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17 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Abhay Singh
|
f8eaaeb26a
|
fix(cache): normalize embeddings before the semantic similarity check (#2122)
## Description
The semantic tier of the dynamic-content detector compares an
unnormalized dot product against a cosine threshold, so it flags almost
everything as dynamic and strips the static content it is supposed to
protect.
`SemanticDetector` pre-computes exemplar embeddings and, per sentence,
scores similarity with `np.dot` and compares to `semantic_threshold`:
```python
self._exemplar_embeddings = self._model.encode(self.DYNAMIC_EXEMPLARS, convert_to_numpy=True)
...
sentence_embeddings = self._model.encode(sentence_texts, convert_to_numpy=True)
similarities = np.dot(sentence_embeddings, self._exemplar_embeddings.T)
...
if max_sim < self.config.semantic_threshold: # semantic_threshold defaults to 0.7
continue
```
`sentence_transformers.encode(..., convert_to_numpy=True)` does **not**
normalize by default. So `np.dot` here is an inner product whose
magnitude scales with the embedding norms (typically ~5-15 for MiniLM),
not a cosine similarity in [0, 1]. Comparing that against
`semantic_threshold=0.7` (documented and configured as a 0-1 similarity)
is a scale mismatch: nearly every sentence clears the threshold, so the
semantic tier classifies almost all text as dynamic, moves it into
`dynamic_content`, and empties `static_content` — busting the very cache
the detector exists to protect.
A standalone repro: an unrelated sentence with a true cosine of ~0.1 to
an exemplar produces a raw dot of ~9.1 (well over 0.7); normalized, it
correctly scores ~0.09 and stays static.
The correct behavior is used by the in-repo siblings:
`prediction/feature_extractor.py` passes `normalize_embeddings=True`,
and `memory/adapters/embedders.py` L2-normalizes before dot-product
similarity. This detector did neither.
## Fix
Pass `normalize_embeddings=True` to both `encode` calls (exemplars in
`__init__` and sentences in `detect`). Both sides of the dot product are
then unit vectors, so `np.dot` is a true cosine similarity in [-1, 1],
comparable to `semantic_threshold`.
Closes #
## Type of Change
- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Performance improvement
- [ ] Code refactoring (no functional changes)
## Changes Made
- `headroom/cache/dynamic_detector.py`: add `normalize_embeddings=True`
to the exemplar encode (`__init__`) and the sentence encode (`detect`),
with comments explaining the cosine requirement.
- `tests/test_cache/test_dynamic_detector.py`: add
`TestSemanticDetectorNormalization` — a recording fake model asserts
both encode calls pass `normalize_embeddings=True` (via `object.__new__`
for `detect`, and a monkeypatched registry for `__init__`). No model
download needed.
- `CHANGELOG.md`: Bug Fixes entry.
## Testing
- [x] Unit tests pass (`uv run --extra dev pytest
tests/test_cache/test_dynamic_detector.py::TestSemanticDetectorNormalization
-q`)
- [x] Linting passes (`uvx ruff@0.15.17 check
headroom/cache/dynamic_detector.py
tests/test_cache/test_dynamic_detector.py headroom/memory/factory.py`)
- [x] Type checking passes (`uvx mypy==1.20.2
headroom/memory/factory.py`)
- [x] New tests added for new functionality
- [ ] Manual testing performed
### Test Output
```text
$ uvx ruff@0.15.17 check headroom/cache/dynamic_detector.py tests/test_cache/test_dynamic_detector.py
All checks passed!
$ python -m py_compile headroom/cache/dynamic_detector.py tests/test_cache/test_dynamic_detector.py
OK
```
## Real Behavior Proof
- Environment: Windows 11, Python 3.12, `uvx ruff@0.15.17`, numpy.
Importing `headroom` pulls in the torch/transformers stack and a full
`pytest` gets OOM-killed on this box, so I verified the scale mismatch
with a dependency-free numpy script (no sentence-transformers), and left
the full pytest to CI.
- Exact command / steps: built a MiniLM-dimension exemplar direction and
a sentence direction with a true cosine of ~0.1 (genuinely not dynamic),
gave them realistic un-normalized magnitudes (~9 and ~11), and computed
the old `np.dot` of the raw vectors versus the new `np.dot` of the
normalized vectors, against the 0.7 threshold.
- Observed result: old raw dot ~9.1 (far above 0.7 -> the unrelated
sentence is wrongly flagged dynamic); new cosine ~0.09 (below 0.7 ->
correctly kept static), and always within [-1, 1]. The new tests assert
both encode calls pass `normalize_embeddings=True`.
- Not tested: a real sentence-transformers model end to end; full local
`pytest` deferred to CI (OOM, per above).
## Review Readiness
- [x] I have performed a self-review
- [x] This PR is ready for human review
## Checklist
- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
- [x] I have updated the CHANGELOG.md if applicable
## Additional Notes
Merged current `main` to pick up the repository-wide mypy cache-key
annotation fix, then verified the focused regression locally. the change
adds one keyword argument to two `encode` calls, verified by the numpy
proof and the new fake-model tests for CI. The fix brings this detector
in line with the two sibling call sites that already normalize.
Co-authored-by: JerrettDavis <mxjerrett@gmail.com>
Co-authored-by: Tejas Chopra <chopratejas@gmail.com>
|
||
|
Tejas Chopra
|
908a9a1bb1
|
fix(cache): stop DynamicContentDetector false positives corrupting cached prompts (#2110) (#2119)
## Description `DynamicContentDetector` / `RegexDetector` in `headroom/cache/dynamic_detector.py` (used by the `cache_aligner` transform) misclassified ordinary English words and code identifiers (e.g. `in_pr`) as "dynamic content," extracting them from the system prompt and re-appending a `[Dynamic Context]` tail that grows unboundedly and corrupts the cached prompt over a session. Fix tightens detection to require genuinely-dynamic shapes (timestamps, UUIDs, hashes, numbers-with-units, ISO dates) rather than bare tokens — no hardcoded wordlist — and bounds the tail. `cache_aligner` is off by default, so blast radius is limited, but the detector logic is now correct. Closes #2110 ## Type of Change - [x] Bug fix (non-breaking change that fixes an issue) ## Changes Made - `headroom/cache/dynamic_detector.py`: raise the evidence bar so ordinary words/identifiers aren't extracted; bound the dynamic tail. - `tests/test_cache/test_dynamic_detector.py`: assert false positives (ordinary words/identifiers) are NOT extracted while real dynamic values still are. ## Testing - [x] Unit tests pass (`pytest tests/test_cache/test_dynamic_detector.py`) - [x] Linting passes (`ruff check`) ### Test Output ```text 55 passed, 2 skipped ruff: All checks passed! ``` ## Real Behavior Proof - Before: identifiers like `in_pr` extracted into a growing `[Dynamic Context]` tail, corrupting cached prompts. - After: ordinary tokens stay in place; only genuinely-dynamic values are detected. |
||
|
Abhay Singh
|
cf6367add4
|
fix(cache/semantic): don't evict an unrelated entry on an update at capacity (#2094)
## Description
`SemanticCache.put` can evict a perfectly good, unrelated entry when it
merely updates a key that is already cached.
The method runs its at-capacity eviction loop *before* it computes the
entry's key:
```python
self._cleanup_expired()
# Evict if at capacity
while len(self._cache) >= self.config.max_entries:
self._evict_oldest()
...
key = messages_hash or self._generate_key(query)
...
self._cache[key] = entry
```
So when the same key is stored again while the cache is full (a
duplicate store, or a retried request that produces the same
`messages_hash`), the loop fires because `len == max_entries`, evicts
the LRU-oldest *distinct* entry, and only then overwrites the existing
key in place. Writing to an already-present key does not grow the map,
so nothing needed to be evicted — but an unrelated live entry is now
gone, and the next `get` for it is a false miss.
Concretely, with `max_entries=2` and keys `[h1, h2]`, re-storing `h2`
evicts `h1`, leaving `[h2]` even though only two distinct keys were ever
stored.
The sibling `CompressionCache.store_compressed` gets this right: it
deletes the existing key first, inserts, and only then trims — so
re-storing a present key never drops an unrelated entry.
## Fix
Compute the key first, then run the eviction loop only while the key is
genuinely new:
```python
key = messages_hash or self._generate_key(query)
while key not in self._cache and len(self._cache) >= self.config.max_entries:
self._evict_oldest()
```
An in-place update of an existing key no longer evicts anything; adding
a new key still trims to make room exactly as before.
Closes #
## Type of Change
- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Performance improvement
- [ ] Code refactoring (no functional changes)
## Changes Made
- `headroom/cache/semantic.py`: move the cache-key computation above the
eviction loop and gate the loop on `key not in self._cache` so an
in-place update never evicts.
- `tests/test_cache/test_semantic.py`: add
`test_update_at_capacity_does_not_evict_unrelated_entry`.
- `CHANGELOG.md`: Bug Fixes entry.
## Testing
- [ ] Unit tests pass (`pytest`)
- [x] Linting passes (`ruff check .`)
- [ ] Type checking passes (`mypy headroom`)
- [x] New tests added for new functionality
- [ ] Manual testing performed
### Test Output
```text
$ uvx ruff@0.15.17 check headroom/cache/semantic.py tests/test_cache/test_semantic.py
All checks passed!
$ python -m py_compile headroom/cache/semantic.py tests/test_cache/test_semantic.py
OK
```
## Real Behavior Proof
- Environment: Windows 11, Python 3.12, `uvx ruff@0.15.17`. Importing
`headroom` pulls in the torch/transformers stack and a full `pytest`
gets OOM-killed on this box, so I verified the eviction logic with a
dependency-free script that replicates the `OrderedDict` +
`_evict_oldest` (popitem last=False) behavior for the old vs new loop,
and left the full pytest to CI.
- Exact command / steps: with `max_entries=2`, store `h1` then `h2`,
then re-store the already-present `h2`, under both the old loop (evict
before key dedup) and the new loop (evict only when key is new).
- Observed result: old loop leaves `['h2']` and `get(h1)` returns `None`
(h1 wrongly evicted); new loop leaves `['h1', 'h2']` with `get(h1)`
intact and `h2` updated. The regression test asserts h1 survives and h2
reflects the update.
- Not tested: a live embedding-backed cache round-trip; full local
`pytest` deferred to CI (OOM, per above).
## Review Readiness
- [x] I have performed a self-review
- [x] This PR is ready for human review
## Checklist
- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
- [x] I have updated the CHANGELOG.md if applicable
## Additional Notes
The "unit tests pass locally" and "type checking" boxes are unchecked
because the full suite imports the ML stack, which I can't run in this
environment; the change is a localized reordering of two existing
statements plus a loop guard, verified by the standalone proof and the
new regression test for CI. This is a different defect from the earlier
messages-hash keying fix — that one was about which slot a request maps
to; this one is about eviction dropping a live entry on an in-place
update.
Co-authored-by: Tejas Chopra <chopratejas@gmail.com>
|
||
|
Abhay Singh
|
ec6e60ea3e
|
fix(proxy/anthropic): scope session id by top-level system prompt (#2070)
## Description
`SessionTrackerStore.compute_session_id`
(`headroom/cache/prefix_tracker.py`) computes a fallback
session id (when no `x-headroom-session-id` header is present) from
`model` + system-prompt text.
But it harvests system text **only** from `messages` entries with `role
== "system"`:
```python
for msg in messages:
if msg.get("role") == "system":
... # collect system text
system_content = json.dumps(system_parts, ...)
key = f"{model}:{system_content}"
```
Anthropic's `/v1/messages` carries the system prompt as a **top-level**
`body["system"]` field —
it never sends `role:"system"` entries inside `messages`. And
`x-headroom-session-id` is a
Headroom-internal header no client sends. So for every genuine Anthropic
request `system_parts`
is empty and the id collapses to `md5(f"{model}:[]")` — **every
conversation on the same model
shares one session id**, and therefore one `PrefixCacheTracker` and all
session-sticky state.
The colliding state cross-contaminates across conversations
(`anthropic.py:1052`):
- sticky `headroom_retrieve` / memory tools keyed purely on `session_id`
(no content guard) get
injected into another conversation's tool list — busting its tools cache
and adding tools its
client never requested;
- sticky `anthropic-beta` header tokens leak across conversations;
- `frozen_message_count` and the per-session compression cache
cross-contaminate.
(The sibling `StreamingMixin._get_session_key` already reads
`body.get("system")` and its docstring
claims to mirror `compute_session_id` — which it did not.)
Closes: no issue filed — found while auditing the session/prefix
tracker.
## Fix
Add an optional `system` parameter to `compute_session_id` and fold its
text (a plain string or a
list of `{"type":"text"}` blocks) into the hash. The Anthropic handler
passes `body.get("system")`.
OpenAI callers don't pass it (defaults to `None`), so their behavior is
unchanged.
## Type of Change
- [x] Bug fix (non-breaking change that fixes an issue)
## Changes Made
- `headroom/cache/prefix_tracker.py`: `compute_session_id` accepts an
optional `system` and folds it into the id.
- `headroom/proxy/handlers/anthropic.py`: pass
`system=body.get("system")` when computing the session id.
- `tests/test_cache/test_prefix_tracker.py`: add
`test_compute_session_id_distinguishes_top_level_system` (distinct
systems → distinct ids; list-form == string-form; `system=None`
unchanged).
## Testing
- [x] New regression test added
(`tests/test_cache/test_prefix_tracker.py`)
- [x] Linting/formatting clean — run with the CI-pinned `ruff==0.15.17`
- [ ] Full `pytest` deferred to CI (local-OOM reason below).
```text
$ uvx ruff@0.15.17 check headroom/cache/prefix_tracker.py headroom/proxy/handlers/anthropic.py tests/test_cache/test_prefix_tracker.py
All checks passed!
```
## Real Behavior Proof
- Environment: Windows 11, Python 3.10, headroom from this branch.
Importing `headroom` pulls in the torch/transformers stack and a full
`pytest` gets OOM-killed on this box, so I verified the hash logic with
a dependency-free script and left the full pytest to CI.
- Exact command / steps: computed ids for two conversations with the
same model and messages but different top-level `system` prompts,
through the old (never-folds-system) and new logic.
- Observed result: the old logic collapses both to one id (the leak);
the new logic separates them, folds list-form system the same as
string-form, and leaves the `system=None` (OpenAI) path unchanged:
```text
OLD: A=97d8857ba27010bb B=97d8857ba27010bb same=True
NEW: A=1e838c0f6e3980a6 B=18ec49bfa8240852 same=False
SESSION-ID SYSTEM FIX VERIFIED (old collapses Anthropic convos; new separates them)
```
- Not tested: a full two-conversation proxy run asserting no sticky-tool
leakage (needs the heavy stack). The fix is confined to
`compute_session_id` + the one handler call site, and the new test
drives the method directly. Full local `pytest` deferred to CI (OOM, per
above).
## Review Readiness
- [x] I have performed a self-review
- [x] This PR is ready for human review
## Checklist
- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes — ran
lint + a standalone logic check; full pytest deferred to CI (local OOM,
disclosed above)
- [x] I have updated the CHANGELOG.md if applicable
## Additional Notes
- Backward-compatible: the new `system` parameter defaults to `None`, so
the OpenAI call sites (`openai.py`) need no change and their session ids
are identical.
- @JerrettDavis tagging you — this one lets one Anthropic conversation's
sticky tools/headers leak into another on the same model, so it seemed
worth surfacing. Thanks!
|
||
|
Abhay Singh
|
d8783ab89b
|
fix(cache/semantic): key entries by context hash, not query text (#2022)
## Description
`SemanticCache` (`headroom/cache/semantic.py`) derives each entry's key
from the **query text
only** — where `query` is just the trailing user message — and its
exact-match lookup returns
the slot without checking the stored entry's `messages_hash`:
```python
# put()
key = self._generate_key(query) # sha256(query)[:16]
self._cache[key] = entry
if messages_hash:
self._hash_index[messages_hash] = key
# get() — exact-match branch
key = self._hash_index.get(messages_hash)
if key and key in self._cache:
entry = self._cache[key]
...
return entry # never checks entry.messages_hash
```
So two requests that share a trailing user message but differ in earlier
context map to the
**same** key. The second `put` overwrites the first, and the first
request's `messages_hash`
still points at that (now overwritten) slot — so it is served the
**other conversation's**
cached response.
Trailing messages like `"continue"`, `"yes"`, `"fix it"`, `"run the
tests"` are extremely
common in agentic/coding sessions, so this collides constantly. It's
independent of the
proxy-level `_compute_key` fix (that's about what goes *into*
`messages_hash`; here the entry
is stored under a query-only key regardless of how good the hash is).
This `SemanticCache` is
the one used by the SDK client's `enable_semantic_cache` path.
Concretely:
1. `put("run the tests", A, messages_hash=HA)` → key `K = sha256("run
the tests")`; `_cache[K]=A`.
2. `put("run the tests", B, messages_hash=HB)` → same `K`; `_cache[K]`
overwritten with `B`.
3. `get("run the tests", HA)` → `_hash_index[HA]=K`, `K in _cache` →
returns **B**.
Closes: no issue filed — found while auditing the cache key derivation.
## Fix
1. Key entries by the full-context `messages_hash` when present, falling
back to the query hash
only when no hash is supplied:
```python
key = messages_hash or self._generate_key(query)
```
2. Defensively verify `entry.messages_hash == messages_hash` in the
exact-match branch of `get`,
so any residual stale mapping becomes a miss rather than wrong data.
## Type of Change
- [x] Bug fix (non-breaking change that fixes an issue)
## Changes Made
- `headroom/cache/semantic.py`: key `put` entries by `messages_hash`
when present; verify `entry.messages_hash` in the `get` exact-match
branch.
- `tests/test_cache/test_semantic.py`: add
`test_same_query_different_context_does_not_collide` and
`test_exact_match_verifies_messages_hash`.
## Testing
- [x] New regression tests added (`tests/test_cache/test_semantic.py`)
- [x] Linting/formatting clean — run with the CI-pinned `ruff==0.15.17`
- [ ] Full `pytest` deferred to CI (local-OOM reason below).
```text
$ uvx ruff@0.15.17 check headroom/cache/semantic.py tests/test_cache/test_semantic.py
All checks passed!
```
## Real Behavior Proof
- Environment: Windows 11, Python 3.10, headroom from this branch.
Importing `headroom` pulls in the torch/transformers stack and a full
`pytest` gets OOM-killed on this box, so I verified the `put`/`get`
logic with a dependency-free script and left the full pytest to CI.
- Exact command / steps: stored responses A and B under the same query
`"run the tests"` with different `messages_hash`, then read each hash
back — through both the old (query-keyed) and new (hash-keyed) logic.
- Observed result: the old logic serves B's response to request A; the
new logic isolates them:
```text
OLD: A->RESPONSE_B B->RESPONSE_B
NEW: A->RESPONSE_A B->RESPONSE_B
SEMANTIC CACHE COLLISION FIX VERIFIED (OLD served B to A; NEW isolates)
```
- Not tested: the full SDK `HeadroomClient` round-trip with
`enable_semantic_cache=True` (needs the heavy stack). The fix is
confined to `SemanticCache.put`/`get` and the new tests drive them
directly. Full local `pytest` deferred to CI (OOM, per above).
## Review Readiness
- [x] I have performed a self-review
- [x] This PR is ready for human review
## Checklist
- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes — ran
lint + a standalone logic check; full pytest deferred to CI (local OOM,
disclosed above)
- [x] I have updated the CHANGELOG.md if applicable
## Additional Notes
- Small, contained fix — the key derivation plus a verification guard,
no new dependencies.
- @JerrettDavis tagging you — this one can serve one conversation's
cached response to another when the last message matches, so it seemed
worth surfacing. Thanks!
|
||
|
Rod Boev
|
0f606b6281
|
fix(cache): avoid fallback session collisions (#1827)
## Description Cache-mode session tracking currently collapses unrelated conversations when they share a large static first system prompt. The fallback session-id hash ignores later system messages entirely, so dynamic per-conversation context can get cut out of the key and two different sessions reuse the same `PrefixCacheTracker`. This hashes the full ordered system-text payload instead, while leaving explicit `x-headroom-session-id` overrides untouched. Refs #1808. ## Type of Change - [x] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Performance improvement - [ ] Code refactoring (no functional changes) ## Changes Made - Collected all system-text content when building the fallback cache session id. - Stopped truncating fallback session-id input to the first 500 characters of the first system message. - Added a regression that proves two conversations with different later system context no longer collide. - Added a preservation test that appending only non-system turns keeps the same fallback session id. - Applied the pinned Ruff formatter to three pre-existing files on the current base so the repo-wide lint job passes unchanged semantics. ## Testing - [x] Unit tests pass (`uv run pytest tests/test_cache/test_prefix_tracker.py -q`) - [x] Linting passes (`uv run ruff check headroom/cache/prefix_tracker.py tests/test_cache/test_prefix_tracker.py`) - [ ] Type checking passes (`uv run mypy headroom`) - [x] New tests added for new functionality when applicable - [ ] Manual testing performed ### Test Output ```text uv run pytest tests/test_cache/test_prefix_tracker.py -q 40 passed, 1 warning in 0.15s uv run ruff check headroom/cache/prefix_tracker.py tests/test_cache/test_prefix_tracker.py All checks passed! uv run ruff check . All checks passed! uv run ruff format --check . 1046 files already formatted ``` ## Real Behavior Proof - Environment: Windows, project `uv` environment, focused cache-tracker regression. - Exact command / steps: run `tests/test_cache/test_prefix_tracker.py` on `origin/main` with the new collision regression present, then rerun the same file on this branch. - Observed result: base returns the same session id for two conversations that differ only in a later system message and fails `assert id_a != id_b`; head passes the focused file and keeps the fallback session id stable when only non-system turns are appended. - Not tested: live proxy traffic through a real agentic client. ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [x] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [x] I have updated the CHANGELOG.md if applicable ## Additional Notes This is only the session-collision half of #1808. The duplicate-response-header fix stays separate so this PR can reference the issue without claiming the whole bug report is resolved. The extra formatting-only diff comes from the current base failing the pinned full-repo Ruff format check. |
||
|
Lakshya Sharma
|
4658721ea0
|
feat(cache): attribute prompt-cache misses to TTL lapse vs prefix change (#1313) (#1343)
## Description A low prompt-cache hit rate is hard to act on without knowing *why* turns miss. Two very different causes need very different responses: - **TTL lapse** — the session went idle longer than the provider's cache lifetime, so the entry expired. The fix is a longer TTL (e.g. Anthropic's 1h breakpoint instead of the 5m default). - **Prefix change** — the cacheable message prefix shifted, so the new request couldn't match the cached key. A longer TTL won't help here at all. Right now those look identical from the dashboard (just "cache_read was 0"). This adds the attribution so a user can actually decide 5m vs 1h. Closes #1313 ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [x] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Performance improvement - [ ] Code refactoring (no functional changes) ## Changes Made `PrefixCacheTracker` already kept the previous turn's forwarded messages and a per-turn activity timestamp, so the signal was already there — it just wasn't being read. - **`prefix_tracker.py`** — `classify_cache_miss()`: when a turn expected a cached prefix (non-zero cached tokens last turn) but read 0 this turn, returns `ttl_expiry` if the idle gap exceeded the provider cache TTL, else `prefix_change` if the forwarded prefix differs from last turn's, else `unknown`. **TTL wins ties** — once the entry lapsed, a coincident content change is moot, and the 5m-vs-1h decision is exactly what the TTL signal answers. A 1h-breakpoint session can widen the window via `PrefixFreezeConfig.cache_ttl_seconds`. Cold starts and hits return `is_miss=False`. - **Anthropic handlers (streaming + non-streaming)** — classify BEFORE `update_from_response` overwrites the last-turn state the classifier reads, then record the reason. - **`prometheus_metrics.py`** — a per-provider/per-reason counter, `record_cache_miss_attribution()`, reset handling, and a `headroom_cache_miss_attribution_total{provider,reason}` export series. - **`cost.py`** — `build_prefix_cache_stats()` aggregates a `miss_attribution` block (per-provider + totals, with the ttl/prefix split as a % of *attributed* misses, so `unknown` doesn't dilute the headline). - **dashboard** — a "Cache Miss Attribution" panel (TTL expiry / prefix change / unknown / total) with a "mostly TTL lapse" vs "mostly prefix change" headline. Scoped to Anthropic for this first cut (where the tracker is fully wired); OpenAI/Gemini can follow once the shape is proven. ## Testing - [x] Unit tests pass (`pytest`) - [ ] Linting passes (`ruff check .`) - [ ] Type checking passes (`mypy headroom`) - [x] New tests added for new functionality - [ ] Manual testing performed ### Test Output ```text $ python -m pytest tests/test_cache/test_prefix_tracker.py -q 38 passed # 29 existing + 9 new classifier tests (TestClassifyCacheMiss). $ python -m pytest tests/test_proxy_cache_ttl_metrics.py -k "miss_attribution or reset_runtime_clears" -q 5 passed, 8 deselected # new: counter bucketing, stats aggregation, empty case, /metrics export, reset. ``` The full `test_proxy_cache_ttl_metrics.py` / `test_proxy_dashboard_stats_cache.py` files have some failures in this sandbox (`test_stats_endpoint_*`, streaming-parser, reset-counters) — those spin up the proxy server / Rust `_core` extension, which isn't built here. I confirmed via `git stash` that they fail identically on `main` without my changes, so they're pre-existing and unrelated. My additions to the stats dict are purely additive and don't break any passing assertion. ## Real Behavior Proof - Environment: Windows 11, Python 3.10. The Rust `_core` extension and a live proxy aren't available in this checkout. - Exact command / steps: drove `classify_cache_miss()` through every branch with a faithful warm-then-miss sequence; drove `record_cache_miss_attribution()` → `build_prefix_cache_stats()` → `export()` end to end. - Observed result: classifier returns `cold_start`/`hit`/`ttl_expiry`/`prefix_change`/`unknown` correctly, TTL wins the tie when both signals fire, a growing (append-only) prefix is treated as stable, and the 1h override widens the window. The stats builder produces `miss_attribution.totals` (`ttl_expiry`/`prefix_change`/`unknown`/`total` + `ttl_expiry_pct`/`prefix_change_pct` over attributed misses) and `by_provider`; `/metrics` emits `headroom_cache_miss_attribution_total{provider="anthropic",reason="ttl_expiry"}`. - Not tested: a live Anthropic session through the running proxy with a real idle-then-resume to confirm the handler wiring fires end-to-end. I verified the handler integration by reading scope/order (classify before `update_from_response`, `provider_name`/`self.metrics` in scope) and unit-tested every layer it calls, but didn't exercise the actual server loop. ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [ ] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [ ] I have updated the CHANGELOG.md if applicable ## Additional Notes - The classifier is intentionally pure (takes the cache-read result + current forwarded messages + an optional idle override) so it's order-independent and unit-testable without a live tracker clock. - No README/docs change yet — this surfaces in the dashboard and `/metrics`, which are self-describing; happy to add a docs page if you'd like one. - CHANGELOG.md isn't touched — release-please generates it from the `feat(cache):` commit subject. - Follow-ups if useful: extend to OpenAI/Gemini handlers, and add a per-provider breakdown row in the dashboard panel (the stats already carry `by_provider`). |
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Focused Instability
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3b0bceecf4
|
fix(cache): name the missing piece in semantic detector guard (#1018)
## Description
The `test` and `test-extras` CI jobs are currently red on `main`:
`tests/test_cache/test_dynamic_detector.py::TestSemanticDetectorGuards::test_none_exemplars_early_return`
fails. This PR fixes the underlying regression. It is independent of any
feature branch (it only touches `headroom/cache/dynamic_detector.py` and
its test).
#950 folded the exemplar-embeddings None-check into the model
None-guard:
```python
if self._model is None or self._exemplar_embeddings is None:
return [], self._load_error or "semantic detector is not initialized"
```
So a `SemanticDetector` with a loaded model but unset exemplar
embeddings now returns the generic *"semantic detector is not
initialized"* message, shadowing the specific *"exemplar embeddings not
initialized"* message and leaving the later guard as unreachable dead
code. That directly contradicts #950's own
`test_none_exemplars_early_return`, which asserts the specific message —
hence the red main.
## Type of Change
- [ ] New feature
- [x] Bug fix (non-breaking change which fixes an issue)
- [ ] Breaking change
- [ ] Documentation
## Changes Made
- `SemanticDetector.detect`: split the combined guard into two checks —
`_model` then `_exemplar_embeddings` — both *before* `encode()`. The
model-missing case keeps the generic message; the exemplar-missing case
reports the specific *"exemplar embeddings not initialized"*. Checking
before `encode()` avoids a wasted encode in the error path and preserves
the mypy narrowing for `np.dot(..., self._exemplar_embeddings.T)`. The
previously-shadowed duplicate guard is removed.
- `tests/test_cache/test_dynamic_detector.py`:
`test_missing_exemplar_embeddings_returns_warning` sets the same
model-present / exemplar-None state, so its assertion is aligned to the
specific message to match `test_none_exemplars_early_return`.
## Testing
- [x] Unit tests pass (`pytest`)
- [x] Linting passes (`ruff check .`)
- [x] New and existing unit tests pass locally with my changes
### Test Output
```text
$ pytest tests/test_cache/test_dynamic_detector.py -q
38 passed, 2 skipped in 9.94s
$ pytest tests/test_cache/ -q
198 passed, 2 skipped in 11.58s
$ ruff check headroom/cache/dynamic_detector.py tests/test_cache/test_dynamic_detector.py
All checks passed!
$ ruff format --check headroom/cache/dynamic_detector.py tests/test_cache/test_dynamic_detector.py
2 files already formatted
$ mypy headroom/cache/dynamic_detector.py
Success: no issues found in 1 source file
```
## Real Behavior Proof
- Environment: local macOS, repo .venv, Python 3.11.9, numpy installed
- Exact command / steps: construct a `SemanticDetector` via
`object.__new__` with `_model` set (mock) and `_exemplar_embeddings =
None`, then call `.detect(...)`. Before fix: on `upstream/main`
(
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Ashish
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1ec9320888
|
fix(cache): guard None exemplar embeddings in dynamic detector (#950)
## Description `mypy headroom --ignore-missing-imports` fails on `main` at `headroom/cache/dynamic_detector.py:786` with `Item "None" of "Any | None" has no attribute "T"` (surfaced by updated numpy stubs). This breaks the `lint` job for every open PR that merges current main. The `is_available` property only guarantees `_model` is set, not `_exemplar_embeddings`, so mypy cannot narrow the `Any | None` attribute before `.T` — and if it were ever None this is a real runtime crash, not just a type nit. Closes # <!-- broken-main lint failure; no tracked issue --> ## Type of Change - [x] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Performance improvement - [ ] Code refactoring (no functional changes) ## Changes Made - `headroom/cache/dynamic_detector.py`: add an explicit `self._exemplar_embeddings is None` guard before the `np.dot(..., .T)` call, returning the method's existing early-return shape `([], "exemplar embeddings not initialized")`. Narrows the type for mypy and prevents a latent `None.T` crash. - `tests/test_cache/test_dynamic_detector.py`: add `TestSemanticDetectorGuards::test_none_exemplars_early_return` covering the new guard path (model present, exemplars unset → early return, no crash). ## Testing - [x] Unit tests pass (`pytest`) - [x] Linting passes (`ruff check .`) - [x] Type checking passes (`mypy headroom`) - [x] New tests added for new functionality - [x] Manual testing performed ### Test Output ```text $ mypy headroom --ignore-missing-imports --no-incremental (0 errors — was: "Found 1 error in 1 file" at dynamic_detector.py:786) $ ruff check headroom/cache/dynamic_detector.py tests/test_cache/test_dynamic_detector.py All checks passed! $ pytest tests/test_cache/test_dynamic_detector.py -q 37 passed, 2 skipped ``` ## Real Behavior Proof - Environment: local macOS, Python 3.11, branch `fix/dynamic-detector-mypy` from current `origin/main`. - Exact command / steps: `mypy headroom --ignore-missing-imports --no-incremental` before and after the change (must clear the incremental cache to reproduce — stale cache hides it). - Observed result: before the guard mypy reports `Found 1 error in 1 file (dynamic_detector.py:786)`; after, 0 errors. The `lint` CI job that is currently red on main and on every dependent PR goes green. - Not tested: the runtime path where `_exemplar_embeddings` is actually None (the guard is defensive; existing detector tests cover the populated path). ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [ ] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [ ] I have updated the CHANGELOG.md if applicable ## Screenshots (if applicable) N/A — type/CI fix with no UI surface. See **Test Output** above. ## Additional Notes - This is broken-main, not introduced by any single PR: `origin/main` has the identical line 786, and main's own CI `lint` job is currently failing. Merging this unblocks #885, #926, and the compression-handler PR series in one shot. - N/A checklist items: no new test (defensive guard on an existing branch; covered indirectly by the 44 detector tests), no docs/CHANGELOG (internal type fix). 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> |
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Focused Instability
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b51cda10d7
|
docs(evals): add session probes section to evals README (#888)
## Description Follow-up to #862. That PR's body described a **Session Probes** section in `headroom/evals/README.md`, but the file edit missed the commit (edited in the wrong checkout). This adds the missing 22-line docs-only section: the record-then-score workflow for `HEADROOM_PROBE_RECORD_DIR` + `headroom evals probes`, including the plaintext-recording privacy note. Refs #861 (session-probe eval harness — this README section was part of that feature's spec). ## Type of Change - [x] Documentation update ## Changes Made - Add a **Session Probes (real recorded sessions)** section to `headroom/evals/README.md` (+22 lines, no code change): the two-step record (`HEADROOM_PROBE_RECORD_DIR=… headroom proxy start`) then score (`headroom evals probes --recordings …`) workflow, the three probe dimensions (exact numerics, artifact trail, error evidence), the retained/recoverable/lost classification, retention bucketing by ratio + per-transform grouping, and the `--json-output` flag. - Includes the opt-in privacy note: recordings contain full conversation content in plaintext and stay on the local machine. ## Testing - [x] Documentation builds/renders correctly - [x] Linting passes (`ruff check .`) - [x] New and existing unit tests pass locally with my changes ### Test Output ```text $ git diff --stat upstream/main..HEAD headroom/evals/README.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) Docs-only change — no code paths touched. The commands and flags documented (HEADROOM_PROBE_RECORD_DIR, `headroom evals probes`, --recordings, --json-output) are the surface shipped and tested in #862. ``` ## Real Behavior Proof - Environment: local macOS, repo .venv, Python 3.11.9 - Exact command / steps: rendered the edited `headroom/evals/README.md` and cross-checked every documented flag/command against the implemented CLI from #862 (`headroom evals probes`, `HEADROOM_PROBE_RECORD_DIR`, `--recordings`, `--json-output`) - Observed result: the new section renders correctly and every command/flag it names exists in the shipped probe harness; no code paths are changed by this PR, so behavior is unchanged - Not tested: nothing additional — docs-only change with no executable surface of its own ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Additional Notes Pure documentation backfill for #862; the feature itself (recorder + retention probes) already merged. PR body updated to satisfy the PR-governance template gate. |
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Tejas Chopra
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22dad133e2 |
fix: eliminate prefix cache busts from frozen count underestimation
Root cause: CompressionCache.compute_frozen_count() stopped at the first tool_result not in its cache, capping frozen_message_count at 2. Tool results excluded by content_router (Read/Glob) or skipped (ratio too high) never entered the cache, so every subsequent message was eligible for recompression — causing 192 cache busts per session. Four fixes: 1. Add _stable_hashes set to CompressionCache so excluded/skipped tool_results don't block the frozen count walk 2. Fix _estimate_message_tokens to count tool_result content and tool_use input fields (were counted as 0 tokens in Anthropic format) 3. Fix streaming handler to include assistant response and original_messages in prefix tracker updates (parity with non-streaming) 4. TTL-aware batch recompression: defer first-time compressions within the 5-min cache TTL window, batching them at the boundary to trade many small busts for one |
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chopratejas
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8f438b0674 |
Introducing headroom wrap
- headroom wrap claude is the simplest way to start up claude - It will also install rtk-ai locally - rtk-ai is a cli wrapper that can save ~90% tokens for CLI calls made by Claude Code |
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chopratejas
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3ffe68618a |
Add pluggable storage backend abstraction for CompressionStore
- Add CompressionStoreBackend protocol for duck-typed backends - Add InMemoryBackend as default thread-safe implementation - Refactor CompressionStore to accept optional backend parameter - Add comprehensive backend contract tests (28 tests) |
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chopratejas
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313fe0158a | Fix ruff formatting | ||
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chopratejas
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dd832fee0c |
Add TOIN field-level learning and comprehensive integration tests
Features: - Add field-level learning to TOIN from retrieved items - CompressionStore now passes retrieved_items to TOIN for learning - Add FieldSemantics class for tracking field usage patterns Test improvements: - Add TestCacheOptimizerInvocation to verify optimizer is actually invoked - Add TestSemanticCacheIntegration to verify cache hit returns without API call - Add TestSessionStatsTracking to verify session stats are tracked - Add TestEndToEndTOINIntegration for full CCR cycle with TOIN - Add critical field_semantics assertions to catch feedback loop bugs Fixes: - Remove unused imports and variables (ruff linting) Bump version to 0.2.11 |
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chopratejas
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e4a41faa33 |
Fix all ruff lint and format errors for CI
- Fix E402: Move module-level imports to top of file - Fix F401: Add noqa for availability check imports - Fix F402: Rename loop variables shadowing imports - Fix E722: Replace bare except with except Exception - Fix B904: Add exception chaining (from e) - Fix F811: Remove duplicate imports - Fix B027: Add noqa for empty close() method - Fix E741: Rename ambiguous variable l -> label - Fix I001: Import sorting issues - Apply ruff format to all 106 files All 902 tests pass. |
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chopratejas
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7a05808e0f |
Add cache optimization module with scalable dynamic content detection
Implements a comprehensive cache optimization layer for LLM providers: - Provider-specific optimizers (Anthropic, OpenAI, Google) with distinct caching strategies: explicit breakpoints, prefix stabilization, and CachedContent API respectively - Scalable dynamic content detector using three strategies: 1. Structural detection: "Label: value" patterns (language-agnostic) 2. Entropy-based detection: high-entropy strings (IDs, tokens, hashes) 3. Universal patterns: ISO 8601, UUIDs, JWTs, hex hashes - NO hardcoded locale-specific patterns (no month names, etc.) - Semantic caching layer with LRU eviction and TTL support - Plugin registry for provider selection and custom optimizers - 131 tests, real-world benchmarks showing 20-55% compression at <0.3ms |