diagnostics/app.py

@@ -13,11 +13,16 @@ import logging
 import os
 import sqlite3
 import statistics
+import sys
 import urllib.request
 from datetime import datetime, timezone
 from pathlib import Path
 
+# Add pipeline lib to path so we can import shared modules
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "pipeline"))
+
 from aiohttp import web
+from lib.search import search as kb_search, embed_query, search_qdrant
 
 logger = logging.getLogger("argus")
 
@@ -27,12 +32,7 @@ PORT = int(os.environ.get("ARGUS_PORT", "8081"))
 REPO_DIR = Path(os.environ.get("REPO_DIR", "/opt/teleo-eval/workspaces/main"))
 CLAIM_INDEX_URL = os.environ.get("CLAIM_INDEX_URL", "http://localhost:8080/claim-index")
 
-# Search config
-QDRANT_URL = os.environ.get("QDRANT_URL", "http://localhost:6333")
-QDRANT_COLLECTION = os.environ.get("QDRANT_COLLECTION", "teleo-claims")
-OPENROUTER_KEY_FILE = Path(os.environ.get("OPENROUTER_KEY_FILE", "/opt/teleo-eval/secrets/openrouter-key"))
-EMBEDDING_MODEL = "text-embedding-3-small"
-EMBEDDING_DIMS = 1536
+# Search config — moved to lib/search.py (shared with Telegram bot + agents)
 
 # Auth config
 API_KEY_FILE = Path(os.environ.get("ARGUS_API_KEY_FILE", "/opt/teleo-eval/secrets/argus-api-key"))
@@ -483,82 +483,7 @@ async def auth_middleware(request, handler):
 
 
 # ─── Embedding + Search ──────────────────────────────────────────────────────
-
-
-def _get_embedding_key() -> str | None:
-    """Load OpenRouter API key for embeddings."""
-    return _load_secret(OPENROUTER_KEY_FILE)
-
-
-def _embed_query(text: str, api_key: str) -> list[float] | None:
-    """Embed a query string via OpenRouter (OpenAI-compatible endpoint).
-
-    Uses urllib to avoid adding httpx/openai as dependencies.
-    """
-    payload = json.dumps({
-        "model": f"openai/{EMBEDDING_MODEL}",
-        "input": text,
-    }).encode()
-    req = urllib.request.Request(
-        "https://openrouter.ai/api/v1/embeddings",
-        data=payload,
-        headers={
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json",
-        },
-    )
-    try:
-        with urllib.request.urlopen(req, timeout=10) as resp:
-            data = json.loads(resp.read())
-            return data["data"][0]["embedding"]
-    except Exception as e:
-        logger.error("Embedding failed: %s", e)
-        return None
-
-
-def _search_qdrant(vector: list[float], limit: int = 10,
-                   domain: str | None = None, confidence: str | None = None,
-                   exclude: list[str] | None = None) -> list[dict]:
-    """Search Qdrant collection for nearest claims.
-
-    Uses urllib for zero-dependency Qdrant access (REST API).
-    """
-    must_filters = []
-    if domain:
-        must_filters.append({"key": "domain", "match": {"value": domain}})
-    if confidence:
-        must_filters.append({"key": "confidence", "match": {"value": confidence}})
-
-    must_not_filters = []
-    if exclude:
-        for path in exclude:
-            must_not_filters.append({"key": "claim_path", "match": {"value": path}})
-
-    payload = {
-        "vector": vector,
-        "limit": limit,
-        "with_payload": True,
-        "score_threshold": 0.3,
-    }
-    if must_filters or must_not_filters:
-        payload["filter"] = {}
-        if must_filters:
-            payload["filter"]["must"] = must_filters
-        if must_not_filters:
-            payload["filter"]["must_not"] = must_not_filters
-
-    req = urllib.request.Request(
-        f"{QDRANT_URL}/collections/{QDRANT_COLLECTION}/points/search",
-        data=json.dumps(payload).encode(),
-        headers={"Content-Type": "application/json"},
-    )
-    try:
-        with urllib.request.urlopen(req, timeout=10) as resp:
-            data = json.loads(resp.read())
-            return data.get("result", [])
-    except Exception as e:
-        logger.error("Qdrant search failed: %s", e)
-        return []
+# Moved to lib/search.py — imported at top of file as kb_search, embed_query, search_qdrant
 
 
 # ─── Usage logging ───────────────────────────────────────────────────────────
@@ -701,7 +626,7 @@ async def handle_api_domains(request):
 
 
 async def handle_api_search(request):
-    """GET /api/search — semantic search over claims via Qdrant.
+    """GET /api/search — semantic search over claims via Qdrant + graph expansion.
 
     Query params:
       q:          search query (required)
@@ -709,6 +634,7 @@ async def handle_api_search(request):
       confidence: filter by confidence level (optional)
       limit:      max results, default 10 (optional)
       exclude:    comma-separated claim paths to exclude (optional)
+      expand:     enable graph expansion, default true (optional)
     """
     query = request.query.get("q", "").strip()
     if not query:
@@ -719,36 +645,19 @@ async def handle_api_search(request):
     limit = min(int(request.query.get("limit", "10")), 50)
     exclude_raw = request.query.get("exclude", "")
     exclude = [p.strip() for p in exclude_raw.split(",") if p.strip()] if exclude_raw else None
+    expand = request.query.get("expand", "true").lower() != "false"
 
-    # Embed the query
-    api_key = _get_embedding_key()
-    if not api_key:
-        return web.json_response({"error": "embedding service unavailable"}, status=503)
+    # Use shared search library (Layer 1 + Layer 2)
+    result = kb_search(query, expand=expand,
+                       domain=domain, confidence=confidence, exclude=exclude)
 
-    vector = _embed_query(query, api_key)
-    if vector is None:
-        return web.json_response({"error": "embedding failed"}, status=502)
+    if "error" in result:
+        error = result["error"]
+        if error == "embedding_failed":
+            return web.json_response({"error": "embedding failed"}, status=502)
+        return web.json_response({"error": error}, status=500)
 
-    # Search Qdrant
-    results = _search_qdrant(vector, limit=limit, domain=domain,
-                             confidence=confidence, exclude=exclude)
-
-    # Format response
-    claims = []
-    for hit in results:
-        payload = hit.get("payload", {})
-        claims.append({
-            "claim_title": payload.get("claim_title", ""),
-            "claim_path": payload.get("claim_path", ""),
-            "similarity_score": round(hit.get("score", 0), 4),
-            "domain": payload.get("domain", ""),
-            "confidence": payload.get("confidence", ""),
-            "snippet": payload.get("snippet", "")[:200],
-            "depends_on": payload.get("depends_on", []),
-            "challenged_by": payload.get("challenged_by", []),
-        })
-
-    return web.json_response(claims)
+    return web.json_response(result)
 
 
 async def handle_api_usage(request):

lib/search.py

@@ -0,0 +1,415 @@
+"""Shared Qdrant vector search library for the Teleo knowledge base.
+
+Provides embed + search + graph expansion as a reusable library.
+Any consumer (Argus dashboard, Telegram bot, agent research) imports from here.
+
+Layer 1: Qdrant vector search (semantic similarity)
+Layer 2: Graph expansion (1-hop via frontmatter edges)
+Layer 3: Left to the caller (agent context, domain filtering)
+
+Owner: Epimetheus
+"""
+
+import json
+import logging
+import os
+import re
+from pathlib import Path
+
+import urllib.request
+
+from . import config
+
+logger = logging.getLogger("pipeline.search")
+
+# --- Config (all from environment or config.py defaults) ---
+QDRANT_URL = os.environ.get("QDRANT_URL", "http://localhost:6333")
+QDRANT_COLLECTION = os.environ.get("QDRANT_COLLECTION", "teleo-claims")
+EMBEDDING_MODEL = "text-embedding-3-small"
+
+_OPENROUTER_KEY: str | None = None
+
+WIKI_LINK_RE = re.compile(r"\[\[([^\]]+)\]\]")
+
+# Structural files that should never be included in graph expansion results.
+# These are indexes/MOCs, not claims — expanding them pulls entire domains.
+STRUCTURAL_FILES = {"_map.md", "_overview.md"}
+
+
+def _get_api_key() -> str | None:
+    """Load OpenRouter API key (cached after first read)."""
+    global _OPENROUTER_KEY
+    if _OPENROUTER_KEY:
+        return _OPENROUTER_KEY
+    key_file = config.SECRETS_DIR / "openrouter-key"
+    if key_file.exists():
+        _OPENROUTER_KEY = key_file.read_text().strip()
+        return _OPENROUTER_KEY
+    _OPENROUTER_KEY = os.environ.get("OPENROUTER_API_KEY")
+    return _OPENROUTER_KEY
+
+
+# --- Layer 1: Vector search ---
+
+
+def embed_query(text: str) -> list[float] | None:
+    """Embed a query string via OpenRouter (OpenAI-compatible endpoint).
+
+    Returns 1536-dim vector or None on failure.
+    """
+    api_key = _get_api_key()
+    if not api_key:
+        logger.error("No OpenRouter API key available for embedding")
+        return None
+
+    payload = json.dumps({
+        "model": f"openai/{EMBEDDING_MODEL}",
+        "input": text[:8000],
+    }).encode()
+    req = urllib.request.Request(
+        "https://openrouter.ai/api/v1/embeddings",
+        data=payload,
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        },
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=15) as resp:
+            data = json.loads(resp.read())
+            return data["data"][0]["embedding"]
+    except Exception as e:
+        logger.error("Embedding failed: %s", e)
+        return None
+
+
+def search_qdrant(vector: list[float], limit: int = 10,
+                  domain: str | None = None, confidence: str | None = None,
+                  exclude: list[str] | None = None,
+                  score_threshold: float = 0.3,
+                  offset: int = 0) -> list[dict]:
+    """Search Qdrant collection for nearest claims.
+
+    Args:
+        offset: Skip first N results (Qdrant native offset for pagination).
+
+    Returns list of hits: [{id, score, payload: {claim_path, claim_title, ...}}]
+    """
+    must_filters = []
+    if domain:
+        must_filters.append({"key": "domain", "match": {"value": domain}})
+    if confidence:
+        must_filters.append({"key": "confidence", "match": {"value": confidence}})
+
+    must_not_filters = []
+    if exclude:
+        for path in exclude:
+            must_not_filters.append({"key": "claim_path", "match": {"value": path}})
+
+    body = {
+        "vector": vector,
+        "limit": limit,
+        "with_payload": True,
+        "score_threshold": score_threshold,
+    }
+    if offset > 0:
+        body["offset"] = offset
+    if must_filters or must_not_filters:
+        body["filter"] = {}
+        if must_filters:
+            body["filter"]["must"] = must_filters
+        if must_not_filters:
+            body["filter"]["must_not"] = must_not_filters
+
+    req = urllib.request.Request(
+        f"{QDRANT_URL}/collections/{QDRANT_COLLECTION}/points/search",
+        data=json.dumps(body).encode(),
+        headers={"Content-Type": "application/json"},
+    )
+    try:
+        with urllib.request.urlopen(req, timeout=10) as resp:
+            data = json.loads(resp.read())
+            return data.get("result", [])
+    except Exception as e:
+        logger.error("Qdrant search failed: %s", e)
+        return []
+
+
+# --- Layer 2: Graph expansion ---
+
+
+def _parse_frontmatter_edges(path: Path) -> dict:
+    """Extract relationship edges from a claim's frontmatter.
+
+    Handles both YAML formats:
+      depends_on: ["item1", "item2"]   (inline list)
+      depends_on:                       (multi-line list)
+        - item1
+        - item2
+
+    Returns {supports: [...], challenges: [...], depends_on: [...], related: [...], wiki_links: [...]}.
+    wiki_links are separated from explicit related edges for differential weighting.
+    """
+    edges = {"supports": [], "challenges": [], "depends_on": [], "related": [], "wiki_links": []}
+    try:
+        text = path.read_text(errors="replace")
+    except Exception:
+        return edges
+
+    if not text.startswith("---"):
+        return edges
+    end = text.find("\n---", 3)
+    if end == -1:
+        return edges
+
+    fm_text = text[3:end]
+
+    # Use YAML parser for reliable edge extraction
+    try:
+        import yaml
+        fm = yaml.safe_load(fm_text)
+        if isinstance(fm, dict):
+            for field in ("supports", "challenges", "depends_on", "related"):
+                val = fm.get(field)
+                if isinstance(val, list):
+                    edges[field] = [str(v).strip() for v in val if v]
+                elif isinstance(val, str) and val.strip():
+                    edges[field] = [val.strip()]
+    except Exception:
+        pass
+
+    # Extract wiki links from body as separate edge type (lower weight)
+    body = text[end + 4:]
+    all_explicit = set()
+    for field in ("supports", "challenges", "depends_on", "related"):
+        all_explicit.update(edges[field])
+
+    wiki_links = WIKI_LINK_RE.findall(body)
+    for link in wiki_links:
+        link = link.strip()
+        if link and link not in all_explicit and link not in edges["wiki_links"]:
+            edges["wiki_links"].append(link)
+
+    return edges
+
+
+def _resolve_claim_path(name: str, repo_root: Path) -> Path | None:
+    """Resolve a claim name (from frontmatter edge or wiki link) to a file path.
+
+    Handles both naming conventions:
+      - "GLP-1 receptor agonists are..." → "GLP-1 receptor agonists are....md" (spaces)
+      - "glp-1-persistence-drops..." → "glp-1-persistence-drops....md" (slugified)
+
+    Checks domains/, core/, foundations/, decisions/ subdirectories.
+    """
+    # Try exact name first (spaces in filename), then slugified
+    candidates = [name]
+    slug = name.lower().replace(" ", "-").replace("_", "-")
+    if slug != name:
+        candidates.append(slug)
+
+    for subdir in ["domains", "core", "foundations", "decisions"]:
+        base = repo_root / subdir
+        if not base.is_dir():
+            continue
+        for candidate_name in candidates:
+            for md in base.rglob(f"{candidate_name}.md"):
+                return md
+    return None
+
+
+def graph_expand(seed_paths: list[str], repo_root: Path | None = None,
+                 max_expanded: int = 30,
+                 challenge_weight: float = 1.5,
+                 seen: set[str] | None = None) -> list[dict]:
+    """Layer 2: Expand seed claims 1-hop through knowledge graph edges.
+
+    Traverses supports/challenges/depends_on/related/wiki_links edges in frontmatter.
+    Edge weights: challenges 1.5x, depends_on 1.25x, supports/related 1.0x, wiki_links 0.5x.
+    Results sorted by weight descending so cap cuts low-value edges first.
+
+    Args:
+        seen: Optional set of paths already matched (e.g. from keyword search) to exclude.
+
+    Returns list of {claim_path, claim_title, edge_type, edge_weight, from_claim}.
+    Excludes claims already in seed_paths or seen set.
+    """
+    EDGE_WEIGHTS = {
+        "challenges": 1.5,
+        "depends_on": 1.25,
+        "supports": 1.0,
+        "related": 1.0,
+        "wiki_links": 0.5,
+    }
+
+    root = repo_root or config.MAIN_WORKTREE
+    all_expanded = []
+    visited = set(seed_paths)
+    if seen:
+        visited.update(seen)
+
+    for seed_path in seed_paths:
+        full_path = root / seed_path
+        if not full_path.exists():
+            continue
+
+        edges = _parse_frontmatter_edges(full_path)
+
+        for edge_type, targets in edges.items():
+            weight = EDGE_WEIGHTS.get(edge_type, 1.0)
+
+            for target_name in targets:
+                target_path = _resolve_claim_path(target_name, root)
+                if target_path is None:
+                    continue
+
+                rel_path = str(target_path.relative_to(root))
+                if rel_path in visited:
+                    continue
+                # Skip structural files (MOCs/indexes) — they pull entire domains
+                if target_path.name in STRUCTURAL_FILES:
+                    continue
+                visited.add(rel_path)
+
+                # Read title from frontmatter
+                title = target_name
+                try:
+                    text = target_path.read_text(errors="replace")
+                    if text.startswith("---"):
+                        end = text.find("\n---", 3)
+                        if end > 0:
+                            import yaml
+                            fm = yaml.safe_load(text[3:end])
+                            if isinstance(fm, dict):
+                                title = fm.get("name", fm.get("title", target_name))
+                except Exception:
+                    pass
+
+                all_expanded.append({
+                    "claim_path": rel_path,
+                    "claim_title": str(title),
+                    "edge_type": edge_type,
+                    "edge_weight": weight,
+                    "from_claim": seed_path,
+                })
+
+    # Sort by weight descending so cap cuts lowest-value edges first
+    all_expanded.sort(key=lambda x: x["edge_weight"], reverse=True)
+    return all_expanded[:max_expanded]
+
+
+# --- Combined search (Layer 1 + Layer 2) ---
+
+# Default thresholds — calibrated with Leo's retrieval audits
+PASS1_LIMIT = 5
+PASS1_THRESHOLD = 0.70
+PASS2_LIMIT = 5
+PASS2_THRESHOLD = 0.60
+HARD_CAP = 10
+
+
+def _dedup_hits(hits: list[dict], seen: set[str]) -> list[dict]:
+    """Filter Qdrant hits: dedup by claim_path, exclude structural files."""
+    results = []
+    for hit in hits:
+        payload = hit.get("payload", {})
+        claim_path = payload.get("claim_path", "")
+        if claim_path in seen:
+            continue
+        if claim_path.split("/")[-1] in STRUCTURAL_FILES:
+            continue
+        seen.add(claim_path)
+        results.append({
+            "claim_title": payload.get("claim_title", ""),
+            "claim_path": claim_path,
+            "score": round(hit.get("score", 0), 4),
+            "domain": payload.get("domain", ""),
+            "confidence": payload.get("confidence", ""),
+            "snippet": payload.get("snippet", "")[:200],
+            "type": payload.get("type", "claim"),
+        })
+    return results
+
+
+def _sort_results(direct: list[dict], expanded: list[dict]) -> list[dict]:
+    """Sort combined results: similarity desc → challenged_by → other expansion.
+
+    Sort order is load-bearing: LLMs have primacy bias, so best claims first.
+    """
+    # Direct results already sorted by Qdrant (cosine desc)
+    sorted_direct = sorted(direct, key=lambda x: x.get("score", 0), reverse=True)
+
+    # Expansion: challenged_by first (counterpoints), then rest by weight
+    challenged = [e for e in expanded if e.get("edge_type") == "challenges"]
+    other_expanded = [e for e in expanded if e.get("edge_type") != "challenges"]
+    challenged.sort(key=lambda x: x.get("edge_weight", 0), reverse=True)
+    other_expanded.sort(key=lambda x: x.get("edge_weight", 0), reverse=True)
+
+    return sorted_direct + challenged + other_expanded
+
+
+def search(query: str, expand: bool = False,
+           domain: str | None = None, confidence: str | None = None,
+           exclude: list[str] | None = None) -> dict:
+    """Two-pass semantic search: embed query, search Qdrant, optionally expand.
+
+    Pass 1 (expand=False, default): Top 5 claims from Qdrant, score >= 0.70.
+    Sufficient for ~80% of queries. Fast and focused.
+
+    Pass 2 (expand=True): Next 5 claims (offset=5, score >= 0.60) plus
+    graph-expanded claims (challenged_by, related edges). Hard cap 10 total.
+    Agent calls this only when pass 1 didn't answer the question.
+
+    Returns {
+        "query": str,
+        "direct_results": [...],     # Layer 1 Qdrant hits (sorted by score desc)
+        "expanded_results": [...],   # Layer 2 graph expansion (challenges first)
+        "total": int,
+    }
+    """
+    vector = embed_query(query)
+    if vector is None:
+        return {"query": query, "direct_results": [], "expanded_results": [],
+                "total": 0, "error": "embedding_failed"}
+
+    # --- Pass 1: Top 5, high threshold ---
+    hits = search_qdrant(vector, limit=PASS1_LIMIT, domain=domain,
+                         confidence=confidence, exclude=exclude,
+                         score_threshold=PASS1_THRESHOLD)
+
+    seen_paths: set[str] = set()
+    if exclude:
+        seen_paths.update(exclude)
+    direct = _dedup_hits(hits, seen_paths)
+
+    expanded = []
+    if expand:
+        # --- Pass 2: Next 5 from Qdrant (lower threshold, offset) ---
+        pass2_hits = search_qdrant(vector, limit=PASS2_LIMIT, domain=domain,
+                                   confidence=confidence, exclude=exclude,
+                                   score_threshold=PASS2_THRESHOLD,
+                                   offset=PASS1_LIMIT)
+        pass2_direct = _dedup_hits(pass2_hits, seen_paths)
+        direct.extend(pass2_direct)
+
+        # Graph expansion on all direct results (pass 1 + pass 2 seeds)
+        seed_paths = [r["claim_path"] for r in direct]
+        remaining_cap = HARD_CAP - len(direct)
+        if remaining_cap > 0:
+            expanded = graph_expand(seed_paths, max_expanded=remaining_cap,
+                                    seen=seen_paths)
+
+    # Enforce hard cap across all results
+    all_sorted = _sort_results(direct, expanded)[:HARD_CAP]
+
+    # Split back into direct vs expanded for backward compat
+    direct_paths = {r["claim_path"] for r in direct}
+    final_direct = [r for r in all_sorted if r.get("claim_path") in direct_paths]
+    final_expanded = [r for r in all_sorted if r.get("claim_path") not in direct_paths]
+
+    return {
+        "query": query,
+        "direct_results": final_direct,
+        "expanded_results": final_expanded,
+        "total": len(all_sorted),
+    }

telegram/kb_retrieval.py

@@ -621,3 +621,107 @@ def format_context_for_prompt(ctx: KBContext) -> str:
                     f"{ctx.stats.get('claims_matched', 0)} claims.")
 
     return "\n".join(sections)
+
+
+# --- Qdrant vector search integration ---
+
+# Module-level import guard for lib.search (Fix 3: no per-call sys.path manipulation)
+_vector_search = None
+try:
+    import sys as _sys
+    import os as _os
+    _pipeline_root = _os.path.dirname(_os.path.dirname(_os.path.abspath(__file__)))
+    if _pipeline_root not in _sys.path:
+        _sys.path.insert(0, _pipeline_root)
+    from lib.search import search as _vector_search
+except ImportError:
+    logger.warning("Qdrant search unavailable at module load (lib.search not found)")
+
+
+def retrieve_vector_context(query: str,
+                            keyword_paths: list[str] | None = None) -> tuple[str, dict]:
+    """Semantic search via Qdrant — returns (formatted_text, metadata).
+
+    Complements retrieve_context() (symbolic/keyword) with semantic similarity.
+    Falls back gracefully if Qdrant is unavailable.
+
+    Args:
+        keyword_paths: Claim paths already matched by keyword search. These are
+            excluded at the Qdrant query level AND from graph expansion to avoid
+            duplicates in the prompt.
+
+    Returns:
+        (formatted_text, metadata_dict)
+        metadata_dict: {direct_results: [...], expanded_results: [...],
+                        layers_hit: [...], duration_ms: int}
+    """
+    import time as _time
+    t0 = _time.monotonic()
+    empty_meta = {"direct_results": [], "expanded_results": [],
+                  "layers_hit": [], "duration_ms": 0}
+
+    if _vector_search is None:
+        return "", empty_meta
+
+    try:
+        results = _vector_search(query, expand=True,
+                                 exclude=keyword_paths)
+    except Exception as e:
+        logger.warning("Qdrant search failed: %s", e)
+        return "", empty_meta
+
+    duration = int((_time.monotonic() - t0) * 1000)
+
+    if results.get("error") or not results.get("direct_results"):
+        return "", {**empty_meta, "duration_ms": duration,
+                    "error": results.get("error")}
+
+    layers_hit = ["qdrant"]
+    if results.get("expanded_results"):
+        layers_hit.append("graph")
+
+    # Build structured metadata for audit
+    meta = {
+        "direct_results": [
+            {"path": r["claim_path"], "title": r["claim_title"],
+             "score": r["score"], "domain": r.get("domain", ""),
+             "source": "qdrant"}
+            for r in results["direct_results"]
+        ],
+        "expanded_results": [
+            {"path": r["claim_path"], "title": r["claim_title"],
+             "edge_type": r.get("edge_type", "related"),
+             "from_claim": r.get("from_claim", ""), "source": "graph"}
+            for r in results.get("expanded_results", [])
+        ],
+        "layers_hit": layers_hit,
+        "duration_ms": duration,
+    }
+
+    # Build formatted text for prompt (Fix 4: subsection headers)
+    sections = []
+    sections.append("## Semantic Search Results (Qdrant)")
+    sections.append("")
+    sections.append("### Direct matches")
+
+    for r in results["direct_results"]:
+        score_pct = int(r["score"] * 100)
+        line = f"- **{r['claim_title']}** ({score_pct}% match"
+        if r.get("domain"):
+            line += f", {r['domain']}"
+        if r.get("confidence"):
+            line += f", {r['confidence']}"
+        line += ")"
+        sections.append(line)
+        if r.get("snippet"):
+            sections.append(f"  {r['snippet']}")
+
+    if results.get("expanded_results"):
+        sections.append("")
+        sections.append("### Related claims (graph expansion)")
+        for r in results["expanded_results"]:
+            edge = r.get("edge_type", "related")
+            weight_str = f" ×{r.get('edge_weight', 1.0)}" if r.get("edge_weight", 1.0) != 1.0 else ""
+            sections.append(f"- {r['claim_title']} ({edge}{weight_str} → {r.get('from_claim', '').split('/')[-1]})")
+
+    return "\n".join(sections), meta

tests/test_search.py

@@ -0,0 +1,604 @@
+"""Tests for lib/search.py — vector search and graph expansion."""
+
+import json
+from pathlib import Path
+from unittest.mock import patch, MagicMock
+
+import pytest
+
+from lib.search import (
+    _parse_frontmatter_edges,
+    _resolve_claim_path,
+    graph_expand,
+    search,
+    search_qdrant,
+    WIKI_LINK_RE,
+)
+
+
+# ─── Fixtures ──────────────────────────────────────────────────────────────
+
+
+@pytest.fixture
+def repo(tmp_path):
+    """Minimal KB repo structure with claim files."""
+    domains = tmp_path / "domains"
+
+    # ai-alignment domain
+    ai = domains / "ai-alignment"
+    ai.mkdir(parents=True)
+
+    (ai / "capability-scoping.md").write_text(
+        "---\nname: AI agent capability scoping\ntype: claim\n"
+        "supports:\n  - capability-matched escalation\n"
+        "related:\n  - multi-agent coordination\n"
+        "---\n\nBody text with [[wiki-linked claim]].\n"
+    )
+    (ai / "capability-matched-escalation.md").write_text(
+        "---\nname: capability-matched escalation\ntype: claim\n"
+        "challenges:\n  - unconstrained autonomy\n"
+        "---\n\nEscalation body.\n"
+    )
+    (ai / "multi-agent-coordination.md").write_text(
+        "---\nname: multi-agent coordination\ntype: claim\n---\n\nCoord body.\n"
+    )
+    (ai / "wiki-linked-claim.md").write_text(
+        "---\nname: wiki-linked claim\ntype: claim\n---\n\nWiki body.\n"
+    )
+    (ai / "unconstrained-autonomy.md").write_text(
+        "---\nname: unconstrained autonomy\ntype: claim\n---\n\nAutonomy body.\n"
+    )
+
+    # internet-finance domain
+    fin = domains / "internet-finance"
+    fin.mkdir(parents=True)
+
+    (fin / "futarchy-governance.md").write_text(
+        "---\nname: futarchy governance\ntype: claim\n"
+        "depends_on:\n  - prediction market accuracy\n"
+        "---\n\nFutarchy body.\n"
+    )
+    (fin / "prediction-market-accuracy.md").write_text(
+        "---\nname: prediction market accuracy\ntype: claim\n---\n\nPM body.\n"
+    )
+
+    return tmp_path
+
+
+@pytest.fixture
+def claim_no_frontmatter(tmp_path):
+    """Claim file with no frontmatter."""
+    domains = tmp_path / "domains" / "misc"
+    domains.mkdir(parents=True)
+    f = domains / "bare.md"
+    f.write_text("Just a body, no frontmatter.\n")
+    return f
+
+
+# ─── _parse_frontmatter_edges ─────────────────────────────────────────────
+
+
+class TestParseFrontmatterEdges:
+
+    def test_supports_and_related(self, repo):
+        path = repo / "domains" / "ai-alignment" / "capability-scoping.md"
+        edges = _parse_frontmatter_edges(path)
+        assert edges["supports"] == ["capability-matched escalation"]
+        assert edges["related"] == ["multi-agent coordination"]
+
+    def test_challenges(self, repo):
+        path = repo / "domains" / "ai-alignment" / "capability-matched-escalation.md"
+        edges = _parse_frontmatter_edges(path)
+        assert edges["challenges"] == ["unconstrained autonomy"]
+
+    def test_depends_on(self, repo):
+        path = repo / "domains" / "internet-finance" / "futarchy-governance.md"
+        edges = _parse_frontmatter_edges(path)
+        assert edges["depends_on"] == ["prediction market accuracy"]
+
+    def test_wiki_links_extracted_separately(self, repo):
+        path = repo / "domains" / "ai-alignment" / "capability-scoping.md"
+        edges = _parse_frontmatter_edges(path)
+        assert "wiki-linked claim" in edges["wiki_links"]
+        # Wiki links should NOT appear in explicit related
+        assert "wiki-linked claim" not in edges["related"]
+
+    def test_wiki_links_deduped_from_explicit(self, tmp_path):
+        """If a wiki link matches an explicit edge, it's excluded from wiki_links."""
+        d = tmp_path / "domains" / "test"
+        d.mkdir(parents=True)
+        f = d / "overlap.md"
+        f.write_text(
+            "---\nname: overlap test\nrelated:\n  - shared target\n---\n\n"
+            "Body with [[shared target]] link.\n"
+        )
+        edges = _parse_frontmatter_edges(f)
+        assert edges["related"] == ["shared target"]
+        assert edges["wiki_links"] == []
+
+    def test_no_frontmatter(self, claim_no_frontmatter):
+        edges = _parse_frontmatter_edges(claim_no_frontmatter)
+        assert all(v == [] for v in edges.values())
+
+    def test_missing_file(self, tmp_path):
+        edges = _parse_frontmatter_edges(tmp_path / "nonexistent.md")
+        assert all(v == [] for v in edges.values())
+
+    def test_inline_list_format(self, tmp_path):
+        """Handles YAML inline list: depends_on: ["a", "b"]."""
+        d = tmp_path / "domains" / "test"
+        d.mkdir(parents=True)
+        f = d / "inline.md"
+        f.write_text('---\nname: inline\ndepends_on: ["alpha", "beta"]\n---\n\nBody.\n')
+        edges = _parse_frontmatter_edges(f)
+        assert edges["depends_on"] == ["alpha", "beta"]
+
+
+# ─── _resolve_claim_path ──────────────────────────────────────────────────
+
+
+class TestResolveClaimPath:
+
+    def test_slugified_name(self, repo):
+        result = _resolve_claim_path("capability-scoping", repo)
+        assert result is not None
+        assert result.name == "capability-scoping.md"
+
+    def test_name_with_spaces(self, repo):
+        """Resolves 'multi-agent coordination' via slug matching."""
+        result = _resolve_claim_path("multi-agent coordination", repo)
+        assert result is not None
+        assert result.name == "multi-agent-coordination.md"
+
+    def test_not_found(self, repo):
+        result = _resolve_claim_path("nonexistent claim", repo)
+        assert result is None
+
+    def test_cross_domain_resolution(self, repo):
+        """Can resolve a claim in a different domain subdirectory."""
+        result = _resolve_claim_path("futarchy-governance", repo)
+        assert result is not None
+        assert "internet-finance" in str(result)
+
+
+# ─── graph_expand ─────────────────────────────────────────────────────────
+
+
+class TestGraphExpand:
+
+    def test_basic_expansion(self, repo):
+        """Expanding from capability-scoping should find its edges."""
+        results = graph_expand(
+            ["domains/ai-alignment/capability-scoping.md"],
+            repo_root=repo,
+        )
+        titles = [r["claim_title"] for r in results]
+        assert "capability-matched escalation" in titles
+        assert "multi-agent coordination" in titles
+
+    def test_wiki_links_included(self, repo):
+        results = graph_expand(
+            ["domains/ai-alignment/capability-scoping.md"],
+            repo_root=repo,
+        )
+        titles = [r["claim_title"] for r in results]
+        assert "wiki-linked claim" in titles
+
+    def test_edge_weights(self, repo):
+        """challenges edges get 1.5x weight, wiki_links get 0.5x."""
+        # Expand from capability-matched-escalation (has challenges edge)
+        results = graph_expand(
+            ["domains/ai-alignment/capability-matched-escalation.md"],
+            repo_root=repo,
+        )
+        challenges_result = [r for r in results if r["edge_type"] == "challenges"]
+        assert len(challenges_result) == 1
+        assert challenges_result[0]["edge_weight"] == 1.5
+
+    def test_wiki_link_weight(self, repo):
+        results = graph_expand(
+            ["domains/ai-alignment/capability-scoping.md"],
+            repo_root=repo,
+        )
+        wiki_results = [r for r in results if r["edge_type"] == "wiki_links"]
+        assert all(r["edge_weight"] == 0.5 for r in wiki_results)
+
+    def test_depends_on_weight(self, repo):
+        results = graph_expand(
+            ["domains/internet-finance/futarchy-governance.md"],
+            repo_root=repo,
+        )
+        dep_results = [r for r in results if r["edge_type"] == "depends_on"]
+        assert len(dep_results) == 1
+        assert dep_results[0]["edge_weight"] == 1.25
+
+    def test_sorted_by_weight_descending(self, repo):
+        results = graph_expand(
+            ["domains/ai-alignment/capability-scoping.md"],
+            repo_root=repo,
+        )
+        weights = [r["edge_weight"] for r in results]
+        assert weights == sorted(weights, reverse=True)
+
+    def test_seed_excluded_from_results(self, repo):
+        seed = "domains/ai-alignment/capability-scoping.md"
+        results = graph_expand([seed], repo_root=repo)
+        paths = [r["claim_path"] for r in results]
+        assert seed not in paths
+
+    def test_seen_set_excludes(self, repo):
+        """Paths in seen set are excluded from expansion results."""
+        already_matched = {"domains/ai-alignment/multi-agent-coordination.md"}
+        results = graph_expand(
+            ["domains/ai-alignment/capability-scoping.md"],
+            repo_root=repo,
+            seen=already_matched,
+        )
+        paths = [r["claim_path"] for r in results]
+        assert "domains/ai-alignment/multi-agent-coordination.md" not in paths
+
+    def test_max_expanded_cap(self, repo):
+        results = graph_expand(
+            ["domains/ai-alignment/capability-scoping.md"],
+            repo_root=repo,
+            max_expanded=1,
+        )
+        assert len(results) <= 1
+
+    def test_cap_cuts_lowest_weight(self, repo):
+        """With cap=2, wiki_links (0.5x) should be cut before supports (1.0x)."""
+        results = graph_expand(
+            ["domains/ai-alignment/capability-scoping.md"],
+            repo_root=repo,
+            max_expanded=2,
+        )
+        edge_types = [r["edge_type"] for r in results]
+        assert "wiki_links" not in edge_types
+
+    def test_nonexistent_seed(self, repo):
+        results = graph_expand(["domains/nonexistent.md"], repo_root=repo)
+        assert results == []
+
+
+# ─── search_qdrant (mocked HTTP) ─────────────────────────────────────────
+
+
+class TestSearchQdrant:
+
+    def _mock_qdrant_response(self, results):
+        """Build a mock urllib response returning Qdrant results."""
+        resp = MagicMock()
+        resp.read.return_value = json.dumps({"result": results}).encode()
+        resp.__enter__ = lambda s: s
+        resp.__exit__ = MagicMock(return_value=False)
+        return resp
+
+    @patch("lib.search.urllib.request.urlopen")
+    def test_basic_search(self, mock_urlopen):
+        mock_urlopen.return_value = self._mock_qdrant_response([
+            {"id": 1, "score": 0.85, "payload": {
+                "claim_title": "test claim", "claim_path": "domains/test.md",
+                "domain": "ai", "confidence": "high",
+            }},
+        ])
+        results = search_qdrant([0.1] * 1536, limit=5)
+        assert len(results) == 1
+        assert results[0]["score"] == 0.85
+
+    @patch("lib.search.urllib.request.urlopen")
+    def test_domain_filter(self, mock_urlopen):
+        mock_urlopen.return_value = self._mock_qdrant_response([])
+        search_qdrant([0.1] * 1536, domain="ai-alignment")
+        # Verify the request body includes domain filter
+        call_args = mock_urlopen.call_args
+        req = call_args[0][0]
+        body = json.loads(req.data)
+        assert body["filter"]["must"][0]["key"] == "domain"
+        assert body["filter"]["must"][0]["match"]["value"] == "ai-alignment"
+
+    @patch("lib.search.urllib.request.urlopen")
+    def test_exclude_filter(self, mock_urlopen):
+        mock_urlopen.return_value = self._mock_qdrant_response([])
+        search_qdrant([0.1] * 1536, exclude=["domains/a.md", "domains/b.md"])
+        call_args = mock_urlopen.call_args
+        req = call_args[0][0]
+        body = json.loads(req.data)
+        must_not = body["filter"]["must_not"]
+        excluded_paths = [f["match"]["value"] for f in must_not]
+        assert "domains/a.md" in excluded_paths
+        assert "domains/b.md" in excluded_paths
+
+    @patch("lib.search.urllib.request.urlopen")
+    def test_no_filters_no_filter_key(self, mock_urlopen):
+        mock_urlopen.return_value = self._mock_qdrant_response([])
+        search_qdrant([0.1] * 1536)
+        call_args = mock_urlopen.call_args
+        req = call_args[0][0]
+        body = json.loads(req.data)
+        assert "filter" not in body
+
+    @patch("lib.search.urllib.request.urlopen")
+    def test_http_failure_returns_empty(self, mock_urlopen):
+        mock_urlopen.side_effect = Exception("connection refused")
+        results = search_qdrant([0.1] * 1536)
+        assert results == []
+
+
+# ─── search() integration (mocked network) ───────────────────────────────
+
+
+class TestSearch:
+
+    @patch("lib.search.embed_query")
+    @patch("lib.search.search_qdrant")
+    def test_embedding_failure(self, mock_qdrant, mock_embed):
+        mock_embed.return_value = None
+        result = search("test query")
+        assert result["error"] == "embedding_failed"
+        assert result["direct_results"] == []
+        mock_qdrant.assert_not_called()
+
+    @patch("lib.search.graph_expand")
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_full_pipeline(self, mock_embed, mock_qdrant, mock_expand):
+        mock_embed.return_value = [0.1] * 1536
+        # Pass 1 returns one hit, pass 2 returns empty (nothing above lower threshold)
+        mock_qdrant.side_effect = [
+            [{"id": 1, "score": 0.82, "payload": {
+                "claim_title": "direct hit", "claim_path": "domains/hit.md",
+                "domain": "ai", "confidence": "high", "snippet": "snippet text",
+                "type": "claim",
+            }}],
+            [],  # pass 2 returns nothing
+        ]
+        mock_expand.return_value = [
+            {"claim_path": "domains/expanded.md", "claim_title": "expanded",
+             "edge_type": "supports", "edge_weight": 1.0, "from_claim": "domains/hit.md"},
+        ]
+        # expand=True triggers two-pass: pass 1 + pass 2 + graph expansion
+        result = search("test query", expand=True)
+        assert len(result["direct_results"]) == 1
+        assert result["direct_results"][0]["claim_title"] == "direct hit"
+        assert len(result["expanded_results"]) == 1
+        assert result["total"] == 2
+
+    @patch("lib.search.graph_expand")
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_exclude_passed_through(self, mock_embed, mock_qdrant, mock_expand):
+        """Exclude list reaches both Qdrant and graph_expand."""
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.side_effect = [
+            [{"id": 1, "score": 0.8, "payload": {
+                "claim_title": "hit", "claim_path": "domains/hit.md",
+            }}],
+            [],  # pass 2
+        ]
+        mock_expand.return_value = []
+        exclude = ["domains/already-matched.md"]
+        search("query", expand=True, exclude=exclude)
+
+        # Qdrant should get exclude (called twice with expand=True: pass 1 + pass 2)
+        assert mock_qdrant.call_count == 2
+        # Both calls should include exclude
+        for call in mock_qdrant.call_args_list:
+            assert call.kwargs.get("exclude") == exclude \
+                or call[1].get("exclude") == exclude
+
+        # graph_expand should get seen set containing exclude paths
+        mock_expand.assert_called_once()
+        call_kwargs = mock_expand.call_args[1] if mock_expand.call_args[1] else {}
+        seen = call_kwargs.get("seen")
+        assert seen is not None
+        assert "domains/already-matched.md" in seen
+
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_no_expand_when_disabled(self, mock_embed, mock_qdrant):
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.return_value = []
+        result = search("query", expand=False)
+        assert result["expanded_results"] == []
+
+
+# ─── WIKI_LINK_RE ─────────────────────────────────────────────────────────
+
+
+class TestWikiLinkRegex:
+
+    def test_basic(self):
+        assert WIKI_LINK_RE.findall("See [[some claim]]") == ["some claim"]
+
+    def test_multiple(self):
+        text = "Links to [[claim A]] and [[claim B]]"
+        assert WIKI_LINK_RE.findall(text) == ["claim A", "claim B"]
+
+    def test_no_nested(self):
+        assert WIKI_LINK_RE.findall("[[outer [[inner]]]]") != ["outer [[inner]]"]
+
+
+# ─── Structural file exclusion ───────────────────────────────────────────
+
+
+class TestStructuralFileExclusion:
+
+    def test_map_excluded_from_expansion(self, tmp_path):
+        """_map.md files should be skipped during graph expansion."""
+        domains = tmp_path / "domains" / "test"
+        domains.mkdir(parents=True)
+        (domains / "seed-claim.md").write_text(
+            "---\nname: seed claim\ntype: claim\n"
+            "related:\n  - domain map\n---\nBody.\n"
+        )
+        (domains / "_map.md").write_text(
+            "---\nname: domain map\ntype: moc\n---\nDomain index.\n"
+        )
+        seed = "domains/test/seed-claim.md"
+        result = graph_expand([seed], repo_root=tmp_path)
+        paths = [r["claim_path"] for r in result]
+        assert "domains/test/_map.md" not in paths
+
+    def test_overview_excluded_from_expansion(self, tmp_path):
+        """_overview.md files should be skipped during graph expansion."""
+        domains = tmp_path / "domains" / "test"
+        domains.mkdir(parents=True)
+        (domains / "seed-claim.md").write_text(
+            "---\nname: seed claim\ntype: claim\n"
+            "related:\n  - domain overview\n---\nBody.\n"
+        )
+        (domains / "_overview.md").write_text(
+            "---\nname: domain overview\ntype: moc\n---\nOverview.\n"
+        )
+        seed = "domains/test/seed-claim.md"
+        result = graph_expand([seed], repo_root=tmp_path)
+        paths = [r["claim_path"] for r in result]
+        assert "domains/test/_overview.md" not in paths
+
+    def test_regular_claims_still_expand(self, repo):
+        """Non-structural files should still expand normally."""
+        seed = "domains/ai-alignment/capability-scoping.md"
+        result = graph_expand([seed], repo_root=repo)
+        paths = [r["claim_path"] for r in result]
+        assert len(paths) > 0
+        assert "domains/ai-alignment/capability-matched-escalation.md" in paths
+
+
+# ─── Dedup within vector results ─────────────────────────────────────────
+
+
+class TestSearchDedup:
+
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_duplicate_paths_deduped(self, mock_embed, mock_qdrant):
+        """Duplicate claim_paths in Qdrant results should be collapsed."""
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.return_value = [
+            {"score": 0.9, "payload": {"claim_title": "Claim A", "claim_path": "domains/x/a.md",
+                                        "domain": "x", "confidence": "high", "snippet": "..."}},
+            {"score": 0.85, "payload": {"claim_title": "Claim A dupe", "claim_path": "domains/x/a.md",
+                                         "domain": "x", "confidence": "high", "snippet": "..."}},
+            {"score": 0.8, "payload": {"claim_title": "Claim B", "claim_path": "domains/x/b.md",
+                                        "domain": "x", "confidence": "high", "snippet": "..."}},
+        ]
+        result = search("test query", expand=False)
+        paths = [r["claim_path"] for r in result["direct_results"]]
+        assert paths == ["domains/x/a.md", "domains/x/b.md"]
+        assert result["direct_results"][0]["claim_title"] == "Claim A"
+
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_structural_files_excluded_from_direct(self, mock_embed, mock_qdrant):
+        """_map.md should be excluded from direct Qdrant results too."""
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.return_value = [
+            {"score": 0.9, "payload": {"claim_title": "Domain Map", "claim_path": "domains/x/_map.md",
+                                        "domain": "x", "confidence": "", "snippet": ""}},
+            {"score": 0.85, "payload": {"claim_title": "Real Claim", "claim_path": "domains/x/real.md",
+                                         "domain": "x", "confidence": "high", "snippet": "..."}},
+        ]
+        result = search("test query", expand=False)
+        paths = [r["claim_path"] for r in result["direct_results"]]
+        assert "domains/x/_map.md" not in paths
+        assert "domains/x/real.md" in paths
+
+
+# ─── Two-pass retrieval ──────────────────────────────────────────────────
+
+
+class TestTwoPassRetrieval:
+
+    @patch("lib.search.graph_expand")
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_pass1_only_default(self, mock_embed, mock_qdrant, mock_expand):
+        """Default search (expand=False) only calls Qdrant once with high threshold."""
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.return_value = [
+            {"score": 0.85, "payload": {"claim_title": "Hit", "claim_path": "d/a.md"}},
+        ]
+        result = search("query")
+        mock_qdrant.assert_called_once()
+        # Should use PASS1_THRESHOLD (0.70)
+        call_kwargs = mock_qdrant.call_args
+        assert call_kwargs.kwargs.get("score_threshold") == 0.70 \
+            or call_kwargs[1].get("score_threshold") == 0.70
+        mock_expand.assert_not_called()
+        assert len(result["direct_results"]) == 1
+
+    @patch("lib.search.graph_expand")
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_pass2_expands(self, mock_embed, mock_qdrant, mock_expand):
+        """expand=True calls Qdrant twice (pass 1 + pass 2) and runs graph expansion."""
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.side_effect = [
+            [{"score": 0.85, "payload": {"claim_title": "P1", "claim_path": "d/a.md"}}],
+            [{"score": 0.65, "payload": {"claim_title": "P2", "claim_path": "d/b.md"}}],
+        ]
+        mock_expand.return_value = []
+        result = search("query", expand=True)
+        assert mock_qdrant.call_count == 2
+        # Pass 2 should use offset=5 and lower threshold
+        pass2_call = mock_qdrant.call_args_list[1]
+        assert pass2_call.kwargs.get("offset") == 5 \
+            or pass2_call[1].get("offset") == 5
+        assert len(result["direct_results"]) == 2
+
+    @patch("lib.search.graph_expand")
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_hard_cap_enforced(self, mock_embed, mock_qdrant, mock_expand):
+        """Total results never exceed HARD_CAP (10)."""
+        mock_embed.return_value = [0.1] * 1536
+        # 5 from pass 1, 5 from pass 2
+        p1 = [{"score": 0.9 - i * 0.02, "payload": {
+            "claim_title": f"P1-{i}", "claim_path": f"d/p1-{i}.md"
+        }} for i in range(5)]
+        p2 = [{"score": 0.65 - i * 0.01, "payload": {
+            "claim_title": f"P2-{i}", "claim_path": f"d/p2-{i}.md"
+        }} for i in range(5)]
+        mock_qdrant.side_effect = [p1, p2]
+        # Graph expansion returns 5 more
+        mock_expand.return_value = [
+            {"claim_path": f"d/exp-{i}.md", "claim_title": f"Exp-{i}",
+             "edge_type": "related", "edge_weight": 1.0, "from_claim": "d/p1-0.md"}
+            for i in range(5)
+        ]
+        result = search("query", expand=True)
+        assert result["total"] <= 10
+
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_sort_order_similarity_first(self, mock_embed, mock_qdrant):
+        """Direct results are sorted by cosine similarity descending."""
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.return_value = [
+            {"score": 0.75, "payload": {"claim_title": "Low", "claim_path": "d/low.md"}},
+            {"score": 0.95, "payload": {"claim_title": "High", "claim_path": "d/high.md"}},
+            {"score": 0.85, "payload": {"claim_title": "Mid", "claim_path": "d/mid.md"}},
+        ]
+        result = search("query")
+        titles = [r["claim_title"] for r in result["direct_results"]]
+        assert titles == ["High", "Mid", "Low"]
+
+    @patch("lib.search.graph_expand")
+    @patch("lib.search.search_qdrant")
+    @patch("lib.search.embed_query")
+    def test_challenges_before_other_expansion(self, mock_embed, mock_qdrant, mock_expand):
+        """challenged_by claims appear before other expanded claims."""
+        mock_embed.return_value = [0.1] * 1536
+        mock_qdrant.side_effect = [
+            [{"score": 0.85, "payload": {"claim_title": "Seed", "claim_path": "d/seed.md"}}],
+            [],
+        ]
+        mock_expand.return_value = [
+            {"claim_path": "d/related.md", "claim_title": "Related",
+             "edge_type": "related", "edge_weight": 1.0, "from_claim": "d/seed.md"},
+            {"claim_path": "d/challenge.md", "claim_title": "Challenge",
+             "edge_type": "challenges", "edge_weight": 1.5, "from_claim": "d/seed.md"},
+        ]
+        result = search("query", expand=True)
+        expanded_titles = [r["claim_title"] for r in result["expanded_results"]]
+        assert expanded_titles.index("Challenge") < expanded_titles.index("Related")