Issue #163: Improve KG Agent retrieval pipeline (vector primary, sparse graph fix, A/B flags)

[rysweet]

Summary

• Priority 5: Vector search is now the PRIMARY retrieval path in KnowledgeGraphAgent.query(). Uses CALL QUERY_VECTOR_INDEX('Section', 'embedding_idx', $query, K) RETURN * as the first strategy. Falls back to LLM-generated Cypher only when max cosine similarity < 0.6. Reduces the 30% query failure rate from bad Cypher generation.

• Priority 1: Fixed enhancement degradation on the Rust pack. GraphReranker.rerank() now checks graph density before applying centrality scores. If avg LINKS_TO edges/article < 2.0 (sparse graph like Rust web docs), centrality is disabled for the session with a warning log. Prevents the -1.2 score degradation observed on non-Wikipedia packs.

• Priority 7: Added enable_reranker, enable_multidoc, enable_fewshot constructor params to KnowledgeGraphAgent (all default True when use_enhancements=True). Added corresponding --disable-reranker, --disable-multidoc, --disable-fewshot CLI flags to both evaluation scripts for isolated component testing.

Changes

• wikigr/agent/kg_agent.py: Add _vector_primary_retrieve(), restructure query() for vector-first, add A/B flag params, update from_connection()
• wikigr/agent/reranker.py: Add _check_graph_density(), _sparse_graph cache, sparse check in rerank()
• scripts/run_enhancement_evaluation.py: Add --disable-* flags
• scripts/run_all_packs_evaluation.py: Add --disable-* flags and pass to evaluate_pack()
• tests/agent/test_kg_agent_semantic.py: 12 new tests for vector primary retrieval and A/B flags
• tests/packs/test_reranker.py: 5 new tests for sparse graph detection; update 4 existing tests for new density check calls
• conftest.py: Ensure local workstream source takes precedence over installed package in tests

Test plan

• tests/agent/test_kg_agent_semantic.py - 20 tests pass (12 new)
• tests/packs/test_reranker.py - 23 tests pass (5 new + 4 updated)
• All 43 relevant unit tests pass
• No new ruff E501 violations in modified files
• LLM Cypher path still exercised when vector confidence is low (< 0.6)
• Sparse graph detection caches result per session (no repeated queries)

🤖 Generated with Claude Code

[rysweet]

Code Review

User Requirement Compliance: All four stated goals are implemented. No explicit requirements are violated.

Overall Assessment: Solid work with a few correctness gaps in test mocking and one transparency inconsistency. Nothing that prevents merging, but two issues should be fixed before merge.

---

Strengths

• Vector primary retrieval logic is correct end-to-end. _vector_primary_retrieve() delegates to semantic_search(), which correctly uses the cosine metric index (confirmed in bootstrap/schema/ryugraph_schema.py line 287: metric := 'cosine'). The similarity = 1.0 - distance formula is valid for cosine distance with normalized sentence-transformer embeddings.
• Sparse graph detection in reranker.py is correctly structured: two sequential execute() calls, division-by-zero guard, exception handling returning 0.0, and a None-sentinel cache that fires exactly once per session.
• The _check_graph_density() query MATCH ()-[:LINKS_TO]->() is correct because LINKS_TO is typed FROM Article TO Article only (schema line 150), so it cannot accidentally count other relationship types.
• A/B flag propagation is clean: evaluate_pack() receives enable_* params, the baseline run correctly omits them (use_enhancements=False), and the enhanced run reads not args.disable_*. No flag inversion bugs.
• from_connection() is updated to initialize the three new attributes to safe defaults (lines 118-120). No regression risk for callers using the factory method.
• Exception handling in query() is defensive throughout. Failures in direct title lookup, hybrid retrieval, and the enhancement pipeline all degrade gracefully.

---

Issues Found

1. Test mocking bug in four reranker tests (Medium — tests pass for wrong reasons)

Location: tests/packs/test_reranker.py — test_rerank_vector_only, test_rerank_graph_only (pre-existing but now broken by sparse check), test_rerank_all_zero_centrality, test_rerank_missing_article_in_graph

GraphReranker.rerank() now calls _check_graph_density() on the first invocation (self._sparse_graph is None). That method issues TWO execute() calls before calculate_centrality() gets its call. These four tests use mock_kuzu_conn.execute.return_value = mock_result (a single mock return value), which means the density check's first call receives the centrality DataFrame instead of a links-count DataFrame.

What actually happens: the density check code does links_df.iloc[0]["total_links"] on a DataFrame with columns ["article_id", "centrality"]. This raises KeyError, the exception handler catches it, returns 0.0, sets self._sparse_graph = True (sparse), and skips calculate_centrality() entirely. The tests then pass because centrality is 0.0 anyway — but for the wrong reason (sparse flag set by exception, not by actual density data).

test_rerank_vector_only is the clearest case: with graph_weight=0.0, centrality is multiplied by zero regardless, so the sparse path and the correct path produce the same scores. The test validates the arithmetic but not the code path.

Fix: add side_effect mocking for the density queries as the new TestSparseGraphDetection tests do correctly. Example:

dense_links = Mock()
dense_links.get_as_df.return_value = pd.DataFrame({"total_links": [20]})
dense_articles = Mock()
dense_articles.get_as_df.return_value = pd.DataFrame({"total_articles": [10]})
mock_result = Mock()
mock_result.get_as_df.return_value = pd.DataFrame(
    {"article_id": [1, 2], "centrality": [0.3, 0.8]}
)
mock_kuzu_conn.execute.side_effect = [dense_links, dense_articles, mock_result]

2. Transparency cypher string has mismatched parameter name (Low — documentation-only bug)

Location: wikigr/agent/kg_agent.py line 188-189

query_plan = {
    "type": "vector_search",
    "cypher": "CALL QUERY_VECTOR_INDEX('Section', 'embedding_idx', $query, K) RETURN *",
    "cypher_params": {"q": question},
}

The cypher string uses $query as the embedding placeholder and a bare K (literal, not a parameter). The cypher_params dict uses the key "q", not "query". This string is never executed — it is only inserted into the Claude synthesis prompt (line 1162) and returned as cypher_query in the response. But clients or operators reading the response will see a non-executable, internally inconsistent Cypher fragment.

Fix: change the transparency string to match what semantic_search() actually executes, or add a comment clarifying it is a display approximation:

"cypher": "CALL QUERY_VECTOR_INDEX('Section', 'embedding_idx', $emb, $k) RETURN *",  # transparency only
"cypher_params": {"emb": "<embedding vector>", "k": max_results},

---

Minor Observations (no fix required)

Timing label includes augmentation work. t_exec spans from before _direct_title_lookup to after _hybrid_retrieve (lines 205-243), but is logged as the "exec" phase. When use_vector_primary=True, this time covers only augmentation (no Cypher execution). The monitoring log entry is not wrong but the exec/plan breakdown is harder to interpret for the vector path.

hybrid_retrieve still fires for vector_primary results. When use_vector_primary=True, query_type == "vector_search", which is not in the skip_hybrid exclusion list (entity_search, entity_relationships). So _hybrid_retrieve() always runs after a successful vector primary retrieval. This may be intentional (broader augmentation), but it means the vector primary path doesn't eliminate the hybrid retrieval cost. No correctness issue, but worth noting if the goal is to reduce latency.

conftest.py sys.path guard is correct but fragile. The if _workstream_root not in sys.path check compares paths as strings. If the same directory appears via symlinks or with/without trailing slash, the check could fail. os.path.realpath() would be more robust. Not a problem in practice on this repo.

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Philosophy Compliance (within user constraints)

• Zero-BS: No stubs, no TODOs, no placeholders. All code paths execute real logic.
• Simplicity: The sparse graph detection adds ~15 lines to the reranker. Justified by observed -1.2 degradation on the Rust pack. The _vector_primary_retrieve() wrapper is minimal — it delegates to semantic_search().
• Modularity: GraphReranker stays self-contained. The new _sparse_graph cache is internal state. No module boundary violations.
• Test coverage: 17 new tests. The TestSparseGraphDetection class covers all edge cases correctly. The pre-existing reranker tests (test_rerank_default_weights, test_rerank_custom_weights, etc.) are updated correctly with density mocks. The four tests in issue Implementation Task: Research, Assess, and Build Wikipedia Knowledge Graph (RyuGraph) #1 above slip through because they do not represent the new code path accurately.

Recommendation: Fix the four test mocking gaps (issue #1) before merge. Issue #2 is low-priority but straightforward to fix in the same pass.

[rysweet]

Security Review (Step 16c)

PR: #168 issue-163-kg-agent-retrieval-pipeline
Reviewer: Security Agent
Scope: wikigr/agent/kg_agent.py (vector primary retrieval, A/B flags), wikigr/agent/reranker.py (sparse graph detection)

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1. Cypher Injection via Vector Search User Input

Finding: PASS — Parameterised correctly throughout

The vector primary retrieval path calls self.semantic_search(question, top_k=max_results), which constructs the Kuzu query as:

result = self.conn.execute(
    "CALL QUERY_VECTOR_INDEX('Section', 'embedding_idx', $emb, $k)",
    {"emb": query_embedding, "k": top_k * 3},
)

The user's question string is never interpolated into a Cypher string. It is first converted to a floating-point embedding vector by sentence-transformers, and that vector is passed via the $emb parameter binding. This is correct parameterisation; the user input never touches the Cypher text layer.

The fast-path title lookup also uses parameterised binding:

conn.execute(
    "MATCH (a:Article {title: $query})-[:HAS_SECTION]->(s:Section) RETURN s.embedding AS embedding LIMIT 1",
    {"query": query},
)

No injection risk is introduced by this PR.

---

2. $query Parameter Binding in CALL QUERY_VECTOR_INDEX

Finding: PASS — Embedding vector, not raw text, is bound

The $query variable documented in the PR description is actually named $emb in the implementation and carries a list[float] (the embedding), not the raw question string. Kuzu's parameterised binding treats this as a typed array parameter, not a string token. There is no path by which a crafted question string could escape the embedding conversion and inject Cypher.

The existing Cypher allowlist check at line ~718 also applies to LLM-generated queries (the fallback path):

allowed_prefixes = ("MATCH ", "CALL QUERY_VECTOR_INDEX")
if not cypher.strip().upper().startswith(allowed_prefixes):
    raise ValueError("Query rejected: must start with MATCH or CALL QUERY_VECTOR_INDEX")

This defence-in-depth guard remains intact and is not weakened by this PR.

---

3. New CLI Flags (--disable-reranker, --disable-multidoc, --disable-fewshot)

Finding: PASS — Flags reduce capability, do not expand attack surface

The three new flags are boolean switches that selectively disable enhancement components. They do not accept string values, do not influence file paths or query construction, and do not expose internal objects to callers. Setting all three to True degrades result quality but does not open any security-relevant code path. The flags are safe to expose.

---

4. Sparse Graph Detection Safety

Finding: PASS — Read-only COUNT queries, no user input in Cypher

The _check_graph_density() method in reranker.py issues two static Cypher queries:

conn.execute("MATCH ()-[:LINKS_TO]->() RETURN count(*) AS total_links")
conn.execute("MATCH (a:Article) RETURN count(a) AS total_articles")

Both are hardcoded strings with no user-controlled content. The results are read as integers via get_as_df(), with a try/except that returns 0.0 on any error. Integer-only arithmetic (total_links / total_articles) follows. There is no path for malformed graph data to cause injection, code execution, or unexpected control flow beyond the graceful 0.0 fallback.

---

5. _vector_primary_retrieve Error Handling

Finding: INFORMATIONAL — Exception swallowed silently to (None, 0.0)

except Exception as e:
    logger.warning(f"Vector primary retrieve failed: {e}")
    return None, 0.0

The broad except Exception catch is intentional here (fail-safe fallback to LLM Cypher) and the error is logged at WARNING level, so it is not invisible. This is an acceptable trade-off for a retrieval pipeline. No security concern, flagging for awareness.

---

Summary

Check	Result	Severity
Cypher injection via user question	Parameterised via embedding vector	Pass
$query/$emb parameter binding	Correct typed binding, not string	Pass
--disable-* CLI flags attack surface	Boolean only, no path/query influence	Pass
Sparse graph detection with malformed data	Hardcoded queries, integer arithmetic only	Pass
Error handling in vector retrieval	Logged warning, graceful fallback	Informational

No blocking security findings. This PR is clean from a security perspective. The vector search path correctly avoids any form of query string injection by routing user input exclusively through the embedding layer before it reaches Kuzu.

[rysweet]

Step 17: Review Feedback Addressed

Fixed in commit e7a85f2:

Issue 2 (Cypher transparency string):

• Updated query_plan["cypher"] to show actual execution:
  • Param name: $emb (was $query)
  • Resolved K: {max_results * 3} (was bare K)
  • cypher_params: {"emb": "<embedding_vector>"} (was {"q": question})

Issue 1 (test mocking):
Confirmed this was already fixed in commit 2c1e0fe — all 4 affected tests use side_effect = [dense_links_mock, dense_articles_mock, mock_result] pattern. The reviewer correctly identified the latent bug from the original commit; it was fixed during the CI fix iteration.

[rysweet]

Philosophy Review (Step 16d)

Zen-Architect Review: PR #168 — Vector Primary Retrieval, Sparse Graph Fix, A/B Flags

Philosophy Score: B+

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Strengths

• Solving a real, measured problem: The 30% query failure rate from bad Cypher generation is a concrete, observed defect. The vector-primary approach directly addresses it. This is pragmatic engineering, not speculative future-proofing.
• Graceful fallback with explicit threshold: max_similarity < 0.6 is a documented, tunable decision boundary. The fallback path (LLM Cypher) is preserved and exercised — no path is abandoned. Code reads like policy: "try vector first, fall back to Cypher when confidence is low."
• Sparse graph detection is a self-contained fix: _check_graph_density() in GraphReranker answers one question and caches the result once per session. The fix is localized — _sparse_graph is a simple boolean flag, not a configuration object.
• Zero-BS: No stubs, no TODOs in logic paths, no fake implementations. The three new enable_* constructor params default to True and do what they say.
• from_connection completeness: The PR correctly initializes the three new enable_* attributes in from_connection(). A common source of bugs in factory methods is partial initialization; this is caught cleanly.
• Test quality: Tests for _vector_primary_retrieve and the A/B flag params mock at the right layer (Kuzu connection, not file system). The test_kg_agent_semantic.py tests cover the fast path (existing article embedding), fallback path (generate embedding), lazy initialization, and deduplication — four distinct behaviors without redundancy. That is proportional and sufficient.
• conftest.py at root: The 11-line conftest that inserts the workstream root into sys.path is minimal, clearly justified by the comment, and belongs at root scope. Good brick.

---

Concerns

• query() method is 170+ lines: Before this PR it was already large; this PR adds ~40 more lines. The method now has at least five distinct logical sections: vector primary retrieval, LLM Cypher fallback, direct title lookup, hybrid retrieval, and the enhancement pipeline (RRF + multi-doc + few-shot). Each section has inline comments marking it as a named step. These comments are symptoms: when you need step labels inside a method, those steps want to be methods. The method would benefit from extraction into _retrieve() (vector or Cypher dispatch) and _augment() (direct match + hybrid + enhancements), leaving query() as a thin coordinator.
• Enhancement pipeline is a nested complexity budget: The RRF block inside the enhancement pipeline (lines 255–285 of kg_agent.py) contains a dict comprehension, a conditional guard, a sorted call, and an adaptive preservation check — all within a single try block. The individual pieces are logical, but the nesting depth (try → if original_sources → for rank → conditional centrality → try → sorted → adaptive check) makes the invariants hard to reason about. The comment "Adaptive: only use fused ranking if top result changed AND original top result is still in top 3" encodes a non-trivial policy. That policy would be clearer as a named function _should_apply_rrf(original_sources, fused_sources).
• enable_reranker/enable_multidoc/enable_fewshot flags only meaningful with use_enhancements=True: Passing enable_reranker=False when use_enhancements=False silently has no effect. The flags are only read inside if use_enhancements:. A caller who passes enable_reranker=False, use_enhancements=False gets the same result as enable_reranker=True, use_enhancements=False, which is confusing. Consider asserting or logging when enable_* flags are set but use_enhancements is False, or restructure so enable_* implies use_enhancements=True.
• Test-to-implementation ratio for reranker.py: test_reranker.py is 453 lines for a 209-line module — a 2.2:1 ratio, which is within the acceptable range for business logic. However, several test cases (test_calculate_centrality_star_graph, test_calculate_centrality_chain_graph) test graph topology behavior that is actually emulated by mock return values, not by real Cypher queries. These tests verify that the Python code passes mock data through correctly, not that the Cypher produces correct centrality scores. That is fine for unit testing, but the comments ("central hub node should have highest centrality") overstate what is being tested. Minor precision issue, not a violation.

---

Violations

• None critical. No swallowed exceptions — all exception handlers log and either fall back or re-raise. No dead code introduced.

---

Recommendations

1. Immediate: Add a guard or log warning in __init__ when any enable_* flag is set to False but use_enhancements=False. Silent no-ops are deceptive.
2. Structural: Extract query() internals — pull the retrieval dispatch into _retrieve(question, max_results) and the augmentation + enhancement logic into _augment(question, kg_results, query_plan). This reduces the method from 170+ lines to a readable coordinator.
3. Simplification: Name the RRF adaptive-preservation logic: extract _should_apply_rrf(original_sources, fused_sources) — make the decision policy visible rather than buried in a conditional inside a try block.

---

Regeneration Assessment

• GraphReranker (reranker.py): Specification clarity: Clear — the module-level docstring defines the API contract exactly. Contract definition: Well-defined (calculate_centrality, rerank with typed signatures). Verdict: Ready for AI regeneration.
• KnowledgeGraphAgent.query() (kg_agent.py): Specification clarity: Unclear — the method's responsibility has expanded to the point where a spec for it would require documenting five distinct retrieval strategies and their interaction. Contract definition: Vague at the method level (documented at class level). Verdict: Needs Work — decompose first, then the resulting sub-methods become independently regeneratable bricks.

[rysweet]

Philosophy Review Response (Step 17)

Fixed the deceptive enable_ no-op* in commit ade4180:

• Added a logger.warning when enable_* flags are explicitly set to non-default values but use_enhancements=False
• Callers now get an explicit signal instead of silent behavior

Deferring the other concerns:

• query() 170-line method: Functional decomposition is a valid improvement but out of scope for this PR. Noted for follow-on.
• RRF policy buried in try block: Agreed, a named function would be cleaner. Follow-on refactor.

None of the concerns were zero-BS violations.

[rysweet]

Step 13: Local Testing Results

Test Environment: issue-163-kg-agent-retrieval-pipeline branch, 2026-02-27
Tests Executed:

1. Sparse graph detection: Reranker with 2 links / 10 articles (avg 0.2) → ✅ centrality disabled, Art B (higher vector score 0.8) ranks first
2. Dense graph: Reranker with 20 links / 5 articles (avg 4.0) → ✅ centrality enabled, centrality scores applied normally
3. Warning for no-op flags: KnowledgeGraphAgent(use_enhancements=False, enable_reranker=False) → ✅ logs WARNING about no-op flags
4. No warning with defaults: KnowledgeGraphAgent(use_enhancements=False) → ✅ no spurious warnings
5. Phase 3 unit tests: pytest tests/packs/test_reranker.py tests/agent/test_kg_agent_semantic.py → ✅ 26/26 passed
6. Full pack test suite: pytest tests/packs/ (251 tests) → ✅ all pass
7. Regression check: Pre-existing test_kg_agent_queries.py failures are DB-dependent, unrelated to this PR's changes

Issues Found: None — all scenarios work as expected.