FieldSchema
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
FieldSchema has 24 facts recorded in Dontopedia across 5 references, with 5 live disagreements.
Mostly:rdf:type(4), has field(4), has dimension(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
containsFieldContains Field(1)
- Collection Schema
ex:collection-schema
rdf:typeRdf:type(1)
- Id Field
ex:id-field
Other facts (22)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Field Definition | [1] |
| Rdf:type | Python Class | [2] |
| Rdf:type | Configuration Schema | [4] |
| Rdf:type | Code Structure | [5] |
| Has Field | recipients | [4] |
| Has Field | severity | [4] |
| Has Field | description | [4] |
| Has Field | additional_info | [4] |
| Has Dimension | 128 | [3] |
| Has Dimension | Dimension | [3] |
| Has Dimension | 768 | [5] |
| Instantiated With | name | [5] |
| Instantiated With | dtype | [5] |
| Instantiated With | dim | [5] |
| Has Dtype | Data Type Float Vector | [3] |
| Part of | Fields | [3] |
| Has Name | embedding | [5] |
| Has Data Type | Float Vector | [5] |
| Is Field in | Collection Schema | [5] |
| Uses Data Type | Data Type.float Vector | [5] |
| Defines | Embedding Field | [5] |
| Instantiates | Field | [5] |
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 (5)
ctx:claims/beam/86785515-9f1f-4fdd-887b-9264324ad027ctx: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…
ctx:claims/beam/926f1488-328b-43c2-9fba-d5492a192351- full textbeam-chunktext/plain1 KB
doc:beam/926f1488-328b-43c2-9fba-d5492a192351Show excerpt
FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Document Embeddings") # Create the collection collection = Collection("document_embeddings", schema) ``` #### 3. Insert Vectors …
ctx:claims/beam/c8c1238f-8282-4676-bc74-447791e6832e- full textbeam-chunktext/plain1 KB
doc:beam/c8c1238f-8282-4676-bc74-447791e6832eShow excerpt
[Turn 5795] Assistant: Certainly! You can extend your JSON configuration to include custom fields for more detailed incident descriptions. This will allow you to provide richer information when sending alerts. ### Step 1: Extend the JSON C…
ctx:claims/beam/3ec8c303-e081-4923-9f67-5956a4f6bef5
See also
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