vestige/crates/vestige-core/Cargo.toml
Sam Valladares 14b061f124
Release v2.1.23 Receipt Lock hardening
Hardens Sanhedrin Receipt Lock for model-agnostic use, adds fail-open telemetry and receipt docs, fixes smart_ingest batch safety, wires opt-in CUDA Qwen3 device selection, and refreshes dashboard/release assets.\n\nFixes #54\nFixes #58\nFixes #60\nRefs #59
2026-05-27 19:03:16 -05:00

146 lines
5.6 KiB
TOML

[package]
name = "vestige-core"
version = "2.1.23"
edition = "2024"
rust-version = "1.91"
authors = ["Vestige Team"]
description = "Cognitive memory engine - FSRS-6 spaced repetition, semantic embeddings, and temporal memory"
license = "AGPL-3.0-only"
repository = "https://github.com/samvallad33/vestige"
keywords = ["memory", "spaced-repetition", "fsrs", "embeddings", "knowledge-graph"]
categories = ["science", "database"]
[features]
default = ["embeddings", "ort-download", "vector-search", "bundled-sqlite"]
# SQLite backend (default, unencrypted)
bundled-sqlite = ["rusqlite/bundled"]
# Encrypted SQLite via SQLCipher (mutually exclusive with bundled-sqlite)
# Use: --no-default-features --features encryption,embeddings,ort-download,vector-search
# Set VESTIGE_ENCRYPTION_KEY env var to enable encryption
encryption = ["rusqlite/bundled-sqlcipher"]
# Embedding code paths (fastembed dep, hf-hub, image-models). This feature
# enables the #[cfg(feature = "embeddings")] gates throughout the crate but
# does NOT pick an ort backend. Pair with EXACTLY ONE of `ort-download`
# (prebuilt ONNX Runtime, default) or `ort-dynamic` (runtime-linked system
# libonnxruntime, required on targets without prebuilts).
embeddings = ["dep:fastembed", "fastembed/hf-hub-native-tls", "fastembed/image-models"]
# Default ort backend: ort-sys downloads prebuilt ONNX Runtime at build time.
# Requires glibc >= 2.38. Fails on x86_64-apple-darwin (Microsoft is
# discontinuing Intel Mac prebuilts after ONNX Runtime v1.23.0).
ort-download = ["embeddings", "fastembed/ort-download-binaries-native-tls"]
# HNSW vector search with USearch (20x faster than FAISS)
vector-search = ["dep:usearch"]
# Alternative ort backend: runtime-linked against a system libonnxruntime via
# dlopen. Required on Intel Mac and on systems with glibc < 2.38 (Ubuntu
# 22.04, Debian 12, RHEL/Rocky 9). Transitively enables `embeddings` so the
# #[cfg] gates stay active.
#
# Usage: cargo build --no-default-features \
# --features ort-dynamic,vector-search,bundled-sqlite
# Runtime: export ORT_DYLIB_PATH=/path/to/libonnxruntime.{dylib,so}
# (e.g. $(brew --prefix onnxruntime)/lib/libonnxruntime.dylib)
ort-dynamic = ["embeddings", "fastembed/ort-load-dynamic"]
# Nomic Embed Text v2 MoE (475M params, 305M active, Candle backend)
# Requires: fastembed with nomic-v2-moe feature
nomic-v2 = ["embeddings", "fastembed/nomic-v2-moe"]
# Qwen3 Embeddings (Candle backend, opt-in for v2.1.1 re-embedding)
qwen3-embeddings = ["embeddings", "fastembed/qwen3", "dep:candle-core"]
# Backwards-compatible feature alias from the original v2.1.0 naming.
qwen3-reranker = ["qwen3-embeddings"]
# Metal GPU acceleration on Apple Silicon (significantly faster inference)
metal = ["fastembed/metal"]
# CUDA GPU acceleration on NVIDIA hardware (Windows / Linux, x86_64 + aarch64).
# Propagates to `candle-core/cuda`, which pulls in `cudarc` and `candle-kernels`
# for a per-build nvcc compile pass. Pair with `qwen3-embeddings` so the Candle
# backend is present in the build graph.
#
# Build: cargo build --release -p vestige-mcp --features qwen3-embeddings,cuda
cuda = ["qwen3-embeddings", "candle-core/cuda"]
# cuDNN on top of CUDA — additional fused kernels and faster inference paths.
cudnn = ["cuda", "candle-core/cudnn"]
[dependencies]
# Serialization
serde = { version = "1", features = ["derive"] }
serde_json = "1"
# Date/Time with full timezone support
chrono = { version = "0.4", features = ["serde"] }
# UUID v4 generation
uuid = { version = "1", features = ["v4", "serde"] }
# Error handling
thiserror = "2"
# Database - SQLite with FTS5 full-text search and JSON
# Note: "bundled" or "bundled-sqlcipher" added via feature flags above
rusqlite = { version = "0.38", features = ["chrono", "serde_json"] }
# Platform-specific directories
directories = "6"
# Async runtime (required for codebase module)
tokio = { version = "1", features = ["sync", "rt-multi-thread", "macros"] }
# Tracing for structured logging
tracing = "0.1"
# Git integration for codebase memory
# vendored-openssl: Compile OpenSSL from source for cross-compilation support
git2 = { version = "0.20", features = ["vendored-openssl"] }
# File watching for codebase memory
notify = "8"
# ============================================================================
# OPTIONAL: Embeddings (fastembed v5 - local ONNX inference, 2026 bleeding edge)
# ============================================================================
# nomic-embed-text-v1.5: 768 dimensions, 8192 token context, Matryoshka support
# fastembed v5 provides Nomic v2 MoE and Qwen3 feature-gated model loaders.
fastembed = { version = "5.11", default-features = false, features = ["hf-hub-native-tls", "image-models"], optional = true }
candle-core = { version = "0.10.2", optional = true }
# ============================================================================
# OPTIONAL: Vector Search (USearch - HNSW, 20x faster than FAISS)
# ============================================================================
# Pinned to 2.23.0 — 2.24.0 introduced a Windows MSVC compile break because
# its memory_mapping_allocator_gt template references the POSIX MAP_FAILED
# macro from <sys/mman.h>, which doesn't exist on MSVC. Tracked upstream in
# unum-cloud/usearch#746. Unpin when the upstream fix lands.
usearch = { version = "=2.23.0", optional = true }
# LRU cache for query embeddings
lru = "0.16"
[dev-dependencies]
tempfile = "3"
criterion = { version = "0.5", features = ["html_reports"] }
[[bench]]
name = "search_bench"
harness = false
[lib]
name = "vestige_core"
path = "src/lib.rs"
# Enable doctests
doctest = true
[package.metadata.docs.rs]
all-features = true
rustdoc-args = ["--cfg", "docsrs"]