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Technology#Data#Machine Learning

FAISS

FAISS is a C++/Python library from Facebook AI Research for efficient similarity search over large vector collections. It provides approximate nearest neighbor indexes, GPU acceleration and multiple distance metrics for embedding-based retrieval. The library supports production use and exposes index types and knobs to trade accuracy for latency.

This block bundles baseline information, context, and relations as a neutral reference in the model.

Reference building block

This building block serves as a structured reference in the knowledge model, with core data, context, and direct relationships.

What is this view?

This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.

Baseline data

Context
Organizational level
Domain
Organizational maturity
Intermediate
Impact area
Technical
Decision
Decision type
Technical
Value stream stage
Build
Assessment
Complexity
High
Maturity
Established
Cognitive load
High

Context in the model

Structural placement

Where this block lives in the structure.

No structure path available.

Relations

Connected blocks

Directly linked content elements.

Dependency · Uses
(1)