Intelligent query interface for the Sovereign AI Stack

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1. KNOWLEDGE GRAPH: Stack Component Registry
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📚 Sovereign AI Stack Layers:
  Compute Primitives: 5 components
    - trueno-graph v0.1.1
    - trueno-viz v0.1.1
    - trueno v0.7.3
    - trueno-db v0.3.3
    - trueno-rag v0.1.0
  ML Algorithms: 1 components
    - aprender v0.12.0
  Training & Inference: 2 components
    - entrenar v0.2.1
    - realizar v0.2.1
  Transpilers: 4 components
    - depyler v0.1.0
    - ruchy v3.213.0
    - bashrs v6.41.0
    - decy v0.1.0
  Orchestration: 3 components
    - pforge v0.1.2
    - repartir v1.0.0
    - batuta v0.1.0
  Quality & Profiling: 3 components
    - pmat v2.205.0
    - renacer v0.6.5
    - certeza v0.1.0
  Data Loading: 2 components
    - alimentar v0.1.0
    - pacha v0.1.0

🔍 Finding components with SIMD capability:
  - trueno (Compute Primitives)

🧠 Finding components for Supervised Learning domain:
  - aprender: Next-generation machine learning library in pure Rust
  - aprender: Next-generation machine learning library in pure Rust
  - aprender: Next-generation machine learning library in pure Rust
  - aprender: Next-generation machine learning library in pure Rust
  - aprender: Next-generation machine learning library in pure Rust
  - aprender: Next-generation machine learning library in pure Rust
  - aprender: Next-generation machine learning library in pure Rust
  - aprender: Next-generation machine learning library in pure Rust

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2. NATURAL LANGUAGE QUERIES
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📝 Query: "How do I run K-means clustering on a large dataset?"
   Problem: Unsupervised Learning
   Algorithm: k_means
   Primary: aprender (85% confidence)
   Backend: Scalar

📝 Query: "What's the fastest way to do matrix multiplication?"
   Problem: Linear Algebra
   Primary: trueno (85% confidence)
   Backend: Scalar

📝 Query: "I need to transpile PyTorch to Rust with GPU support"
   Problem: Python Migration
   Primary: depyler (85% confidence)
   Backend: Scalar

📝 Query: "Help me optimize random forest training for 10GB of data"
   Problem: Supervised Learning
   Algorithm: random_forest
   Primary: aprender (90% confidence)
   Backend: Scalar

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3. RECOMMENDER: Intelligent Component Selection
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🎯 Backend Selection (Amdahl's Law + PCIe 5× Rule):

Scenario: Small matrix (64×64)
  Backend: SIMD
  Rationale: SIMD vectorization optimal for 4K samples with High complexity

Scenario: Neural network training (1M samples, GPU available)
  Backend: GPU
  Rationale: GPU acceleration recommended for 1M samples with High complexity - PCIe overhead amortized

Scenario: Billion-scale random forest (8 nodes)
  Backend: SIMD
  Distribution: No
  Rationale: Distribution overhead (80%) outweighs benefits

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4. INTEGRATION PATTERNS
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🔗 Integration patterns from trueno:

🔗 Components that integrate with aprender:
  depyler → aprender: Convert sklearn code to aprender
  alimentar → aprender: Load data for ML training

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5. CODE GENERATION EXAMPLES
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💻 Code example for SIMD matrix operations:
use trueno::prelude::*;

// Create tensors with SIMD acceleration
let a = Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0]);
let b = Tensor::from_vec(vec![5.0, 6.0, 7.0, 8.0]);

// SIMD-accelerated operations
let result = a.dot(&b);
println!("Dot product: {}", result);

💻 Code example for distributed computing:
use repartir::{Executor, Task};

// Create distributed executor
let executor = Executor::new()
    .with_workers(4)
    .with_scheduler(WorkStealing);

// Distribute computation
let results = executor.map(data.chunks(1000), |chunk| {
    process_chunk(chunk)
}).await?;

// Aggregate results
let final_result = results.reduce(|a, b| a + b);

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6. BACKEND DECISION CRITERIA
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Backend selection follows Gregg & Hazelwood (2011):

  Backend      | Criteria
  -------------+---------------------------------------------------
  Scalar       | Simple operations, small data
  SIMD | SIMD-friendly (matmul, conv, reductions)
  GPU | compute_time > 5× PCIe_transfer_time
  Distributed | Data > single node capacity OR Amdahl P > 0.95

✅ Oracle Mode ready for production queries!
   Run: batuta oracle --interactive
% time     seconds  usecs/call     calls    errors syscall
------ ----------- ----------- --------- --------- ----------------
 62.98    0.000922           7       117           write
  8.20    0.000120           9        13           mmap
  4.03    0.000059           9         6           mprotect
  2.12    0.000031           6         5           rt_sigaction
  3.48    0.000051          10         5           read
  1.84    0.000027           6         4           newfstatat
  1.78    0.000026           6         4           unknown
  2.73    0.000040          10         4           openat
  2.39    0.000035           8         4           brk
  1.84    0.000027           6         4           close
  1.71    0.000025           6         4           pread64
  1.30    0.000019           6         3           sigaltstack
  1.30    0.000019           9         2           getrandom
  1.57    0.000023          11         2           munmap
  0.96    0.000014           7         2         1 arch_prctl
  0.41    0.000006           6         1           set_robust_list
  0.41    0.000006           6         1           set_tid_address
  0.48    0.000007           7         1           poll
  0.48    0.000007           7         1         1 access
------ ----------- ----------- --------- --------- ----------------
100.00    0.001464           8       183         2 total
