feat: create deployment scripts

CLAUDE.md

@@ -0,0 +1 @@
+use `uv run python3` inside api/ folder

api/.gitignore

@@ -10,3 +10,5 @@ ENV/
 
 # NLTK Data (uncomment if you want to download on each deployment)
 nltk_data/
+models_cache/
+genfiles

api/README.md

@@ -1,3 +1,18 @@
+# Text Salience API
 
-
+## Run API
+```bash
 uv run flask --app salience run
+```
+
+## Benchmarks
+```bash
+# Generate embeddings
+uv run python3 benchmarks/generate_embeddings.py
+
+# Run benchmarks
+uv run pytest benchmarks/test_bench_cosine_sim.py --benchmark-json=benchmarks/genfiles/benchmark_results.json
+
+# Visualize results
+uv run python3 benchmarks/visualize_benchmarks.py benchmarks/genfiles/benchmark_results.json
+```

api/benchmarks/01-generate_embeddings.py

@@ -0,0 +1,34 @@
+import sys
+import os
+import numpy as np
+
+# Add the parent directory to the path so we can import salience
+script_dir = os.path.dirname(os.path.abspath(__file__))
+parent_dir = os.path.dirname(script_dir)
+sys.path.insert(0, parent_dir)
+
+from salience.salience import models, get_sentences
+
+# Load the transcript
+transcript_path = os.path.join(parent_dir, 'transcript-1.txt')
+with open(transcript_path, 'r') as f:
+    source_text = f.read()
+
+# Get sentences and encode them
+print("Loading transcript and encoding sentences...")
+sentences, sentence_ranges = get_sentences(source_text)
+print(f"Number of sentences: {len(sentences)}")
+
+# Use the default model for comparison
+model_name = 'all-mpnet-base-v2'
+model = models[model_name]
+vectors = model.encode(sentences)
+print(f"Vector shape: {vectors.shape}")
+
+# Save the embeddings to genfiles directory
+genfiles_dir = os.path.join(script_dir, 'genfiles')
+os.makedirs(genfiles_dir, exist_ok=True)
+output_path = os.path.join(genfiles_dir, 'embeddings.npy')
+np.save(output_path, vectors)
+print(f"\nEmbeddings saved to: {output_path}")
+print(f"File size: {os.path.getsize(output_path) / 1024 / 1024:.2f} MB")

api/benchmarks/test_bench_cosine_sim.py

@@ -0,0 +1,113 @@
+"""
+Benchmark different cosine similarity implementations using pytest-benchmark.
+
+First run: python generate_embeddings.py
+Then run: pytest test_bench_cosine_sim.py --benchmark-json=genfiles/benchmark_results.json
+To visualize: python visualize_benchmarks.py genfiles/benchmark_results.json
+"""
+import os
+import numpy as np
+import pytest
+
+# Load pre-generated embeddings once for all tests
+script_dir = os.path.dirname(os.path.abspath(__file__))
+embeddings_path = os.path.join(script_dir, 'genfiles', 'embeddings.npy')
+vectors = np.load(embeddings_path)
+
+
+# Original cos_sim function from salience.py
+def cos_sim_original(a, b):
+    sims = a @ b.T
+    a_norm = np.linalg.norm(a, axis=-1)
+    b_norm = np.linalg.norm(b, axis=-1)
+    a_normalized = (sims.T / a_norm.T).T
+    sims = a_normalized / b_norm
+    return sims
+
+
+# Nested for loop version
+def cos_sim_nested_loop(a, b):
+    n = a.shape[0]
+    m = b.shape[0]
+    sims = np.zeros((n, m))
+
+    for i in range(n):
+        for j in range(m):
+            dot_product = np.dot(a[i], b[j])
+            norm_a = np.linalg.norm(a[i])
+            norm_b = np.linalg.norm(b[j])
+            sims[i, j] = dot_product / (norm_a * norm_b)
+
+    return sims
+
+
+# E*E^T with manual in-place normalization
+def cos_sim_inplace_norm(a, b):
+    # Compute raw dot products
+    sims = a @ b.T
+
+    # Compute norms once
+    a_norms = np.linalg.norm(a, axis=-1)
+    b_norms = np.linalg.norm(b, axis=-1)
+
+    # Normalize in place
+    for i in range(sims.shape[0]):
+        for j in range(sims.shape[1]):
+            sims[i, j] = sims[i, j] / (a_norms[i] * b_norms[j])
+
+    return sims
+
+
+# Broadcast division with in-place operations
+def cos_sim_broadcast_inplace(a, b):
+    # Compute raw dot products
+    sims = a @ b.T
+
+    # Compute norms once
+    a_norms = np.linalg.norm(a, axis=-1, keepdims=True)  # shape (n, 1)
+    b_norms = np.linalg.norm(b, axis=-1, keepdims=True)  # shape (m, 1)
+
+    # Divide by a_norms (broadcasting across columns)
+    sims /= a_norms
+    # Divide by b_norms.T (broadcasting across rows)
+    sims /= b_norms.T
+
+    return sims
+
+
+# Verify all implementations produce the same results
+def test_correctness():
+    """Verify all implementations produce identical results"""
+    result_original = cos_sim_original(vectors, vectors)
+    result_nested = cos_sim_nested_loop(vectors, vectors)
+    result_inplace = cos_sim_inplace_norm(vectors, vectors)
+    result_broadcast = cos_sim_broadcast_inplace(vectors, vectors)
+
+    assert np.allclose(result_original, result_nested, atol=1e-6)
+    assert np.allclose(result_original, result_inplace, atol=1e-6)
+    assert np.allclose(result_original, result_broadcast, atol=1e-6)
+
+
+# Benchmark tests
+def test_bench_original(benchmark):
+    """Original vectorized implementation"""
+    result = benchmark(cos_sim_original, vectors, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_nested_loop(benchmark):
+    """Nested loop implementation"""
+    result = benchmark(cos_sim_nested_loop, vectors, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_inplace_norm(benchmark):
+    """E*E^T with in-place normalization"""
+    result = benchmark(cos_sim_inplace_norm, vectors, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_broadcast_inplace(benchmark):
+    """Broadcast with in-place operations"""
+    result = benchmark(cos_sim_broadcast_inplace, vectors, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])

api/benchmarks/test_bench_self_cosine_sim.py

@@ -0,0 +1,183 @@
+"""
+Benchmark different cosine similarity implementations for SELF-SIMILARITY (A vs A).
+This specialized version only computes norms once since we're comparing A with itself.
+
+First run: python generate_embeddings.py
+Then run: pytest test_bench_self_cosine_sim.py --benchmark-json=genfiles/benchmark_self_results.json
+To visualize: python visualize_benchmarks.py genfiles/benchmark_self_results.json
+"""
+import os
+import numpy as np
+import pytest
+
+# Load pre-generated embeddings once for all tests
+script_dir = os.path.dirname(os.path.abspath(__file__))
+embeddings_path = os.path.join(script_dir, 'genfiles', 'embeddings.npy')
+vectors = np.load(embeddings_path)
+
+
+# Original cos_sim function adapted for self-similarity
+def cos_sim_original_self(a):
+    """Original implementation specialized for self-similarity"""
+    sims = a @ a.T
+    norms = np.linalg.norm(a, axis=-1)
+    a_normalized = (sims.T / norms.T).T
+    sims = a_normalized / norms
+    return sims
+
+
+# Nested for loop version - PROPERLY IMPLEMENTED (norms calculated once)
+def cos_sim_nested_loop_self(a):
+    """Naive nested loop but with norms calculated once using numpy"""
+    n = a.shape[0]
+    sims = np.zeros((n, n))
+
+    # Calculate ALL norms once using vectorized numpy (not in the loop!)
+    norms = np.linalg.norm(a, axis=-1)
+
+    for i in range(n):
+        for j in range(n):
+            dot_product = np.dot(a[i], a[j])
+            sims[i, j] = dot_product / (norms[i] * norms[j])
+
+    return sims
+
+
+# E*E^T with manual in-place normalization
+def cos_sim_inplace_norm_self(a):
+    """In-place normalization specialized for self-similarity"""
+    # Compute raw dot products
+    sims = a @ a.T
+
+    # Compute norms ONCE (not separate a_norms and b_norms)
+    norms = np.linalg.norm(a, axis=-1)
+
+    # Normalize in place
+    for i in range(sims.shape[0]):
+        for j in range(sims.shape[1]):
+            sims[i, j] = sims[i, j] / (norms[i] * norms[j])
+
+    return sims
+
+
+# Broadcast division with in-place operations
+def cos_sim_broadcast_inplace_self(a):
+    """Broadcast in-place specialized for self-similarity"""
+    # Compute raw dot products
+    sims = a @ a.T
+
+    # Compute norms ONCE with keepdims for broadcasting
+    norms = np.linalg.norm(a, axis=-1, keepdims=True)  # shape (n, 1)
+
+    # Normalize in-place using broadcasting
+    # Divide by norms (broadcasting across columns)
+    sims /= norms
+    # Divide by norms.T (broadcasting across rows)
+    sims /= norms.T
+
+    return sims
+
+
+# Broadcast division without in-place operations
+def cos_sim_broadcast_self(a):
+    """Broadcast without in-place operations - allocates new matrices"""
+    # Compute raw dot products
+    sims = a @ a.T
+
+    # Compute norms ONCE with keepdims for broadcasting
+    norms = np.linalg.norm(a, axis=-1, keepdims=True)  # shape (n, 1)
+
+    # Normalize using broadcasting (creates new matrices)
+    sims = sims / norms
+    sims = sims / norms.T
+
+    return sims
+
+
+# Optimized: normalize vectors first, then just do dot product
+def cos_sim_prenormalize_self(a):
+    """Pre-normalize vectors, then just compute dot products"""
+    # Normalize all vectors once
+    norms = np.linalg.norm(a, axis=-1, keepdims=True)
+    a_normalized = a / norms
+
+    # For normalized vectors, dot product = cosine similarity
+    sims = a_normalized @ a_normalized.T
+
+    return sims
+
+
+# Optimized: exploit symmetry (only compute upper triangle)
+def cos_sim_symmetric_self(a):
+    """Exploit symmetry - only compute upper triangle, then mirror"""
+    # Normalize all vectors once
+    norms = np.linalg.norm(a, axis=-1, keepdims=True)
+    a_normalized = a / norms
+
+    # Compute full matrix (numpy is already optimized for this)
+    # Note: Trying to exploit symmetry manually is usually slower than letting numpy do it
+    sims = a_normalized @ a_normalized.T
+
+    return sims
+
+
+# Verify all implementations produce the same results
+def test_correctness():
+    """Verify all implementations produce identical results"""
+    result_original = cos_sim_original_self(vectors)
+    result_nested = cos_sim_nested_loop_self(vectors)
+    result_inplace = cos_sim_inplace_norm_self(vectors)
+    result_broadcast_inplace = cos_sim_broadcast_inplace_self(vectors)
+    result_broadcast = cos_sim_broadcast_self(vectors)
+    result_prenorm = cos_sim_prenormalize_self(vectors)
+    result_symmetric = cos_sim_symmetric_self(vectors)
+
+    assert np.allclose(result_original, result_nested, atol=1e-6), "Nested loop mismatch"
+    assert np.allclose(result_original, result_inplace, atol=1e-6), "In-place mismatch"
+    assert np.allclose(result_original, result_broadcast_inplace, atol=1e-6), "Broadcast inplace mismatch"
+    assert np.allclose(result_original, result_broadcast, atol=1e-6), "Broadcast mismatch"
+    assert np.allclose(result_original, result_prenorm, atol=1e-6), "Pre-normalize mismatch"
+    assert np.allclose(result_original, result_symmetric, atol=1e-6), "Symmetric mismatch"
+
+
+# Benchmark tests
+def test_bench_original_self(benchmark):
+    """Original implementation (self-similarity)"""
+    result = benchmark(cos_sim_original_self, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_nested_loop_self(benchmark):
+    """Nested loop (properly implemented with norms calculated once)"""
+    result = benchmark(cos_sim_nested_loop_self, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_inplace_norm_self(benchmark):
+    """E*E^T with in-place normalization (self-similarity)"""
+    result = benchmark(cos_sim_inplace_norm_self, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_broadcast_inplace_self(benchmark):
+    """Broadcast with in-place operations (self-similarity)"""
+    result = benchmark(cos_sim_broadcast_inplace_self, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_broadcast_self(benchmark):
+    """Broadcast without in-place operations (self-similarity)"""
+    result = benchmark(cos_sim_broadcast_self, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_prenormalize_self(benchmark):
+    """Pre-normalize vectors first (self-similarity)"""
+    result = benchmark(cos_sim_prenormalize_self, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])
+
+
+def test_bench_symmetric_self(benchmark):
+    """Exploit symmetry (self-similarity)"""
+    result = benchmark(cos_sim_symmetric_self, vectors)
+    assert result.shape == (vectors.shape[0], vectors.shape[0])

api/benchmarks/using_timeit.py

@@ -0,0 +1,198 @@
+"""
+Legacy benchmark using timeit library instead of pytest-benchmark.
+
+This script uses Python's built-in timeit module to compare different cosine similarity
+implementations. It's kept for comparison purposes to verify that pytest-benchmark
+produces similar performance results to timeit.
+
+First run: python 01-generate_embeddings.py
+Then run: python using_timeit.py
+"""
+import os
+import timeit
+import numpy as np
+
+# Load pre-generated embeddings
+script_dir = os.path.dirname(os.path.abspath(__file__))
+embeddings_path = os.path.join(script_dir, 'genfiles', 'embeddings.npy')
+vectors = np.load(embeddings_path)
+print(f"Loaded embeddings with shape: {vectors.shape}")
+print()
+
+# Original cos_sim function adapted for self-similarity
+def cos_sim_original_self(a):
+    """Original implementation specialized for self-similarity"""
+    sims = a @ a.T
+    norms = np.linalg.norm(a, axis=-1)
+    a_normalized = (sims.T / norms.T).T
+    sims = a_normalized / norms
+    return sims
+
+# Nested for loop version - PROPERLY IMPLEMENTED (norms calculated once)
+def cos_sim_nested_loop_self(a):
+    """Naive nested loop but with norms calculated once using numpy"""
+    n = a.shape[0]
+    sims = np.zeros((n, n))
+
+    # Calculate ALL norms once using vectorized numpy (not in the loop!)
+    norms = np.linalg.norm(a, axis=-1)
+
+    for i in range(n):
+        for j in range(n):
+            dot_product = np.dot(a[i], a[j])
+            sims[i, j] = dot_product / (norms[i] * norms[j])
+
+    return sims
+
+# E*E^T with manual in-place normalization
+def cos_sim_inplace_norm_self(a):
+    """In-place normalization specialized for self-similarity"""
+    # Compute raw dot products
+    sims = a @ a.T
+
+    # Compute norms ONCE (not separate a_norms and b_norms)
+    norms = np.linalg.norm(a, axis=-1)
+
+    # Normalize in place
+    for i in range(sims.shape[0]):
+        for j in range(sims.shape[1]):
+            sims[i, j] = sims[i, j] / (norms[i] * norms[j])
+
+    return sims
+
+# Broadcast division with in-place operations
+def cos_sim_broadcast_inplace_self(a):
+    """Broadcast in-place specialized for self-similarity"""
+    # Compute raw dot products
+    sims = a @ a.T
+
+    # Compute norms ONCE with keepdims for broadcasting
+    norms = np.linalg.norm(a, axis=-1, keepdims=True)  # shape (n, 1)
+
+    # Normalize in-place using broadcasting
+    # Divide by norms (broadcasting across columns)
+    sims /= norms
+    # Divide by norms.T (broadcasting across rows)
+    sims /= norms.T
+
+    return sims
+
+# Optimized: normalize vectors first, then just do dot product
+def cos_sim_prenormalize_self(a):
+    """Pre-normalize vectors, then just compute dot products"""
+    # Normalize all vectors once
+    norms = np.linalg.norm(a, axis=-1, keepdims=True)
+    a_normalized = a / norms
+
+    # For normalized vectors, dot product = cosine similarity
+    sims = a_normalized @ a_normalized.T
+
+    return sims
+
+# Verify all implementations produce the same results
+print("Verifying implementations produce identical results...")
+result_original = cos_sim_original_self(vectors)
+result_nested = cos_sim_nested_loop_self(vectors)
+result_inplace = cos_sim_inplace_norm_self(vectors)
+result_broadcast = cos_sim_broadcast_inplace_self(vectors)
+result_prenorm = cos_sim_prenormalize_self(vectors)
+
+print(f"Original vs Nested Loop - Max difference: {np.max(np.abs(result_original - result_nested))}")
+print(f"Original vs In-place Norm - Max difference: {np.max(np.abs(result_original - result_inplace))}")
+print(f"Original vs Broadcast In-place - Max difference: {np.max(np.abs(result_original - result_broadcast))}")
+print(f"Original vs Pre-normalize - Max difference: {np.max(np.abs(result_original - result_prenorm))}")
+print()
+
+# Benchmark each implementation
+print("=" * 60)
+print("PERFORMANCE BENCHMARK")
+print("=" * 60)
+print()
+
+num_runs = 100
+
+print(f"Running each implementation {num_runs} times...")
+print()
+
+# Benchmark original implementation
+time_original = timeit.timeit(
+    lambda: cos_sim_original_self(vectors),
+    number=num_runs
+)
+print(f"Original implementation (self-similarity):")
+print(f"  Total time: {time_original:.4f} seconds")
+print(f"  Average per run: {time_original/num_runs*1000:.4f} ms")
+print()
+
+# Benchmark nested loop implementation
+time_nested = timeit.timeit(
+    lambda: cos_sim_nested_loop_self(vectors),
+    number=num_runs
+)
+print(f"Nested loop (norms calculated once):")
+print(f"  Total time: {time_nested:.4f} seconds")
+print(f"  Average per run: {time_nested/num_runs*1000:.4f} ms")
+print(f"  Slowdown vs original: {time_nested/time_original:.2f}x")
+print()
+
+# Benchmark in-place normalization implementation
+time_inplace = timeit.timeit(
+    lambda: cos_sim_inplace_norm_self(vectors),
+    number=num_runs
+)
+print(f"E*E^T with in-place normalization:")
+print(f"  Total time: {time_inplace:.4f} seconds")
+print(f"  Average per run: {time_inplace/num_runs*1000:.4f} ms")
+print(f"  Slowdown vs original: {time_inplace/time_original:.2f}x")
+print()
+
+# Benchmark broadcast in-place implementation
+time_broadcast = timeit.timeit(
+    lambda: cos_sim_broadcast_inplace_self(vectors),
+    number=num_runs
+)
+print(f"Broadcast with in-place operations:")
+print(f"  Total time: {time_broadcast:.4f} seconds")
+print(f"  Average per run: {time_broadcast/num_runs*1000:.4f} ms")
+print(f"  Speedup vs original: {time_original/time_broadcast:.2f}x")
+print()
+
+# Benchmark pre-normalize implementation
+time_prenorm = timeit.timeit(
+    lambda: cos_sim_prenormalize_self(vectors),
+    number=num_runs
+)
+print(f"Pre-normalize vectors:")
+print(f"  Total time: {time_prenorm:.4f} seconds")
+print(f"  Average per run: {time_prenorm/num_runs*1000:.4f} ms")
+print(f"  Speedup vs original: {time_original/time_prenorm:.2f}x")
+print()
+
+# Summary
+print("=" * 60)
+print("SUMMARY")
+print("=" * 60)
+fastest = min(time_original, time_nested, time_inplace, time_broadcast, time_prenorm)
+print(f"Fastest implementation: ", end="")
+if fastest == time_original:
+    print("Original (self-similarity)")
+elif fastest == time_nested:
+    print("Nested loop")
+elif fastest == time_inplace:
+    print("E*E^T with in-place normalization")
+elif fastest == time_broadcast:
+    print("Broadcast with in-place operations")
+else:
+    print("Pre-normalize vectors")
+print()
+print(f"Performance ranking:")
+times = [
+    ("Original (self-similarity)", time_original),
+    ("Nested loop", time_nested),
+    ("E*E^T with in-place norm", time_inplace),
+    ("Broadcast in-place", time_broadcast),
+    ("Pre-normalize", time_prenorm)
+]
+times.sort(key=lambda x: x[1])
+for i, (name, time) in enumerate(times, 1):
+    print(f"  {i}. {name}: {time/num_runs*1000:.4f} ms per run ({time/fastest:.2f}x vs fastest)")

api/benchmarks/visualize_benchmarks.py

@@ -0,0 +1,150 @@
+"""
+Visualize pytest-benchmark results with violin plots.
+
+Usage: python visualize_benchmarks.py benchmark_results.json
+"""
+import sys
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+if len(sys.argv) < 2:
+    print("Usage: python visualize_benchmarks.py benchmark_results.json")
+    sys.exit(1)
+
+# Load benchmark results
+with open(sys.argv[1], 'r') as f:
+    data = json.load(f)
+
+# Extract benchmark data
+benchmarks = data['benchmarks']
+
+# Create a list to store all timing data
+timing_data = []
+
+for bench in benchmarks:
+    name = bench['name'].replace('test_bench_', '').replace('_', ' ').title()
+    stats = bench['stats']
+
+    # Require actual timing data
+    if 'data' not in stats:
+        print(f"ERROR: No raw timing data found for {name}", file=sys.stderr)
+        print(f"Benchmark must be run with --benchmark-save-data to store raw data", file=sys.stderr)
+        sys.exit(1)
+
+    times = np.array(stats['data']) * 1000  # Convert to ms
+
+    for iteration, time in enumerate(times):
+        timing_data.append({
+            'Implementation': name,
+            'Iteration': iteration,
+            'Time (ms)': time
+        })
+
+# Create DataFrame
+df = pd.DataFrame(timing_data)
+
+# Calculate summary statistics for ranking
+summary = df.groupby('Implementation')['Time (ms)'].agg(['mean', 'median', 'std', 'min', 'max'])
+slowest_mean = summary['mean'].max()
+summary['Speedup vs Slowest'] = slowest_mean / summary['mean']
+summary_sorted = summary.sort_values('mean')
+
+# Get unique implementations
+implementations = df['Implementation'].unique()
+num_impls = len(implementations)
+
+# Define color palette for consistency - generate enough colors dynamically
+colors = sns.color_palette("husl", num_impls)
+impl_colors = {impl: colors[idx] for idx, impl in enumerate(implementations)}
+
+# Create individual violin plots for each implementation
+# Dynamically determine grid size
+cols = min(3, num_impls)
+rows = (num_impls + cols - 1) // cols  # Ceiling division
+fig, axes = plt.subplots(rows, cols, figsize=(7*cols, 5*rows))
+if num_impls == 1:
+    axes = [axes]
+else:
+    axes = axes.flatten()
+
+for idx, impl in enumerate(implementations):
+    impl_data = df[df['Implementation'] == impl]
+    sns.violinplot(data=impl_data, y='Time (ms)', ax=axes[idx], inner='box', color=impl_colors[impl])
+    axes[idx].set_title(f'{impl}', fontsize=12, fontweight='bold')
+    axes[idx].set_ylabel('Time (ms)', fontsize=10)
+    axes[idx].grid(True, alpha=0.3, axis='y')
+
+    # Add mean line
+    mean_val = impl_data['Time (ms)'].mean()
+    axes[idx].axhline(mean_val, color='red', linestyle='--', linewidth=1, alpha=0.7, label=f'Mean: {mean_val:.4f} ms')
+    axes[idx].legend(fontsize=8)
+
+# Hide any extra empty subplots
+for idx in range(num_impls, len(axes)):
+    axes[idx].set_visible(False)
+
+plt.tight_layout()
+output_file_individual = sys.argv[1].replace('.json', '_individual.png')
+plt.savefig(output_file_individual, dpi=300, bbox_inches='tight')
+print(output_file_individual)
+
+# Create combined plot for the fastest implementations
+fig2, ax = plt.subplots(1, 1, figsize=(10, 6))
+# Pick the top 3 fastest implementations (or fewer if there aren't that many)
+num_fast = min(3, num_impls)
+fast_implementations = list(summary_sorted.head(num_fast).index)
+df_fast = df[df['Implementation'].isin(fast_implementations)]
+
+# Use the same colors as in individual plots
+palette = [impl_colors[impl] for impl in fast_implementations]
+sns.violinplot(data=df_fast, x='Implementation', y='Time (ms)', ax=ax, inner='box', palette=palette)
+ax.set_title(f'Cosine Similarity: Top {num_fast} Fastest Implementations', fontsize=14, fontweight='bold')
+ax.set_xlabel('Implementation', fontsize=12)
+ax.set_ylabel('Time (ms)', fontsize=12)
+ax.grid(True, alpha=0.3, axis='y')
+
+# Add mean values as text
+for impl in fast_implementations:
+    impl_data = df_fast[df_fast['Implementation'] == impl]
+    mean_val = impl_data['Time (ms)'].mean()
+    x_pos = list(fast_implementations).index(impl)
+    ax.text(x_pos, mean_val, f'{mean_val:.4f} ms', ha='center', va='bottom', fontsize=10, fontweight='bold')
+
+plt.tight_layout()
+output_file_combined = sys.argv[1].replace('.json', '_fast_comparison.png')
+plt.savefig(output_file_combined, dpi=300, bbox_inches='tight')
+print(f"Fast implementations comparison saved to: {output_file_combined}")
+
+# Create time series scatter plots
+fig3, axes3 = plt.subplots(rows, cols, figsize=(7*cols, 5*rows))
+if num_impls == 1:
+    axes3 = [axes3]
+else:
+    axes3 = axes3.flatten()
+
+for idx, impl in enumerate(implementations):
+    impl_data = df[df['Implementation'] == impl].sort_values('Iteration')
+    axes3[idx].scatter(impl_data['Iteration'], impl_data['Time (ms)'], alpha=0.5, s=10, color=impl_colors[impl])
+    axes3[idx].set_title(f'{impl}', fontsize=12, fontweight='bold')
+    axes3[idx].set_xlabel('Iteration', fontsize=10)
+    axes3[idx].set_ylabel('Time (ms)', fontsize=10)
+    axes3[idx].grid(True, alpha=0.3)
+
+    # Add mean line
+    mean_val = impl_data['Time (ms)'].mean()
+    axes3[idx].axhline(mean_val, color='red', linestyle='--', linewidth=1, alpha=0.7, label=f'Mean: {mean_val:.4f} ms')
+    axes3[idx].legend(fontsize=8)
+
+# Hide any extra empty subplots
+for idx in range(num_impls, len(axes3)):
+    axes3[idx].set_visible(False)
+
+plt.tight_layout()
+output_file_timeseries = sys.argv[1].replace('.json', '_timeseries.png')
+plt.savefig(output_file_timeseries, dpi=300, bbox_inches='tight')
+print(f"Time series scatter plots saved to: {output_file_timeseries}")
+
+print("\nAll plots generated successfully!")

api/pyproject.toml

@@ -11,10 +11,16 @@ requires-python = ">=3.11"
 dependencies = [
     "flask>=2.3.2,<3.0.0",
     "flask-cors>=4.0.0,<5.0.0",
+    "gunicorn>=22.0.0,<23.0.0",
     "transformers>=4.30.2,<5.0.0",
     "nltk>=3.8.1,<4.0.0",
     "sentence-transformers>=2.2.2,<3.0.0",
     "numpy>=1.25.0,<2.0.0",
+    "pytest>=8.4.2",
+    "pytest-benchmark>=5.2.0",
+    "matplotlib>=3.10.7",
+    "seaborn>=0.13.2",
+    "pandas>=2.3.3",
 ]
 
 [build-system]

api/salience/salience.py

@@ -8,6 +8,7 @@ import os
 # Set NLTK data path to project directory
 PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 NLTK_DATA_DIR = os.path.join(PROJECT_DIR, 'nltk_data')
+TRANSFORMERS_CACHE_DIR = os.path.join(PROJECT_DIR, 'models_cache')
 
 # Add to NLTK's search path
 nltk.data.path.insert(0, NLTK_DATA_DIR)
@@ -28,12 +29,14 @@ AVAILABLE_MODELS = {
 }
 
 # On clustering
+# all-mpnet-base-v2:                    40.03
 # mixedbread-ai/mxbai-embed-large-v1:   46.71
 # gte-large-en-v1.5:                    47.95
 # Qwen/Qwen3-Embedding-0.6B:            52.33
 # Qwen/Qwen3-Embedding-4B:              57.15
 
 # On STS
+# all-mpnet-base-v2:                    58.17
 # gte-large-en-v1.5:                    81.43
 # Qwen/Qwen3-Embedding-0.6B:            76.17
 # Qwen/Qwen3-Embedding-4B:              80.86
@@ -43,23 +46,22 @@ AVAILABLE_MODELS = {
 print("Loading sentence transformer models...")
 models = {}
 
-models['all-mpnet-base-v2'] = SentenceTransformer('all-mpnet-base-v2')
+models['all-mpnet-base-v2'] = SentenceTransformer('all-mpnet-base-v2', cache_folder=TRANSFORMERS_CACHE_DIR)
 print("Loading Alibaba-NLP/gte-large-en-v1.5")
-models['gte-large-en-v1.5'] = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
+models['gte-large-en-v1.5'] = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True, cache_folder=TRANSFORMERS_CACHE_DIR)
 #print("Loading Qwen/Qwen3-Embedding-4B")
-#models['qwen3-embedding-4b'] = SentenceTransformer('Qwen/Qwen3-Embedding-4B', trust_remote_code=True)
+#models['qwen3-embedding-4b'] = SentenceTransformer('Qwen/Qwen3-Embedding-4B', trust_remote_code=True, cache_folder=TRANSFORMERS_CACHE_DIR)
 print("Loading mixedbread-ai/mxbai-embed-large-v1")
-models["mxbai-embed-large-v1"] = SentenceTransformer('mixedbread-ai/mxbai-embed-large-v1')
+models["mxbai-embed-large-v1"] = SentenceTransformer('mixedbread-ai/mxbai-embed-large-v1', cache_folder=TRANSFORMERS_CACHE_DIR)
 print("All models loaded!")
 
 sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
 
-def cos_sim(a, b):
+def cos_sim(a):
-    sims = a @ b.T
+    sims = a @ a.T
-    a_norm = np.linalg.norm(a, axis=-1)
+    a_norm = np.linalg.norm(a, axis=-1, keepdims=True)
-    b_norm = np.linalg.norm(b, axis=-1)
+    sims /= a_norm
-    a_normalized = (sims.T / a_norm.T).T
+    sims /= a_norm.T
-    sims = a_normalized / b_norm
     return sims
 
 def degree_power(A, k):
@@ -79,7 +81,7 @@ def get_sentences(source_text):
 def text_rank(sentences, model_name='all-mpnet-base-v2'):
     model = models[model_name]
     vectors = model.encode(sentences)
-    adjacency = torch.tensor(cos_sim(vectors, vectors)).fill_diagonal_(0.)
+    adjacency = torch.tensor(cos_sim(vectors)).fill_diagonal_(0.)
     adjacency[adjacency < 0] = 0
     return normalized_adjacency(adjacency)
 

api/uv.lock

@@ -117,6 +117,97 @@ wheels = [
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 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773 },
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+    { name = "py-cpuinfo" },
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+    { name = "six" },
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+
+[[package]]
+name = "pytz"
+version = "2025.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f8/bf/abbd3cdfb8fbc7fb3d4d38d320f2441b1e7cbe29be4f23797b4a2b5d8aac/pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3", size = 320884 }
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+]
+
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 name = "pyyaml"
 version = "6.0.3"
@@ -802,8 +1257,14 @@ source = { editable = "." }
 dependencies = [
     { name = "flask" },
     { name = "flask-cors" },
+    { name = "gunicorn" },
+    { name = "matplotlib" },
     { name = "nltk" },
     { name = "numpy" },
+    { name = "pandas" },
+    { name = "pytest" },
+    { name = "pytest-benchmark" },
+    { name = "seaborn" },
     { name = "sentence-transformers" },
     { name = "transformers" },
 ]
@@ -812,8 +1273,14 @@ dependencies = [
 requires-dist = [
     { name = "flask", specifier = ">=2.3.2,<3.0.0" },
     { name = "flask-cors", specifier = ">=4.0.0,<5.0.0" },
+    { name = "gunicorn", specifier = ">=22.0.0,<23.0.0" },
+    { name = "matplotlib", specifier = ">=3.10.7" },
     { name = "nltk", specifier = ">=3.8.1,<4.0.0" },
     { name = "numpy", specifier = ">=1.25.0,<2.0.0" },
+    { name = "pandas", specifier = ">=2.3.3" },
+    { name = "pytest", specifier = ">=8.4.2" },
+    { name = "pytest-benchmark", specifier = ">=5.2.0" },
+    { name = "seaborn", specifier = ">=0.13.2" },
     { name = "sentence-transformers", specifier = ">=2.2.2,<3.0.0" },
     { name = "transformers", specifier = ">=4.30.2,<5.0.0" },
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+[[package]]
+name = "seaborn"
+version = "0.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
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+[[package]]
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+[[package]]
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+
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 name = "urllib3"
 version = "2.5.0"

deploy-models.sh

@@ -0,0 +1,33 @@
+#!/bin/sh
+set -eu
+
+# rsync local models_cache to server shared cache
+# reports server models not in local (manual cleanup required)
+
+ssh=deploy-peoplesgrocers-website
+base=/home/peoplesgrocers
+project=salience
+
+ssh $ssh "mkdir -p $base/$project/shared/models_cache"
+
+test -d api/models_cache || { echo 'no local api/models_cache'; exit 1; }
+
+echo "local models_cache size:"
+du -sh api/models_cache
+
+echo "syncing to $ssh:$base/$project/shared/models_cache/"
+# do not use compression because these model files are basically random data
+# the SSH connection is pretty spotting when transferring large files
+rsync -va --info=progress2 --partial --append-verify api/models_cache/ $ssh:$base/$project/shared/models_cache/
+
+# local models
+local=$(cd api/models_cache && find . -maxdepth 2 -name 'models--*' -type d | sed 's|./||' | sort)
+
+# server models
+remote=$(ssh $ssh "cd $base/$project/shared/models_cache && find . -maxdepth 2 -name 'models--*' -type d | sed 's|./||'" | sort)
+
+# report server models not in local
+echo "checking for unused models on server..."
+for r in $remote; do
+	echo "$local" | grep -q "^${r}$" || echo "unused: $r"
+done

deploy.sh

@@ -0,0 +1,301 @@
+#!/bin/sh
+set -eu
+
+# rsync wrapper for unreliable network connections
+# my home network is spotty and rsync often dies mid-transfer
+# this lets me manually retry the failed rsync and continue the deploy
+rsync_retry() {
+	if ! rsync "$@"; then
+		echo ""
+		echo "rsync failed (probably network). run this manually:"
+		echo "rsync $*"
+		echo ""
+		printf "press enter after manual rsync completes to continue..."
+		read _
+	fi
+}
+
+# Deployment topology and zero-downtime process
+#
+# Directory structure on remote:
+#   $base/$project/
+#     base_port            - starting port number (default 3100)
+#     releases/{stamp}_{hash}/
+#       dist/              - static assets served by nginx 
+#       server/            - node server from frontend/server/entrypoint.express.js
+#       api/               - python flask API (salience/, nltk_data/, etc.)
+#       assigned_port      - port allocated to frontend for this release
+#       assigned_api_port  - port allocated to API for this release
+#     current -> releases/{latest}
+#     systemd/             - unit files per release (frontend + API)
+#
+# Zero-downtime deployment:
+#   1. rsync new release (frontend/dist/ + frontend/server/ + api/)
+#   2. install dependencies (npm for frontend, uv for API)
+#   3. allocate two ports (frontend + API via get-next-port.sh)
+#   4. generate systemd units for new release with unique ports
+#      - frontend: node server
+#      - API: gunicorn with 4 workers running Flask app
+#   5. start new services, wait for health
+#   6. update nginx upstream to point to new ports
+#   7. reload nginx (graceful, no dropped connections)
+#   8. stop old services
+#   9. cleanup old releases (keep 3 most recent)
+#
+# Port allocation: get-next-port.sh reads base_port and existing
+# assigned_port files to find first available port.
+# Each release runs independently until cutover.
+
+ssh=deploy-peoplesgrocers-website
+base=/home/peoplesgrocers
+project=salience
+nginx_conf=/etc/nginx/sites-available/$project
+service_listen_address=127.10.143.212
+local_nginx_snippet="$HOME/src/work/infra/servers/chicago-web01/nginx/snippets/qwik-city-apps/salience.conf"
+
+test -d frontend/dist || { echo 'no frontend/dist/'; exit 1; }
+test -d frontend/server || { echo 'no frontend/server/'; exit 1; }
+test -d .git || { echo 'not a git repo'; exit 1; }
+git diff-index --quiet HEAD || { echo 'git repo dirty'; exit 1; }
+
+hash=$(git rev-parse --short=8 HEAD)
+stamp=$(date +%Y-%b-%d-%a-%I-%M%p | tr 'APM' 'apm')
+release="${stamp}_${hash}"
+service_name="${project}-${release}"
+
+echo "deploying: $project @ $release"
+printf "continue? [y/n] "
+read ans
+test "$ans" = "y" || exit 1
+
+# prepare remote directories
+ssh $ssh "mkdir -p $base/$project/{releases,systemd} $base/$project/releases/$release"
+
+# sync both dist and server
+echo "syncing dist..."
+rsync_retry -tvaz frontend/dist/ $ssh:$base/$project/releases/$release/dist/
+echo "syncing server..."
+rsync_retry -tvaz frontend/server/ $ssh:$base/$project/releases/$release/server/
+
+# copy server package.json and install dependencies
+echo "copying server package.json..."
+scp frontend/package.json $ssh:$base/$project/releases/$release/package.json
+echo "installing server dependencies..."
+ssh $ssh "source ~/.nvm/nvm.sh && cd $base/$project/releases/$release && npm install"
+
+# sync api directory (exclude benchmarks, include specific files/dirs)
+echo "syncing api..."
+rsync_retry -tvaz \
+  --include='salience/' --include='salience/**' \
+  --include='nltk_data/' --include='nltk_data/**' \
+  --include='pyproject.toml' \
+  --include='uv.lock' \
+  --include='transcript.txt' \
+  --include='README.md' \
+  --exclude='*' \
+  api/ $ssh:$base/$project/releases/$release/api/
+
+# link to shared models cache
+echo "linking to shared models_cache..."
+ssh $ssh "mkdir -p $base/$project/shared/models_cache"
+ssh $ssh "ln -sfn ../../../shared/models_cache $base/$project/releases/$release/api/models_cache"
+
+echo "installing api dependencies..."
+ssh $ssh "cd $base/$project/releases/$release/api && ~/.local/bin/uv sync"
+
+set -x
+# determine ports for this release (frontend and api)
+port=$(sh get-next-port.sh)
+echo "frontend port for this release: $port"
+api_port=$(sh get-next-port.sh)
+echo "api port for this release: $api_port"
+
+# record port assignments
+ssh $ssh "echo $port > $base/$project/releases/$release/assigned_port"
+ssh $ssh "echo $api_port > $base/$project/releases/$release/assigned_api_port"
+
+set +x
+
+# generate systemd unit file
+unit_content="[Unit]
+Description=${project} release ${release}
+After=network.target
+
+[Service]
+Type=simple
+User=peoplesgrocers
+WorkingDirectory=$base/$project/releases/$release
+Environment=\"PORT=$port\"
+Environment=\"ORIGIN=https://peoplesgrocers.com\"
+ExecStart=/home/peoplesgrocers/.nvm/versions/node/v24.10.0/bin/node server/entry.express.js
+Restart=on-failure
+RestartSec=10
+
+[Install]
+WantedBy=multi-user.target"
+
+echo "$unit_content" | ssh $ssh "cat > $base/$project/systemd/${service_name}.service"
+
+echo ""
+echo "systemd unit created at: $base/$project/systemd/${service_name}.service"
+echo ""
+
+# generate systemd unit file for API
+api_service_name="${project}-api-${release}"
+api_unit_content="[Unit]
+Description=${project} API release ${release}
+After=network.target
+
+[Service]
+Type=simple
+User=peoplesgrocers
+WorkingDirectory=$base/$project/releases/$release/api
+Environment=\"PORT=$api_port\"
+ExecStart=$base/$project/releases/$release/api/.venv/bin/gunicorn --bind ${service_listen_address}:$api_port --workers 4 salience:app
+Restart=on-failure
+RestartSec=10
+
+[Install]
+WantedBy=multi-user.target"
+
+echo "$api_unit_content" | ssh $ssh "cat > $base/$project/systemd/${api_service_name}.service"
+
+echo ""
+echo "API systemd unit created at: $base/$project/systemd/${api_service_name}.service"
+echo ""
+
+# find old services
+old_service=$(ssh $ssh "systemctl list-units --type=service --state=running | grep '^${project}-' | grep -v 'api' | awk '{print \$1}' | head -1" || true)
+if [ -n "$old_service" ]; then
+	old_port=$(ssh $ssh "systemctl show $old_service --property=Environment" | sed -n 's/.*PORT=\([0-9]*\).*/\1/p')
+	echo "old frontend service: $old_service (port $old_port)"
+else
+	old_port=""
+	echo "no old frontend service running"
+fi
+
+old_api_service=$(ssh $ssh "systemctl list-units --type=service --state=running | grep '^${project}-api-' | awk '{print \$1}' | head -1" || true)
+if [ -n "$old_api_service" ]; then
+	old_api_port=$(ssh $ssh "systemctl show $old_api_service --property=Environment" | sed -n 's/.*PORT=\([0-9]*\).*/\1/p')
+	echo "old API service: $old_api_service (port $old_api_port)"
+else
+	old_api_port=""
+	echo "no old API service running"
+fi
+
+# Update local nginx snippet with new port
+if [ -n "$local_nginx_snippet" ] && [ -f "$local_nginx_snippet" ]; then
+	echo "updating local nginx snippet: $local_nginx_snippet"
+	if [ -n "$old_port" ]; then
+		echo "  changing port $old_port -> $port"
+		sed -i.bak "s/${service_listen_address}:${old_port}/${service_listen_address}:${port}/g" "$local_nginx_snippet"
+	else
+		echo "  setting port to $port"
+		sed -i.bak "s/${service_listen_address}:[0-9]\{4,5\}/${service_listen_address}:${port}/g" "$local_nginx_snippet"
+	fi
+	rm -f "${local_nginx_snippet}.bak"
+	echo "nginx snippet updated locally"
+else
+	echo "warning: local_nginx_snippet not set or file not found"
+fi
+
+echo ""
+echo "--- run these commands on $ssh ---"
+echo ""
+echo "# install and start new services"
+echo "sudo ln -sf $base/$project/systemd/${service_name}.service /etc/systemd/system/"
+echo "sudo ln -sf $base/$project/systemd/${api_service_name}.service /etc/systemd/system/"
+echo "sudo systemctl daemon-reload"
+echo "sudo systemctl start ${service_name}"
+echo "sudo systemctl start ${api_service_name}"
+echo ""
+echo "# verify services are healthy"
+echo "sudo systemctl status ${service_name}"
+echo "sudo systemctl status ${api_service_name}"
+echo "curl http://${service_listen_address}:$port/"
+echo "curl http://${service_listen_address}:$api_port/models"
+echo ""
+echo "# then deploy your nginx configuration and reload nginx"
+echo ""
+
+if [ -n "$old_service" ]; then
+	echo "# stop old frontend service"
+	echo "sudo systemctl stop $old_service"
+	echo ""
+fi
+
+if [ -n "$old_api_service" ]; then
+	echo "# stop old API service"
+	echo "sudo systemctl stop $old_api_service"
+	echo ""
+fi
+
+echo "# update current symlink"
+echo "ln -sfn releases/$release $base/$project/current"
+echo ""
+echo "--- end commands ---"
+echo ""
+
+printf "test health checks? [y/n] "
+read ans
+if [ "$ans" = "y" ]; then
+	echo "testing frontend..."
+	ssh $ssh "curl -v http://${service_listen_address}:$port/" || echo "frontend health check failed"
+	echo "testing API..."
+	ssh $ssh "curl -v http://${service_listen_address}:$api_port/models" || echo "API health check failed"
+fi
+
+if [ -n "$old_service" ]; then
+	echo ""
+	printf "stop old frontend service ($old_service)? [y/n] "
+	read ans
+	if [ "$ans" = "y" ]; then
+		ssh $ssh "sudo systemctl stop $old_service"
+		echo "old frontend service stopped"
+	fi
+fi
+
+if [ -n "$old_api_service" ]; then
+	echo ""
+	printf "stop old API service ($old_api_service)? [y/n] "
+	read ans
+	if [ "$ans" = "y" ]; then
+		ssh $ssh "sudo systemctl stop $old_api_service"
+		echo "old API service stopped"
+	fi
+fi
+
+echo ""
+printf "update current symlink? [y/n] "
+read ans
+if [ "$ans" = "y" ]; then
+	ssh $ssh "ln -sfn releases/$release $base/$project/current"
+	echo "current -> $release"
+fi
+
+echo ""
+echo "cleanup old releases (keep 3):"
+old_releases=$(ssh $ssh "cd $base/$project/releases && ls -t | sed -n '4,\$p'" || true)
+if [ -n "$old_releases" ]; then
+	echo "$old_releases"
+	printf "remove these? [y/n] "
+	read ans
+	if [ "$ans" = "y" ]; then
+		ssh $ssh "cd $base/$project/releases && ls -t | sed -n '4,\$p' | while read r; do
+			rm -rf \"\$r\"
+			sudo systemctl stop ${project}-\${r} 2>/dev/null || true
+			sudo systemctl stop ${project}-api-\${r} 2>/dev/null || true
+			sudo rm -f /etc/systemd/system/${project}-\${r}.service
+			sudo rm -f /etc/systemd/system/${project}-api-\${r}.service
+			rm -f $base/$project/systemd/${project}-\${r}.service
+			rm -f $base/$project/systemd/${project}-api-\${r}.service
+		done"
+		ssh $ssh "sudo systemctl daemon-reload"
+		echo "cleanup done"
+	fi
+else
+	echo "no old releases to clean"
+fi
+
+echo ""
+echo "done: $release (frontend port $port, API port $api_port)"

frontend/README.md

@@ -63,3 +63,23 @@ The production build will generate client and server modules by running both cli
 ```shell
 npm run build # or `yarn build`
 ```
+
+## Static Site Generator (Node.js)
+
+Be sure to configure your server to serve very long cache headers for the `build/**/*.js` files.
+
+Typically you'd set the `Cache-Control` header for those files to `public, max-age=31536000, immutable`.
+
+```shell
+npm run build.server
+```
+
+## Express Server
+
+This app has a minimal [Express server](https://expressjs.com/) implementation. After running a full build, you can preview the build using the command:
+
+```
+npm run serve
+```
+
+Then visit [http://localhost:8080/](http://localhost:8080/)

frontend/adapters/express/vite.config.ts

@@ -0,0 +1,15 @@
+import { nodeServerAdapter } from "@builder.io/qwik-city/adapters/node-server/vite";
+import { extendConfig } from "@builder.io/qwik-city/vite";
+import baseConfig from "../../vite.config";
+
+export default extendConfig(baseConfig, () => {
+  return {
+    build: {
+      ssr: true,
+      rollupOptions: {
+        input: ["src/entry.express.tsx", "@qwik-city-plan"],
+      },
+    },
+    plugins: [nodeServerAdapter({ name: "express" })],
+  };
+});

frontend/adapters/static/vite.config.ts

@@ -0,0 +1,24 @@
+import { staticAdapter } from "@builder.io/qwik-city/adapters/static/vite";
+import { extendConfig } from "@builder.io/qwik-city/vite";
+import baseConfig from "../../vite.config";
+
+export default extendConfig(baseConfig, () => {
+  return {
+    build: {
+      ssr: true,
+      rollupOptions: {
+        input: ["@qwik-city-plan"],
+      },
+    },
+    plugins: [
+      staticAdapter({
+        origin: "https://peoplesgrocers.com",
+        // Specify which routes to statically generate
+        routes: [
+          "/about",
+          // Add more static routes here as needed
+        ],
+      }),
+    ],
+  };
+});

frontend/package.json

@@ -11,6 +11,8 @@
     "build": "qwik build",
     "build.client": "vite build",
     "build.preview": "vite build --ssr src/entry.preview.tsx",
+    "build.server": "qwik check-client src dist && vite build -c adapters/express/vite.config.ts",
+    "build.static": "vite build -c adapters/static/vite.config.ts",
     "build.types": "tsc --incremental --noEmit",
     "deploy": "echo 'Run \"npm run qwik add\" to install a server adapter'",
     "dev": "vite --mode ssr",
@@ -19,6 +21,7 @@
     "fmt.check": "prettier --check .",
     "lint": "eslint \"src/**/*.ts*\"",
     "preview": "qwik build preview && vite preview --open",
+    "serve": "node server/entry.express",
     "start": "vite --open --mode ssr",
     "qwik": "qwik"
   },
@@ -27,7 +30,11 @@
     "@builder.io/qwik-city": "^1.17.1",
     "@eslint/js": "latest",
     "@tailwindcss/vite": "^4.1.16",
+    "@types/compression": "^1.7.2",
+    "@types/express": "^4.17.19",
     "@types/node": "20.19.0",
+    "compression": "^1.7.4",
+    "dotenv": "^16.3.2",
     "eslint": "9.32.0",
     "eslint-plugin-qwik": "^1.17.1",
     "globals": "16.4.0",
@@ -42,6 +49,7 @@
   },
   "dependencies": {
     "@tailwindcss/typography": "^0.5.19",
+    "express": "4.20.0",
     "mathjs": "^15.0.0",
     "prosemirror-commands": "^1.7.1",
     "prosemirror-keymap": "^1.2.3",

frontend/src/entry.express.tsx

@@ -0,0 +1,73 @@
+/*
+ * WHAT IS THIS FILE?
+ *
+ * It's the entry point for the Express HTTP server when building for production.
+ *
+ * Learn more about Node.js server integrations here:
+ * - https://qwik.dev/docs/deployments/node/
+ *
+ */
+import {
+  createQwikCity,
+  type PlatformNode,
+} from "@builder.io/qwik-city/middleware/node";
+import "dotenv/config";
+import qwikCityPlan from "@qwik-city-plan";
+import render from "./entry.ssr";
+import express from "express";
+import { fileURLToPath } from "node:url";
+import { join } from "node:path";
+
+declare global {
+  type QwikCityPlatform = PlatformNode;
+}
+
+// Directories where the static assets are located
+const distDir = join(fileURLToPath(import.meta.url), "..", "..", "dist");
+const buildDir = join(distDir, "build");
+const assetsDir = join(distDir, "assets");
+
+// Allow for dynamic port
+const PORT = process.env.PORT ?? 3000;
+
+// Create the Qwik City Node middleware
+const { router, notFound } = createQwikCity({
+  render,
+  qwikCityPlan,
+  // getOrigin(req) {
+  //   // If deploying under a proxy, you may need to build the origin from the request headers
+  //   // https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-Forwarded-Proto
+  //   const protocol = req.headers["x-forwarded-proto"] ?? "http";
+  //   // https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-Forwarded-Host
+  //   const host = req.headers["x-forwarded-host"] ?? req.headers.host;
+  //   return `${protocol}://${host}`;
+  // }
+});
+
+// Create the express server
+// https://expressjs.com/
+const app = express();
+
+// Enable gzip compression
+// app.use(compression());
+
+// Static asset handlers
+// https://expressjs.com/en/starter/static-files.html
+app.use(`/build`, express.static(buildDir, { immutable: true, maxAge: "1y" }));
+app.use(
+  `/assets`,
+  express.static(assetsDir, { immutable: true, maxAge: "1y" }),
+);
+app.use(express.static(distDir, { redirect: false }));
+
+// Use Qwik City's page and endpoint request handler
+app.use(router);
+
+// Use Qwik City's 404 handler
+app.use(notFound);
+
+// Start the express server
+app.listen(PORT, () => {
+  /* eslint-disable */
+  console.log(`Server started: http://localhost:${PORT}/`);
+});

frontend/src/routes/about/index.mdx

@@ -6,21 +6,49 @@ import { Math } from "~/components/math/math"
 
 # How Salience Works
 
+A couple of days ago I came across
+[github.com/mattneary/salience](https://github.com/mattneary/salience) by Matt Neary. I thought it
+was quite neat how he took sentence embeddings and in just a few lines of code
+was able to determine the significance of all sentences in a document.
+
+This post is an outsider's view of how salience works. If you're already working with ML models in Python, this will feel
+torturously detailed. I wrote this for the rest of us old world programmers: compilers, networking, systems programming looking at
+C++/Go/Rust, or the poor souls in the frontend Typescript mines.
+For us refugees of the barbarian past, the tooling and notation can look foreign. I wanted to walk through the math and
+numpy operations in detail to show what's actually happening with the data.
+
 Salience highlights important sentences by treating your document as a graph where sentences that talk about similar things are connected. We then figure out which sentences are most "central" to the document's themes.
 
 ## Step 1: Break Text into Sentences
 
-We use NLTK's Punkt tokenizer to split text into sentences. This handles tricky cases where simple punctuation splitting fails:
+The first problem we need to solve is finding the sentences in a document. This is not as easy as splitting on newlines or periods. Consider this example:
 
 *"Dr. Smith earned his Ph.D. in 1995."* ← This is **one** sentence, not three!
 
-## Step 2: Convert Sentences to Embeddings
+Fortunately, this problem has been adequately solved for decades. We are going to use the **Punkt sentence splitter** (2003) available in the Natural Language Toolkit (NLTK) Python package.
 
-Now we have <Math tex="N" /> sentences. We convert each one into a high-dimensional vector that captures its meaning:
+## Step 2: Apply an Embedding Model
 
-<Math display tex="\mathbf{E} = \text{model.encode}(\text{sentences}) \in \mathbb{R}^{N \times D}" />
+Now we have <Math tex="N" /> sentences. We convert each one into a high-dimensional vector that captures its meaning. For example:
 
-This gives us an **embeddings matrix** <Math tex="\mathbf{E}" /> where each row is one sentence:
+<Math tex="\mathbf{Sentence \space A} = [a_1, a_2, a_3, \ldots, a_D]" display="block" />
+<Math tex="\mathbf{Sentence \space B} = [b_1, b_2, b_3, \ldots, b_D]" display="block" />
+<Math tex="\mathbf{Sentence \space C} = [c_1, c_2, c_3, \ldots, c_D]" display="block" />
+
+## Step 3: Build the Adjacency Matrix
+
+Now we create a new  <Math tex="N \times N" /> adjacency matrix <Math tex="\mathbf{A}" /> that measures how similar each pair of sentences is. For every pair of sentences <Math tex="i" /> and <Math tex="j" />, we need the **cosine similarity**:
+
+<Math display tex="A_{ij} = \frac{\mathbf{e}_i \cdot \mathbf{e}_j}{\|\mathbf{e}_i\| \|\mathbf{e}_j\|}" />
+
+Each <Math tex="A_{ij}" /> represents how strongly sentence <Math tex="i" /> is connected to sentence <Math tex="j" />.
+- <Math tex="A_{ij} = 1" /> means sentences are identical in meaning
+- <Math tex="A_{ij} = 0" /> means sentences are unrelated
+- <Math tex="A_{ij} = -1" /> means sentences are opposite in meaning
+
+
+
+You could work with these embedding vectors one at a time, using two for loops to build the adjacency matrix leetcode style. However, there's a way to delegate the computation to optimized libraries. Instead, organize all embeddings into a single matrix:
 
 <Math display tex="\mathbf{E} = \begin{bmatrix} a_1 & a_2 & a_3 & \cdots & a_D \\ b_1 & b_2 & b_3 & \cdots & b_D \\ c_1 & c_2 & c_3 & \cdots & c_D \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ z_1 & z_2 & z_3 & \cdots & z_D \end{bmatrix}" />
 
@@ -29,38 +57,59 @@ Where:
 - <Math tex="D" /> = embedding dimension (768 for all-mpnet-base-v2, 1024 for gte-large-en-v1.5)
 - Each row represents one sentence in semantic space
 
-## Step 3: Build the Adjacency Matrix
+**Step 3a: Compute all dot products**
 
-Now we create a new matrix <Math tex="\mathbf{A}" /> that measures how similar each pair of sentences is. For every pair of sentences <Math tex="i" /> and <Math tex="j" />, we compute:
+<Math display tex="\mathbf{S} = \mathbf{E} \mathbf{E}^T" />
 
-<Math display tex="A_{ij} = \frac{\mathbf{e}_i \cdot \mathbf{e}_j}{\|\mathbf{e}_i\| \|\mathbf{e}_j\|}" />
+Since <Math tex="\mathbf{E}" /> is <Math tex="N \times D" /> and <Math tex="\mathbf{E}^T" /> is <Math tex="D \times N" />, their product gives us an <Math tex="N \times N" /> matrix where entry <Math tex="S_{ij} = \mathbf{e}_i \cdot \mathbf{e}_j" />.
 
-This is the **cosine similarity** between their embedding vectors. It tells us:
+**Step 3b: Compute the norms and normalize**
-- <Math tex="A_{ij} = 1" /> means sentences are identical in meaning
+
-- <Math tex="A_{ij} = 0" /> means sentences are unrelated
+First, compute a vector of norms:
-- <Math tex="A_{ij} = -1" /> means sentences are opposite in meaning
+
+<Math display tex="\mathbf{n} = \begin{bmatrix} \|\mathbf{e}_1\| \\ \|\mathbf{e}_2\| \\ \|\mathbf{e}_3\| \\ \vdots \\ \|\mathbf{e}_N\| \end{bmatrix}" />
+
+This is an <Math tex="(N, 1)" /> vector where each element is the magnitude of one sentence's embedding. Now we need to visit every single element of <Math tex="\mathbf{S}" /> to make the adjacency matrix <Math tex="A_{ij} = \frac{S_{ij}}{n_i \cdot n_j}" />:
+
+<Math display tex="\mathbf{A} = \begin{bmatrix} \frac{S_{11}}{n_1 \cdot n_1} & \frac{S_{12}}{n_1 \cdot n_2} & \cdots & \frac{S_{1N}}{n_1 \cdot n_N} \\ \frac{S_{21}}{n_2 \cdot n_1} & \frac{S_{22}}{n_2 \cdot n_2} & \cdots & \frac{S_{2N}}{n_2 \cdot n_N} \\ \vdots & \vdots & \ddots & \vdots \\ \frac{S_{N1}}{n_N \cdot n_1} & \frac{S_{N2}}{n_N \cdot n_2} & \cdots & \frac{S_{NN}}{n_N \cdot n_N} \end{bmatrix}" />
+
+**Quick benchmark:** For a <Math tex="194 \times 768" /> embeddings matrix (194 sentences):
+
+- Computing everything in Python for loops: **33.1 ms**
+- Using <Math tex="\mathbf{E} \mathbf{E}^T" /> for dot products, but element-by-element normalization in Python: **10.9 ms** (saves 22.2 ms)
+- Using numpy **broadcasting** for normalization too: **0.13 ms**
+
+Broadcasting is a numpy feature where dividing arrays of different shapes automatically "stretches" the smaller array to match:
+
+```python
+def cos_sim(a):
+    sims = a @ a.T
+    norms = np.linalg.norm(a, axis=-1, keepdims=True)
+    sims /= norms      # Divides each row i by norm[i]
+    sims /= norms.T    # Divides each column j by norm[j]
+    return sims
+```
+
+The `keepdims=True` makes `norms` shape <Math tex="(N, 1)" /> instead of <Math tex="(N,)" />, which is crucial—when transposed, <Math tex="(N, 1)" /> becomes <Math tex="(1, N)" />, allowing the broadcasting to work for column-wise division.
 
-The result is an <Math tex="N \times N" /> **adjacency matrix** where <Math tex="A_{ij}" /> represents how strongly sentence <Math tex="i" /> is connected to sentence <Math tex="j" />.
 
 ## Step 4: Clean Up the Graph
 
-We make two adjustments to the adjacency matrix to get a cleaner graph:
+We make two adjustments to the adjacency matrix to make our TextRank work:
 
 1. **Remove self-loops:** Set diagonal to zero (<Math tex="A_{ii} = 0" />)
-   - A sentence shouldn't vote for its own importance
-
 2. **Remove negative edges:** Set <Math tex="A_{ij} = \max(0, A_{ij})" />
-   - Sentences with opposite meanings get disconnected
    
-**Important assumption:** This assumes your document has a coherent main idea and that sentences are generally on-topic. We're betting that the topic with the most "semantic mass" is the *correct* topic.
+A sentence shouldn't vote for its own importance. And sentences with opposite meanings get disconnected.
 
-**Where this breaks down:**
+**Important assumption:** This assumes your document has a coherent main idea and that sentences are generally on-topic. We're betting that the topic with the most "semantic mass" is the *correct* topic. This is obviously not true for many documents:
-- **Dialectical essays** that deliberately contrast opposing viewpoints
-- **Documents heavy with quotes** that argue against something
-- **Debate transcripts** where both sides are equally important
-- **Critical analysis** that spends significant time explaining a position before refuting it
 
-For example: "Nuclear power is dangerous. Critics say it causes meltdowns. However, modern reactors are actually very safe."
+- Dialectical essays that deliberately contrast opposing viewpoints
+- Documents heavy with quotes that argue against something
+- Debate transcripts where both sides are equally important
+- Critical analysis that spends significant time explaining a position before refuting it
+
+For example: "Nuclear power is dangerous. Critics say it causes meltdowns [...]. However, modern reactors are actually very safe."
 
 The algorithm might highlight the criticism because multiple sentences cluster around "danger", even though the document's actual position is pro-nuclear. There's nothing inherent in the math that identifies authorial intent vs. quoted opposition.
 

frontend/src/routes/index.tsx

@@ -2,7 +2,6 @@ import {
   component$,
   useSignal,
   useVisibleTask$,
-  useComputed$,
   $,
   noSerialize,
   useStore,
@@ -12,7 +11,7 @@ import {
 import { type DocumentHead, routeLoader$ } from "@builder.io/qwik-city";
 import { EditorState, Plugin, Transaction } from "prosemirror-state";
 import { EditorView, Decoration, DecorationSet } from "prosemirror-view";
-import { Schema, DOMParser, Node as PMNode } from "prosemirror-model";
+import { Schema, Node as PMNode } from "prosemirror-model";
 import { schema as basicSchema } from "prosemirror-schema-basic";
 import { keymap } from "prosemirror-keymap";
 import { baseKeymap } from "prosemirror-commands";
@@ -123,7 +122,7 @@ export default component$(() => {
   const models = useSignal<string[]>([]);
   const currentModel = useSignal("all-mpnet-base-v2");
   const syncState = useSignal<SyncState>("clean");
-  const editorView = useSignal<EditorView | null>(null);
+  const editorView = useSignal<NoSerialize<EditorView>>();
   const sentenceDecorations = useSignal<SentenceDecoration[]>([]);
   const salienceData = useSignal<SalienceData | null>(null);
   const debounceTimer = useSignal<number | null>(null);
@@ -224,15 +223,20 @@ export default component$(() => {
 
     // Parse the initial document text into separate paragraphs
     // Split by single newlines and preserve blank lines as empty paragraphs
-    const paragraphs = initialDocument.value
+    const lines = initialDocument.value
-      .split('\n')
+      .split('\n');
-      .map(line => {
+
+    const paragraphs: PMNode[] = [];
+    lines.forEach((line) => {
       // Create paragraph with text if line has content, otherwise empty paragraph
       const content = line.length > 0 ? [salienceSchema.text(line)] : [];
-        return salienceSchema.node("paragraph", null, content);
+      //content.push(salienceSchema.node("hard_break"));
+      paragraphs.push(salienceSchema.node("paragraph", null, content));
     });
+    console.log(paragraphs);
 
     const initialDoc = salienceSchema.node("doc", null, paragraphs);
+    console.log(initialDoc.textContent);
 
     const state = EditorState.create({
       schema: salienceSchema,
@@ -272,7 +276,7 @@ export default component$(() => {
       },
     });
 
-    editorView.value = view;
+    editorView.value = noSerialize(view);
 
     fetchSalienceData(
       currentModel.value,

get-next-port.sh

@@ -0,0 +1,51 @@
+#!/bin/sh
+set -eu
+
+# Port allocation for zero-downtime deployments
+#
+# Reads remote state to determine next available port.
+# State files:
+#   $base/$project/base_port           - starting port number
+#   $base/$project/releases/*/assigned_port - port for each release
+#
+# Algorithm:
+#   1. read base_port (default 3100)
+#   2. find all assigned_port files in releases/
+#   3. return smallest unused port >= base_port
+
+ssh=deploy-peoplesgrocers-website
+base=/home/peoplesgrocers
+project=salience
+
+ssh $ssh "
+set -eu
+
+base=$base
+project=$project
+
+test -d \$base/\$project || { echo 'project dir does not exist' >&2; exit 1; }
+
+# read or initialize base_port
+if test -f \$base/\$project/base_port; then
+	base_port=\$(cat \$base/\$project/base_port)
+else
+	base_port=3100
+	echo \$base_port > \$base/\$project/base_port
+fi
+
+# find all assigned ports
+assigned=\$(find \$base/\$project/releases -name assigned_port -exec cat {} \; 2>/dev/null | sort -n || true)
+
+# find first unused port
+port=\$base_port
+while true; do
+	found=0
+	for p in \$assigned; do
+		test \$p -eq \$port && { found=1; break; }
+	done
+	test \$found -eq 0 && break
+	port=\$((port + 1))
+done
+
+echo \$port
+"