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Merge origin/main into claude/quantum-engine-adrs-6OsEO - resolve Cargo.toml conflict
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Cargo.lock

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Cargo.toml

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"crates/ruvector-delta-graph",
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"crates/ruvector-delta-consensus",
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"crates/ruvector-crv",
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"crates/ruvector-temporal-tensor",
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"crates/ruqu-core",
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"crates/ruqu-algorithms",
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"crates/ruqu-wasm",
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resolver = "2"
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[workspace.package]
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version = "2.0.1"
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version = "2.0.2"
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edition = "2021"
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rust-version = "1.77"
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license = "MIT"

README.md

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| Crate | Description | crates.io |
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|-------|-------------|-----------|
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| [ruvllm](./crates/ruvllm) | LLM serving runtime with SONA, paged attention, KV cache | [![crates.io](https://img.shields.io/crates/v/ruvllm.svg)](https://crates.io/crates/ruvllm) |
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| [ruvllm](./crates/ruvllm) | LLM serving runtime with SONA, paged attention, KV cache, BitNet | [![crates.io](https://img.shields.io/crates/v/ruvllm.svg)](https://crates.io/crates/ruvllm) |
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| [ruvllm-cli](./crates/ruvllm-cli) | CLI for model inference and benchmarking | [![crates.io](https://img.shields.io/crates/v/ruvllm-cli.svg)](https://crates.io/crates/ruvllm-cli) |
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| [ruvllm-wasm](./crates/ruvllm-wasm) | WASM bindings for browser LLM inference | [![crates.io](https://img.shields.io/crates/v/ruvllm-wasm.svg)](https://crates.io/crates/ruvllm-wasm) |
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**Features:** Candle backend, Metal/CUDA acceleration, Apple Neural Engine, GGUF support, SONA learning integration.
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**Features:** Candle backend, Metal/CUDA acceleration, Apple Neural Engine, GGUF support, SONA learning, **BitNet 1.58-bit quantization** (TL1 kernels, AVX2/WASM).
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```bash
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cargo add ruvllm --features inference-metal # Mac with Metal
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See [ruvector-dag README](./crates/ruvector-dag/README.md) for full documentation.
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### Temporal Tensor Store
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| Crate | Description | crates.io |
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|-------|-------------|-----------|
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| [ruvector-temporal-tensor](./crates/ruvector-temporal-tensor) | Time-series tensor storage with tiered quantization | [![crates.io](https://img.shields.io/crates/v/ruvector-temporal-tensor.svg)](https://crates.io/crates/ruvector-temporal-tensor) |
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| [ruvector-temporal-tensor-wasm](./crates/ruvector-temporal-tensor-wasm) | WASM bindings for browser temporal tensors | [![crates.io](https://img.shields.io/crates/v/ruvector-temporal-tensor-wasm.svg)](https://crates.io/crates/ruvector-temporal-tensor-wasm) |
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**High-performance temporal embedding storage** optimized for AI agent memory systems:
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| Feature | Description |
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|---------|-------------|
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| **Block-Based Storage** | 4KB aligned blocks with SIMD-optimized I/O ([ADR-018](./docs/adr/temporal-tensor-store/ADR-018-block-based-storage-engine.md)) |
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| **Tiered Quantization** | F32 → F16 → INT8 → INT4 with <1% accuracy loss ([ADR-019](./docs/adr/temporal-tensor-store/ADR-019-tiered-quantization-formats.md)) |
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| **Temporal Scoring** | Access frequency + recency decay for automatic tier migration ([ADR-020](./docs/adr/temporal-tensor-store/ADR-020-temporal-scoring-tier-migration.md)) |
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| **Delta Compression** | 60-80% storage reduction via temporal differencing ([ADR-021](./docs/adr/temporal-tensor-store/ADR-021-delta-compression-reconstruction.md)) |
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| **Cross-Platform WASM** | Unified API for browser, Node.js, and edge ([ADR-022](./docs/adr/temporal-tensor-store/ADR-022-wasm-api-cross-platform.md)) |
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| **AgentDB Integration** | Native coherence scoring and memory persistence |
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**Performance Targets:** >100K writes/sec, <1ms p99 read latency, 4-32x compression ([ADR-023](./docs/adr/temporal-tensor-store/ADR-023-benchmarking-acceptance-criteria.md))
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See [Domain-Driven Design](./docs/architecture/temporal-tensor-store-ddd.md) for architecture details.
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### CRV Signal Line Protocol
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| Crate | Description | crates.io |
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|-------|-------------|-----------|
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| [ruvector-crv](./crates/ruvector-crv) | 6-stage CRV signal line methodology for vector search | [![crates.io](https://img.shields.io/crates/v/ruvector-crv.svg)](https://crates.io/crates/ruvector-crv) |
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**Maps CRV stages to ruvector subsystems:**
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- Stage I (Ideograms) → Poincaré ball hyperbolic embeddings
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- Stage II (Sensory) → Multi-head attention vectors
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- Stage III (Dimensional) → GNN graph topology
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- Stage IV (Emotional) → SNN temporal encoding
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- Stage V (Interrogation) → Differentiable search
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- Stage VI (3D Model) → MinCut partitioning
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### Distributed Systems (Raft & Replication)
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| Crate | Description | crates.io |

crates/ruvector-cluster/Cargo.toml

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description = "Distributed clustering and sharding for ruvector"
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[dependencies]
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ruvector-core = {path = "../ruvector-core" }
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ruvector-core = { version = "2.0.1", path = "../ruvector-core" }
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tokio = { workspace = true, features = ["time"] }
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serde = { workspace = true }
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serde_json = { workspace = true }

crates/ruvector-crv/Cargo.toml

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[package]
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name = "ruvector-crv"
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version = "0.1.0"
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version = "0.1.1"
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edition = "2021"
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authors = ["ruvector contributors"]
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description = "CRV (Coordinate Remote Viewing) protocol integration for ruvector - maps 6-stage signal line methodology to vector database subsystems"
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license = "MIT OR Apache-2.0"
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repository = "https://github.com/ruvnet/ruvector"
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readme = "README.md"
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keywords = ["crv", "signal-line", "vector-search", "attention", "hyperbolic"]
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categories = ["algorithms", "science"]
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default = []
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[dependencies]
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ruvector-attention = { path = "../ruvector-attention" }
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ruvector-gnn = { path = "../ruvector-gnn" }
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ruvector-mincut = { path = "../ruvector-mincut", default-features = false, features = ["exact"] }
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ruvector-attention = { version = "0.1.31", path = "../ruvector-attention" }
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ruvector-gnn = { version = "2.0.1", path = "../ruvector-gnn", default-features = false }
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ruvector-mincut = { version = "2.0.1", path = "../ruvector-mincut", default-features = false, features = ["exact"] }
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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thiserror = "1.0"

crates/ruvector-crv/README.md

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# ruvector-crv
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CRV (Coordinate Remote Viewing) protocol integration for ruvector.
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Maps the 6-stage CRV signal line methodology to ruvector's subsystems:
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| CRV Stage | Data Type | ruvector Component |
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|-----------|-----------|-------------------|
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| Stage I (Ideograms) | Gestalt primitives | Poincaré ball hyperbolic embeddings |
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| Stage II (Sensory) | Textures, colors, temps | Multi-head attention vectors |
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| Stage III (Dimensional) | Spatial sketches | GNN graph topology |
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| Stage IV (Emotional) | AOL, intangibles | SNN temporal encoding |
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| Stage V (Interrogation) | Signal line probing | Differentiable search |
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| Stage VI (3D Model) | Composite model | MinCut partitioning |
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## Quick Start
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```rust
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use ruvector_crv::{CrvConfig, CrvSessionManager, GestaltType, StageIData};
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// Create session manager with default config (384 dimensions)
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let config = CrvConfig::default();
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let mut manager = CrvSessionManager::new(config);
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// Create a session for a target coordinate
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manager.create_session("session-001".to_string(), "1234-5678".to_string()).unwrap();
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// Add Stage I ideogram data
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let stage_i = StageIData {
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stroke: vec![(0.0, 0.0), (1.0, 0.5), (2.0, 1.0), (3.0, 0.5)],
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spontaneous_descriptor: "angular rising".to_string(),
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classification: GestaltType::Manmade,
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confidence: 0.85,
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};
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let embedding = manager.add_stage_i("session-001", &stage_i).unwrap();
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assert_eq!(embedding.len(), 384);
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```
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## Architecture
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The Poincaré ball embedding for Stage I gestalts encodes the hierarchical
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gestalt taxonomy (root → manmade/natural/movement/energy/water/land) with
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exponentially less distortion than Euclidean space.
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For AOL (Analytical Overlay) separation, the spiking neural network temporal
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encoding models signal-vs-noise discrimination: high-frequency spike bursts
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correlate with AOL contamination, while sustained low-frequency patterns
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indicate clean signal line data.
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MinCut partitioning in Stage VI identifies natural cluster boundaries in the
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accumulated session graph, separating distinct target aspects.
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## Cross-Session Convergence
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Multiple sessions targeting the same coordinate can be analyzed for
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convergence — agreement between independent viewers strengthens the
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signal validity:
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```rust
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// After adding data to multiple sessions for "1234-5678"...
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let convergence = manager.find_convergence("1234-5678", 0.75).unwrap();
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// convergence.scores contains similarity values for converging entries
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```
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## License
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MIT OR Apache-2.0

crates/ruvector-gnn/Cargo.toml

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[dependencies]
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# Core
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ruvector-core = { path = "../ruvector-core", default-features = false }
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ruvector-core = { version = "2.0.1", path = "../ruvector-core", default-features = false }
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# Math and numerics
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ndarray = { workspace = true, features = ["serde"] }

crates/ruvector-graph/Cargo.toml

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[dependencies]
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# RuVector dependencies
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ruvector-core = { path = "../ruvector-core", default-features = false, features = ["simd", "parallel"] }
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ruvector-raft = { path = "../ruvector-raft", optional = true }
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ruvector-cluster = { path = "../ruvector-cluster", optional = true }
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ruvector-replication = { path = "../ruvector-replication", optional = true }
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ruvector-core = { version = "2.0.1", path = "../ruvector-core", default-features = false, features = ["simd", "parallel"] }
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ruvector-raft = { version = "2.0.1", path = "../ruvector-raft", optional = true }
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ruvector-cluster = { version = "2.0.1", path = "../ruvector-cluster", optional = true }
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ruvector-replication = { version = "2.0.1", path = "../ruvector-replication", optional = true }
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# Storage and indexing (optional for WASM)
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redb = { workspace = true, optional = true }

crates/ruvector-mincut/Cargo.toml

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# RuVector dependencies
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ruvector-core = {path = "../ruvector-core", default-features = false }
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ruvector-graph = { path = "../ruvector-graph", default-features = false, optional = true }
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ruvector-core = { version = "2.0.1", path = "../ruvector-core", default-features = false }
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ruvector-graph = { version = "2.0.1", path = "../ruvector-graph", default-features = false, optional = true }
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# Core dependencies
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petgraph = "0.6"

crates/ruvector-raft/Cargo.toml

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description = "Raft consensus implementation for ruvector distributed metadata"
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[dependencies]
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ruvector-core = {path = "../ruvector-core" }
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ruvector-core = { version = "2.0.1", path = "../ruvector-core" }
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tokio = { workspace = true, features = ["time"] }
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serde = { workspace = true }
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serde_json = { workspace = true }

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