From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Query Vector has 93 facts recorded in Dontopedia across 31 references, with 15 live disagreements.
Mostly:rdf:type(23), rdfs:label(5), generated by(4)
rdfs:labeldescriptionhasValuegeneratedByisInputTodimensionalitycreatedBytypehasShapepurposedimensionOther 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(6)ex:contextual_similarityex:log_vector_lookupex:searchex:search_similar_vectorsex:search_similar_vectorssearch_similar_vectorsusesUses(4)ex:normalizationStepex:search-operationex:similarity_computationex:testExecutionparameterParameter(3)ex:searchex:search-methodex:search-methodappliedToApplied to(2)ex:reshape-operationex:reshapingOperationargumentArgument(1)ex:searchbetweenBetween(1)ex:contextual_similaritycalledOnCalled on(1)ex:normalize_L2computedFromComputed From(1)ex:similarity_scorescomputesComputes(1)ex:sparse_retrievalconsistsOfConsists of(1)ex:search_parameterscontainsContains(1)ex:query_vector_arraycontainsElementContains Element(1)ex:query_vector_as_arrayderivedFromDerived From(1)ex:query_vector_reshapedgeneratedFromGenerated From(1)ex:query_keyhasArgumentHas Argument(1)ex:search_callmethodOfMethod of(1)ex:reshaperequiresRequires(1)ex:search_operationreshapesReshapes(1)ex:search_similar_vectorstakesArgumentTakes Argument(1)ex:search_similar_vectorstakesQueryVectorTakes Query Vector(1)ex:search_similar_vectorsThe 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 |
|---|---|---|
| Element at Index | 1:4 | [8] |
| Element at Index | 2:5 | [8] |
| Element at Index | 3:6 | [8] |
| Has Element | 4 | [8] |
| Has Element | 5 | [8] |
| Has Element | 6 | [8] |
| Used in Search | Hnsw Index | [26] |
| Used in Search | Ivf Pq Index | [26] |
| Represents | Query Information | [15] |
| Should Be Derived From | query data | [8] |
| Is Parameter of | Contextual Similarity | [8] |
| Placeholder | true | [17] |
| Used for | search | [17] |
| Shape | 1D | [1] |
| Computed by Method | transform | [1] |
| Computed Using | matrix_multiplication | [1] |
| Transformed by | Vectorizer | [1] |
| Derived From | Preprocessed Query | [1] |
| Data Source Post | Json Data | [4] |
| Data Source Get | Request Args | [4] |
| Data Source | Json Data | [4] |
| Requires2 D Shape | for_normalization | [30] |
| Data Structure | numpy array | [5] |
| Is Created After | faiss_index | [14] |
| Is Used for | search | [14] |
| Is Converted to | float32 | [14] |
| Is Created Using | np.random.rand | [14] |
| Has Type | float32 | [14] |
| Is Initialized With | np.random.rand(1, 128).astype("float32") | [14] |
| Encrypted by | Encrypt Vector Function | [11] |
| Converted to List | Query Vector.tolist | [2] |
| Is Similar to | Test Data Instance 1 Vector | [9] |
| Differs From | Test Data Instance 2 | [9] |
| Matches | Test Data Instance 1 | [9] |
| Generation Method | random generation | [6] |
| Data Type | float32 | [6] |
| Element Type | Float32 | [10] |
| Has Element Type | Float32 | [12] |
| Used in | Index Search Call | [21] |
| Is Parameter | Calculate Accuracy | [16] |
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.
doc:beam/a1e88667-d286-4285-99ae-a5a39b7d9de2reformulated_query = f"{query} in {context['location']}" return reformulated_query # Example context and query context = {'location': 'New York', 'previous_searches': ['coffee shops']} query = "coffee shops" # Reformulate the quer…
doc:beam/fdf8898b-efa0-4bd1-8940-8157d32e6ff0# For demonstration, let's assume we have a function `perform_vector_search` results = perform_vector_search(query_vector, top_k) return jsonify(results) api.add_resource(VectorSearch, '/vector-search') ```…
doc:beam/05970489-d0ac-4332-acb3-da3b56efd23dfaiss.normalize_L2(query_vector) # Search for similar vectors distances, indices = index.search(query_vector.reshape(1, -1), k) return distances, indices # Test the function query_vector = np.random.rand(128).asty…
doc:beam/adbf517e-1335-405d-8a65-aca63a92c7f3# Perform search results = search(COLLECTION_NAME, query_vector, TOP_K) print(results) ``` ### Explanation 1. **Collection Creation**: - `create_collection`: Creates a collection with specified parameters, including dimensi…
doc:beam/8bd9c45a-1ecf-4ac0-b993-6f3a0df4a404vector = decrypt(encrypted_vector) return vector # Define a function to perform vector search def search_vectors(query_vector, required_roles): token = request.headers.get('Authorization').split(' ')[1] check_roles(token, r…
doc:beam/fc7cf36b-fb78-4d1e-89ff-75395398d5c6"dimension": dimension, "index_file_size": 1024, # Size of each segment file in MB "metric_type": METRIC_TYPE } milvus.create_collection(param) # Create an index def create_index(name, index_type, nlist): …
doc:beam/4efeeb64-8572-49af-812f-e5accd46c4adquery_vector = np.random.rand(1, 128).astype("float32") # Search for nearest neighbors k = 10 # number of nearest neighbors to retrieve D, I = index.search(query_vector, k) # Print the results print("Distances:", D) print("Indices:", I) …
doc:beam/c75986d9-237e-4635-ab0b-7e072dc32b3b2. **Analyze Results**: Review the reformulated query and the contextual similarity to understand how well the context aligns with the query. 3. **Refine Implementation**: Based on the results, refine the context extraction and reformulatio…
doc:beam/1f683966-b96e-483a-9ff6-00f44bcce021# Generate ground truth for evaluation ground_truth_indices = np.random.choice(len(db.vectors), size=10, replace=False) # Evaluate the accuracy accuracy = calculate_accuracy(db, query_vector, ground_truth_indices) print(f"Search accuracy: …
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