ironclaw 0.22.0

Secure personal AI assistant that protects your data and expands its capabilities on the fly
Documentation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
//! LRU embedding cache wrapping any [`EmbeddingProvider`].
//!
//! Avoids redundant HTTP calls for identical texts by caching embeddings
//! in memory keyed by `SHA-256(model_name + "\0" + text)`.
//!
//! Uses `lru::LruCache` for O(1) insertion, lookup, and eviction.

use std::num::NonZeroUsize;
use std::sync::{Arc, Mutex};

use async_trait::async_trait;
use lru::LruCache;
use sha2::{Digest, Sha256};

use crate::workspace::embeddings::{EmbeddingError, EmbeddingProvider};

/// Configuration for the embedding cache.
#[derive(Debug, Clone)]
pub struct EmbeddingCacheConfig {
    /// Maximum number of cached embeddings (default 10,000).
    ///
    /// Approximate raw embedding payload: `max_entries × dimension × 4 bytes`.
    /// At 10,000 entries × 1536 floats ≈ 58 MB (payload only; actual memory
    /// is higher due to per-entry overhead in the linked-list LRU).
    pub max_entries: usize,
}

impl Default for EmbeddingCacheConfig {
    fn default() -> Self {
        Self {
            max_entries: crate::config::DEFAULT_EMBEDDING_CACHE_SIZE,
        }
    }
}

/// Embedding provider wrapper that caches results in memory.
///
/// Thread-safe via `std::sync::Mutex`. The lock is **never held**
/// across `.await` points (all critical sections are scoped blocks),
/// so a synchronous mutex is cheaper than `tokio::sync::Mutex`.
pub struct CachedEmbeddingProvider {
    inner: Arc<dyn EmbeddingProvider>,
    cache: Mutex<LruCache<[u8; 32], Vec<f32>>>,
}

impl CachedEmbeddingProvider {
    /// Wrap a provider with LRU caching.
    ///
    /// `config.max_entries` is clamped to at least 1.
    pub fn new(inner: Arc<dyn EmbeddingProvider>, config: EmbeddingCacheConfig) -> Self {
        let max_entries = config.max_entries.max(1);
        if max_entries > 100_000 {
            tracing::warn!(
                max_entries,
                "Embedding cache size exceeds 100,000 entries; memory usage may be significant"
            );
        }
        // safety: max_entries >= 1 due to .max(1) above
        let cap = NonZeroUsize::new(max_entries).expect("clamped to >= 1"); // safety: always >= 1
        Self {
            inner,
            cache: Mutex::new(LruCache::new(cap)),
        }
    }

    /// Number of entries currently in the cache.
    pub fn len(&self) -> usize {
        self.cache.lock().unwrap_or_else(|e| e.into_inner()).len()
    }

    /// Whether the cache is empty.
    pub fn is_empty(&self) -> bool {
        self.cache
            .lock()
            .unwrap_or_else(|e| e.into_inner())
            .is_empty()
    }

    /// Clear all cached entries.
    pub fn clear(&self) {
        self.cache.lock().unwrap_or_else(|e| e.into_inner()).clear();
    }

    /// Build a deterministic cache key: `SHA-256(model_name + "\0" + text)`.
    ///
    /// Returns raw 32-byte hash to avoid a 64-char hex String allocation per lookup.
    fn cache_key(&self, text: &str) -> [u8; 32] {
        let mut hasher = Sha256::new();
        hasher.update(self.inner.model_name().as_bytes());
        hasher.update(b"\0");
        hasher.update(text.as_bytes());
        hasher.finalize().into()
    }
}

#[async_trait]
impl EmbeddingProvider for CachedEmbeddingProvider {
    fn dimension(&self) -> usize {
        self.inner.dimension()
    }

    fn model_name(&self) -> &str {
        self.inner.model_name()
    }

    fn max_input_length(&self) -> usize {
        self.inner.max_input_length()
    }

    async fn embed(&self, text: &str) -> Result<Vec<f32>, EmbeddingError> {
        let key = self.cache_key(text);

        // Check cache (short critical section). LruCache::get promotes the
        // entry to most-recently-used automatically.
        {
            let mut guard = self.cache.lock().unwrap_or_else(|e| e.into_inner());
            if let Some(embedding) = guard.get(&key) {
                tracing::trace!("embedding cache hit");
                return Ok(embedding.clone());
            }
        }
        // Lock released before HTTP call.
        // NOTE: Thundering herd — multiple concurrent callers with the same
        // uncached key will each call the inner provider. This is acceptable:
        // embeddings are idempotent and the last writer wins in the LruCache.

        let embedding = self.inner.embed(text).await?;

        // Store result under lock. Re-check first: another concurrent caller
        // may have already cached this key while the lock was released.
        {
            let mut guard = self.cache.lock().unwrap_or_else(|e| e.into_inner());
            if guard.get(&key).is_some() {
                // Thundering herd — another caller beat us. LruCache::get
                // already promoted it to most-recently-used; skip the clone.
                tracing::trace!("embedding cache: concurrent insert, skipping clone");
            } else {
                guard.push(key, embedding.clone());
            }
        }

        tracing::trace!("embedding cache miss");
        Ok(embedding)
    }

    async fn embed_batch(&self, texts: &[String]) -> Result<Vec<Vec<f32>>, EmbeddingError> {
        if texts.is_empty() {
            return Ok(Vec::new());
        }

        // Partition into hits and misses
        let keys: Vec<[u8; 32]> = texts.iter().map(|t| self.cache_key(t)).collect();
        let mut results: Vec<Option<Vec<f32>>> = vec![None; texts.len()];
        let mut miss_indices: Vec<usize> = Vec::new();

        {
            let mut guard = self.cache.lock().unwrap_or_else(|e| e.into_inner());
            for (i, key) in keys.iter().enumerate() {
                if let Some(embedding) = guard.get(key) {
                    results[i] = Some(embedding.clone());
                } else {
                    miss_indices.push(i);
                }
            }
        }
        // Lock released before HTTP call

        if miss_indices.is_empty() {
            tracing::trace!(count = texts.len(), "embedding batch: all cache hits");
            return results
                .into_iter()
                .enumerate()
                .map(|(i, slot)| {
                    slot.ok_or_else(|| {
                        EmbeddingError::InvalidResponse(format!(
                            "embedding slot {i} was not populated"
                        ))
                    })
                })
                .collect::<Result<Vec<_>, _>>();
        }

        // Fetch missing embeddings
        let miss_texts: Vec<String> = miss_indices.iter().map(|&i| texts[i].clone()).collect();
        let new_embeddings = self.inner.embed_batch(&miss_texts).await?;

        if new_embeddings.len() != miss_indices.len() {
            return Err(EmbeddingError::InvalidResponse(format!(
                "embed_batch returned {} embeddings, expected {}",
                new_embeddings.len(),
                miss_indices.len()
            )));
        }

        tracing::trace!(
            hits = texts.len() - miss_indices.len(),
            misses = miss_indices.len(),
            "embedding batch: partial cache"
        );

        // Cache only the last `cap` new embeddings — caching more than the
        // cache capacity wastes clone work on entries that are immediately evicted.
        {
            let mut guard = self.cache.lock().unwrap_or_else(|e| e.into_inner());
            let cap = guard.cap().get();
            let skip = miss_indices.len().saturating_sub(cap);
            for (&orig_idx, emb) in miss_indices[skip..].iter().zip(&new_embeddings[skip..]) {
                guard.push(keys[orig_idx], emb.clone());
            }
        }

        // Move originals into results (zero-copy).
        for (orig_idx, emb) in miss_indices.iter().copied().zip(new_embeddings) {
            results[orig_idx] = Some(emb);
        }

        results
            .into_iter()
            .enumerate()
            .map(|(i, slot)| {
                slot.ok_or_else(|| {
                    EmbeddingError::InvalidResponse(format!("embedding slot {i} was not populated"))
                })
            })
            .collect()
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::sync::atomic::{AtomicU32, Ordering};

    /// Mock embedding provider that counts calls.
    struct CountingMock {
        dimension: usize,
        model: String,
        embed_calls: AtomicU32,
        batch_calls: AtomicU32,
    }

    impl CountingMock {
        fn new(dimension: usize, model: &str) -> Self {
            Self {
                dimension,
                model: model.to_string(),
                embed_calls: AtomicU32::new(0),
                batch_calls: AtomicU32::new(0),
            }
        }

        fn embed_calls(&self) -> u32 {
            self.embed_calls.load(Ordering::SeqCst)
        }

        fn batch_calls(&self) -> u32 {
            self.batch_calls.load(Ordering::SeqCst)
        }
    }

    #[async_trait]
    impl EmbeddingProvider for CountingMock {
        fn dimension(&self) -> usize {
            self.dimension
        }
        fn model_name(&self) -> &str {
            &self.model
        }
        fn max_input_length(&self) -> usize {
            10_000
        }
        async fn embed(&self, text: &str) -> Result<Vec<f32>, EmbeddingError> {
            self.embed_calls.fetch_add(1, Ordering::SeqCst);
            // Simple deterministic embedding: val = text.len() / 100.0
            let val = text.len() as f32 / 100.0;
            Ok(vec![val; self.dimension])
        }
        async fn embed_batch(&self, texts: &[String]) -> Result<Vec<Vec<f32>>, EmbeddingError> {
            self.batch_calls.fetch_add(1, Ordering::SeqCst);
            texts
                .iter()
                .map(|t| {
                    let val = t.len() as f32 / 100.0;
                    Ok(vec![val; self.dimension])
                })
                .collect()
        }
    }

    #[tokio::test]
    async fn cache_hit_avoids_inner_call() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 100 });

        let r1 = cached.embed("hello").await.unwrap();
        assert_eq!(inner.embed_calls(), 1);

        let r2 = cached.embed("hello").await.unwrap();
        assert_eq!(inner.embed_calls(), 1); // still 1 -- cache hit
        assert_eq!(r1, r2);

        assert_eq!(cached.len(), 1);
    }

    #[tokio::test]
    async fn cache_miss_calls_inner() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 100 });

        cached.embed("hello").await.unwrap();
        cached.embed("world").await.unwrap();
        assert_eq!(inner.embed_calls(), 2);
        assert_eq!(cached.len(), 2);
    }

    #[tokio::test]
    async fn cache_key_includes_model() {
        let inner_a = Arc::new(CountingMock::new(4, "model-a"));
        let inner_b = Arc::new(CountingMock::new(4, "model-b"));

        let cached_a = CachedEmbeddingProvider::new(
            inner_a.clone(),
            EmbeddingCacheConfig { max_entries: 100 },
        );
        let cached_b = CachedEmbeddingProvider::new(
            inner_b.clone(),
            EmbeddingCacheConfig { max_entries: 100 },
        );

        // Same text, different models -> different cache keys
        let key_a = cached_a.cache_key("hello");
        let key_b = cached_b.cache_key("hello");
        assert_ne!(key_a, key_b);
    }

    #[tokio::test]
    async fn lru_eviction() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 2 });

        cached.embed("first").await.unwrap();
        cached.embed("second").await.unwrap();
        assert_eq!(cached.len(), 2);

        // Third entry should evict the oldest ("first")
        cached.embed("third").await.unwrap();
        assert_eq!(cached.len(), 2);
        assert_eq!(inner.embed_calls(), 3);

        // "first" should be a cache miss now
        cached.embed("first").await.unwrap();
        assert_eq!(inner.embed_calls(), 4);
    }

    #[tokio::test]
    async fn embed_batch_partial_hits() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 100 });

        // Pre-cache one text
        cached.embed("cached").await.unwrap();
        assert_eq!(inner.embed_calls(), 1);

        // Batch with 1 cached + 2 new
        let texts = vec![
            "cached".to_string(),
            "new_one".to_string(),
            "new_two".to_string(),
        ];
        let results = cached.embed_batch(&texts).await.unwrap();

        // Should have called embed_batch on inner for 2 misses
        assert_eq!(inner.batch_calls(), 1);
        assert_eq!(results.len(), 3);
        assert_eq!(cached.len(), 3);
    }

    #[tokio::test]
    async fn batch_preserves_order() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 100 });

        // Pre-cache "bb" (len 2)
        cached.embed("bb").await.unwrap();

        // Batch: "a" (miss, len 1), "bb" (hit, len 2), "ccc" (miss, len 3)
        let texts = vec!["a".to_string(), "bb".to_string(), "ccc".to_string()];
        let results = cached.embed_batch(&texts).await.unwrap();

        assert_eq!(results.len(), 3);
        let expected_a = vec![1.0_f32 / 100.0; 4];
        let expected_bb = vec![2.0_f32 / 100.0; 4];
        let expected_ccc = vec![3.0_f32 / 100.0; 4];
        assert_eq!(results[0], expected_a);
        assert_eq!(results[1], expected_bb);
        assert_eq!(results[2], expected_ccc);
    }

    #[tokio::test]
    async fn batch_exceeding_capacity_respects_max_entries() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 3 });

        // Batch with 5 misses but cache capacity is 3
        let texts: Vec<String> = (0..5).map(|i| format!("text_{i}")).collect();
        let results = cached.embed_batch(&texts).await.unwrap();

        assert_eq!(results.len(), 5);
        let len = cached.len();
        assert!(len <= 3, "cache len {len} exceeds max 3");
    }

    /// Mock embedding provider that fails the first N calls, then succeeds.
    struct FailThenSucceedMock {
        dimension: usize,
        model: String,
        remaining_failures: AtomicU32,
    }

    impl FailThenSucceedMock {
        fn new(dimension: usize, fail_count: u32) -> Self {
            Self {
                dimension,
                model: "fail-mock".to_string(),
                remaining_failures: AtomicU32::new(fail_count),
            }
        }
    }

    #[async_trait]
    impl EmbeddingProvider for FailThenSucceedMock {
        fn dimension(&self) -> usize {
            self.dimension
        }
        fn model_name(&self) -> &str {
            &self.model
        }
        fn max_input_length(&self) -> usize {
            10_000
        }
        async fn embed(&self, text: &str) -> Result<Vec<f32>, EmbeddingError> {
            let prev =
                self.remaining_failures
                    .fetch_update(Ordering::SeqCst, Ordering::SeqCst, |v| {
                        if v > 0 { Some(v - 1) } else { None }
                    });
            if prev.is_ok() {
                return Err(EmbeddingError::HttpError("simulated failure".to_string()));
            }
            let val = text.len() as f32 / 100.0;
            Ok(vec![val; self.dimension])
        }
        async fn embed_batch(&self, texts: &[String]) -> Result<Vec<Vec<f32>>, EmbeddingError> {
            let prev =
                self.remaining_failures
                    .fetch_update(Ordering::SeqCst, Ordering::SeqCst, |v| {
                        if v > 0 { Some(v - 1) } else { None }
                    });
            if prev.is_ok() {
                return Err(EmbeddingError::HttpError("simulated failure".to_string()));
            }
            texts
                .iter()
                .map(|t| {
                    let val = t.len() as f32 / 100.0;
                    Ok(vec![val; self.dimension])
                })
                .collect()
        }
    }

    #[tokio::test]
    async fn error_does_not_pollute_cache() {
        let inner = Arc::new(FailThenSucceedMock::new(4, 1));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 100 });

        // First call fails
        let err = cached.embed("hello").await;
        assert!(err.is_err());
        assert!(cached.is_empty(), "cache should be empty after error");

        // Second call succeeds and should call the inner provider (not serve stale error)
        let result = cached.embed("hello").await;
        assert!(result.is_ok());
        assert_eq!(cached.len(), 1);
    }

    #[tokio::test]
    async fn embed_batch_empty_input() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 100 });

        let results = cached.embed_batch(&[]).await.unwrap();
        assert!(results.is_empty());
        assert_eq!(inner.batch_calls(), 0);
    }

    #[tokio::test]
    async fn embed_batch_all_misses() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 100 });

        // Nothing cached — every text is a miss
        let texts: Vec<String> = vec!["alpha".into(), "beta".into(), "gamma".into()];
        let results = cached.embed_batch(&texts).await.unwrap();
        assert_eq!(results.len(), 3);
        assert_eq!(inner.batch_calls(), 1, "inner called once for misses");
        assert_eq!(cached.len(), 3, "all results should be cached");

        // Second call should be all hits — no new inner calls
        let results2 = cached.embed_batch(&texts).await.unwrap();
        assert_eq!(results2.len(), 3);
        assert_eq!(inner.batch_calls(), 1, "no new inner calls");
    }

    #[tokio::test]
    async fn zero_max_entries_clamped_to_one() {
        let inner = Arc::new(CountingMock::new(4, "test-model"));
        let cached =
            CachedEmbeddingProvider::new(inner.clone(), EmbeddingCacheConfig { max_entries: 0 });

        // Should behave as max_entries=1 (clamped in constructor)
        cached.embed("hello").await.unwrap();
        assert_eq!(cached.len(), 1);

        // Second entry evicts the first
        cached.embed("world").await.unwrap();
        assert_eq!(cached.len(), 1);
        assert_eq!(inner.embed_calls(), 2);
    }
}