Bring your own data

You can ingest your own typed knowledge graph into OWLGraph and query it via the SDK. v1 supports the “I already have typed data” path — you bring four files (ontology + entities + triples + passages); OWLGraph loads them. If you have only a corpus and want OWLGraph to induce the ontology + triples, that’s Phase 2E.2 (LLM-driven extraction) and not shipping yet.

Total size limit today: 50 MB combined (nginx body cap). With 1536-dim embeddings, that’s room for ~3000 passages. Larger corpora need the S3-backed upload path which isn’t built yet.

Standard OWL 2 RL Turtle. Declare your classes, properties, and (optionally) owlcore:textEmbedding if you want HNSW vector search.

@prefix : <http://your-domain.example.org/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix owlcore: <https://owlgraph.ai/owl/2026/core#> .
@prefix owlv: <https://owlgraph.ai/owl/2026/vector#> .

:Drug a owl:Class .
:Disease a owl:Class .
:Approval a owl:Class .

:treats a owl:ObjectProperty ;
    rdfs:domain :Drug ;
    rdfs:range :Disease .

:approvedFor a owl:ObjectProperty ;
    rdfs:domain :Drug ;
    rdfs:range :Disease .

owlcore:textEmbedding a owl:DatatypeProperty ;
    rdfs:domain owlcore:Passage ;
    rdfs:range owlv:Float32Vector ;
    owlv:vectorDimension 1536 ;
    owlv:vectorIndex "hnsw(metric: \"cosine\", exponent: \"5\")" ;
    owlv:embedderHint "openai/text-embedding-3-small" .

One JSON object per line. Required: name, top_type. Optional but recommended: aliases, passage_ids.

{"name": "Pembrolizumab", "top_type": "Drug", "all_types": ["Drug"],
 "aliases": ["Keytruda", "MK-3475"],
 "passage_ids": ["doc-0001", "doc-0017", "doc-0042"]}
{"name": "Melanoma", "top_type": "Disease", "all_types": ["Disease"],
 "aliases": ["malignant melanoma"],
 "passage_ids": ["doc-0001", "doc-0042"]}

One JSON object per line. Required: subject, subject_top_type, predicate, object, object_top_type.

{"subject": "Pembrolizumab", "subject_top_type": "Drug",
 "predicate": "treats",
 "object": "Melanoma", "object_top_type": "Disease",
 "evidence": "approved for unresectable or metastatic melanoma",
 "confidence": 1.0}

One JSON object per line.

{"id": "doc-0001", "text": "Pembrolizumab (Keytruda) is approved for...",
 "source": "FDA-Drug-Approvals/2014/pembrolizumab.pdf",
 "vec": [0.0123, -0.0456, 0.1234, ...]}

1. Sign up + mint an API key. Quick start covers the first half.

2. Install the SDK.

   pip install owlgraph-core

3. Create a database.

   owlgraph db create --name my-knowledge-graph --size s

   Record the UUID it prints — you’ll pass it to subsequent commands.

4. Ingest your four files.

   owlgraph ingest custom <db_id> \
       --ontology  ontology.ttl \
       --entities  entities.jsonl \
       --triples   triples.jsonl \
       --passages  passages.jsonl \
       --follow

   --follow streams per-stage events to stdout. Without it, the command returns the job id and you tail it later with owlgraph ingest logs <db_id> <job_id> --follow.

5. Query it.

   from owlgraph_core import sdk as owl
   
   db = owl.connect(database_id="<db_id>", api_key="<key>")
   result = db.retrieve("Which drugs treat melanoma?")
   print(result.answer)
   for p in result.passages:
       print(p.source, p.text[:200])

invalid_jsonl in entities.jsonl line 14 — one of your JSONL rows isn’t valid JSON. Check trailing commas, unescaped quotes, missing braces.

entity name not found: <foo> — a triple references a subject or object that isn’t in entities.jsonl. Make sure every entity in the triples has a row in entities.

Schema conflicts — if the ontology declares a property with a different type than your data writes (e.g. textEmbedding as string instead of float32vector), Dgraph rejects later writes. Drop the database and re-ingest cleanly if you change the ontology.

Body too large — the API rejects total payloads >50 MB. Either drop the vec field (platform will embed at ingest cost) or split the corpus into multiple databases.

• Re-ingest of just one file. v1 reloads everything per call. Re-ingest deletes existing data first (drop_all on the alpha) — back up before re-running.
• S3-backed upload for >50 MB corpora. Tracked.
• LLM-driven entity/triple extraction. If you have only a corpus and want OWLGraph to build the graph for you, that’s Phase 2E.2 — talk to us about design-partner access.