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