e1f37fa4

Get File Contents

Calls GET /files/{id}/content internally. Saves the content to a new file.

Reference:

via_client_cli.py file-content [-h] [--backend BACKEND]
[--print-curl-command] file_id

Example:

python3 via_client_cli.py file-content
7ce1127a-2009-4bfa-bdf8-efa9e1f37fa4

Delete File

Calls DELETE /files/{id} internally. Prints the delete status and file details.

Reference:

via_client_cli.py delete-file [-h] [--backend BACKEND]
[--print-curl-command] file_id

Example:

python3 via_client_cli.py delete-file
7ce1127a-2009-4bfa-bdf8-efa9e1f37fa4

Live Stream Commands

Add Live Stream

Calls POST /live-stream internally. Prints the live-stream id if it is added successfully.

Reference:

via_client_cli.py add-live-stream [-h] [--description DESCRIPTION]
[--username USERNAME] [--password PASSWORD] [--backend BACKEND]
[--print-curl-command]

live_stream_url

Example:

python3 via_client_cli.py add-live-stream --description "Some live stream description" \
   rtsp://192.168.1.100:8554/video/media1

List Live Streams

Calls GET /live-stream internally. Prints the list of live-streams and their details in a tabular format.

Reference:

via_client_cli.py list-live-streams [-h] [--backend BACKEND]
[--print-curl-command]

Example:

python3 via_client_cli.py list-live-streams

Delete Live Stream

Calls DELETE /live-stream/{id} internally. Prints the status confirming deletion of the live stream.

Reference:

via_client_cli.py delete-live-stream [-h] [--backend BACKEND][--print-curl-command]video_id

Example:

python3 via_client_cli.py delete-live-stream
ea071500-3a47-4e6f-87da-1bc796075344

Models Commands

List Models

Calls GET /models internally. Prints the list of models loaded by the server and their details in a tabular format.

Reference:

via_client_cli.py list-models [-h] [--backend BACKEND][--print-curl-command]

Example:

python3 via_client_cli.py list-models

Summarization Command

Calls POST /summarize internally. Triggers summarization on a file or live-stream and blocks it until summarization is complete or you interrupt the process.

The command allows some configurable parameters with the summarize request.

For files, results are available after the entire file is summarized. The command then prints the results.

For live-streams, results are periodically available. The period depends on the chunk_duration and summary_duration. Interrupting the summarize command does not stop the summarization on the server side. You can re-connect to the live-stream by re-running the summarize command with the same id as the live-stream.

Multiple image files can be summarized together by specifying --id <image-file-id> multiple times. This works only for image files.

To enable later chat or Q&A based on the prompts in the current summarize API call, add --enable-chat.

To enable audio transcription and use of audio transcripts in summarization, and later Q&A if enabled, add --enable-audio.

To enable CV metadata generation and usage add --enable-cv-metadata.

To provide custom prompt to the object detector in CV pipeline, add --cv-pipeline-prompt <prompt>. For example to detect persons and forklifts add --cv-pipeline-prompt "person . forklift .".

To enable alerts for live-streams and files using Server-Sent Events, specify the events to be alerted for using --alert <alert_name>:<event1>,<event2>,.... For example --alert safety-issues:workers not wearing ppe,boxes falling. This argument can be specified multiple times for multiple alerts.

For more details on each argument, see the help command and the VSS API reference. Detailed VSS API documentation is available at http://<VSS_API_ENDPOINT>/docs after VSS is deployed.

Reference:

via_client_cli.py summarize [-h] --id ID --model MODEL
   [--stream]
   [--chunk-duration CHUNK_DURATION]
   [--chunk-overlap-duration CHUNK_OVERLAP_DURATION]
   [--summary-duration SUMMARY_DURATION]
   [--prompt PROMPT]
   [--caption-summarization-prompt CAPTION_SUMMARIZATION_PROMPT]
   [--summary-aggregation-prompt SUMMARY_AGGREGATION_PROMPT]
   [--file-start-offset FILE_START_OFFSET]
   [--file-end-offset FILE_END_OFFSET]
   [--model-temperature MODEL_TEMPERATURE]
   [--model-top-p MODEL_TOP_P]
   [--model-top-k MODEL_TOP_K]
   [--model-max-tokens MODEL_MAX_TOKENS]
   [--model-seed MODEL_SEED]
   [--alert ALERT]
   [--enable-chat]
   [--enable-audio]
   [--enable-cv-metadata]
   [--cv-pipeline-prompt CV_PIPELINE_PROMPT]
   [--response-format {json_object,text}]
   [--backend BACKEND]
   [--print-curl-command]
   [--summarize-batch-size SUMMARIZE_BATCH_SIZE]
   [--rag-batch-size RAG_BATCH_SIZE]
   [--rag-type {graph-rag, vector-rag}]
   [--rag-top-k RAG_TOP_K]
   [--summarize-top-p SUMMARIZE_TOP_P]
   [--summarize-temperature SUMMARIZE_TEMPERATURE]
   [--summarize-max-tokens SUMMARIZE_MAX_TOKENS]
   [--chat-top-p CHAT_TOP_P]
   [--chat-temperature CHAT_TEMPERATURE]
   [--chat-max-tokens CHAT_MAX_TOKENS]
   [--notification-top-p NOTIFICATION_TOP_P]
   [--notification-temperature NOTIFICATION_TEMPERATURE]
   [--notification-max-tokens NOTIFICATION_MAX_TOKENS]

Example:

python3 via_client_cli.py summarize \
   --id ea071500-3a47-4e6f-87da-1bc796075344 \
   --model gpt-4o \
   --chunk-duration 60 \
   --stream \
   --prompt "Write a dense caption about the video containing events like ..." \
   --model-temperature 0.8

python3 via_client_cli.py summarize \
   --id ea071500-3a47-4e6f-87da-1bc796075344 \
   --model gpt-4o \
   --chunk-duration 10

python3 via_client_cli.py summarize \
   --id ea071500-3a47-4e6f-87da-1bc796075344 \
   --model gpt-4o \
   --chunk-duration 60 \
   --prompt "Write a dense caption about the video containing events like ..." \
   --caption-summarization-prompt "Summarize the events in the video using provided video description and audio transcripts. ..." \
   --summary-aggregation-prompt "Given the video descriptions and audio transcripts, aggregate them to a concise summary with timestamps. ..." \
   --enable-chat \
   --enable-audio

python3 via_client_cli.py summarize \
   --id ea071500-3a47-4e6f-87da-1bc796075344 \
   --model gpt-4o \
   --chunk-duration 60 \
   --enable-cv-metadata \
   --cv-pipeline-prompt "person . forklift ."

Chat Command

Calls POST /chat/completions internally. Triggers a Q&A query on a file or live-stream and blocks it until results are received.

The command allows sending the prompt(question) along with some configurable parameters for the Q&A request.

For chat command to work, a summarize query with --enable-chat must already have completed,

For more details on each argument, see the help command and the VSS API reference. Detailed VSS API documentation is available at http://<VSS_API_ENDPOINT>/docs after VSS is deployed.

Reference:

via_client_cli.py chat [-h] --id ID --model MODEL
   [--stream]
   [--prompt PROMPT]
   [--file-start-offset FILE_START_OFFSET]
   [--file-end-offset FILE_END_OFFSET]
   [--model-temperature MODEL_TEMPERATURE]
   [--model-top-p MODEL_TOP_P]
   [--model-top-k MODEL_TOP_K]
   [--model-max-tokens MODEL_MAX_TOKENS]
   [--model-seed MODEL_SEED]
   [--response-format {json_object,text}]
   [--backend BACKEND] [--print-curl-command]

Example:

python3 via_client_cli.py chat \
   --id d0b997f6-869a-4b2b-aee4-ea92d204d6bf \
   --model vila-1.5 \
   --prompt "Is there a person wearing white shirt?"

Alerts Commands

Add Live Stream Alert

Call POST /alerts internally. Adds an alert callback for a live-stream based on currently running summarization prompts. Prints the alert id if added successfully.

Whenever an alert is detected, VSS will POST a request to the configured URL with alert details.

Reference:

via_client_cli.py add-alert [-h] --live-stream-id LIVE_STREAM_ID
   --callback-url CALLBACK_URL --events EVENTS
   [--callback-json-template CALLBACK_JSON_TEMPLATE]
   [--callback-token CALLBACK_TOKEN]
   [--backend BACKEND] [--print-curl-command]

Example:

python3 via_client_cli.py add-alert \
   --live-stream-id ea071500-3a47-4e6f-87da-1bc796075344 \
   --callback-url http://localhost:14000/via-alert-callback \
   --events "worker not wearing ppe" --events "boxes falling"

Refer to Alert Callback Test Server for starting a test alert callback server.

List Live Stream Alerts

Call GET /alerts internally. Get the list of all live-stream alerts that have been configured.

Reference:

via_client_cli.py list-alerts [-h] [--backend BACKEND] [--print-curl-command]

Example:

python3 via_client_cli.py list-alerts

Delete Live Stream Alert

Call DELETE /alerts/{id} internally. Delete a live-stream alert using its ID.

Reference:

via_client_cli.py delete-alert [-h] [--backend BACKEND] [--print-curl-command] alert_id

Example:

python3 via_client_cli.py delete-alert f82b2c8b-e5ce-49d7-be7e-053717d0cd49

List Recent Live Stream Alerts

Call GET /alerts internally. Get the list of recent live-stream alerts that were detected.

Reference:

via_client_cli.py list-recent-alerts [-h] [--live-stream-id LIVE_STREAM_ID]
   [--backend BACKEND] [--print-curl-command]

Example:

python3 via_client_cli.py list-recent-alerts

Alert Callback Test Server

Start a test server for receiving live-stream alerts from VSS. The server prints the alerts as received.

Reference:

via_client_cli.py alert-callback-server [-h] [--host HOST] [--port PORT]

Example:

python3 via_client_cli.py alert-callback-server --host 0.0.0.0 --port 5004

Server Health and Metrics Commands

Server Health Check

Calls GET /health/ready internally. Checks the response status code and prints the server health status.

Reference:

via_client_cli.py server-health-check [-h] [--backend BACKEND][--print-curl-command]

Example:

python3 via_client_cli.py server-health-check

Server Metrics

Calls GET /metrics internally. Prints the server metrics. The metrics are in Prometheus format.

Reference:

via_client_cli.py server-metrics [-h] [--backend BACKEND][--print-curl-command]

Example:

python3 via_client_cli.py server-metrics