From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
Query Embedding has 36 facts recorded in Dontopedia across 12 references, with 4 live disagreements.
Mostly:rdf:type(8), rdfs:label(4), derived from(2)
derivedFromgeneratedFromhasShaperdfs:labeldata_typeresultOfproducedBycomputedFromusedInreshapedWithOther 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(2)ex:searchex:util_cos_simrequiresRequires(2)ex:perform-searchex:search stepacceptsAccepts(1)ex:function-signatureappliedToApplied to(1)ex:reshapeassignsVariableAssigns Variable(1)ex:query-embedding-declarationcomputesBetweenComputes Between(1)ex:cosine_similarity_computationgeneratesForGenerates for(1)ex:embedding_generationhasArgumentHas Argument(1)ex:index.searchmethodParameterMethod Parameter(1)ex:searchmethod_searchMethod Search(1)ex:gpu_indexnormalizesNormalizes(1)ex:faiss_normalize_L2_call_2operatesOnOperates on(1)ex:embedding_calculationparameterParameter(1)ex:index-searchreturnsReturns(1)ex:reshapetakesInputTakes Input(1)ex:searchtakesParametersTakes Parameters(1)ex:index.searchusesQueryUses Query(1)ex:vector searchThe 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.
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/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7index = faiss.IndexFlatL2(embedding_dim) # Add the document embeddings to the index index.add(document_embeddings) # Generate a random query embedding query_embedding = np.random.rand(1, embedding_dim).astype('float32') # Search the inde…
doc:beam/c5e65b2e-6289-4399-808e-64fe4e0eddcem = 8 # number of subquantizers index = faiss.IndexIVFPQ(faiss.MetricType.L2, d, nlist, m, 8) # Train the index index.train(embeddings) # Add the embeddings to the index index.add(embeddings) # Generate a query embedding in a different …
doc:beam/57c71698-b5d8-4196-b47b-1b9f597b3034[Turn 10462] User: Sure, let's get started with the implementation. I'll run the code and see how it improves the detection accuracy. I'll also keep an eye on the logged errors to identify any patterns and refine the detection logic further…
doc:beam/a8f9767f-e515-4c18-876d-5a6237129dbequery_embedding = np.random.rand(1, 512).astype('float32') # Search the index distances, indices = index.search(query_embedding, k=10) print(distances) print(indices) ``` ->-> 4,22 [Turn 4869] Assistant: Certainly! FAISS is a powerful li…
doc:beam/926f1488-328b-43c2-9fba-d5492a192351FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Document Embeddings") # Create the collection collection = Collection("document_embeddings", schema) ``` #### 3. Insert Vectors …
doc:beam/bd9543d2-c630-4def-9177-6f94b1d1eb6e4. **Calculate Similarity**: Use cosine similarity to measure the semantic similarity between the queries. 5. **Log Errors**: Log intent misinterpretation errors with detailed information. 6. **Analyze Logs**: Regularly review the logs to i…
Dontopedia is in a read-only public launch. Follow the references and disputed branches now; contributions will open after durable identity and moderation are in place.