Index Input
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Index Input has 3 facts recorded in Dontopedia across 3 references.
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servesAsServes As(1)
- Document Embeddings
ex:document-embeddings
Other facts (3)
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| Predicate | Value | Ref |
|---|---|---|
| Type | embeddings | [1] |
| Rdf:type | Data Structure | [2] |
| Used by | Step 6 | [3] |
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References (3)
ctx:claims/beam/1eb8aa09-e959-4141-bc61-fdce4119df7f- full textbeam-chunktext/plain1 KB
doc:beam/1eb8aa09-e959-4141-bc61-fdce4119df7fShow excerpt
document_embeddings = vectorization_module.vectorize(documents) # Add the document embeddings to the index indexing_module.add_to_index(document_embeddings) ``` ->-> 4,24 [Turn 4863] Assistant: Certainly! To design a modular architecture …
ctx:claims/beam/dec68f27-fa07-4dd3-9e72-4e86e758bea4- full textbeam-chunktext/plain1 KB
doc:beam/dec68f27-fa07-4dd3-9e72-4e86e758bea4Show excerpt
- We use the `search` method to find the 10 nearest neighbors to the query embedding. The method returns the distances and indices of the nearest neighbors. ### Benefits of FAISS - **Reduced Memory Usage**: FAISS can store large number…
ctx:claims/beam/8fff75de-50f4-4374-99db-d3d2973a1ba2- full textbeam-chunktext/plain896 B
doc:beam/8fff75de-50f4-4374-99db-d3d2973a1ba2Show excerpt
raise ValueError(f"Mismatched dimensions: Expected {dimension}, got {normalized_query_vector.shape[1]}") # Perform search distances, indices = index.search(normalized_query_vector, k=10) # Print results print(f"Distances: {distances}"…
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