This Collection is a thorough overview on the implementation of the Curio APIs into your internal systems or workflows. The Curio engine can function as a stand-alone tool or can enrich and enhance the data of an existing system of record.

Each section below are breakout sections for programmatically interacting with or creating Curio metadata.

If using this primarily with Postman it's recommended to create an Environment and use variables as some of the calls make sense to utilize the same envars.

Concepts

This document outlines the high-level concepts modeled in the GrayMeta Platform API.

How Curio works

The GrayMeta Platform ingests content from storage, extracting metadata and making it available via various APIs.

Storage

For example, a connection to an Amazon S3 instance is a Location, and the buckets are Containers. The files inside the buckets are the Items.

Data

Once analyzation has finished, you may access the metadata for an item by using the unique IDs assigned to it with the Items API, alternatively you can use the Search API to perform full-text queries, filters and aggregations against the metadata.

Create an additional Bearer Token (tokens created include all access and functionality of the User who created them - Users with different scopes Bearer Tokens will only include the functionality of that individual user).

This section include the ability to manage users within the Curio system. It is recommeded that Roles and Groups be created in the UI, but these functions are available programmatically as well.

Put container in db/enable - This step actually puts the new bucket into the database, if this step is skipped you will technically still be able to call a harvest against the bucket, but the proxies, etc. will not be indexed or searchable. This step also makes this bucket eligible/enabled to be walked during Live Harvest (if configured).

GET containers/enabled - This is a simple GET request to confirm the success of step one, or can be used separately to audit the list of currently enabled containers across an instance.

Determine stow_url & gm_item_id - This step determines the gm_item_id (also referred to as item_id for certain calls) as well as translates item key into the full stow path Curio actually uses for individual file processing. The gm_item_id is for finding the item once processed, the item_stow_url output is necessary for step 4. note: Item key is not the full S3 path, only the portions after the location name.

Init harvest by stow_url - Step 4 takes the stow_url output from step 3 and uses it to specifically call a harvest on a previously unwalked item. This step also introduces the profile_id, which determines which extractor profile gets used in the harvest. In order to get a list of eligible extractor_profiles make the following call:

however, in order to confirm/adjust extractor profiles, you must log into the UI.

jobstatus - per request_id - Once a harvest has been successfully initiated the response payload will include the request_id. Step 5 allows the user to monitor the status of the harvest. Since each harvest includes different content and possibly different configurations, the processing time will vary. Rather than provide a somewhat misleading processing percentage, we simplify it to walk vs. index. The walked_count is how many files we called to be harvested, the indexed_count is how many have been completed and indexed.

For harvests of more than a single item, or container harvests the above call will clarify which items are included in the request.

activity - extractors errors per request_id Lists all extractors called per request_id and the payload response includes error count per extractor.

activity - errors per request_id/extractor -
In the event of an error, this call will bring back details about the error.

activity - get item_ids with errors per extractor - This endpoint responds with the item_id and item name for every errored file by extractor.

activity - get extractor errors for multiple request_ids - Extractor error count for multiple request_ids.

Instead of individually ingesting files you're also able to call a container harvest which will ingest all files within an entire bucket.

Once an item has been ingested into Curio you may indivually trigger analyzation of a single file against a single service by using only it's Curio unique ID (item_id).

A simple method of determining which ML services have been run on an item-by-item basis.

This is a step-by-step harvesting process for items via API once a location has been added. Remember, depending on the IAM policy a location may have permissions to more than one bucket.

Steps 1 & 2 will typically only per performed once and are sometimes easier through the UI. Step 2 does include data that will be necessary for every call though (location_id and container_id).

Steps 3 & 4 are generally used when there is a large bucket with one specific item that must be processed immediately ahead of the next walk, or if live harvest is disabled and items are called individually programmatically by another system (typically a MAM/DAM workflow).

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","","","","`;","","","// In case you only want a specific property, change it here.","//","// Default: You can set the entire JSON response using pm.response.json()","let tableProps = {"," json: pm.response.json()","};","","pm.visualizer.set(template, tableProps);"],"type":"text/javascript"}}],"id":"3b6e7baf-51ed-4af0-aa0b-4c1df6ac76f1","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"auth":{"type":"bearer","bearer":{"basicConfig":[{"key":"token","value":"{{token}}"}]},"isInherited":false},"method":"POST","header":[],"body":{"mode":"raw","raw":"{\n \"query\": \"\\\"rocks tonic juice magic\\\"\",\n \"filters\": {\n \"full_text\": [],\n \"full_text_phrase\": [],\n \"exact_terms\": [],\n \"multi_terms\": [],\n \"exclude_terms\": [],\n \"exists\": [],\n \"not_exists\": [],\n \"ranges\": []\n },\n \"sort_fields\": [],\n \"page\": 0,\n \"limit\": 25\n}","options":{"raw":{"language":"json"}}},"url":"{{url}}/api/data/search","description":"

Perform top-level \"exact phrase\" search.

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Once you've found an asset that matches your top-level search criteria the asset level search will return all time and metadata type occurences of this search term within the item.

The Curio search options via API are robust and have both filtering and aggregation functionality built it. For a full list of search options please reference the Curio Search API Docs.

Below please find some good examples of the standard search functionality.

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Programmatically pull all logo data for a specific item including confidence scores and bounding box information.

Programmatically pull all logo data for a specific item.

Programmatically pull all OCR data for a specific item.

Programmatically pull all S2T data for a specific item.

Programmatically pull all tag data for a specific item.

Programmatically pull all technical file data for a specific item.

Programmatically pull all audio detection data for a specific item.

Programmatically pull all top level Curio item data for a specific item(includes ingest data, unique IDs for location, item, file paths, etc.

Programmatically pull all caption data for a specific item.

Programmatically pull all associated side-car audio Curio items for a specific item.

Programmatically pull all scene description data for a specific item.

Programmatically pull all unique custom data for a specific item.

Programmatically pull all Known and Unknown People data for a specific item.

Programmatically pull all insights detection data for a specific item.

Programmatically pull all location/landmark data for a specific item(including bounding boxes).

Programmatically pull all location/landmark data for a specific item.

Programmatically pull all mature-content detection data for a specific item.

Programmatically pull combined Technical Cues detection data for a specific item.

Programmatically pull all color bars detection data for a specific item.

Programmatically pull all black scene detection data for a specific item.

Programmatically pull all silence detection data for a specific item.

Programmatically pull all digital slate detection data for a specific item.

Programmatically pull all physical slate(clapperboard) detection data for a specific item.

Programmatically pull true start/end data for a specific item.

Programmatically pull all texted detection data for a specific item.

Programmatically pull all textless detection data for a specific item.

Programmatically pull all sports(soccer) detection data for a specific item.

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item","id":"6c0f4b9b-6bdb-4a68-a80c-79fc8cb0a36b","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"auth":{"type":"bearer","bearer":{"basicConfig":[{"key":"token","value":"{{token}}"}]},"isInherited":false},"method":"GET","header":[],"url":"{{url}}/files/{{item_id}}/video_main_frames/frame-0000000001.jpg","urlObject":{"path":["files","{{item_id}}","video_main_frames","frame-0000000001.jpg"],"host":["{{url}}"],"query":[],"variable":[]}},"response":[],"_postman_id":"6c0f4b9b-6bdb-4a68-a80c-79fc8cb0a36b"},{"name":"Download cumulative metadata file per 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pm.response.to.have.status(200);","});"],"type":"text/javascript"}}],"id":"65d4fab1-df84-4f2c-835c-ce37c9ff1158","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[{"key":"Content-Type","name":"Content-Type","value":"application/json","type":"text"}],"body":{"mode":"raw","raw":""},"url":"{{url}}/api/data/v3/duplicates-footprint","auth":{"type":"bearer","bearer":{"basicConfig":[{"key":"token","value":"{{token}}"}]},"isInherited":true,"source":{"_postman_id":"51ebd78d-1d7d-4632-be82-42ca6387b008","id":"51ebd78d-1d7d-4632-be82-42ca6387b008","name":"Wasabi AiR Implementation Template","type":"collection"}},"urlObject":{"path":["api","data","v3","duplicates-footprint"],"host":["{{url}}"],"query":[],"variable":[]}},"response":[],"_postman_id":"65d4fab1-df84-4f2c-835c-ce37c9ff1158"},{"name":"Get Duplicate items data","event":[{"listen":"test","script":{"id":"a6c0edf9-31f3-47b7-87eb-8cc3ef4fcd0d","exec":["//access the JSON response body","let response = 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pm.response.to.have.status(200);","});"],"type":"text/javascript"}}],"id":"7f07779e-2574-49a8-a047-0468a8590870","protocolProfileBehavior":{"disableBodyPruning":true},"request":{"method":"GET","header":[{"key":"Content-Type","name":"Content-Type","value":"application/json","type":"text"}],"body":{"mode":"raw","raw":""},"url":"{{url}}/api/data/v3/search/categories","auth":{"type":"bearer","bearer":{"basicConfig":[{"key":"token","value":"{{token}}"}]},"isInherited":true,"source":{"_postman_id":"51ebd78d-1d7d-4632-be82-42ca6387b008","id":"51ebd78d-1d7d-4632-be82-42ca6387b008","name":"Wasabi AiR Implementation Template","type":"collection"}},"urlObject":{"path":["api","data","v3","search","categories"],"host":["{{url}}"],"query":[],"variable":[]}},"response":[],"_postman_id":"7f07779e-2574-49a8-a047-0468a8590870"},{"name":"Get Category Items","event":[{"listen":"test","script":{"id":"a6c0edf9-31f3-47b7-87eb-8cc3ef4fcd0d","exec":["//access the JSON response body","let response = 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