Once you have ingested content, how do you find what's in your Graphlit project?
You can use the contents query to return all the content you ingested.
Graphlit uses a paging model for queries, where you can request the offset into the query results, and the limit of results to be returned.
Below is the simplest query possible, which does no filtering on the content itself.
As content is ingested into Graphlit, metadata is indexed and stored in the knowledge graph. Also, as the content progresses through the workflow, knowledge will be extracted via ML models and stored in the knowledge graph.
Query:
queryQueryContents($filter: ContentFilter!) { contents(filter: $filter) { results { id name creationDate state owner { id } originalDate finishedDate workflowDuration uri text type fileType mimeType fileName fileSize masterUri mezzanineUri transcriptUri } }}
Variables:
{"filter": {"offset":0,"limit":100 }}
Response:
{"results": [ {"type":"FILE","originalDate":"2023-06-21T00:44:15Z","mimeType":"application/pdf","fileType":"DOCUMENT","fileName":"Unifying Large Language Models and Knowledge Graphs A Roadmap-2306.08302.pdf","fileSize":3312767,"masterUri":"https://graphlit20230701d31d9453.blob.core.windows.net/files/7ecc766e-b793-4abc-8b6a-13a9a50ebbac/Unifying%20Large%20Language%20Models%20and%20Knowledge%20Graphs%20A%20Roadmap-2306.08302.pdf","mezzanineUri":"https://graphlit20230701d31d9453.blob.core.windows.net/files/7ecc766e-b793-4abc-8b6a-13a9a50ebbac/Mezzanine/Unifying%20Large%20Language%20Models%20and%20Knowledge%20Graphs%20A%20Roadmap-2306.08302.json","uri":"https://graphlitplatform.blob.core.windows.net/samples/Unifying%20Large%20Language%20Models%20and%20Knowledge%20Graphs%20A%20Roadmap-2306.08302.pdf","id":"7ecc766e-b793-4abc-8b6a-13a9a50ebbac","name":"Unifying Large Language Models and Knowledge Graphs A Roadmap-2306.08302.pdf","state":"FINISHED","creationDate":"2023-07-03T22:07:19Z","finishedDate":"2023-07-03T22:34:24Z","workflowDuration":"PT1M2.1412798S","owner": {"id":"9422b73d-f8d6-4faf-b7a9-152250c862a4" } }, {"type":"PAGE","mimeType":"text/html","fileType":"DOCUMENT","fileName":"gpt-4.htm","fileSize":194133,"masterUri":"https://graphlit20230701d31d9453.blob.core.windows.net/files/2fd457d0-5254-444d-b33e-f950f90f12bf/gpt-4.htm","mezzanineUri":"https://graphlit20230701d31d9453.blob.core.windows.net/files/2fd457d0-5254-444d-b33e-f950f90f12bf/Mezzanine/gpt-4.json","uri":"https://openai.com/research/gpt-4","id":"2fd457d0-5254-444d-b33e-f950f90f12bf","name":"GPT-4","state":"FINISHED","creationDate":"2023-07-03T22:38:00Z","finishedDate":"2023-07-03T22:39:06Z","workflowDuration":"PT1M6.0812698S","owner": {"id":"9422b73d-f8d6-4faf-b7a9-152250c862a4" } }, {"type":"FILE","mimeType":"audio/mpeg","fileType":"AUDIO","fileName":"Unstructured Data is Dark Data Podcast.mp3","fileSize":33008244,"masterUri":"https://graphlit20230701d31d9453.blob.core.windows.net/files/c0cc103d-467b-43c1-8256-8b99f346d4f3/Unstructured%20Data%20is%20Dark%20Data%20Podcast.mp3","mezzanineUri":"https://graphlit20230701d31d9453.blob.core.windows.net/files/c0cc103d-467b-43c1-8256-8b99f346d4f3/Mezzanine/Unstructured%20Data%20is%20Dark%20Data%20Podcast.mp3","transcriptUri":"https://graphlit20230701d31d9453.blob.core.windows.net/files/c0cc103d-467b-43c1-8256-8b99f346d4f3/Transcript/Unstructured%20Data%20is%20Dark%20Data%20Podcast.json","uri":"https://graphlitplatform.blob.core.windows.net/samples/Unstructured%20Data%20is%20Dark%20Data%20Podcast.mp3","id":"c0cc103d-467b-43c1-8256-8b99f346d4f3","name":"Unstructured Data is Dark Data Podcast.mp3","state":"FINISHED","creationDate":"2023-07-03T22:24:50Z","finishedDate":"2023-07-03T22:25:46Z","workflowDuration":"PT56.2314332S","owner": {"id":"9422b73d-f8d6-4faf-b7a9-152250c862a4" } } ]}