What Content Do I Have?
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:
query QueryContents($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"
}
}
]
}
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