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Ef Construction has 30 facts recorded in Dontopedia across 7 references, with 4 live disagreements.
Mostly:rdf:type(7), affects(4), rdfs:label(3)
rdfs:labelaffectseffectOfHigherValuehasValueparameterTypespecificTohasDefaultValuecontrolsPhaseOther 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(7)ex:construction-phaseex:constructionPhaseex:faiss-1.7.4ex:hnswex:HNSWex:hnsw-indexex:index_paramsaffectedByAffected by(2)ex:accuracyex:constructionTimedescribesDescribes(2)ex:explanationSectionex:sourceDocumenthasEfConstructionHas Ef Construction(2)ex:hnswex:hnsw-indexusesParameterUses Parameter(2)ex:constructionPhaseex:faissappliesToApplies to(1)ex:parameter-adjustmenthasParameterEfConstructionHas Parameter Ef Construction(1)ex:hnswinvolvesInvolves(1)ex:parameterTuningparameterParameter(1)ex:HNSWrelatedParameterRelated Parameter(1)ex:efSearchThe 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.
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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/8e356af0-5214-4a1f-8615-f270ae5ec1c9- `efConstruction` and `efSearch` parameters control the construction and search phases, respectively. 2. **IVFPQ Index**: - `IndexIVFPQ`: Creates an IVFPQ index with a specified number of clusters (`nlist`), subquantizers (`m`), and…
doc:beam/24609436-74f2-4564-988e-86e3e75d7114If your vectors have a relatively low dimensionality (e.g., less than 128), you can use `IndexHNSWFlat` instead of `IndexHNSW`. This can be faster since it avoids the overhead of the hierarchical structure. ### 4. **Optimize Construction P…
doc:beam/3c7c96d1-549b-4085-8bd9-152174bddc1f- `efConstruction`: Construction parameter. - `efSearch`: Search parameter. 3. **Multi-threading**: - `faiss.omp_set_num_threads(8)` enables multi-threading to take advantage of multiple CPU cores. 4. **Adding Vectors**: - Vec…
doc:beam/ea1c880d-666a-428b-9f18-ae4bdd751abeindex = faiss.IndexHNSWFlat(128, M) index.hnsw.efConstruction = efConstruction index.hnsw.efSearch = efSearch index.add(vectors) # Measure initial performance start_time = time.time() distances, indices = search_similar_vectors(query_vecto…
doc:beam/32c1e7e5-4ce5-48df-a04d-ccdefa61e55d- **Choosing the Right Index Type**: Different index types (e.g., IVF_FLAT, HNSW, ANNOY) have different trade-offs between search speed, memory usage, and accuracy. Choose an index type that best fits your use case. - **Parameter Tuning**: …
doc:beam/19d581bd-9e09-4819-ad3a-f497c9d8b02dFieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True), FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Test Collection") # Create a collection collectio…
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