INT64
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
INT64 has 4 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
fieldTypeField Type(1)
- Id Field
ex:id-field
hasDataTypeHas Data Type(1)
- Field Schema Id
ex:field-schema-id
valueValue(1)
- Dtype Parameter
ex:dtype-parameter
Other facts (2)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (2)
ctx:claims/beam/1c53ac22-55f2-410c-b32e-6b6547174e6f- full textbeam-chunktext/plain1 KB
doc:beam/1c53ac22-55f2-410c-b32e-6b6547174e6fShow excerpt
connections.connect("default", host="localhost", port="19530") # Define the schema fields = [ FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True), FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, d…
ctx:claims/beam/58335043-7a28-4310-8bc8-6b38b5011f99- full textbeam-chunktext/plain1 KB
doc:beam/58335043-7a28-4310-8bc8-6b38b5011f99Show excerpt
Here's how you can set up and use Milvus to store and retrieve document embeddings: ### Step-by-Step Guide 1. **Install Milvus**: - Install Milvus using Docker or from source. - Ensure you have a running Milvus instance. 2. **Desig…
See also
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