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Merge pull request #479 from ggozad/feat/speed-up-duplicates

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Speed up doctor's duplicate-document detection
This commit is contained in:
Yiorgis Gozadinos 2026-06-28 10:51:32 +03:00 committed by GitHub
commit b707dfe848
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7 changed files with 265 additions and 239 deletions
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10
CHANGELOG.md
View file

@ -1,6 +1,16 @@
# Changelog
## [Unreleased]
### Added
- `doctor` shows a spinner naming the check currently in progress while it runs.
### Changed
- `doctor` near-duplicate detection compares whole-document embedding centroids instead of per-chunk overlap, and no longer flags a small document contained in a larger one. The per-chunk check could take hours and tens of GB of memory on corpora with large documents. The centroid check is independent of document size and reduces each document to a centroid during the vector scan without a second copy of the matrix.
- **Breaking:** the `doctor.duplicates` keys `containment_threshold`, `candidate_threshold`, and `twin_similarity` are replaced by a single `similarity_threshold` (default `0.97`). `min_chunks` is unchanged.
- `doctor --duplicates-out` YAML reports `similarity` per document instead of `contained_fraction`.
## [0.62.1] - 2026-06-27
### Fixed

6
docs/cli.md
View file

@ -292,7 +292,9 @@ Check the database for consistency problems and print a pass/warn/fail report:
haiku-rag doctor [--db /path/to/your.lancedb] [--duplicates-out groups.yaml]
```
`--duplicates-out PATH` additionally writes the near-duplicate document groups to a YAML file (one block per group with `keep` and a list of `documents`, each carrying `document_id`, `document`, `chunks`, `contained_fraction`, and `keep_suggested`) for offline review.
While it runs, doctor shows a spinner naming the check currently in progress.
`--duplicates-out PATH` additionally writes the near-duplicate document groups to a YAML file (one block per group with `keep` and a list of `documents`, each carrying `document_id`, `document`, `chunks`, `similarity`, and `keep_suggested`) for offline review.
Checks include:
@ -309,7 +311,7 @@ Checks include:
- the configured embedding identity matches the stored settings
- no database migrations are pending
- the vector index covers all chunks
- near-duplicate documents (revisions sharing most of their chunks) are grouped and reported, with the largest member flagged as the likely one to keep (advisory only, never deleted; tuned via `doctor.duplicates` in config)
- near-identical documents (by embedding-centroid similarity) are grouped and reported, with the largest member flagged as the likely one to keep (advisory only, never deleted, tuned via `doctor.duplicates` in config)
- API keys are set for configured providers
It also probes the external endpoints the config uses and reports them under a Providers section:

4
docs/configuration/index.md
View file

@ -116,9 +116,7 @@ search:
doctor:
duplicates: # Near-duplicate document detection (doctor command)
containment_threshold: 0.75 # flag a group when one doc shares >= this fraction of the smaller's chunks
candidate_threshold: 0.85 # centroid similarity gate for proposing candidate pairs (recall)
twin_similarity: 0.95 # cosine at which two chunks count as the same chunk
similarity_threshold: 0.97 # cosine cutoff on document embedding centroids
min_chunks: 3 # documents with fewer chunks are excluded
prompts:

19
haiku_rag_slim/haiku/rag/app.py
View file

@ -201,6 +201,7 @@ class HaikuRAGApp: # pragma: no cover
async def doctor(self, duplicates_out: Path | None = None) -> bool:
"""Run health checks and print a report. Returns True if any check failed."""
import os
from contextlib import nullcontext
from haiku.rag.doctor import Severity, run_doctor
@ -213,10 +214,24 @@ class HaikuRAGApp: # pragma: no cover
self.console.print("[red]Database path does not exist.[/red]")
return True
report = await run_doctor(
self.config, self.db_path, dict(os.environ), duplicates_out=duplicates_out
status = (
self.console.status("Running checks") if self.console.is_terminal else None
)
def on_progress(label: str) -> None:
if status is not None:
status.update(f"{label}...")
cm = status if status is not None else nullcontext()
with cm:
report = await run_doctor(
self.config,
self.db_path,
dict(os.environ),
duplicates_out=duplicates_out,
on_progress=on_progress,
)
glyphs = {
Severity.OK: "[green]✓[/green]",
Severity.WARN: "[yellow]![/yellow]",

12
haiku_rag_slim/haiku/rag/config/models.py
View file

@ -106,15 +106,13 @@ class AnalysisConfig(BaseModel):
class DuplicateDetectionConfig(BaseModel):
"""Thresholds for doctor's near-duplicate document detection.
Detection clusters documents that share most of their chunks (revisions of
one another). ``containment_threshold`` is the decision knob; the others
tune the cheap centroid pre-filter, what counts as a shared chunk, and which
tiny documents to skip.
Detection clusters whole documents whose embedding centroids are nearly
identical — the same document ingested twice, or a light revision.
``similarity_threshold`` is the cosine cutoff; ``min_chunks`` skips
documents too small to compare meaningfully.
"""
containment_threshold: float = 0.75
candidate_threshold: float = 0.85
twin_similarity: float = 0.95
similarity_threshold: float = 0.97
min_chunks: int = 3

293
haiku_rag_slim/haiku/rag/doctor.py
View file

@ -235,134 +235,90 @@ async def _column_values(table, column: str) -> list:
return [row[column] for row in rows]
# Backstop on how many centroid candidate pairs we verify, bounding memory and
# runtime on a pathologically self-similar corpus.
MAX_CANDIDATE_PAIRS = 200_000
class _DuplicateFamily(BaseModel):
members: list[str]
superset: str
pairs: list[tuple[str, str, float, float]]
keep: str
similarity: dict[str, float]
sizes: dict[str, int]
def _unit(vector: np.ndarray) -> np.ndarray:
norm = np.linalg.norm(vector)
return vector / norm if norm else vector
def _containment(source: np.ndarray, target: np.ndarray, twin: float) -> float:
"""Fraction of ``source`` chunks with a near-identical chunk in ``target``."""
return float(((source @ target.T).max(axis=1) >= twin).mean())
def _duplicate_families(
doc_vectors: dict[str, np.ndarray], cfg: DuplicateDetectionConfig
doc_ids: list[str],
centroids: np.ndarray,
counts: np.ndarray,
cfg: DuplicateDetectionConfig,
) -> list[_DuplicateFamily]:
"""Cluster documents that share most of their chunks (revisions of one another).
"""Cluster documents whose embedding centroids are nearly identical.
Block-then-verify: cheap centroid similarity proposes candidate document
pairs, then directed chunk-overlap containment confirms them. Returns one
entry per connected component of confirmed pairs.
``centroids`` holds one summed (unnormalized) centroid per document and
``counts`` its embedded-chunk count. Documents below the small-document
floor are dropped; the rest are normalized and clustered by union-find over
pairwise cosine above ``similarity_threshold``. One family per component,
each carrying every member's highest cosine to another member.
"""
# Drop unembedded (zero) vectors and documents below the small-document
# floor; normalize the rest to unit length.
normalized: dict[str, np.ndarray] = {}
for doc_id, matrix in doc_vectors.items():
m = np.asarray(matrix, dtype=float)
if m.ndim != 2 or m.shape[0] == 0:
continue
m = m[np.linalg.norm(m, axis=1) > 0]
if m.shape[0] < cfg.min_chunks:
continue
normalized[doc_id] = m / np.linalg.norm(m, axis=1)[:, None]
if len(normalized) < 2:
centroids = np.asarray(centroids, dtype=np.float32)
counts = np.asarray(counts)
norms = np.linalg.norm(centroids, axis=1)
eligible = np.nonzero((counts >= cfg.min_chunks) & (norms > 0))[0]
if eligible.size < 2:
return []
unit = centroids[eligible] / norms[eligible][:, None]
ids = [doc_ids[i] for i in eligible]
sizes = {doc_ids[i]: int(counts[i]) for i in eligible}
n = len(ids)
order = sorted(normalized)
centroids = np.array([_unit(normalized[d].mean(axis=0)) for d in order])
sizes = np.array([normalized[d].shape[0] for d in order], dtype=float)
# Pairwise cosine, block-wise to avoid a full D×D matrix at once. Each row
# only compares against higher-indexed documents (upper triangle). Cluster
# with union-find and keep only each document's best similarity to a twin —
# a self-similar corpus forms one clique, so storing every pair would be
# O(D²) objects.
parent = list(range(n))
# Stage 1: centroid candidate pairs, block-wise to avoid a full D×D matrix.
# The cap is enforced per row (truncating each row's matches) so a
# self-similar corpus can never allocate beyond MAX_CANDIDATE_PAIRS.
candidates: list[tuple[int, int]] = []
def find(x: int) -> int:
while parent[x] != x:
parent[x] = parent[parent[x]]
x = parent[x]
return x
best = np.zeros(n, dtype=np.float32)
linked = False
block = 512
capped = False
for start in range(0, len(order), block):
if capped:
break
sims = centroids[start : start + block] @ centroids.T
for start in range(0, n, block):
sims = unit[start : start + block] @ unit.T
for row in range(sims.shape[0]):
gi = start + row
targets = np.arange(gi + 1, len(order))
if targets.size == 0:
continue
# A smaller document can be fully contained in a larger append-only
# revision even when the fixed centroid threshold would fail:
# with orthogonal chunks, cosine falls to sqrt(small / large).
# Scale the candidate gate by that size ratio, then let directed
# containment make the actual duplicate decision.
ratios = np.minimum(sizes[gi], sizes[targets]) / np.maximum(
sizes[gi], sizes[targets]
cols = (
gi + 1 + np.nonzero(sims[row, gi + 1 :] >= cfg.similarity_threshold)[0]
)
thresholds = cfg.candidate_threshold * np.sqrt(ratios)
above = np.nonzero(sims[row, gi + 1 :] >= thresholds)[0]
remaining = MAX_CANDIDATE_PAIRS - len(candidates)
if len(above) >= remaining:
above = above[:remaining]
capped = True
candidates.extend((gi, gi + 1 + int(j)) for j in above)
if capped:
break
# Stage 2: confirm candidates with directed chunk-overlap containment.
adjacency: dict[int, set[int]] = {}
edges: dict[tuple[int, int], tuple[float, float]] = {}
for i, j in candidates:
a_to_b = _containment(
normalized[order[i]], normalized[order[j]], cfg.twin_similarity
)
b_to_a = _containment(
normalized[order[j]], normalized[order[i]], cfg.twin_similarity
)
if max(a_to_b, b_to_a) >= cfg.containment_threshold:
adjacency.setdefault(i, set()).add(j)
adjacency.setdefault(j, set()).add(i)
edges[(i, j)] = (a_to_b, b_to_a)
if not edges:
if cols.size == 0:
continue
linked = True
row_best = sims[row, cols]
best[gi] = max(best[gi], float(row_best.max()))
best[cols] = np.maximum(best[cols], row_best)
ri = find(gi)
for gj in cols.tolist():
parent[find(gj)] = ri
if not linked:
return []
# Cluster confirmed pairs into families (connected components).
components: dict[int, list[int]] = {}
for idx in range(n):
components.setdefault(find(idx), []).append(idx)
families: list[_DuplicateFamily] = []
seen: set[int] = set()
for node in adjacency:
if node in seen:
for indices in components.values():
if len(indices) < 2:
continue
component: set[int] = set()
stack = [node]
while stack:
cur = stack.pop()
if cur in seen:
continue
seen.add(cur)
component.add(cur)
stack.extend(adjacency[cur] - seen)
members = sorted(order[i] for i in component)
# Largest document (most chunks) is the likely superset; smallest id on a tie.
superset = min(members, key=lambda d: (-normalized[d].shape[0], d))
pairs = sorted(
(order[i], order[j], round(ab, 3), round(ba, 3))
for (i, j), (ab, ba) in edges.items()
if i in component and j in component
)
members = sorted(ids[i] for i in indices)
# Largest document (most chunks) is the one to keep; smallest id on a tie.
keep = min(members, key=lambda d: (-sizes[d], d))
families.append(
_DuplicateFamily(
members=members,
superset=superset,
pairs=pairs,
sizes={d: int(normalized[d].shape[0]) for d in members},
keep=keep,
similarity={ids[i]: round(float(best[i]), 3) for i in indices},
sizes={d: sizes[d] for d in members},
)
)
return sorted(families, key=lambda f: f.members)
@ -387,25 +343,21 @@ def _common_path_prefix(labels: list[str]) -> str:
def _write_duplicates_out(
path: Path, families: list[_DuplicateFamily], label: Callable[[str], str]
) -> None:
"""One block per group; ``keep_suggested`` marks the superset and
``contained_fraction`` is how much of the document is covered by the rest."""
"""One block per group; ``keep_suggested`` marks the document to keep and
``similarity`` is the highest centroid cosine to another group member."""
groups = []
for n, family in enumerate(families, start=1):
contained = dict.fromkeys(family.members, 0.0)
for a, b, a_to_b, b_to_a in family.pairs:
contained[a] = max(contained[a], a_to_b)
contained[b] = max(contained[b], b_to_a)
groups.append(
{
"group": n,
"keep": family.superset,
"keep": family.keep,
"documents": [
{
"document_id": member,
"document": label(member),
"chunks": family.sizes[member],
"contained_fraction": round(contained[member], 3),
"keep_suggested": member == family.superset,
"similarity": family.similarity[member],
"keep_suggested": member == family.keep,
}
for member in family.members
],
@ -416,13 +368,15 @@ def _write_duplicates_out(
def _check_duplicate_documents(
doc_vectors: dict[str, np.ndarray],
doc_ids: list[str],
centroids: np.ndarray,
counts: np.ndarray,
uri_by_doc: Mapping[str, str | None],
title_by_doc: Mapping[str, str | None],
cfg: DuplicateDetectionConfig,
yaml_path: Path | None = None,
) -> CheckResult:
families = _duplicate_families(doc_vectors, cfg)
families = _duplicate_families(doc_ids, centroids, counts, cfg)
def label(doc_id: str) -> str:
return uri_by_doc.get(doc_id) or title_by_doc.get(doc_id) or doc_id
@ -439,7 +393,7 @@ def _check_duplicate_documents(
# The terminal report is a summary: show the first few groups whole and
# point at the YAML export for the rest. One block per shown group — a
# header, each member on its own numbered line, then a compact overlap line.
# header, each member on its own numbered line, then a compact similarity line.
shown = families[:_SAMPLE_LIMIT]
prefix = _common_path_prefix([label(m) for f in shown for m in f.members])
@ -453,15 +407,14 @@ def _check_duplicate_documents(
for n, family in enumerate(shown, start=1):
number = {member: i for i, member in enumerate(family.members, start=1)}
details.append(
f"group {n} — {len(family.members)} docs, keep #{number[family.superset]}:"
f"group {n} — {len(family.members)} docs, keep #{number[family.keep]}:"
)
for member in family.members:
details.append(f" #{number[member]} {short(member)}")
overlaps = ", ".join(
f"#{number[a]}→#{number[b]} {ab:.0%}, #{number[b]}→#{number[a]} {ba:.0%}"
for a, b, ab, ba in family.pairs
sims = ", ".join(
f"#{number[m]} {family.similarity[m]:.0%}" for m in family.members
)
details.append(f" overlap: {overlaps}")
details.append(f" similarity: {sims}")
if len(families) > len(shown):
details.append(
f"... (+{len(families) - len(shown)} more groups; "
@ -473,11 +426,11 @@ def _check_duplicate_documents(
name="duplicate_documents",
severity=Severity.WARN,
message=(
f"{len(families)} group(s) of documents with substantial chunk overlap "
f"(potential duplicates/revisions), {total_docs} documents."
f"{len(families)} group(s) of near-identical documents "
f"(potential duplicates), {total_docs} documents."
),
remediation=(
"Review each group and remove redundant revisions; overlap may be intentional."
"Review each group and remove redundant copies; duplication may be intentional."
),
details=details,
)
@ -488,13 +441,16 @@ async def run_db_checks(
config: AppConfig,
stats: dict,
duplicates_out: Path | None = None,
on_progress: Callable[[str], None] | None = None,
) -> list[CheckResult]:
"""Referential and content-integrity checks against an open read-only Store.
Assumes all required tables exist (the caller short-circuits otherwise).
"""
notify = on_progress or (lambda _label: None)
results: list[CheckResult] = []
notify("Reading document records")
doc_ids = set(await _column_values(store.documents_table, "id"))
meta_rows = (
await store.document_meta_table.query()
@ -511,6 +467,7 @@ async def run_db_checks(
uri_by_doc = {row["document_id"]: row.get("uri") for row in meta_rows}
title_by_doc = {row["document_id"]: row.get("title") for row in meta_rows}
notify("Reading chunks")
chunk_rows = (
await store.chunks_table.query()
.select(["id", "document_id", "metadata"])
@ -518,6 +475,7 @@ async def run_db_checks(
)
chunk_doc_ids = {row["document_id"] for row in chunk_rows}
notify("Reading document items")
item_rows = (
await store.document_items_table.query()
.select(["document_id", "self_ref", "label"])
@ -530,6 +488,7 @@ async def run_db_checks(
self_refs_by_doc.setdefault(row["document_id"], set()).add(row["self_ref"])
labels_by_doc.setdefault(row["document_id"], set()).add(row["label"])
notify("Checking referential integrity")
# documents <-> document_meta must be 1:1.
orphan_docs = doc_ids - meta_doc_ids
orphan_meta = meta_doc_ids - doc_ids
@ -585,6 +544,7 @@ async def run_db_checks(
)
)
notify("Checking document chunking")
# Documents with no chunks, classified by what they contain and whether the
# embedder can index images.
results += _classify_unchunked(
@ -608,6 +568,7 @@ async def run_db_checks(
)
)
notify("Checking chunk references")
# Chunk metadata may reference self_refs that do not exist for that document.
dangling: list[str] = []
for row in chunk_rows:
@ -629,6 +590,7 @@ async def run_db_checks(
)
)
notify("Scanning chunk vectors")
# Vector dimension consistency and unembedded (all-zero) vectors share one
# scan of the vector column — the heaviest check on large corpora.
arrow = (
@ -660,35 +622,60 @@ async def run_db_checks(
)
)
ids = arrow.column("id").to_pylist()
vectors = np.asarray(arrow.column("vector").to_pylist(), dtype=float)
zero_ids: list[str] = []
if vectors.size:
zero_ids = [ids[i] for i in np.nonzero(~vectors.any(axis=1))[0]]
# Reshape the Arrow fixed-size-list child buffer directly into an (N, dim)
# float32 matrix. Going through to_pylist() would box N*dim Python floats
# (tens of GB and most of the wall-clock on large corpora); the stored
# vectors are already float32, so this keeps the layout and the dtype.
vec_col = arrow.column("vector").combine_chunks()
vectors = vec_col.values.to_numpy(zero_copy_only=False).reshape(-1, actual_dim)
embedded = vectors.any(axis=1) if vectors.size else np.zeros(0, dtype=bool)
# Unembedded (all-zero) chunks: report a count and a few sampled ids without
# materializing every chunk id.
zero_rows = np.nonzero(~embedded)[0]
zero_count = int(zero_rows.size)
id_col = arrow.column("id")
zero_sample = [id_col[int(i)].as_py() for i in zero_rows[:_SAMPLE_LIMIT]]
if zero_count > _SAMPLE_LIMIT:
zero_sample.append(f"... (+{zero_count - _SAMPLE_LIMIT} more)")
results.append(
CheckResult(
name="unembedded_chunks",
severity=Severity.WARN if zero_ids else Severity.OK,
severity=Severity.WARN if zero_count else Severity.OK,
message=(
f"{len(zero_ids)} chunk(s) have an all-zero (unembedded) vector."
if zero_ids
f"{zero_count} chunk(s) have an all-zero (unembedded) vector."
if zero_count
else "All chunks are embedded."
),
remediation="haiku-rag rebuild --embed-only" if zero_ids else None,
details=_sample(zero_ids),
remediation="haiku-rag rebuild --embed-only" if zero_count else None,
details=zero_sample,
)
)
# Near-duplicate documents (revisions sharing most chunks), grouped from the
# same vector scan rather than a second pass.
chunk_doc_ids_ordered = arrow.column("document_id").to_pylist()
indices_by_doc: dict[str, list[int]] = {}
for index, doc_id in enumerate(chunk_doc_ids_ordered):
indices_by_doc.setdefault(doc_id, []).append(index)
doc_vectors = {doc_id: vectors[idx] for doc_id, idx in indices_by_doc.items()}
notify("Detecting near-duplicate documents")
# Near-identical documents (centroid cosine). Reduce each document's chunk
# vectors to one summed centroid during the scan: dictionary-encode the
# document ids into integer codes, then sum each document's embedded rows in
# a single pass per document — no second full copy of the vector matrix.
encoded = arrow.column("document_id").combine_chunks().dictionary_encode()
doc_ids = encoded.dictionary.to_pylist()
codes = encoded.indices.to_numpy(zero_copy_only=False)
centroids = np.zeros((len(doc_ids), actual_dim), dtype=np.float32)
counts = np.zeros(len(doc_ids), dtype=np.int64)
order = np.argsort(codes, kind="stable")
bounds = np.searchsorted(codes, np.arange(len(doc_ids) + 1), sorter=order)
for d in range(len(doc_ids)):
rows = order[bounds[d] : bounds[d + 1]]
rows = rows[embedded[rows]]
counts[d] = rows.size
if rows.size:
centroids[d] = vectors[rows].sum(axis=0)
del vectors
results.append(
_check_duplicate_documents(
doc_vectors,
doc_ids,
centroids,
counts,
uri_by_doc,
title_by_doc,
config.doctor.duplicates,
@ -696,6 +683,7 @@ async def run_db_checks(
)
)
notify("Checking picture data")
# Pictures from image/PDF sources should carry raster bytes. Pictures that
# are external image references in a text document (markdown, HTML) have no
# embedded bytes by nature, so a missing raster there is expected.
@ -726,6 +714,7 @@ async def run_db_checks(
)
)
notify("Checking settings and indexes")
# Settings must hold exactly one canonical row.
total_settings = await store.settings_table.count_rows()
canonical = len(
@ -978,12 +967,16 @@ def _endpoint_result(
)
async def run_provider_checks(config: AppConfig) -> list[CheckResult]:
async def run_provider_checks(
config: AppConfig, on_progress: Callable[[str], None] | None = None
) -> list[CheckResult]:
"""Probe the external endpoints the current config actually uses."""
targets, local = _provider_targets(config)
results: list[CheckResult] = []
if targets:
if on_progress is not None:
on_progress("Probing provider endpoints")
async with httpx.AsyncClient(timeout=_PROBE_TIMEOUT_S) as client:
probes = await asyncio.gather(
*(_probe_endpoint(client, url) for url in targets)
@ -1007,12 +1000,15 @@ async def run_doctor(
db_path: Path,
environ: dict[str, str],
duplicates_out: Path | None = None,
on_progress: Callable[[str], None] | None = None,
) -> DoctorReport:
"""Open the database read-only and run every diagnostic check.
Opens with validation and migration checks skipped so a drifted or
pre-migration database can still be diagnosed rather than refusing to open.
"""
notify = on_progress or (lambda _label: None)
notify("Inspecting tables")
db = await connect_lancedb(config, db_path)
stats = await get_database_stats(db)
@ -1038,9 +1034,14 @@ async def run_doctor(
skip_migration_check=True,
) as store:
results += await run_db_checks(
store, config, stats, duplicates_out=duplicates_out
store,
config,
stats,
duplicates_out=duplicates_out,
on_progress=on_progress,
)
notify("Checking API keys")
results.append(_check_api_keys(config, environ))
results += await run_provider_checks(config)
results += await run_provider_checks(config, on_progress=on_progress)
return DoctorReport(results=results)

160
tests/test_doctor.py
View file

@ -172,6 +172,17 @@ async def test_healthy_db_all_ok(temp_db_path):
assert all(r.severity is Severity.OK for r in report.results)
@pytest.mark.asyncio
async def test_doctor_reports_progress(temp_db_path):
await _build_db(temp_db_path)
labels: list[str] = []
await run_doctor(_config(), temp_db_path, {}, on_progress=labels.append)
assert "Inspecting tables" in labels
assert "Scanning chunk vectors" in labels
assert "Detecting near-duplicate documents" in labels
assert "Probing provider endpoints" in labels
@pytest.mark.asyncio
async def test_empty_db_fails(temp_db_path):
report = await run_doctor(_config(), temp_db_path, {})
@ -944,102 +955,87 @@ async def test_probe_endpoint_connection_error():
# --- Duplicate-document detection ----------------------------------------
def _docs(spec: dict[str, list[int]], dim: int = 8) -> dict[str, np.ndarray]:
"""Build per-document chunk matrices from one-hot indices.
def _centroids(
spec: dict[str, list[int]], dim: int = 8
) -> tuple[list[str], np.ndarray, np.ndarray]:
"""Summed one-hot centroids + chunk counts per document, as
``_duplicate_families`` consumes them.
A shared index across documents is a shared (identical) chunk; distinct
indices are orthogonal, so they never count as twins.
Orthogonal one-hot chunks make the centroid cosine of two documents equal to
``shared / sqrt(len(a) * len(b))``: identical documents score 1.0, fully
distinct documents score 0.0.
"""
eye = np.eye(dim)
return {
doc: np.array([eye[i] for i in idxs], dtype=float) for doc, idxs in spec.items()
}
doc_ids = list(spec)
centroids = np.array(
[eye[idxs].sum(axis=0) for idxs in spec.values()], dtype=np.float32
)
counts = np.array([len(idxs) for idxs in spec.values()], dtype=np.int64)
return doc_ids, centroids, counts
# Stage-2 (containment/clustering) unit tests disable the centroid gate
# (candidate_threshold=0.0) so every pair is verified; orthogonal one-hot chunks
# would otherwise drop centroids below the default gate. The gate itself is
# exercised by the end-to-end tests below.
def _stage2_cfg(**kw) -> DuplicateDetectionConfig:
return DuplicateDetectionConfig(candidate_threshold=0.0, **kw)
def test_duplicate_families_revision_pair():
def test_duplicate_families_identical_docs_flagged():
families = _duplicate_families(
_docs({"a": [0, 1, 2, 3], "b": [0, 1, 2, 4]}), _stage2_cfg()
*_centroids({"a": [0, 1, 2, 3], "b": [0, 1, 2, 3]}), DuplicateDetectionConfig()
)
assert len(families) == 1
assert set(families[0].members) == {"a", "b"}
assert families[0].similarity == {"a": pytest.approx(1.0), "b": pytest.approx(1.0)}
def test_duplicate_families_append_only_is_asymmetric():
def test_duplicate_families_append_only_not_flagged():
# A is fully contained in the larger B, but their centroids diverge
# (cosine sqrt(3/6) ~= 0.71), so it stays below the similarity cutoff.
families = _duplicate_families(
_docs({"a": [0, 1, 2], "b": [0, 1, 2, 3, 4, 5]}), _stage2_cfg()
)
assert len(families) == 1
fam = families[0]
assert fam.superset == "b" # the larger document
# directed containment: all of A is in B (1.0); only half of B is in A.
a_to_b = next(p for p in fam.pairs if p[:2] == ("a", "b"))
assert a_to_b[2] == pytest.approx(1.0)
assert a_to_b[3] == pytest.approx(0.5)
def test_duplicate_families_append_only_passes_default_centroid_gate():
# Fixed 0.85 centroid gating misses this: centroid cosine is sqrt(3 / 6),
# but A is fully contained in B and should reach the containment verifier.
families = _duplicate_families(
_docs({"a": [0, 1, 2], "b": [0, 1, 2, 3, 4, 5]}),
*_centroids({"a": [0, 1, 2], "b": [0, 1, 2, 3, 4, 5]}),
DuplicateDetectionConfig(),
)
assert len(families) == 1
assert set(families[0].members) == {"a", "b"}
assert families == []
def test_duplicate_families_distinct_docs_none():
families = _duplicate_families(
_docs({"a": [0, 1, 2], "b": [3, 4, 5]}), _stage2_cfg()
*_centroids({"a": [0, 1, 2], "b": [3, 4, 5]}), DuplicateDetectionConfig()
)
assert families == []
def test_duplicate_families_three_way_chain_one_family():
def test_duplicate_families_three_way_one_family():
families = _duplicate_families(
_docs(
{
"a": [0, 1, 2, 3],
"b": [0, 1, 2, 3, 4],
"c": [0, 1, 2, 3, 4, 5],
}
),
_stage2_cfg(),
*_centroids({"a": [0, 1, 2, 3], "b": [0, 1, 2, 3], "c": [0, 1, 2, 3]}),
DuplicateDetectionConfig(),
)
assert len(families) == 1
assert set(families[0].members) == {"a", "b", "c"}
assert families[0].superset == "c"
# Equal sizes -> smallest id is kept.
assert families[0].keep == "a"
def test_duplicate_families_clique_single_family():
# A self-similar corpus (all identical) is one clique. Union-find collapses
# it to a single family without materializing every pair.
spec = {chr(ord("a") + k): [0, 1, 2, 3] for k in range(8)}
families = _duplicate_families(*_centroids(spec), DuplicateDetectionConfig())
assert len(families) == 1
assert set(families[0].members) == set(spec)
assert all(s == pytest.approx(1.0) for s in families[0].similarity.values())
def test_duplicate_families_tiny_docs_ignored():
# min_chunks = 3 excludes the one-chunk documents.
families = _duplicate_families(_docs({"a": [0], "b": [0]}), _stage2_cfg())
families = _duplicate_families(
*_centroids({"a": [0], "b": [0]}), DuplicateDetectionConfig()
)
assert families == []
def test_duplicate_families_caps_candidates_during_collection(monkeypatch):
# Three mutually-identical docs would yield 3 candidate pairs, but a cap of 1
# must stop collection after the first (a,b), leaving c unconfirmed.
monkeypatch.setattr("haiku.rag.doctor.MAX_CANDIDATE_PAIRS", 1)
docs = _docs({"a": [0, 1, 2], "b": [0, 1, 2], "c": [0, 1, 2]}, dim=3)
families = _duplicate_families(docs, _stage2_cfg())
assert len(families) == 1
assert set(families[0].members) == {"a", "b"}
def test_duplicate_families_threshold_is_configurable():
# Share 3 of 5 chunks each -> containment 0.6 both ways.
spec = {"a": [0, 1, 2, 3, 4], "b": [0, 1, 2, 5, 6]}
assert _duplicate_families(_docs(spec), _stage2_cfg()) == []
flagged = _duplicate_families(_docs(spec), _stage2_cfg(containment_threshold=0.6))
# Share 3 of 4 chunks each -> centroid cosine 0.75.
spec = {"a": [0, 1, 2, 3], "b": [0, 1, 2, 4]}
assert _duplicate_families(*_centroids(spec), DuplicateDetectionConfig()) == []
flagged = _duplicate_families(
*_centroids(spec), DuplicateDetectionConfig(similarity_threshold=0.7)
)
assert len(flagged) == 1
assert set(flagged[0].members) == {"a", "b"}
@ -1051,9 +1047,10 @@ def test_duplicate_documents_report_truncates_summary():
idx = [3 * k, 3 * k + 1, 3 * k + 2]
spec[f"a{k}"] = idx
spec[f"b{k}"] = list(idx)
docs = _docs(spec, dim=3 * pairs)
uris = {d: f"file:///srv/shared/library/docs/{d}.pdf" for d in spec}
result = _check_duplicate_documents(docs, uris, {}, _stage2_cfg())
result = _check_duplicate_documents(
*_centroids(spec, dim=3 * pairs), uris, {}, DuplicateDetectionConfig()
)
assert result.severity is Severity.WARN
# The summary message still reports the full total.
assert f"{pairs} group(s)" in result.message
@ -1064,10 +1061,14 @@ def test_duplicate_documents_report_truncates_summary():
def test_duplicate_documents_report_factors_common_path():
docs = _docs({"a": [0, 1, 2], "b": [0, 1, 2]}, dim=3)
base = "file:///srv/shared/library/docs/"
uris = {"a": base + "alpha.pdf", "b": base + "beta.pdf"}
result = _check_duplicate_documents(docs, uris, {}, _stage2_cfg())
result = _check_duplicate_documents(
*_centroids({"a": [0, 1, 2], "b": [0, 1, 2]}, dim=3),
uris,
{},
DuplicateDetectionConfig(),
)
assert f"common path: {base}" in result.details
member_lines = [d for d in result.details if d.lstrip().startswith("#")]
assert {d.strip() for d in member_lines} == {"#1 alpha.pdf", "#2 beta.pdf"}
@ -1076,10 +1077,12 @@ def test_duplicate_documents_report_factors_common_path():
def test_duplicate_documents_writes_yaml(tmp_path):
# a,b identical (a 4-chunk duplicate); c distinct and excluded.
docs = _docs({"a": [0, 1, 2, 3], "b": [0, 1, 2, 3], "c": [4, 5, 6]}, dim=8)
spec = {"a": [0, 1, 2, 3], "b": [0, 1, 2, 3], "c": [4, 5, 6]}
uris = {"a": "file:///x/a.pdf", "b": "file:///x/b.pdf", "c": "file:///x/c.pdf"}
out = tmp_path / "dups.yaml"
_check_duplicate_documents(docs, uris, {}, _stage2_cfg(), yaml_path=out)
_check_duplicate_documents(
*_centroids(spec, dim=8), uris, {}, DuplicateDetectionConfig(), yaml_path=out
)
data = yaml.safe_load(out.read_text())
assert len(data["groups"]) == 1
group = data["groups"][0]
@ -1088,6 +1091,7 @@ def test_duplicate_documents_writes_yaml(tmp_path):
assert [d["document_id"] for d in docs_out] == ["a", "b"]
assert [d["document"] for d in docs_out] == ["file:///x/a.pdf", "file:///x/b.pdf"]
assert all(d["chunks"] == 4 for d in docs_out)
assert all(d["similarity"] == pytest.approx(1.0) for d in docs_out)
assert {d["document_id"]: d["keep_suggested"] for d in docs_out} == {
"a": True,
"b": False,
@ -1095,10 +1099,14 @@ def test_duplicate_documents_writes_yaml(tmp_path):
def test_duplicate_documents_writes_empty_yaml_when_none(tmp_path):
docs = _docs({"a": [0, 1, 2], "b": [3, 4, 5]}, dim=6) # distinct
spec = {"a": [0, 1, 2], "b": [3, 4, 5]} # distinct
out = tmp_path / "dups.yaml"
_check_duplicate_documents(
docs, {"a": "u", "b": "v"}, {}, _stage2_cfg(), yaml_path=out
*_centroids(spec, dim=6),
{"a": "u", "b": "v"},
{},
DuplicateDetectionConfig(),
yaml_path=out,
)
assert yaml.safe_load(out.read_text()) == {"groups": []}
@ -1162,7 +1170,7 @@ async def _build_dup_db(path, docs: dict[str, list[int]], *, vector_dim: int = 8
@pytest.mark.asyncio
async def test_duplicate_documents_check_warns_end_to_end(temp_db_path):
await _build_dup_db(temp_db_path, {"a": [0, 1, 2, 3], "b": [0, 1, 2, 3, 4]})
await _build_dup_db(temp_db_path, {"a": [0, 1, 2, 3], "b": [0, 1, 2, 3]})
report = await run_doctor(_config(vector_dim=8), temp_db_path, {})
result = _result(report, "duplicate_documents")
assert result.severity is Severity.WARN
@ -1179,14 +1187,10 @@ async def test_duplicate_documents_check_ok_when_distinct(temp_db_path):
@pytest.mark.asyncio
async def test_duplicate_documents_check_reads_config(temp_db_path):
# Share 3 of 5 -> containment 0.6. Disable the centroid gate on both runs so
# only containment_threshold decides the outcome.
# Share 3 of 5 -> centroid cosine 0.6, below the default 0.97 cutoff.
await _build_dup_db(temp_db_path, {"a": [0, 1, 2, 3, 4], "b": [0, 1, 2, 5, 6]})
base = _config(vector_dim=8)
base.doctor = DoctorConfig(
duplicates=DuplicateDetectionConfig(candidate_threshold=0.0)
)
assert (
_result(
await run_doctor(base, temp_db_path, {}), "duplicate_documents"
@ -1196,9 +1200,7 @@ async def test_duplicate_documents_check_reads_config(temp_db_path):
tuned = _config(vector_dim=8)
tuned.doctor = DoctorConfig(
duplicates=DuplicateDetectionConfig(
candidate_threshold=0.0, containment_threshold=0.6
)
duplicates=DuplicateDetectionConfig(similarity_threshold=0.5)
)
assert (
_result(