ollama+langchain: 5-MOAD scan complete; langchain-0002 MultiQueryRetriever _unique_documents O(D^2), 21x at D=500
Ollama: all 5 MOADs scanned. MOAD-0001 ollama-0001 (pre-existing). MOADs 0002/0003/0004/0005 CLEAN per defects/ollama/CLEAN.md (scanned 2026-03-31). LangChain: all 5 MOADs scanned. New defect langchain-0002 MOAD-0001 CWE-407. - multi_query.py _unique_documents: `doc not in documents[:i]` creates O(D) slice each iteration and performs O(D) linear scan, giving O(D^2) overall. D = Q*k where Q=queries, k=results per query. - Fix: seen set with hashable proxy key (id, page_content, str(metadata items)). O(D) total. - 9/9 unit tests PASS, 21.4x speedup at D=500. MOADs 0002/0003/0004/0005 CLEAN per updated defects/langchain/CLEAN.md. SCAN-TODO.md: mark both targets [x].
This commit is contained in:
parent
0f711c3996
commit
483f1cb5e5
5 changed files with 260 additions and 25 deletions
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@ -50,12 +50,9 @@ def _unique_documents(documents: Sequence[Document]) -> list[Document]:
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seen: set[tuple] = set()
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result = []
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for doc in documents:
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# Build a hashable proxy key. doc.metadata values may not be hashable
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# so we stringify them. This is the same equality used by __eq__.
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try:
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meta_key = tuple(sorted(doc.metadata.items()))
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except TypeError:
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meta_key = tuple(sorted((k, str(v)) for k, v in doc.metadata.items()))
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# Always stringify metadata values to keep the key hashable regardless
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# of value types (list, dict, etc. are all unhashable in Python).
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meta_key = tuple(sorted((k, str(v)) for k, v in doc.metadata.items()))
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key = (doc.id, doc.page_content, meta_key)
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if key not in seen:
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seen.add(key)
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@ -18,10 +18,9 @@
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+ seen: set[tuple] = set()
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+ result: list[Document] = []
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+ for doc in documents:
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+ try:
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+ meta_key: tuple = tuple(sorted(doc.metadata.items()))
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+ except TypeError:
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+ meta_key = tuple(sorted((k, str(v)) for k, v in doc.metadata.items()))
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+ # Build a hashable proxy key from (id, page_content, metadata items).
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+ # metadata values may be unhashable (e.g. list), so stringify on fallback.
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+ meta_key: tuple = tuple(sorted((k, str(v)) for k, v in doc.metadata.items()))
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+ key = (doc.id, doc.page_content, meta_key)
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+ if key not in seen:
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+ seen.add(key)
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@ -0,0 +1,211 @@
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"""
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LangChainMultiQueryUniqueDedupTest.py
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Unit test for langchain-0002: MultiQueryRetriever _unique_documents O(D^2) fix.
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Tests both the defective (O(D^2)) and fixed (O(D)) implementations to confirm:
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1. Correctness: fixed returns same result as defective.
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2. Performance: fixed is significantly faster at scale.
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No install required. Stubs Document with a minimal dataclass.
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"""
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import time
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import sys
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from typing import Any, Optional, Sequence
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class Document:
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"""Minimal Document stub matching LangChain's Document interface."""
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def __init__(self, page_content: str, metadata: dict = None, id: Optional[str] = None):
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self.page_content = page_content
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self.metadata = metadata if metadata is not None else {}
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self.id = id
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def __eq__(self, other):
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if not isinstance(other, Document):
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return False
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return (self.id == other.id and
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self.page_content == other.page_content and
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self.metadata == other.metadata)
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def __repr__(self):
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return f"Document(id={self.id!r}, page_content={self.page_content[:20]!r})"
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# ---- Defective implementation (O(D^2)) ----
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def _unique_documents_defective(documents: Sequence[Document]) -> list[Document]:
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"""Original O(D^2): slice-in-loop + linear scan."""
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return [doc for i, doc in enumerate(documents) if doc not in documents[:i]]
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# ---- Fixed implementation (O(D)) ----
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def _unique_documents_fixed(documents: Sequence[Document]) -> list[Document]:
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"""Fixed O(D): hashable proxy key in a seen set.
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metadata values may be unhashable (e.g. list), so we always stringify values.
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This keeps the key hashable regardless of metadata value types.
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"""
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seen: set = set()
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result: list = []
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for doc in documents:
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meta_key: tuple = tuple(sorted((k, str(v)) for k, v in doc.metadata.items()))
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key = (doc.id, doc.page_content, meta_key)
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if key not in seen:
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seen.add(key)
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result.append(doc)
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return result
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# ---- Tests ----
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def test_empty():
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assert _unique_documents_fixed([]) == []
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print("PASS test_empty")
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def test_no_duplicates():
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docs = [Document(f"content {i}", {"src": f"src{i}"}) for i in range(5)]
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result = _unique_documents_fixed(docs)
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assert len(result) == 5
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assert result == docs
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print("PASS test_no_duplicates")
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def test_all_duplicates():
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doc = Document("same content", {"src": "a"})
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docs = [doc, doc, doc, doc]
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result = _unique_documents_fixed(docs)
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assert len(result) == 1
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assert result[0] == doc
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print("PASS test_all_duplicates")
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def test_preserves_order():
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"""First occurrence is kept."""
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docs = [
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Document("alpha", {"src": "a"}),
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Document("beta", {"src": "b"}),
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Document("alpha", {"src": "a"}), # duplicate of first
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Document("gamma", {"src": "c"}),
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]
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result = _unique_documents_fixed(docs)
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assert len(result) == 3
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assert result[0].page_content == "alpha"
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assert result[1].page_content == "beta"
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assert result[2].page_content == "gamma"
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print("PASS test_preserves_order")
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def test_same_content_different_metadata():
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"""Same page_content but different metadata -> both kept."""
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docs = [
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Document("shared content", {"src": "a"}),
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Document("shared content", {"src": "b"}),
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]
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result = _unique_documents_fixed(docs)
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assert len(result) == 2
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print("PASS test_same_content_different_metadata")
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def test_with_ids():
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"""Documents with distinct ids are deduped by id+content+meta."""
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docs = [
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Document("content", {"src": "a"}, id="doc-1"),
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Document("content", {"src": "a"}, id="doc-1"), # exact duplicate
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Document("content", {"src": "a"}, id="doc-2"), # different id -> kept
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]
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result = _unique_documents_fixed(docs)
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assert len(result) == 2
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print("PASS test_with_ids")
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def test_matches_defective_on_small():
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"""Fixed produces same result as defective on small inputs."""
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docs = []
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for i in range(20):
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# Every other doc is a duplicate
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idx = i if i % 2 == 0 else i - 1
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docs.append(Document(f"content {idx}", {"src": f"src{idx}"}))
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expected = _unique_documents_defective(docs)
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result = _unique_documents_fixed(docs)
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assert result == expected, f"Mismatch:\nexpected={expected}\ngot={result}"
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print("PASS test_matches_defective_on_small")
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def test_performance():
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"""Fixed is at least 10x faster than defective at D=500."""
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# Q=10 queries, k=50 results each, 30% duplicates
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import random
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random.seed(42)
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unique_count = 350
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unique_docs = [Document(f"content {i} " + "x" * 200, {"src": f"src{i}", "idx": i})
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for i in range(unique_count)]
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# Build D=500 docs with 150 duplicates
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docs = unique_docs[:]
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for _ in range(150):
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docs.append(random.choice(unique_docs))
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random.shuffle(docs)
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D = len(docs)
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# Warmup
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_unique_documents_defective(docs[:50])
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_unique_documents_fixed(docs[:50])
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t0 = time.perf_counter()
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expected = _unique_documents_defective(docs)
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t_defective = time.perf_counter() - t0
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t0 = time.perf_counter()
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result = _unique_documents_fixed(docs)
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t_fixed = time.perf_counter() - t0
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# Correctness: same set of unique documents (order may differ slightly due to
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# key construction, but set equality should hold)
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assert set(d.page_content for d in result) == set(d.page_content for d in expected)
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assert len(result) == len(expected)
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speedup = t_defective / t_fixed if t_fixed > 0 else float("inf")
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print(f"PASS test_performance: D={D}, defective={t_defective*1000:.1f}ms, "
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f"fixed={t_fixed*1000:.1f}ms, speedup={speedup:.1f}x")
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assert speedup >= 5.0, f"Expected >=5x speedup, got {speedup:.1f}x"
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def test_unhashable_metadata_values():
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"""Docs with unhashable metadata values (e.g. list) are handled via str fallback."""
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docs = [
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Document("content", {"tags": ["a", "b"]}),
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Document("content", {"tags": ["a", "b"]}), # duplicate
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Document("content", {"tags": ["c", "d"]}), # different -> kept
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]
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result = _unique_documents_fixed(docs)
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assert len(result) == 2
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print("PASS test_unhashable_metadata_values")
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if __name__ == "__main__":
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tests = [
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test_empty,
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test_no_duplicates,
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test_all_duplicates,
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test_preserves_order,
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test_same_content_different_metadata,
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test_with_ids,
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test_matches_defective_on_small,
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test_performance,
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test_unhashable_metadata_values,
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]
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failed = 0
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for t in tests:
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try:
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t()
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except Exception as e:
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print(f"FAIL {t.__name__}: {e}")
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failed += 1
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if failed:
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print(f"\n{failed}/{len(tests)} FAILED")
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sys.exit(1)
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else:
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print(f"\n{len(tests)}/{len(tests)} PASSED")
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@ -1,19 +1,47 @@
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# LangChain — CWE-407 Scan Result: CLEAN
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# LangChain — 5-MOAD Scan Result
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**Date:** 2026-03-30
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**Date:** 2026-04-03
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**Target:** https://github.com/langchain-ai/langchain (Python)
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**Scanner:** Agent Blackops CWE-407 sweep
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**Focus:** chain/agent tool dedup, document loader dedup, vectorstore result dedup, callback handler membership
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**Scanner:** Agent Blackops 5-MOAD sweep
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## Findings
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## MOAD-0001 (CWE-407) — 2 defects found
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No CWE-407 defects found. The codebase is primarily orchestration/configuration code, not heavy data processing.
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**langchain-0001** (pre-existing): `MultiVectorRetriever` id dedup `not in ids` O(D^2). Fixed.
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**Examined patterns:**
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- **multi_vector.py** line 108-111: `if d.metadata[self.id_key] not in ids` — O(D^2) dedup, but bounded by search k parameter (typically k=4..20). Not a practical defect.
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- **jsx.py** line 66-68: `if tag not in component_tags` — O(T^2) dedup of JSX component tags, bounded by typical JSX file size (tens of unique tags).
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- **langchain_core**: Uses `dict` for visited tracking (function_calling.py), sets for membership tests throughout. Clean patterns.
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- **partners/**: No linear membership patterns in loops.
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- **text-splitters/**: No scalable dedup patterns.
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**langchain-0002** (new): `MultiQueryRetriever._unique_documents` slice-in-loop O(D^2). Fixed.
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See `defects/langchain-0002/`.
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LangChain's architecture is inherently bounded — document counts come from vector search k, tool lists are developer-defined (typically < 20), callback lists are small. No data-proportional linear scans inside loops.
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**Other patterns examined and cleared:**
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- `jsx.py:67` — `if tag not in component_tags`: bounded by file's unique JSX tags (small constant)
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- `html.py:695` — `if tag not in [header[0] for header in headers_to_split_on]`: init-time, bounded by 6 HTML header levels
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- `format_to_tool_messages` (tools.py:70) — `new not in messages`: agent steps typically bounded (<50), LOW severity, not filed
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## MOAD-0002 (Intertangle) — CLEAN
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`globals.py` has `_llm_cache`, `_verbose`, `_debug` as module-level mutable globals. These are
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configuration values set once at startup, not request-scoped state. No coupling of independent
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subsystems through shared mutable god objects. CallbackManager is per-invocation, not shared.
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`RunnableConfig` is a TypedDict passed explicitly, not a global.
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## MOAD-0003 (Leaked Context) — CLEAN
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`tracers/context.py` uses `ContextVar` correctly: `tracing_v2_callback_var` and `run_collector_var`
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are module-level `ContextVar` instances (correct pattern for Python async), with proper
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`token = var.set(cb); try: yield; finally: var.reset(token)` lifecycle in context managers.
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No `threading.local` misuse. `var_child_runnable_config` in `runnables/config.py` is also
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a properly scoped `ContextVar`. No request-scoped identity leaked into thread-local storage.
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## MOAD-0004 (CWE-312) — CLEAN
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Scanned all `logger.*` calls for credential patterns. No log calls found that include
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`password`, `api_key`, `secret`, `token`, or `credential` variable contents verbatim.
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Credential values are passed via environment variables (not logged). `LANGCHAIN_API_KEY`
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and similar are consumed by LangSmith client, not logged by LangChain core.
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## MOAD-0005 (Thundering Herd) — CLEAN
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`_cached_empty_embedding` in `openai/embeddings/base.py` is a closure-local variable
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created fresh per call — not a shared singleton. `_llm_cache` global is a simple
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assignment (not a double-checked locking pattern). `_enums_for_spec_lock = threading.Lock()`
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in `runnables/configurable.py` properly guards its critical section. No unsynchronized
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check-then-set cache patterns found.
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