add
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
add is Add vectors to the index.
Mostly:rdf:type(6), description(1), has parameter(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
containsContains(3)
- Faiss Methods
ex:faiss-methods - Faiss Section
ex:faiss-section - Hnsw Example
ex:hnsw-example
callsMethodCalls Method(2)
- Index Embeddings Function
ex:index-embeddings-function - Step Add Vectors
ex:step-add-vectors
appliedBeforeApplied Before(1)
- Faiss Normalize L2
ex:faiss-normalize-L2
usesMethodUses Method(1)
- Index Addition
ex:index-addition
Other facts (14)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Method | [1] |
| Rdf:type | Method | [2] |
| Rdf:type | Method | [3] |
| Rdf:type | Method | [4] |
| Rdf:type | Method | [5] |
| Rdf:type | Index Method | [6] |
| Description | Add vectors to the index | [1] |
| Has Parameter | vectors | [2] |
| Function | Adds the vectors to the index | [2] |
| Called Before | Index Search Method | [3] |
| Used for | Add Document Embeddings | [5] |
| Method Name | add | [6] |
| Called on | Faiss Index | [6] |
| Parameter | Embeddings Parameter | [6] |
Timeline
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References (6)
ctx:claims/beam/42a434b2-95aa-4616-a1af-a5af03a4baf6- full textbeam-chunktext/plain1 KB
doc:beam/42a434b2-95aa-4616-a1af-a5af03a4baf6Show excerpt
Here's an example using the `IndexHNSW` index, which is more scalable and efficient for large datasets: ```python import numpy as np import faiss # Assuming I have a dataset of vectors vectors = np.random.rand(1000, 128).astype('float32')…
ctx:claims/beam/a62e0ed1-9011-4f17-b311-aa52982c8569ctx:claims/beam/4acac4d0-910b-4fa1-96b2-afff0416f947- full textbeam-chunktext/plain1 KB
doc:beam/4acac4d0-910b-4fa1-96b2-afff0416f947Show excerpt
# Normalize the vectors for cosine similarity faiss.normalize_L2(vectors) # Create an HNSW index M = 16 # Number of links per node efConstruction = 200 # Number of neighbors to consider during construction efSearch = 64 # Number of neig…
ctx:claims/beam/f4875baf-2de8-4f32-b31f-0e5fd916dd32ctx:claims/beam/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7- full textbeam-chunktext/plain1 KB
doc:beam/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7Show excerpt
index = 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…
ctx:claims/beam/15b9d2ff-0708-4bd3-99bf-6912daafb54c
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