dim
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
dim has 22 facts recorded in Dontopedia across 7 references, with 3 live disagreements.
22 facts·6 predicates·7 sources·3 in dispute
Mostly:rdf:type(7), used in(6), parameter name(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (11)
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.
hasParameterHas Parameter(5)
- Argmax Operation
ex:argmax-operation - Generate Random Vector
ex:generate-random-vector - Initialize Faiss Index
ex:initialize-faiss-index - Initialize Faiss Index Function
ex:initialize-faiss-index-function - Vectorizer Init
ex:vectorizer-init
hasDimensionHas Dimension(2)
- Document Embeddings Array
ex:document-embeddings-array - Query Embedding Array
ex:query-embedding-array
describesDescribes(1)
- Dim Parameter Role
ex:dim-parameter-role
parameterParameter(1)
- Initialize Function
ex:initialize-function
Other facts (19)
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.
19 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Parameter | [1] |
| Rdf:type | Dimension Parameter | [2] |
| Rdf:type | Function Parameter | [3] |
| Rdf:type | Parameter | [4] |
| Rdf:type | Function Parameter | [5] |
| Rdf:type | Parameter | [6] |
| Rdf:type | Function Parameter | [7] |
| Used in | Initialize Faiss Index | [1] |
| Used in | Gpu Index Creation | [1] |
| Used in | Cpu Index Creation | [1] |
| Used in | Initialize Faiss Index Function | [2] |
| Used in | Document Embeddings | [2] |
| Used in | Query Embedding | [2] |
| Parameter Name | dim | [3] |
| Parameter Name | dim | [5] |
| Parameter Name | dim | [6] |
| Parameter Default | 128 | [5] |
| Parameter Value | 0 | [6] |
| Has Value | -1 | [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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usedInbeam/e4762ba4-92ad-42cd-b666-a7f736830e81
ex:initialize-faiss-index
—
typebeam/e4762ba4-92ad-42cd-b666-a7f736830e81
ex:Parameter
—
usedInbeam/e4762ba4-92ad-42cd-b666-a7f736830e81
ex:gpu-index-creation
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usedInbeam/e4762ba4-92ad-42cd-b666-a7f736830e81
ex:cpu-index-creation
—
typebeam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12
ex:dimension-parameter
—
labelbeam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12
dim
—
usedInbeam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12
ex:initialize-faiss-index-function
—
usedInbeam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12
ex:document-embeddings
—
usedInbeam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12
ex:query-embedding
—
typebeam/c93f21b2-5d63-4700-acd2-ac16decca67b
ex:FunctionParameter
—
parameterNamebeam/c93f21b2-5d63-4700-acd2-ac16decca67b
dim
—
typebeam/8db83f0d-819a-4f3b-b500-3a38a63092b2
ex:Parameter
—
labelbeam/8db83f0d-819a-4f3b-b500-3a38a63092b2
dim
—
typebeam/1e47faff-9001-4475-b47f-aee14dcc46af
ex:FunctionParameter
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parameterNamebeam/1e47faff-9001-4475-b47f-aee14dcc46af
dim
—
parameterDefaultbeam/1e47faff-9001-4475-b47f-aee14dcc46af
128
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typebeam/b04fbb01-0357-4127-b979-b3b93c026864
ex:Parameter
—
parameterNamebeam/b04fbb01-0357-4127-b979-b3b93c026864
dim
—
parameterValuebeam/b04fbb01-0357-4127-b979-b3b93c026864
0
—
typebeam/98b5f18a-bd85-4023-b6af-9de1b7642a01
ex:FunctionParameter
—
labelbeam/98b5f18a-bd85-4023-b6af-9de1b7642a01
dim
—
hasValuebeam/98b5f18a-bd85-4023-b6af-9de1b7642a01
-1
References (7)
7 references
ctx:claims/beam/e4762ba4-92ad-42cd-b666-a7f736830e81- full textbeam-chunktext/plain1 KB
doc:beam/e4762ba4-92ad-42cd-b666-a7f736830e81Show excerpt
Here's an improved version of your code incorporating these suggestions: ```python import numpy as np import faiss # Initialize the FAISS index outside the function def initialize_faiss_index(dim, use_gpu=False): if use_gpu: r…
ctx:claims/beam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12- full textbeam-chunktext/plain1 KB
doc:beam/d1235175-e1c4-4a66-a955-c9f6ddbcfd12Show excerpt
use_gpu = False # Set to True if you want to use GPU acceleration index = initialize_faiss_index(dim, use_gpu) # Generate random document embeddings and a query embedding document_embeddings = np.random.rand(200000, dim).astype('float32')…
ctx:claims/beam/c93f21b2-5d63-4700-acd2-ac16decca67bctx:claims/beam/8db83f0d-819a-4f3b-b500-3a38a63092b2ctx:claims/beam/1e47faff-9001-4475-b47f-aee14dcc46af- full textbeam-chunktext/plain1 KB
doc:beam/1e47faff-9001-4475-b47f-aee14dcc46afShow excerpt
Create a Python script named `setup_milvus.py` with the following content: ```python from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection # Connect to Milvus connections.connect("default", ho…
ctx:claims/beam/b04fbb01-0357-4127-b979-b3b93c026864- full textbeam-chunktext/plain1 KB
doc:beam/b04fbb01-0357-4127-b979-b3b93c026864Show excerpt
- Ensure the new model integrates seamlessly with the rest of the retrieval pipeline. ### Example Implementation #### Step 1: Data Preparation Prepare your dataset for training and validation: ```python from transformers import AutoT…
ctx:claims/beam/98b5f18a-bd85-4023-b6af-9de1b7642a01
See also
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