mirror of
https://github.com/trustgraph-ai/trustgraph.git
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101 lines
3.2 KiB
Text
101 lines
3.2 KiB
Text
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#!/usr/bin/env python3
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import requests
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import json
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import sys
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import base64
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url = "http://localhost:8088/api/v1/"
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############################################################################
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text = open("docs/README.cats", "rb").read()
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# Some random identifiers. The doc ID is important, as extracted knowledge
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# is linked back to this identifier
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org_id = "https://trustgraph.ai/org/3c35111a-f8ce-54b2-4dd6-c673f8bf0d09"
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doc_id = "https://trustgraph.ai/doc/4faa45c1-f91a-a96a-d44f-2e57b9813db8"
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pub_id = "https://trustgraph.ai/pubev/a847d950-a281-4099-aaab-c5e35333ff61"
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# Organization metadata
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org_facts = [
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[org_id, "http://www.w3.org/1999/02/22-rdf-syntax-ns#type",
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"https://schema.org/Organization"],
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[org_id, "http://www.w3.org/2000/01/rdf-schema#label", "trustgraph.ai"],
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[org_id, "https://schema.org/name", "trustgraph.ai"]
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]
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# Publication metadata. Note how it links to the Organization
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pub_facts = [
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[pub_id, "http://www.w3.org/1999/02/22-rdf-syntax-ns#type",
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"https://schema.org/PublicationEvent"],
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[pub_id, "https://schema.org/description", "Uploading to Github"],
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[pub_id, "https://schema.org/endDate", "2024-10-23"],
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[pub_id, "https://schema.org/publishedBy", org_id],
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[pub_id, "https://schema.org/startDate", "2024-10-23"]
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]
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# Document metadata. Note how it links to the publication event
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doc_facts = [
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[doc_id, "http://www.w3.org/1999/02/22-rdf-syntax-ns#type",
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"https://schema.org/DigitalDocument"],
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[doc_id, "http://www.w3.org/2000/01/rdf-schema#label", "Mark's cats"],
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[doc_id, "https://schema.org/copyrightHolder", "trustgraph.ai"],
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[doc_id, "https://schema.org/copyrightNotice", "Public domain"],
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[doc_id, "https://schema.org/copyrightYear", "2024"],
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[doc_id, "https://schema.org/description",
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"This document describes Mark's cats"],
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[doc_id, "https://schema.org/keywords", "animals"],
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[doc_id, "https://schema.org/keywords", "cats"],
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[doc_id, "https://schema.org/keywords", "home-life"],
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[doc_id, "https://schema.org/name", "Mark's cats"],
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[doc_id, "https://schema.org/publication", pub_id],
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[doc_id, "https://schema.org/url", "https://example.com"]
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]
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def to_value(x):
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if x.startswith("https://"):
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return { "v": x, "e": True }
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if x.startswith("http://"):
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return { "v": x, "e": True }
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return { "v": x, "e": False }
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# Convert the above metadata into the right form
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metadata = [
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{ "s": to_value(t[0]), "p": to_value(t[1]), "o": to_value(t[2]) }
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for t in org_facts + pub_facts + doc_facts
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]
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input = {
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# Document identifer. Knowledge derived by TrustGraph is linked to this
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# identifier, so the additional metadata specified above is linked to the
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# derived knowledge and users of the knowledge graph could see
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# information about the source of knowledge
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"id": doc_id,
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# Additional metadata in the form of RDF triples
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"metadata": metadata,
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# Text character set. Default is UTF-8
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"charset": "utf-8",
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# The PDF document, is presented as a base64 encoded document.
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"text": base64.b64encode(text).decode("utf-8")
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}
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resp = requests.post(
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f"{url}load/text",
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json=input,
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)
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resp = resp.json()
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if "error" in resp:
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print(f"Error: {resp['error']}")
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sys.exit(1)
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print(resp)
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