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4 commits

Author SHA1 Message Date
Leoy
88177bd7a6
feat(relevance): weight BM25 score_batch by corpus IDF (#646)
BM25Scorer.score_batch() ranks a real corpus of documents but weighted
every matched term with a constant idf=log(2.0), so a ubiquitous noise
word counted the same as a discriminative UUID. The _compute_idf() helper
needed to do this properly already existed (and was unit-tested) but was
never wired into scoring.

- Implement _compute_idf() with the standard floored BM25 IDF its docstring
  documents: log((N - n + 0.5) / (n + 0.5) + 1).
- Thread an optional per-term idf_map through _bm25_score(); single-document
  score() keeps the neutral log(2.0) weight (no corpus to estimate from).
- score_batch() now computes document frequency across the batch and builds
  the IDF map, so rare/discriminative terms outrank corpus-wide terms in the
  ranking that CompressionStore.search() and HybridScorer consume.

Adds tests covering the IDF formula and the batch ranking behaviour.
2026-06-05 14:15:44 -08:00
chopratejas
c765c53bf8 feat(rust): retire python smart_crusher, ship rust-only via pyo3
Stage 3c.1b step 2 + cleanup. The python `SmartCrusher` (3669 lines)
is replaced by a thin pyo3-backed shim (~290 lines) that delegates
every byte to `headroom._core.SmartCrusher` (built from
`crates/headroom-py`, landed in the previous commit). There is no
python implementation and no env-var fallback — the wheel is a hard
import.

Why now: parity was already proven across 17 fixtures + the python-
side bridge test (1+17 in `test_smart_crusher_rust_parity.py`).
Keeping a shadow python impl behind a flag is a permanent maintenance
cost with no operational benefit. Stage 3c.1b deletes ~3380 lines of
python parser/scorer/analyzer/orchestrator code; the rust crate has
its own coverage (388 unit tests + property tests in headroom-core).

Surface preserved (drop-in for every production caller):
- `headroom.transforms.smart_crusher.SmartCrusher` — same class name,
  same `__init__(config, relevance_config, scorer, ccr_config)`
  signature (the latter three are accepted for source-compat and
  silently dropped — rust port keeps those subsystems disabled in
  Stage 3c.1, they re-attach in Stage 3c.2).
- `SmartCrusherConfig` and `CrushResult` dataclasses kept as python
  dataclasses (callers use `asdict()` / dataclass matching on them).
- `crush(content, query, bias)`, `_smart_crush_content(content, ...)`,
  `apply(messages, tokenizer, **kwargs)`, and
  `_extract_context_from_messages(messages)` all preserved.
- `smart_crush_tool_output(content, config, ccr_config)` thin wrapper.

The transform-protocol `apply()` orchestration stays python (message
walking, digest-marker insertion, token counting); only the per-
message compression call delegates to rust.

Removed:
- Python parser / planner / scorer / analyzer / classifier (~3380 lines).
- Internal helpers `_classify_array`, `_detect_sequential_pattern`,
  `_detect_rare_status_values`, `_detect_items_by_learned_semantics`,
  `_percentile_linear`, `_compute_k_split`, `_crush_number_array`,
  `_process_value`, etc. — rust crate has parallel coverage.
- `SmartAnalyzer`, `ArrayType`, `CompressionStrategy`,
  `extract_query_anchors` — internals; not used by any production
  caller (only tests probed them).

Tests deleted (probed deleted internals — same precedent as Stage 3b):
- `tests/test_transforms/test_smart_crusher.py` (40 tests)
- `tests/test_transforms/test_universal_json_crush.py` (45)
- `tests/test_transforms/test_anchor_selector.py` (49)
- `tests/test_toin_field_learning.py` (21)
- `tests/test_crushability.py` (20)

Tests trimmed (removed methods/classes that probe deferred subsystems
— scorer injection, CCR marker injection, TOIN feedback recording —
all of which re-attach in Stage 3c.2):
- `tests/test_transforms/test_smart_crusher_bugs.py`:
  TestNumberArraySchemaPreservation, TestStage3c1BugFixes.
- `tests/test_relevance.py`: 2 scorer-injection tests.
- `tests/test_ccr.py`: TestSmartCrusherCCRIntegration class +
  test_custom_marker_template.
- `tests/test_toin_integration.py`: TestTOINIntegration +
  TestStoreToTOINHash classes.
- `tests/test_critical_fixes.py`: TestSmartCrusherTOINIntegration +
  test_full_feedback_loop.
- `tests/test_acceptance.py::TestQueryAnchorExtraction`: dropped the
  `extract_query_anchors` probe; kept the end-to-end "Alice
  preserved" assertion.

Bug fixes from Stage 3c.1 (#1 percentile linear interp, #2 zero-
padded sequential, #3 rare-status pareto, #4 k-split overshoot) are
pinned by the rust crate and the parity fixtures
(`tests/parity/fixtures/smart_crusher/`).

Tests:
- 517 passed in the smart_crusher-adjacent file set
  (test_transforms/, test_relevance*, test_ccr, test_toin_integration,
  test_quality_retention, test_acceptance, test_critical_fixes).
- 18 in `test_smart_crusher_rust_parity.py` (1 sanity + 17 fixtures).
- 388 rust unit tests still green.

One stale-error-message regex in `test_relevance_extra.py` updated
from "requires sentence-transformers" → "requires fastembed".
2026-04-27 00:52:21 -07:00
chopratejas
bf779b54e6 Fix all mypy type errors and flaky embedding test
- Fix 34 mypy errors across 17 files with type annotations and casts
- Add type: ignore comments for legitimate dynamic patterns
- Handle None operands with (value or 0) pattern
- Cast return values to proper types (int, float, str, bool)
- Add EstimatingTokenCounter imports where needed
- Use getattr() for potentially missing attributes
- Fix flaky test_paraphrase_match with more distinct semantic examples
- Add mlx to mypy ignore list (broken third-party stubs)
2026-01-10 18:27:33 -08:00
chopratejas
9c7d4512d6 Initial commit: Headroom SDK - LLM context optimization toolkit
A comprehensive SDK for optimizing LLM context windows, reducing token
usage while preserving critical information for AI agents.

Core Features:
- SmartCrusher: Statistical compression of tool outputs (70-85% reduction)
- CacheAligner: Prefix optimization for prompt cache hits
- RollingWindow: Intelligent context window management
- BM25/Hybrid relevance scoring for smart item selection

Integrations:
- OpenAI and Anthropic provider support
- LangChain integration (ChatModel, Callbacks, Runnable)
- MCP (Model Context Protocol) integration for tool compression

Test Coverage:
- 372 tests passing across all modules
- 35 performance benchmarks
- Real-world agent evaluations with 88% token savings

Key Components:
- headroom/transforms/: Core compression transforms
- headroom/providers/: OpenAI and Anthropic support
- headroom/integrations/: LangChain and MCP integrations
- headroom/relevance/: BM25 and hybrid scoring
- headroom/pricing/: Model pricing registry
- benchmarks/: Performance benchmark suite
- examples/: Usage examples and demos
2026-01-06 23:16:58 -08:00