vector
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
vector has 12 facts recorded in Dontopedia across 7 references, with 2 live disagreements.
12 facts·3 predicates·7 sources·2 in dispute
Maturity scale
raw canonical shape-checked rule-derived certifiedFull NamefullName
- DataType.FLOAT_VECTOR[5]sourceall time · Eedd69ea 628c 47ec A0dd 4f8d515c0c1d
Inbound mentions (7)
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.
hasDataTypeHas Data Type(2)
- Property Vector
ex:property-vector - Vector Field
ex:vector-field
dataFormatData Format(1)
- Vector Dataset
ex:vector-dataset
describesDescribes(1)
- Float32
float32
hasDataTypeArrayElementsHas Data Type Array Elements(1)
- Property Vector Data
ex:property-vector-data
typeHintType Hint(1)
- Vector Parameter
ex:vector-parameter
validationTargetValidation Target(1)
- Check Dtype Function
ex:check-dtype-function
Other facts (8)
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.
8 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Data Type | [1] |
| Rdf:type | Data Type Element | [2] |
| Rdf:type | Data Type | [3] |
| Rdf:type | Data Type Enum | [4] |
| Rdf:type | Data Type | [5] |
| Rdf:type | Data Attribute | [6] |
| Rdf:type | Data Format | [7] |
| Expected Type | Float32 | [6] |
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/cbaeb875-e16f-44dd-bc0f-36b3945d0935
ex:DataType
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labelbeam/cbaeb875-e16f-44dd-bc0f-36b3945d0935
Vector Data Type
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typebeam/df58a3ab-2df5-43d0-a3c7-d866e2d0138c
ex:DataTypeElement
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typebeam/d7afc1e8-622c-4a16-b0a5-c6289c0cac34
ex:DataType
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labelbeam/d7afc1e8-622c-4a16-b0a5-c6289c0cac34
vector
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typebeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
ex:DataTypeEnum
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labelbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
FLOAT_VECTOR
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typebeam/eedd69ea-628c-47ec-a0dd-4f8d515c0c1d
ex:DataType
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fullNamebeam/eedd69ea-628c-47ec-a0dd-4f8d515c0c1d
DataType.FLOAT_VECTOR
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typebeam/a980ff53-f4b6-4edc-b34c-d483c453a7f5
ex:DataAttribute
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expectedTypebeam/a980ff53-f4b6-4edc-b34c-d483c453a7f5
ex:float32
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typebeam/b81bf9d3-a669-43d9-8289-e9bbbd96847e
ex:DataFormat
References (7)
7 references
ctx:claims/beam/cbaeb875-e16f-44dd-bc0f-36b3945d0935- full textbeam-chunktext/plain1 KB
doc:beam/cbaeb875-e16f-44dd-bc0f-36b3945d0935Show excerpt
print("Query successful:") print(result) ``` ### Example with Vector Search If you want to perform a vector search and retrieve both text and vector data, you can use the `nearVector` filter: ```python # Perform a vector search query_vec…
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/d7afc1e8-622c-4a16-b0a5-c6289c0cac34- full textbeam-chunktext/plain1 KB
doc:beam/d7afc1e8-622c-4a16-b0a5-c6289c0cac34Show excerpt
[Turn 2411] Assistant: Absolutely, let's ensure that encryption standards are included in the solution for securing your data in Weaviate. Given your requirements, we'll focus on using AES-256 encryption to secure the vectors before storing…
ctx:claims/beam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69- full textbeam-chunktext/plain1 KB
doc:beam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69Show excerpt
FieldSchema(name="id", dtype=DataType.INT64, is_primary=True), FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=3) ] schema = CollectionSchema(fields, "RAG Vector Collection") collection = Collection("rag_vectors", schema…
ctx:claims/beam/eedd69ea-628c-47ec-a0dd-4f8d515c0c1d- full textbeam-chunktext/plain1 KB
doc:beam/eedd69ea-628c-47ec-a0dd-4f8d515c0c1dShow excerpt
# Connect to MongoDB client = MongoClient('mongodb://localhost:27017/') db = client['rag_db'] document_collection = db['documents'] # Connect to Milvus connections.connect("default", host="localhost", port="19530") # Define schema for Mil…
ctx:claims/beam/a980ff53-f4b6-4edc-b34c-d483c453a7f5ctx:claims/beam/b81bf9d3-a669-43d9-8289-e9bbbd96847e- full textbeam-chunktext/plain1 KB
doc:beam/b81bf9d3-a669-43d9-8289-e9bbbd96847eShow excerpt
- **Distributed Indexing**: Use distributed indexing techniques to distribute the workload across multiple machines. - **Profiling**: Use profiling tools to measure the performance and identify bottlenecks. ### Alternative: Using `IndexHNS…
See also
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