semantic-memory 0.6.0

Hybrid semantic search with SQLite, FTS5, and HNSW — built for AI agents
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# semantic-memory

Hybrid semantic search with SQLite — BM25 + vector + Reciprocal Rank Fusion. Built for AI agents, runs everywhere from ESP32 to servers.

## Features

- **Hybrid search**: BM25 (FTS5) + dense vectors (HNSW or brute-force) + RRF fusion
- **PerDim compressed scoring** (default): 4× compression, 100% recall@10, 205ms artifact generation
- **Multi-codec dispatch**: Runtime-switchable backends — Disabled, PerDim, TurboQuant, FibQuant
- **Digest-verified artifact pipeline**: SQLite generations with blake3 integrity
- **Feature-gated compile**: Zero-cost when disabled — `per-dim-codec`, `fib-quant-codec`, `turbo-quant-codec`
- **no_std compatible** (via `compressed-scorer`): Runs on ESP32-S3 microcontrollers

## Quick start

```toml
[dependencies]
semantic-memory = { version = "0.6", features = ["per-dim-codec"] }
```

```rust
use semantic_memory::{MemoryStore, SearchConfig, DerivedVectorBackendPolicy};

// Default: PerDim compressed with exact rerank (100% recall)
let config = SearchConfig::default();

// Fast path: skip exact rerank (~1.2× faster, ~1% recall loss)
let mut config = SearchConfig::default();
config.per_dim_require_exact_rerank = false;

// Fall back to brute force exact search
config.derived_vector_backend = DerivedVectorBackendPolicy::Disabled;
```

## Multi-codec dispatch

| Backend | Compression | Recall@10 | Build time | Use case |
|---------|-----------|-----------|-----------|----------|
| Disabled (brute force) | 1× | 1.000 | — | Exact search |
| **PerDim** (default) | 3.98× | 1.000 | 205ms | Production default |
| TurboQuant | ~5× | 0.95+ | seconds | Rotation-based |
| FibQuant | 5.9× | 0.08-0.14 | 78s | Max compression |

## Performance

Benchmarked on AMD Ryzen 7 7730U, 1,865 vectors, 768-dim all-MiniLM-L6-v2 embeddings:

- **Artifact generation**: 205ms for full corpus
- **PerDim exact rerank**: 17.8 ms/query, 100% recall@10
- **PerDim skip-rerank**: 15.0 ms/query, 100% recall@10 (1.19× speedup)
- **Space savings**: 5.73MB → 1.44MB (3.98×)

## Council Graph usage

semantic-memory is developed with Agent Graph council deliberation:

```
┌─ Coordinator ────────────────────────────────────┐
│  → Fanout → Analyst 0 (research)                  │
│          → Analyst 1 (implementation)             │
│          → Analyst 2 (validation)                 │
│  → Join → Synthesize → Plan                       │
└───────────────────────────────────────────────────┘
```

Multi-perspective research, spec generation, and implementation planning use the council_deliberation template. See [RecursiveIntell doctrine](https://github.com/recursiveintell) for the full workflow.

## Feature flags

| Feature | Description | Dependencies |
|---------|-------------|-------------|
| `hnsw` (default) | Approximate nearest neighbor via HNSW | `hnsw_rs` |
| `per-dim-codec` | Per-dimension uniform quantization (recommended) | `compressed-scorer` |
| `fib-quant-codec` | Fibonacci quantization with Gram-lookup | `fib-quant`, `scr-runtime-compression` |
| `turbo-quant-codec` | TurboQuant polar-coordinate encoding | `turbo-quant`, `scr-runtime-compression` |
| `poly-kv-codec` | Shared KV pool encoding | `poly-kv` |

## License

Apache-2.0