sample dataset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
sample dataset is random vectors.
61 facts·25 predicates·10 sources·8 in dispute
Mostly:rdf:type(10), has field(6), has value(6)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
- Data Entity[1]all time · Eaa80ff9 95f4 4aca A89f 3b0f0a7cdfc0
- Dataset[2]all time · 233f71d1 90fb 465f B655 D5a578f6247b
- Dataset[3]all time · 18f4ab71 A5f8 4e4c Bddd 45b5cd6d411f
- Dataset[4]all time · 9bbaf7ec D1f0 4843 9bbf E2b297fec107
- Dataset[5]all time · 830f9da6 6442 415f B959 4e810c077604
- Dataset[6]all time · Be6814ba Aa07 4fc4 B58d D8d7b642906f
- Dataset[7]all time · D4c82979 1650 4b89 A2fa A0ec5b37bb69
- Dataset[8]sourceall time · C39988e0 Db33 4984 8c77 56ffcecd919a
- Dataset[9]all time · 2fcc4e7a D497 4bfa B889 84fb8a9dfe40
- Dataset[10]all time · B4174542 E9f5 41d0 809f Ec6511b667bb
Inbound mentions (19)
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.
inputInput(4)
- Adding Step
ex:adding-step - Adding Vectors
ex:adding-vectors - Training
ex:training - Training Step
ex:training-step
usedInUsed in(4)
- Id Field
ex:id-field - Name Field
ex:name-field - Text Content Field
ex:text-content-field - Vector Field
ex:vector-field
appliesToApplies to(1)
- Sample Data Purpose
ex:sample-data-purpose
createsCreates(1)
- Step 1
ex:step-1
definesDefines(1)
- Python Script
ex:python-script
describesDescribes(1)
- Comment Define Dataset
ex:comment-define-dataset
memberOfMember of(1)
- Documents Example
ex:documents-example
mentionsMentions(1)
- Assistant Turn 2413
ex:assistant-turn-2413
partOfPart of(1)
- Vectors
ex:vectors
partOfDatasetPart of Dataset(1)
- Shakespeare Works
ex:shakespeare-works
rdf:typeRdf:type(1)
- Sample Data
ex:sampleData
resultsInResults in(1)
- Step 1
ex:step-1
storesStores(1)
- Dataframe
ex:dataframe
Other facts (47)
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.
47 facts
| Predicate | Value | Ref |
|---|---|---|
| Has Field | Id Field | [4] |
| Has Field | Name Field | [4] |
| Has Field | Id Field | [8] |
| Has Field | Name Field | [8] |
| Has Field | Vector Field | [8] |
| Has Field | Text Content Field | [8] |
| Has Value | 1 | [4] |
| Has Value | 2 | [4] |
| Has Value | 3 | [4] |
| Has Value | John | [4] |
| Has Value | Jane | [4] |
| Has Value | Bob | [4] |
| Contains Record | Record 1 | [6] |
| Contains Record | Record 2 | [6] |
| Contains Record | Record 3 | [6] |
| Contains Record | Sample Record 1 | [7] |
| Contains Record | Sample Record 2 | [7] |
| Contains Record | Sample Record 3 | [7] |
| Contains | Vectors | [3] |
| Contains | Id Field | [6] |
| Contains | Name Field | [6] |
| Contains | Text Content Field | [6] |
| Contains | Documents Example | [10] |
| Has Column | id | [7] |
| Has Column | name | [7] |
| Has Column | text_content | [7] |
| Has Key | Id Key | [4] |
| Has Key | Name Key | [4] |
| Has Record Count | 3 | [6] |
| Has Record Count | 3 | [8] |
| Description | random vectors | [2] |
| Vector Count | 100 | [2] |
| Vector Dimension | 10 | [2] |
| Data Format | float32 | [2] |
| Added to | Annoy Index Object | [2] |
| Has Number of Rows | 3 | [4] |
| Has Number of Columns | 2 | [4] |
| Is Instance | Dictionary | [4] |
| Has Number of Records | 3 | [5] |
| Stored in | Dataframe | [6] |
| Illustrates | Typical Rag Data | [7] |
| Converted to | Dataframe | [8] |
| Number of Vectors | 10000 | [9] |
| Vector Dimensions | 128 | [9] |
| Generated by | Numpy Random Rand | [9] |
| Shape | (10000, 128) | [9] |
| Dtype | float32 | [9] |
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.
—
typebeam/eaa80ff9-95f4-4aca-a89f-3b0f0a7cdfc0
ex:DataEntity
—
labelbeam/eaa80ff9-95f4-4aca-a89f-3b0f0a7cdfc0
sample dataset
—
typebeam/233f71d1-90fb-465f-b655-d5a578f6247b
ex:Dataset
—
descriptionbeam/233f71d1-90fb-465f-b655-d5a578f6247b
random vectors
—
vectorCountbeam/233f71d1-90fb-465f-b655-d5a578f6247b
100
—
vectorDimensionbeam/233f71d1-90fb-465f-b655-d5a578f6247b
10
—
dataFormatbeam/233f71d1-90fb-465f-b655-d5a578f6247b
float32
—
addedTobeam/233f71d1-90fb-465f-b655-d5a578f6247b
ex:annoy-index-object
—
typebeam/18f4ab71-a5f8-4e4c-bddd-45b5cd6d411f
ex:Dataset
—
labelbeam/18f4ab71-a5f8-4e4c-bddd-45b5cd6d411f
sample dataset
—
containsbeam/18f4ab71-a5f8-4e4c-bddd-45b5cd6d411f
ex:vectors
—
typebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
ex:Dataset
—
hasFieldbeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
ex:id-field
—
hasFieldbeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
ex:name-field
—
hasValuebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
1
—
hasValuebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
2
—
hasValuebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
3
—
hasValuebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
John
—
hasValuebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
Jane
—
hasValuebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
Bob
—
hasNumberOfRowsbeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
3
—
hasNumberOfColumnsbeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
2
—
isInstancebeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
ex:dictionary
—
hasKeybeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
ex:id-key
—
hasKeybeam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107
ex:name-key
—
typebeam/830f9da6-6442-415f-b959-4e810c077604
ex:Dataset
—
hasNumberOfRecordsbeam/830f9da6-6442-415f-b959-4e810c077604
3
—
typebeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:Dataset
—
labelbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
Sample Dataset
—
containsbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:id-field
—
containsbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:name-field
—
containsbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:text-content-field
—
containsRecordbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:record-1
—
containsRecordbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:record-2
—
containsRecordbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:record-3
—
hasRecordCountbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
3
—
storedInbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:dataframe
—
typebeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
ex:Dataset
—
hasColumnbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
id
—
hasColumnbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
name
—
hasColumnbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
text_content
—
containsRecordbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
ex:sample-record-1
—
containsRecordbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
ex:sample-record-2
—
containsRecordbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
ex:sample-record-3
—
illustratesbeam/d4c82979-1650-4b89-a2fa-a0ec5b37bb69
ex:typical-rag-data
—
typebeam/c39988e0-db33-4984-8c77-56ffcecd919a
ex:Dataset
—
hasRecordCountbeam/c39988e0-db33-4984-8c77-56ffcecd919a
3
—
hasFieldbeam/c39988e0-db33-4984-8c77-56ffcecd919a
ex:id-field
—
hasFieldbeam/c39988e0-db33-4984-8c77-56ffcecd919a
ex:name-field
—
hasFieldbeam/c39988e0-db33-4984-8c77-56ffcecd919a
ex:vector-field
—
convertedTobeam/c39988e0-db33-4984-8c77-56ffcecd919a
ex:dataframe
—
hasFieldbeam/c39988e0-db33-4984-8c77-56ffcecd919a
ex:text_content-field
—
typebeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
ex:Dataset
—
numberOfVectorsbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
10000
—
vectorDimensionsbeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
128
—
generatedBybeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
ex:numpy-random-rand
—
shapebeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
(10000, 128)
—
dtypebeam/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40
float32
—
typebeam/b4174542-e9f5-41d0-809f-ec6511b667bb
ex:Dataset
—
labelbeam/b4174542-e9f5-41d0-809f-ec6511b667bb
Sample Documents Dataset
—
containsbeam/b4174542-e9f5-41d0-809f-ec6511b667bb
ex:documents-example
References (10)
10 references
ctx:claims/beam/eaa80ff9-95f4-4aca-a89f-3b0f0a7cdfc0ctx:claims/beam/233f71d1-90fb-465f-b655-d5a578f6247bctx:claims/beam/18f4ab71-a5f8-4e4c-bddd-45b5cd6d411f- full textbeam-chunktext/plain1 KB
doc:beam/18f4ab71-a5f8-4e4c-bddd-45b5cd6d411fShow excerpt
1. **Sample Dataset Creation**: - `num_vectors`: Number of vectors in the dataset. - `vector_dim`: Dimensionality of each vector. - `vectors`: Randomly generated vectors. 2. **Annoy Index Initialization**: - `AnnoyIndex(vector_…
ctx:claims/beam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107- full textbeam-chunktext/plain1 KB
doc:beam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107Show excerpt
Would you like to explore any specific aspect of these configurations further, such as setting up detailed monitoring or configuring more advanced ASG settings? [Turn 2658] User: I need help designing a data modeling approach for my RAG sy…
ctx:claims/beam/830f9da6-6442-415f-b959-4e810c077604- full textbeam-chunktext/plain1 KB
doc:beam/830f9da6-6442-415f-b959-4e810c077604Show excerpt
First, define the structure of your data. For simplicity, let's assume you have documents with text content and associated vectors. ```python import pandas as pd from pymongo import MongoClient from pymilvus import connections, FieldSchema…
ctx:claims/beam/be6814ba-aa07-4fc4-b58d-d8d7b642906fctx: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/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/2fcc4e7a-d497-4bfa-b889-84fb8a9dfe40ctx:claims/beam/b4174542-e9f5-41d0-809f-ec6511b667bb- full textbeam-chunktext/plain1 KB
doc:beam/b4174542-e9f5-41d0-809f-ec6511b667bbShow excerpt
dense_scores = get_embeddings([query]).dot(embeddings.T) combined_scores = 0.5 * sparse_scores + 0.5 * dense_scores return combined_scores # Example usage documents = ["This is a sample document.", "Este es un documento de mues…
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