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Retention Target System: auto-GC low-retention memories during consolidation (VESTIGE_RETENTION_TARGET env var, default 0.8). Auto-Promote: memories accessed 3+ times in 24h get frequency-dependent potentiation. Waking SWR Tagging: promoted memories get preferential 70/30 dream replay. Improved Consolidation Scheduler: triggers on 6h staleness or 2h active use. New tools: memory_health (retention dashboard with distribution buckets, trend tracking, recommendations) and memory_graph (subgraph export with Fruchterman-Reingold force-directed layout, up to 200 nodes). Dream connections now persist to database via save_connection(), enabling memory_graph traversal. Schema Migration V8 adds waking_tag, utility_score, times_retrieved/useful columns and retention_snapshots table. 21 MCP tools. v1.9.1 fixes: ConnectionRecord export, UTF-8 safe truncation, link_type normalization, utility_score clamping, only-new-connections persistence, 70/30 split capacity fill, nonexistent center_id error handling. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
96 lines
2.9 KiB
TOML
96 lines
2.9 KiB
TOML
[package]
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name = "vestige-core"
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version = "1.9.1"
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edition = "2024"
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rust-version = "1.85"
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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", "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,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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# Core embeddings with fastembed (ONNX-based, local inference)
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embeddings = ["dep:fastembed"]
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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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# Full feature set including MCP protocol support
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full = ["embeddings", "vector-search"]
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# MCP (Model Context Protocol) support for Claude integration
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mcp = []
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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 = { version = "5", 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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usearch = { version = "2", 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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[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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