Perform Search
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
Perform Search is Perform the search using the FAISS index.
Mostly:requires(3), returns(2), ex:depends on(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (9)
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.
ex:usedInEx:used in(1)
- Vectors
ex:vectors
hasStepHas Step(1)
- Workflow Sequence
ex:workflow-sequence
operationOperation(1)
- Refine Indexing Logic Function
ex:refine-indexing-logic-function
precedesPrecedes(1)
- Add Embeddings to Index
ex:add-embeddings-to-index
step-3Step 3(1)
- Index Creation Sequence
ex:index-creation-sequence
step5Step5(1)
- Code Sequence
ex:code-sequence
usedByUsed by(1)
- Faiss Index
ex:faiss-index
Other facts (10)
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 |
|---|---|---|
| Requires | Index | [2] |
| Requires | Query Embedding | [2] |
| Requires | K | [2] |
| Returns | Distances | [2] |
| Returns | Indices | [2] |
| Ex:depends on | Set Nprobe | [1] |
| Rdf:type | Operation | [3] |
| Depends on | Add Vectors | [4] |
| Description | Perform the search using the FAISS index | [5] |
| Uses Index | Faiss Index | [5] |
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.
References (5)
ctx:claims/beam/9f354551-a9f5-474b-a587-082e952c4a41- full textbeam-chunktext/plain1 KB
doc:beam/9f354551-a9f5-474b-a587-082e952c4a41Show excerpt
faiss.omp_set_num_threads(4) # Adjust based on your system's capabilities # Create an IVFFlat index quantizer = faiss.IndexFlatL2(128) index = faiss.IndexIVFFlat(quantizer, 128, nlist, faiss.METRIC_L2) # Train the index index.train(vecto…
ctx: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/926f1488-328b-43c2-9fba-d5492a192351- full textbeam-chunktext/plain1 KB
doc:beam/926f1488-328b-43c2-9fba-d5492a192351Show excerpt
FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Document Embeddings") # Create the collection collection = Collection("document_embeddings", schema) ``` #### 3. Insert Vectors …
ctx:claims/beam/49101dfd-4fc4-460c-9cd9-8e0457730c83- full textbeam-chunktext/plain1 KB
doc:beam/49101dfd-4fc4-460c-9cd9-8e0457730c83Show excerpt
- Adjust the search parameters like `efSearch` for `IndexHNSW` to balance between speed and accuracy. ### Example Implementation Here's an optimized version of your code using `IndexIVFPQ` and enabling multi-threading: ```python impor…
ctx:claims/beam/ab7dd67d-8391-46bb-9eeb-cac9e6f35962- full textbeam-chunktext/plain1 KB
doc:beam/ab7dd67d-8391-46bb-9eeb-cac9e6f35962Show excerpt
- Add the embeddings to the index. 4. **Querying**: - Generate query embeddings using the same multilingual model. - Perform the search using the FAISS index. ### Example Code Here's an example of how to handle multi-language em…
See also
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