# ll-core
Hybrid semantic search engine. SQLite-backed, on-device ONNX inference, no external services.
- **Embedding trait** — model-agnostic interface for ONNX-backed embedders (BGE, mE5, etc.)
- **Hybrid scoring** — BM25 (FTS5) + vector cosine fused via RRF, with configurable FTS table schema
- **Graph scoring** — personalized PageRank over pre-loaded edges, for link/citation/participant graphs
- **Cross-encoder reranker** — ms-marco-MiniLM-L-6-v2 bundled via `include_bytes!`, no runtime download
- **Embedding store** — in-memory cache with O(n) cosine over all vectors
- **Rocchio PRF** — pseudo-relevance feedback for query expansion
Built for local-first tools that need search over SQLite data without shipping a vector database or hitting an API.
## Projects using ll-core
**[learning-loop](https://github.com/robinslange/learning-loop/tree/main/native)** — A Claude Code plugin that turns your Obsidian vault into a queryable knowledge graph. ll-core powers semantic search, link-graph traversal, and note clustering across thousands of atomic notes. Runs entirely on your machine.
## Usage
```rust
use ll_core::embed::EmbeddingProvider;
use ll_core::scoring::{fts_bm25_query, FtsConfig};
use ll_core::store::EmbeddingStore;
// Implement EmbeddingProvider for your model, then:
let store = EmbeddingStore::from_data(vec![(1, "key".into(), vec![0.1; 384])]);
// `messages_fts` is an FTS5 table whose rowid is `messages.id`. The content
// table must be a real table (not the FTS table itself), and `id_column` must
// equal the FTS rowid. One bm25 weight per FTS column.
let fts_config = FtsConfig::new(
"messages_fts", // fts_table: queried with MATCH
"messages", // content_table: joined on id = messages_fts.rowid
"messages", // items_table: joined on id, supplies path_column
"id", // id_column
"message_id", // path_column
"5.0, 10.0", // bm25_weights
);
let hits = fts_bm25_query(&conn, "search text", 20, &fts_config);
// Fuse with vector search via RRF:
let mut rrf_scores = std::collections::HashMap::new();