github-mcp 0.4.2

// GitHub v3 REST API MCP server — generated by mcpify. Do not hand-edit.
//
// Single source of truth for embedding computation. bin/populate_embeddings.rs
// (indexing time) and tools/search_tool.rs (live query time) both call
// `embed()` from here, so the two are structurally guaranteed to share the
// same model and vector space — see the plan's embeddings decision.
// `all-MiniLM-L6-v2` at its native 384 dimensions, matching
// `mcp_store.db`'s `semantic_endpoints` schema exactly (`FLOAT[384]`).
// Switched from `all-mpnet-base-v2` (768-dim) so the packaged crate's
// embedded stores fit under crates.io's 10MiB upload limit even at max
// zstd compression — see the "switch to all-MiniLM-L6-v2" fix.
//
// This intentionally diverges from `targets::typescript`'s
// `@xenova/transformers`-based embedding-service.ts, which still uses
// `all-mpnet-base-v2`/768 dims — `-l rust` and `-l typescript` output are
// no longer search-behavior equivalent for the same spec as a result of
// this crate-size-driven change.

use std::sync::{Mutex, OnceLock};

use fastembed::{EmbeddingModel, TextEmbedding, TextInitOptions};

fn model() -> &'static Mutex<TextEmbedding> {
    static MODEL: OnceLock<Mutex<TextEmbedding>> = OnceLock::new();
    MODEL.get_or_init(|| {
        // Downloads on first use and caches locally afterward (no network
        // needed once cached) — mirrors `@xenova/transformers`' own
        // caching UX. `.expect()` here matches the TS target's own
        // failure mode: an unrecoverable startup error either way if the
        // model can't be fetched/loaded.
        Mutex::new(
            TextEmbedding::try_new(TextInitOptions::new(EmbeddingModel::AllMiniLML6V2))
                .expect("failed to load the all-MiniLM-L6-v2 embedding model"),
        )
    })
}

/// Computes a 384-dim embedding vector for `text`, mean-pooled and
/// normalized (fastembed's default behavior for this model, replicating
/// the sentence-transformers reference implementation).
pub fn embed(text: &str) -> anyhow::Result<Vec<f32>> {
    let model = model();
    let mut model = model.lock().unwrap();
    let mut embeddings = model.embed(vec![text], None)?;
    embeddings
        .pop()
        .ok_or_else(|| anyhow::anyhow!("embedding model returned no output for the given text"))
}