diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6900eed0..0da1cddb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,10 @@
 # Changelog
 ## [Unreleased]
 
+### Added
+
+- `doctor` reports groups of near-duplicate documents (revisions sharing most of their chunks), flagging the largest member as the likely one to keep. Tuned via the `doctor.duplicates` config block (`containment_threshold`, `candidate_threshold`, `twin_similarity`, `min_chunks`).
+
 ### Security
 
 - Bumped dependencies in `uv.lock` to patched versions for known advisories: `aiohttp` 3.14.1, `cryptography` 49.0.0, `idna` 3.18, `langchain-core` 1.4.8, `langchain-text-splitters` 1.1.2, `langsmith` 0.9.1, `lxml` 6.1.1, `pillow` 12.2.0, `pydantic-settings` 2.14.2, `pyjwt` 2.13.0, `pytest` 9.1.1, `python-multipart` 0.0.32, `requests` 2.34.2, `starlette` 1.3.1, `urllib3` 2.7.0, `vcrpy` 8.2.1.
diff --git a/docs/cli.md b/docs/cli.md
index 8549016c..589f519e 100644
--- a/docs/cli.md
+++ b/docs/cli.md
@@ -307,6 +307,7 @@ 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)
 - API keys are set for configured providers
 
 It also probes the external endpoints the config uses and reports them under a Providers section:
diff --git a/docs/configuration/index.md b/docs/configuration/index.md
index a05de0a6..300d8f1e 100644
--- a/docs/configuration/index.md
+++ b/docs/configuration/index.md
@@ -114,6 +114,13 @@ search:
   vector_index_metric: cosine  # cosine, l2, or dot
   vector_refine_factor: 30
 
+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: 0.97        # cosine at which two chunks count as the same chunk
+    min_chunks: 3                # documents with fewer chunks are excluded
+
 prompts:
   domain_preamble: ""  # Prepended to skill instructions
 
diff --git a/haiku_rag_slim/haiku/rag/config/models.py b/haiku_rag_slim/haiku/rag/config/models.py
index 7a10e6a4..c5a03057 100644
--- a/haiku_rag_slim/haiku/rag/config/models.py
+++ b/haiku_rag_slim/haiku/rag/config/models.py
@@ -103,6 +103,27 @@ class AnalysisConfig(BaseModel):
     max_executions: int = 15
 
 
+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.
+    """
+
+    containment_threshold: float = 0.75
+    candidate_threshold: float = 0.85
+    twin_similarity: float = 0.97
+    min_chunks: int = 3
+
+
+class DoctorConfig(BaseModel):
+    duplicates: DuplicateDetectionConfig = Field(
+        default_factory=DuplicateDetectionConfig
+    )
+
+
 class PictureDescriptionConfig(BaseModel):
     """How the VLM runs over each picture when it runs at all.
 
@@ -516,6 +537,7 @@ class AppConfig(BaseModel):
     analysis: AnalysisConfig = Field(default_factory=AnalysisConfig)
     processing: ProcessingConfig = Field(default_factory=ProcessingConfig)
     search: SearchConfig = Field(default_factory=SearchConfig)
+    doctor: DoctorConfig = Field(default_factory=DoctorConfig)
     providers: ProvidersConfig = Field(default_factory=ProvidersConfig)
     prompts: PromptsConfig = Field(default_factory=PromptsConfig)
     ingester: IngesterConfig = Field(default_factory=IngesterConfig)
diff --git a/haiku_rag_slim/haiku/rag/doctor.py b/haiku_rag_slim/haiku/rag/doctor.py
index 4bbd48a2..d096ee8e 100644
--- a/haiku_rag_slim/haiku/rag/doctor.py
+++ b/haiku_rag_slim/haiku/rag/doctor.py
@@ -8,6 +8,7 @@ import numpy as np
 from pydantic import BaseModel, Field
 
 from haiku.rag.config import AppConfig
+from haiku.rag.config.models import DuplicateDetectionConfig
 from haiku.rag.store.engine import (
     REQUIRED_TABLES,
     Store,
@@ -232,6 +233,153 @@ 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]]
+
+
+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
+) -> list[_DuplicateFamily]:
+    """Cluster documents that share most of their chunks (revisions of one another).
+
+    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.
+    """
+    # Normalize, drop unembedded (zero) vectors, apply the small-document floor.
+    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
+        norms = np.linalg.norm(m, axis=1)
+        m = m[norms > 0]
+        if m.shape[0] < cfg.min_chunks:
+            continue
+        normalized[doc_id] = m / np.linalg.norm(m, axis=1)[:, None]
+    if len(normalized) < 2:
+        return []
+
+    order = sorted(normalized)
+    centroids = np.array([_unit(normalized[d].mean(axis=0)) for d in order])
+
+    # Stage 1: centroid candidate pairs, block-wise to avoid a full D×D matrix.
+    candidates: list[tuple[int, int]] = []
+    block = 512
+    for start in range(0, len(order), block):
+        sims = centroids[start : start + block] @ centroids.T
+        for row in range(sims.shape[0]):
+            gi = start + row
+            above = np.nonzero(sims[row, gi + 1 :] >= cfg.candidate_threshold)[0]
+            candidates.extend((gi, gi + 1 + int(j)) for j in above)
+        if len(candidates) >= MAX_CANDIDATE_PAIRS:
+            candidates = candidates[:MAX_CANDIDATE_PAIRS]
+            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:
+        return []
+
+    # Cluster confirmed pairs into families (connected components).
+    families: list[_DuplicateFamily] = []
+    seen: set[int] = set()
+    for node in adjacency:
+        if node in seen:
+            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
+        )
+        families.append(
+            _DuplicateFamily(members=members, superset=superset, pairs=pairs)
+        )
+    return sorted(families, key=lambda f: f.members)
+
+
+def _check_duplicate_documents(
+    doc_vectors: dict[str, np.ndarray],
+    uri_by_doc: dict[str, str | None],
+    title_by_doc: dict[str, str | None],
+    cfg: DuplicateDetectionConfig,
+) -> CheckResult:
+    families = _duplicate_families(doc_vectors, cfg)
+    if not families:
+        return CheckResult(
+            name="duplicate_documents",
+            severity=Severity.OK,
+            message="No near-duplicate documents detected.",
+        )
+
+    def label(doc_id: str) -> str:
+        return uri_by_doc.get(doc_id) or title_by_doc.get(doc_id) or doc_id
+
+    lines: list[str] = []
+    for family in families:
+        members = ", ".join(label(m) for m in family.members)
+        overlaps = "; ".join(
+            f"{label(a)}→{label(b)}: {ab:.2f}, {label(b)}→{label(a)}: {ba:.2f}"
+            for a, b, ab, ba in family.pairs
+        )
+        lines.append(f"[{members}] keep≈{label(family.superset)} ({overlaps})")
+
+    total_docs = sum(len(f.members) for f in families)
+    return CheckResult(
+        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."
+        ),
+        remediation=(
+            "Review each group and remove redundant revisions; overlap may be intentional."
+        ),
+        details=_sample(lines),
+    )
+
+
 async def run_db_checks(
     store: Store, config: AppConfig, stats: dict
 ) -> list[CheckResult]:
@@ -244,7 +392,7 @@ async def run_db_checks(
     doc_ids = set(await _column_values(store.documents_table, "id"))
     meta_rows = (
         await store.document_meta_table.query()
-        .select(["document_id", "metadata"])
+        .select(["document_id", "metadata", "uri", "title"])
         .to_list()
     )
     meta_doc_ids = {row["document_id"] for row in meta_rows}
@@ -254,6 +402,8 @@ async def run_db_checks(
         )
         for row in meta_rows
     }
+    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}
 
     chunk_rows = (
         await store.chunks_table.query()
@@ -375,7 +525,11 @@ async def run_db_checks(
 
     # Vector dimension consistency and unembedded (all-zero) vectors share one
     # scan of the vector column — the heaviest check on large corpora.
-    arrow = await store.chunks_table.query().select(["id", "vector"]).to_arrow()
+    arrow = (
+        await store.chunks_table.query()
+        .select(["id", "vector", "document_id"])
+        .to_arrow()
+    )
     stored = await SettingsRepository(store).get_current_settings()
     stored_dim = stored.get("embeddings", {}).get("model", {}).get("vector_dim")
     actual_dim = arrow.schema.field("vector").type.list_size
@@ -419,6 +573,19 @@ async def run_db_checks(
         )
     )
 
+    # 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()}
+    results.append(
+        _check_duplicate_documents(
+            doc_vectors, uri_by_doc, title_by_doc, config.doctor.duplicates
+        )
+    )
+
     # 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.
diff --git a/tests/test_doctor.py b/tests/test_doctor.py
index 5ca7bef3..4c6c0885 100644
--- a/tests/test_doctor.py
+++ b/tests/test_doctor.py
@@ -3,6 +3,7 @@ from importlib import metadata
 from unittest.mock import AsyncMock, MagicMock
 
 import lancedb
+import numpy as np
 import pytest
 from typer.testing import CliRunner
 
@@ -11,6 +12,8 @@ from haiku.rag.config.models import (
     AppConfig,
     ConversionOptions,
     DoclingServeConfig,
+    DoctorConfig,
+    DuplicateDetectionConfig,
     EmbeddingModelConfig,
     EmbeddingsConfig,
     ModelConfig,
@@ -26,6 +29,7 @@ from haiku.rag.doctor import (
     _check_api_keys,
     _check_embedding_drift,
     _check_vector_index,
+    _duplicate_families,
     _model_present,
     _probe_endpoint,
     _provider_targets,
@@ -933,3 +937,190 @@ async def test_probe_endpoint_connection_error():
     reachable, error, _ = await _probe_with_handler(handler)
     assert not reachable
     assert error is not None and "refused" in 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.
+
+    A shared index across documents is a shared (identical) chunk; distinct
+    indices are orthogonal, so they never count as twins.
+    """
+    eye = np.eye(dim)
+    return {
+        doc: np.array([eye[i] for i in idxs], dtype=float) for doc, idxs in spec.items()
+    }
+
+
+# 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():
+    families = _duplicate_families(
+        _docs({"a": [0, 1, 2, 3], "b": [0, 1, 2, 4]}), _stage2_cfg()
+    )
+    assert len(families) == 1
+    assert set(families[0].members) == {"a", "b"}
+
+
+def test_duplicate_families_append_only_is_asymmetric():
+    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_distinct_docs_none():
+    families = _duplicate_families(
+        _docs({"a": [0, 1, 2], "b": [3, 4, 5]}), _stage2_cfg()
+    )
+    assert families == []
+
+
+def test_duplicate_families_three_way_chain_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(),
+    )
+    assert len(families) == 1
+    assert set(families[0].members) == {"a", "b", "c"}
+    assert families[0].superset == "c"
+
+
+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())
+    assert families == []
+
+
+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))
+    assert len(flagged) == 1
+    assert set(flagged[0].members) == {"a", "b"}
+
+
+async def _build_dup_db(path, docs: dict[str, list[int]], *, vector_dim: int = 8):
+    """Build a multi-document database with one-hot chunk vectors."""
+    eye = np.eye(vector_dim)
+    db = await lancedb.connect_async(path)
+    settings_tbl = await db.create_table("settings", schema=SettingsRecord)
+    docs_tbl = await db.create_table("documents", schema=DocumentRecord)
+    meta_tbl = await db.create_table("document_meta", schema=DocumentMetaRecord)
+    chunk_model = create_chunk_model(vector_dim)
+    chunks_tbl = await db.create_table("chunks", schema=chunk_model)
+    items_tbl = await db.create_table("document_items", schema=DocumentItemRecord)
+
+    await settings_tbl.add(
+        [
+            SettingsRecord(
+                id="settings",
+                settings=json.dumps(
+                    {
+                        "version": CURRENT_VERSION,
+                        "embeddings": {
+                            "model": {
+                                "provider": "ollama",
+                                "name": "test",
+                                "vector_dim": vector_dim,
+                            }
+                        },
+                    }
+                ),
+            )
+        ]
+    )
+    for doc_id, idxs in docs.items():
+        await docs_tbl.add([DocumentRecord(id=doc_id, content="x")])
+        await meta_tbl.add(
+            [DocumentMetaRecord(document_id=doc_id, uri=f"test://{doc_id}")]
+        )
+        await items_tbl.add(
+            [
+                DocumentItemRecord(
+                    document_id=doc_id, position=0, self_ref="#/texts/0", text="x"
+                )
+            ]
+        )
+        await chunks_tbl.add(
+            [
+                chunk_model(
+                    id=f"{doc_id}-c{n}",
+                    document_id=doc_id,
+                    content="x",
+                    metadata=json.dumps({"doc_item_refs": ["#/texts/0"]}),
+                    vector=eye[i].tolist(),
+                )
+                for n, i in enumerate(idxs)
+            ]
+        )
+    return db
+
+
+@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]})
+    report = await run_doctor(_config(vector_dim=8), temp_db_path, {})
+    result = _result(report, "duplicate_documents")
+    assert result.severity is Severity.WARN
+    blob = " ".join(result.details)
+    assert "test://a" in blob and "test://b" in blob
+
+
+@pytest.mark.asyncio
+async def test_duplicate_documents_check_ok_when_distinct(temp_db_path):
+    await _build_dup_db(temp_db_path, {"a": [0, 1, 2], "b": [3, 4, 5]})
+    report = await run_doctor(_config(vector_dim=8), temp_db_path, {})
+    assert _result(report, "duplicate_documents").severity is Severity.OK
+
+
+@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.
+    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"
+        ).severity
+        is Severity.OK
+    )
+
+    tuned = _config(vector_dim=8)
+    tuned.doctor = DoctorConfig(
+        duplicates=DuplicateDetectionConfig(
+            candidate_threshold=0.0, containment_threshold=0.6
+        )
+    )
+    assert (
+        _result(
+            await run_doctor(tuned, temp_db_path, {}), "duplicate_documents"
+        ).severity
+        is Severity.WARN
+    )