post-cortex-embeddings 0.3.1

Embedding engines (Model2Vec static + BERT via Candle + static-hash fallback) and HNSW vector database for post-cortex. Self-contained ML stack — depend on this crate when you need vector search without the full post-cortex orchestrator.
Documentation 
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// Copyright (c) 2025, 2026 Julius ML
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.

//! Shared internal helpers: `SearchResult` ordering for heaps + Euclidean distance.

/// Internal search result with ordering support for BinaryHeap
#[derive(Debug, Clone)]
pub(super) struct SearchResult {
    pub(super) id: u32,
    pub(super) similarity: f32,
}

impl PartialEq for SearchResult {
    fn eq(&self, other: &Self) -> bool {
        self.id == other.id
    }
}

impl Eq for SearchResult {}

impl PartialOrd for SearchResult {
    fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
        Some(self.cmp(other))
    }
}

impl Ord for SearchResult {
    fn cmp(&self, other: &Self) -> std::cmp::Ordering {
        // Compare by similarity (max-heap: higher similarity first)
        self.similarity
            .partial_cmp(&other.similarity)
            .unwrap_or(std::cmp::Ordering::Equal)
    }
}

/// Calculate Euclidean distance between two vectors
pub(super) fn euclidean_distance(a: &[f32], b: &[f32]) -> f32 {
    a.iter()
        .zip(b.iter())
        .map(|(x, y)| (x - y).powi(2))
        .sum::<f32>()
        .sqrt()
}