vector
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
vector has 14 facts recorded in Dontopedia across 8 references, with 2 live disagreements.
14 facts·3 predicates·8 sources·2 in dispute
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (9)
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.
containsKeyContains Key(6)
- Data Dictionary
ex:data-dictionary - Id Vector Dict
ex:id-vector-dict - Near Vector Dict
ex:near-vector-dict - Near Vector Dictionary
ex:near-vector-dictionary - Near Vector Param
ex:near-vector-param - Near Vector Structure
ex:near-vector-structure
hasKeyHas Key(3)
- Data Dictionary
ex:data-dictionary - Result Variable
ex:result-variable - Vector Config Object
ex:vector-config-object
Other facts (10)
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.
10 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Dictionary Key | [1] |
| Rdf:type | Parameter Key | [2] |
| Rdf:type | Config Key | [3] |
| Rdf:type | Dictionary Key | [4] |
| Rdf:type | Json Key | [5] |
| Rdf:type | Dictionary Key | [6] |
| Rdf:type | Dictionary Key | [8] |
| Rdf:type | Query Vector Key | [8] |
| Used in | Near Vector Object | [4] |
| Has Value | [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]] | [7] |
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.
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typebeam/70bbc43a-27da-4ee6-abde-0b83af52d874
ex:DictionaryKey
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labelbeam/70bbc43a-27da-4ee6-abde-0b83af52d874
vector
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typebeam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0
ex:ParameterKey
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typebeam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c
ex:ConfigKey
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typebeam/ea34a816-3421-425e-97a9-50206b2c6248
ex:DictionaryKey
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labelbeam/ea34a816-3421-425e-97a9-50206b2c6248
"vector" Key
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usedInbeam/ea34a816-3421-425e-97a9-50206b2c6248
ex:near-vector-object
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typebeam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8
ex:JSONKey
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labelbeam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8
vector key
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typebeam/5cbfc373-2797-488e-9dab-6ae88803e66c
ex:DictionaryKey
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labelbeam/5cbfc373-2797-488e-9dab-6ae88803e66c
vector
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hasValuebeam/c39988e0-db33-4984-8c77-56ffcecd919a
ex:[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
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typebeam/9d96f8cb-54e9-48bd-a699-50a1796601b9
ex:DictionaryKey
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typebeam/9d96f8cb-54e9-48bd-a699-50a1796601b9
ex:QueryVectorKey
References (8)
8 references
ctx:claims/beam/70bbc43a-27da-4ee6-abde-0b83af52d874ctx:claims/beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0- full textbeam-chunktext/plain1 KB
doc:beam/f80d8de8-0d2a-446e-ac9c-fc4672dce4f0Show excerpt
# Create the schema in Weaviate client.schema.create_class(schema) print("Schema created successfully.") ``` #### Inserting Data When inserting data, you can specify which vector property to use based on the vector size. ```python # Add …
ctx:claims/beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c- full textbeam-chunktext/plain1 KB
doc:beam/df58a3ab-2df5-43d0-a3c7-d866e2d0138cShow excerpt
.with_near_vector(near_vector_128) .with_limit(10) .do() ) print("Vector search query successful (size 128):") print(result_128) query_vector_256 = [0.5, 0.6, 0.7, 0.8] * 64 # Example query vector of size 256 near_vector_256 …
ctx:claims/beam/ea34a816-3421-425e-97a9-50206b2c6248ctx:claims/beam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8- full textbeam-chunktext/plain821 B
doc:beam/b199aa18-2d4a-4e37-a971-f1f5b557a5b8Show excerpt
print("Vector search query successful (size 128):") print(result_128) query_vector_256 = [0.5, 0.6, 0.7, 0.8] * 64 # Example query vector of size 256 near_vector_256 = {"vector": query_vector_256} result_256 = ( client.query.get("MyC…
ctx:claims/beam/5cbfc373-2797-488e-9dab-6ae88803e66c- full textbeam-chunktext/plain1 KB
doc:beam/5cbfc373-2797-488e-9dab-6ae88803e66cShow excerpt
decrypted_vector = decrypt_vector(result["vector"]) print(f"Name: {result['name']}, Vector: {decrypted_vector}") ``` ### Explanation 1. **Encryption Functions**: - `encrypt_vector`: Serializes the vector to bytes, encodes it in…
ctx:claims/beam/c39988e0-db33-4984-8c77-56ffcecd919a- full textbeam-chunktext/plain1 KB
doc:beam/c39988e0-db33-4984-8c77-56ffcecd919aShow excerpt
# Vector exists but document does not vector_collection.delete([vec_id]) # Run reconciliation periodically reconcile_data() ``` ### Full Example Script Here is the complete script combining all the steps: ```pyth…
ctx:claims/beam/9d96f8cb-54e9-48bd-a699-50a1796601b9
See also
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