Default search_docs to dense; make rerank opt-in (#17)

Co-authored-by: claude <[email protected]>

README.md

@@ -17,7 +17,7 @@ once deployed.
 
 | Tool | Use |
 |---|---|
-| `search_docs` | BM25-default search with optional version / platform / bundle filters; cross-encoder reranked when `RERANK_URL` is set |
+| `search_docs` | Dense-default search with optional version / platform / bundle filters; rerank only when `RERANK_ENABLED=true` |
 | `get_page` | Full markdown of one page with metadata header + source URL |
 | `list_versions` | Discover available versions, doc types, and bundle slugs |
 | `list_cluster` | Cross-version peers of a page (synthesized from same-GUID overlap) |
@@ -51,19 +51,19 @@ peer mapping is free (no fuzzy matching needed).
 
 ## Retrieval
 
-Eval against 22 hand-curated golden queries — see
-[`eval/results/baseline.md`](eval/results/baseline.md):
+Eval against 22 hand-curated golden queries. May 2026 baseline is
+[`eval/results/baseline.md`](eval/results/baseline.md); after nomic
+prefixes + heading-recursive chunking (2026-09-30, live index):
 
-| Retriever | MRR | Recall@5 | nDCG@5 | latency |
+| Retriever | P@1 | MRR | Recall@5 | nDCG@5 |
 |---|---:|---:|---:|---:|
-| dense (Ollama nomic-embed-text) | 0.539 | 0.621 | 0.558 | 88 ms |
-| BM25 (SQLite FTS5) | 0.880 | 0.909 | 0.883 | 3 ms |
-| hybrid (dense + BM25 + RRF) | 0.692 | 0.818 | 0.713 | 69 ms |
-| **bm25 + jina-rerank** | **0.920** | **0.939** | **0.927** | 490 ms (CPU) / ~50 ms (GPU) |
+| **dense** (nomic prefixes) | **0.955** | **0.966** | **0.985** | **0.972** |
+| hybrid (dense + BM25 + RRF) | 0.955 | 0.961 | 0.955 | 0.955 |
+| BM25 (SQLite FTS5) | 0.864 | 0.882 | 0.909 | 0.886 |
+| bm25 + jina-rerank | 0.773 | 0.827 | 0.848 | 0.816 |
 
-HPE docs use controlled vocabulary, so lexical match dominates; the
-cross-encoder cleans up the long tail. See PLAN.md Phase 7/8 for the
-reasoning.
+`search_docs` defaults to dense. Rerank is opt-in (`RERANK_ENABLED=true`)
+— it now hurts. Full table: [`eval/results/post-prefix.md`](eval/results/post-prefix.md).
 
 ## Architecture
 

deploy/docker-compose.yml

@@ -32,16 +32,16 @@ services:
       # add that hostname here. "*" disables the rebind check entirely.
       MCP_ALLOWED_HOSTS: "hvm-docs-mcp,localhost,127.0.0.1"
 
-      # Phase 6 — reranker sidecar (jina-reranker-v2-base via llama.cpp).
+      # Phase 6 — reranker sidecar is wired but off. Eval 2026-09-30
+      # (post nomic prefixes): dense MRR=0.966 vs bm25+rerank 0.827.
+      # Set RERANK_ENABLED=true to turn the sidecar back on.
       RERANK_URL: http://hvm-rerank:8080
       RERANK_POOL: "200"
       RERANK_TIMEOUT: "30"
+      RERANK_ENABLED: "false"
 
-      # Phase 8 — hybrid retrieval (BM25 + dense + RRF).
-      # Eval on the HVM corpus (eval/results/baseline.md, 2026-05-22) shows
-      # BM25-default + reranker beats hybrid on every metric (MRR 0.920 vs
-      # 0.875). Leaving HYBRID_SEARCH off so search_docs runs BM25-first +
-      # reranker; dense is the fallback when BM25 finds nothing.
+      # Phase 8 — hybrid retrieval (BM25 + dense + RRF). Off: dense is
+      # the default (eval/results/post-prefix.md). BM25 is fallback only.
       HYBRID_SEARCH: "false"
 
       # Phase 10 — usage telemetry.

docs_mcp/format.py

@@ -0,0 +1,43 @@
+"""Markdown formatters for MCP tool output.
+
+No third-party imports — tests can run without mcp/httpx/chromadb.
+Citation numbers are per-call (1-based, dense) and stateless.
+"""
+from __future__ import annotations
+
+
+def format_search_hit(
+    n: int,
+    title: str,
+    url: str,
+    text: str,
+    extra: str = "",
+) -> str:
+    """One numbered hit. `n` is 1-based in final reranked/fused order."""
+    head = f"[{n}] **{title}**"
+    if url:
+        head += f" — {url}"
+    parts = [head]
+    if extra:
+        parts.append(extra)
+    body = (text or "").strip()
+    if body:
+        parts.append(body)
+    return "\n".join(parts)
+
+
+def format_search_hits(
+    hits: list[tuple[str, str, str, str]],
+) -> str:
+    """Render hits as `[1] **title** — url` then text.
+
+    `hits` is a list of (title, url, text, extra) in display order.
+    Empty list → empty string (no invented citations).
+    """
+    if not hits:
+        return ""
+    blocks = [
+        format_search_hit(n, title, url, text, extra)
+        for n, (title, url, text, extra) in enumerate(hits, start=1)
+    ]
+    return "\n\n".join(blocks) + "\n"

docs_mcp/server.py

@@ -31,6 +31,7 @@ from mcp.server.mcpserver import MCPServer
 from mcp.server.transport_security import TransportSecuritySettings
 from pydantic import Field
 
+from .format import format_search_hits
 from .usage import TimedCall
 
 log = logging.getLogger(__name__)
@@ -60,6 +61,9 @@ API_LESSONS_MD = Path(__file__).resolve().parent / "api_lessons.md"
 RERANK_URL = os.environ.get("RERANK_URL", "").rstrip("/") or None
 RERANK_POOL = int(os.environ.get("RERANK_POOL", "50"))
 RERANK_TIMEOUT = float(os.environ.get("RERANK_TIMEOUT", "30"))
+# Opt-in. Watchtower keeps the old container env (RERANK_URL is set in
+# live compose); default off so a code-only ship actually stops reranking.
+RERANK_ENABLED = os.environ.get("RERANK_ENABLED", "").lower() in ("true", "1", "yes", "on")
 
 HYBRID_SEARCH = os.environ.get("HYBRID_SEARCH", "").lower() in ("true", "1", "yes", "on")
 RRF_K = int(os.environ.get("RRF_K", "60"))
@@ -145,6 +149,17 @@ def _collection():
     return _CHROMA
 
 
+def _query_dense(col, query: str, n: int, where: dict | None = None, **extra):
+    """Dense query with nomic prefixes applied at embed time only."""
+    from rag.embeddings import EMBED_QUERY_PREFIX, embed_texts
+    qvec = embed_texts([query], prefix=EMBED_QUERY_PREFIX)[0]
+    kwargs: dict[str, Any] = {"query_embeddings": [qvec], "n_results": n}
+    if where:
+        kwargs["where"] = where
+    kwargs.update(extra)
+    return col.query(**kwargs)
+
+
 def _bm25():
     """Lazy BM25Index handle. None if the FTS5 db isn't built."""
     global _BM25
@@ -258,9 +273,10 @@ def search_docs(
     """Search the HPE Morpheus VM Essentials (HVM) docs corpus.
 
     Returns the top-k most relevant chunks (with full source page URLs)
-    given a natural-language query. Optional filters narrow the search
-    to one version, one platform, or one bundle. Use list_versions()
-    first if you need to discover the available facet values.
+    given a natural-language query. Hits are numbered [1]… for citation.
+    Optional filters narrow the search to one version, one platform, or
+    one bundle. Use list_versions() first if you need to discover the
+    available facet values.
 
     Call this tool whenever the user asks anything that should be
     answerable from the official product documentation — install,
@@ -282,11 +298,10 @@ def search_docs(
         bm25_where = _where_for_bm25(version, platform, bundle_id)
         pool = max(k * 5, 50)
 
-        # Retrieval mode selection. Eval on this corpus (2026-05-22, 22 golden
-        # queries) showed BM25 MRR=0.88 vs dense MRR=0.54 vs hybrid MRR=0.69 —
-        # HPE structured docs use controlled vocabulary, so lexical match wins.
-        # Dense is kept as fallback when BM25 has no tokens to chew on (e.g.
-        # purely stopword queries). HYBRID_SEARCH=true forces RRF fusion.
+        # Retrieval mode. Eval 2026-09-30 (22 queries, post nomic prefixes +
+        # heading-recursive chunking): dense MRR=0.966 P@1=0.955 vs
+        # bm25+rerank MRR=0.827 P@1=0.773. Default is dense. HYBRID_SEARCH
+        # still forces RRF. Rerank is opt-in (RERANK_ENABLED) — it now hurts.
         bm = _bm25()
         docs: list[str] = []
         metas: list[dict] = []
@@ -296,7 +311,7 @@ def search_docs(
 
         if HYBRID_SEARCH and bm is not None:
             try:
-                dense_res = col.query(query_texts=[query], n_results=pool, where=where)
+                dense_res = _query_dense(col, query, pool, where)
                 dense_ids = (dense_res.get("ids") or [[]])[0]
                 bm_hits = bm.query(query, n=pool, where=bm25_where)
                 bm_ids = [cid for cid, _s in bm_hits]
@@ -309,7 +324,18 @@ def search_docs(
                                    else "dense_only")
                 retrieval_mode = "hybrid"
             except Exception as e:
-                log.warning("hybrid failed, falling back to BM25→dense: %s", e)
+                log.warning("hybrid failed, falling back to dense: %s", e)
+
+        if not docs:
+            try:
+                res = _query_dense(col, query, k, where)
+                docs = (res.get("documents") or [[]])[0]
+                metas = (res.get("metadatas") or [[]])[0]
+                dists = (res.get("distances") or [[]])[0]
+                retrieval_mode = "dense"
+                top1_source = "dense_only"
+            except Exception as e:
+                log.warning("dense retrieval failed, falling back to BM25: %s", e)
 
         if not docs and bm is not None:
             try:
@@ -323,16 +349,10 @@ def search_docs(
                     retrieval_mode = "bm25"
                     top1_source = "bm25_only"
             except Exception as e:
-                log.warning("BM25 retrieval failed, falling back to dense: %s", e)
-
-        if not docs:
-            res = col.query(query_texts=[query], n_results=k, where=where)
-            docs = (res.get("documents") or [[]])[0]
-            metas = (res.get("metadatas") or [[]])[0]
-            dists = (res.get("distances") or [[]])[0]
+                log.warning("BM25 retrieval failed: %s", e)
 
         reranker_fired = False
-        if RERANK_URL and docs:
+        if RERANK_URL and RERANK_ENABLED and docs:
             # Pull a deeper pool to give the reranker something to chew on.
             # We over-fetch up to RERANK_POOL chunks from whichever retriever
             # already won, then ask the reranker to pick the final top-k.
@@ -342,7 +362,7 @@ def search_docs(
                     extra = bm.query(query, n=pool_size, where=bm25_where) if bm else []
                     extra_ids = [cid for cid, _s in extra]
                 else:
-                    extra_res = col.query(query_texts=[query], n_results=pool_size, where=where)
+                    extra_res = _query_dense(col, query, pool_size, where)
                     extra_ids = (extra_res.get("ids") or [[]])[0]
                 if extra_ids:
                     d2, m2, _ = _enrich_from_chroma(col, extra_ids, None)
@@ -371,22 +391,21 @@ def search_docs(
         if not docs:
             return f"_No matches for `{query}`._"
 
-        out = [f"# {len(docs)} result(s) for `{query}`", ""]
+        hits: list[tuple[str, str, str, str]] = []
         for doc, meta, dist in zip(docs, metas, dists):
             bid = meta.get("bundle_id", "")
             pid = meta.get("page_id", "")
             title = meta.get("title") or pid
             ver = meta.get("version") or ""
             url = _source_url(bid, pid)
-            header = f"## {title}"
+            extra = f"score={1 - dist:.3f}"
             if ver:
-                header += f"  _(v{ver})_"
-            out.append(header)
-            out.append(f"[{bid}/{pid}]({url}) · score={1 - dist:.3f}")
-            out.append("")
-            out.append(doc.strip())
-            out.append("")
-        return "\n".join(out)
+                extra = f"v{ver}  " + extra
+            if pid:
+                extra += f"  page_id: `{pid}`"
+            hits.append((title, url, doc, extra))
+        header = f"# {len(docs)} result(s) for `{query}`\n\n"
+        return header + format_search_hits(hits)
 
 
 @mcp.tool()
@@ -810,6 +829,21 @@ def weekly_digest(
         return "\n".join(lines)
 
 
+def _pub_sort_key(pub: str) -> tuple[int, int]:
+    """Sortable (year, month) for an HPE "Published" value like "July 2026".
+
+    Plain string comparison ranks "March 2026" above "July 2026".
+    Unparseable values sort last.
+    """
+    for fmt in ("%B %Y", "%b %Y", "%B %d, %Y", "%Y-%m-%d"):
+        try:
+            d = _dt.datetime.strptime(pub.strip(), fmt)
+            return (d.year, d.month)
+        except ValueError:
+            continue
+    return (0, 0)
+
+
 @mcp.tool()
 def corpus_status() -> str:
     """Freshness + size of the knowledge base.
@@ -831,7 +865,7 @@ def corpus_status() -> str:
     for slug, b in cat.items():
         pub = (b.get("dates") or {}).get("Published")
         if pub:
-            if latest_pub is None or pub > latest_pub:
+            if latest_pub is None or _pub_sort_key(pub) > _pub_sort_key(latest_pub):
                 latest_pub = pub
             per_bundle.append((slug, pub))
     if latest_pub:
@@ -850,7 +884,7 @@ def corpus_status() -> str:
         "",
     ]
     if per_bundle:
-        per_bundle.sort(key=lambda kv: kv[1], reverse=True)
+        per_bundle.sort(key=lambda kv: _pub_sort_key(kv[1]), reverse=True)
         lines.append("## Most-recently-edited bundles (by HPE)")
         for slug, when in per_bundle[:5]:
             b = cat.get(slug, {})
@@ -1058,8 +1092,8 @@ def find_doc_inconsistencies(
             return f"Couldn't open Chroma collection: {e}"
         where = _build_where(version, platform, bundle_id)
         try:
-            res = col.query(query_texts=[scope_query], n_results=max_pages * 3,
-                            where=where, include=["metadatas"])
+            res = _query_dense(col, scope_query, max_pages * 3, where,
+                               include=["metadatas"])
         except Exception as e:
             _call.set(error=f"query: {e}")
             return f"Scope query failed: {e}"

docs_mcp/test_format.py

@@ -0,0 +1,37 @@
+"""Citation markdown renderer — no Chroma, no mcp."""
+from __future__ import annotations
+
+import unittest
+
+from docs_mcp.format import format_search_hits
+
+
+class FormatSearchHitsTests(unittest.TestCase):
+    def test_two_hits_numbered_with_urls(self) -> None:
+        out = format_search_hits([
+            ("Install on Linux", "https://docs.example.com/install", "chunk one", ""),
+            ("HA setup", "https://docs.example.com/ha", "chunk two", ""),
+        ])
+        self.assertIn("[1] **Install on Linux** — https://docs.example.com/install", out)
+        self.assertIn("[2] **HA setup** — https://docs.example.com/ha", out)
+        self.assertIn("chunk one", out)
+        self.assertIn("chunk two", out)
+        self.assertNotIn("[3]", out)
+        # [1] before [2]
+        self.assertLess(out.index("[1]"), out.index("[2]"))
+        # each number is followed by its URL on the same logical hit
+        first = out.split("[2]")[0]
+        self.assertIn("https://docs.example.com/install", first)
+        self.assertNotIn("https://docs.example.com/ha", first)
+
+    def test_empty_hits_invents_nothing(self) -> None:
+        self.assertEqual(format_search_hits([]), "")
+
+    def test_missing_url_omits_emdash(self) -> None:
+        out = format_search_hits([("Title only", "", "body", "")])
+        self.assertIn("[1] **Title only**", out)
+        self.assertNotIn("—", out)
+
+
+if __name__ == "__main__":
+    unittest.main()

eval/pvalue.py

@@ -0,0 +1,156 @@
+"""Paired permutation test between two eval JSONL sidecars.
+
+Compares per-query scores from two `eval.run_eval` sidecar files so
+"P@1 went 0.88 → 0.91 on 25 queries" is not treated as a win.
+
+    python -m eval.pvalue \\
+        --a eval/results/baseline.jsonl \\
+        --b eval/results/new.jsonl \\
+        --metric rr
+
+Exit 0 even when the difference is not significant — this is a report,
+not a gate. No third-party deps; `random.Random(seed)` is enough.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+
+def load_sidecar(path: Path) -> list[dict]:
+    rows: list[dict] = []
+    with open(path) as fh:
+        for line in fh:
+            line = line.strip()
+            if line:
+                rows.append(json.loads(line))
+    return rows
+
+
+def paired_permutation(
+    a: list[float],
+    b: list[float],
+    n_resamples: int = 10000,
+    seed: int = 0,
+) -> dict:
+    """Two-sided paired permutation test on per-query scores.
+
+    Null: each pair is exchangeable (randomly flipping the sign of
+    A_i - B_i). p_value is the fraction of permutations whose
+    |mean diff| is at least as large as the observed |mean(A-B)|.
+    """
+    if len(a) != len(b):
+        raise ValueError(f"paired lengths differ: {len(a)} vs {len(b)}")
+    if not a:
+        raise ValueError("no paired queries to compare")
+    diffs = [x - y for x, y in zip(a, b)]
+    n = len(diffs)
+    observed = sum(diffs) / n
+    abs_obs = abs(observed)
+    rng = random.Random(seed)
+    extreme = 0
+    for _ in range(n_resamples):
+        total = 0.0
+        for d in diffs:
+            total += d if rng.random() < 0.5 else -d
+        if abs(total / n) >= abs_obs - 1e-15:
+            extreme += 1
+    p_value = extreme / n_resamples
+    return {
+        "A_mean": sum(a) / n,
+        "B_mean": sum(b) / n,
+        "Diff(A-B)": observed,
+        "p_value": p_value,
+        "significant": p_value < 0.05,
+        "n": n,
+        "n_resamples": n_resamples,
+    }
+
+
+def _index(rows: list[dict], metric: str) -> dict[tuple[str, str], float]:
+    """Map (retriever, query) -> score."""
+    out: dict[tuple[str, str], float] = {}
+    for row in rows:
+        retriever = str(row.get("retriever") or "")
+        query = str(row.get("query") or "")
+        if metric == "p_at_1":
+            score = float(row.get("p_at_1") or 0)
+        else:
+            score = float(row.get("rr") or 0)
+        out[(retriever, query)] = score
+    return out
+
+
+def compare(
+    rows_a: list[dict],
+    rows_b: list[dict],
+    metric: str = "rr",
+    retriever: str | None = None,
+    n_resamples: int = 10000,
+    seed: int = 0,
+) -> list[dict]:
+    """Join on (retriever, query). One result dict per shared retriever."""
+    ia, ib = _index(rows_a, metric), _index(rows_b, metric)
+    retrievers = sorted({r for r, _ in ia} & {r for r, _ in ib})
+    if retriever:
+        retrievers = [r for r in retrievers if r == retriever]
+        if not retrievers:
+            raise ValueError(f"retriever {retriever!r} not in both sidecars")
+    reports = []
+    for name in retrievers:
+        queries = sorted({q for r, q in ia if r == name} & {q for r, q in ib if r == name})
+        if not queries:
+            continue
+        a_scores = [ia[(name, q)] for q in queries]
+        b_scores = [ib[(name, q)] for q in queries]
+        report = paired_permutation(a_scores, b_scores, n_resamples=n_resamples, seed=seed)
+        report["retriever"] = name
+        report["metric"] = metric
+        reports.append(report)
+    if not reports:
+        raise ValueError("no overlapping (retriever, query) pairs")
+    return reports
+
+
+def render(reports: list[dict]) -> str:
+    lines = ["# Permutation test", ""]
+    for r in reports:
+        sig = "yes" if r["significant"] else "no"
+        lines += [
+            f"## `{r['retriever']}` ({r['metric']}, n={r['n']})",
+            "",
+            f"- A_mean: `{r['A_mean']:.4f}`",
+            f"- B_mean: `{r['B_mean']:.4f}`",
+            f"- Diff(A-B): `{r['Diff(A-B)']:.4f}`",
+            f"- p_value: `{r['p_value']:.4f}` ({r['n_resamples']} resamples)",
+            f"- significant (p < 0.05): **{sig}**",
+            "",
+        ]
+    return "\n".join(lines)
+
+
+def main() -> int:
+    p = argparse.ArgumentParser(description="Paired permutation test on two eval JSONL sidecars.")
+    p.add_argument("--a", type=Path, required=True, help="sidecar JSONL (system A)")
+    p.add_argument("--b", type=Path, required=True, help="sidecar JSONL (system B)")
+    p.add_argument("--metric", choices=("rr", "p_at_1"), default="rr")
+    p.add_argument("--retriever", default=None, help="restrict to one retriever name")
+    p.add_argument("--n-resamples", type=int, default=10000)
+    p.add_argument("--seed", type=int, default=0)
+    args = p.parse_args()
+    reports = compare(
+        load_sidecar(args.a),
+        load_sidecar(args.b),
+        metric=args.metric,
+        retriever=args.retriever,
+        n_resamples=args.n_resamples,
+        seed=args.seed,
+    )
+    print(render(reports), end="")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

eval/results/post-prefix.md

@@ -0,0 +1,21 @@
+# Retrieval eval — k=5
+
+_22 hand-curated queries, generated 2026-09-30 02:40:07_
+
+Live container after nomic prefixes + heading-recursive chunking
+(`hvm-docs` image `b200c5f76c56`, Watchtower 2026-09-30T02:36Z).
+Compared with [`baseline.md`](baseline.md) (2026-05-22, pre-prefix).
+
+| Retriever | P@1 | MRR | Recall@5 | nDCG@5 | avg latency |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| `dense` | 0.955 | 0.966 | 0.985 | 0.972 | 183ms |
+| `bm25` | 0.864 | 0.882 | 0.909 | 0.886 | 8ms |
+| `hybrid_rrf` | 0.955 | 0.961 | 0.955 | 0.955 | 113ms |
+| `bm25+rerank` | 0.773 | 0.827 | 0.848 | 0.816 | 162ms |
+| `hybrid_rrf+rerank` | 0.727 | 0.813 | 0.894 | 0.821 | 254ms |
+
+Dense flipped from worst (May MRR 0.539) to best. Rerank now hurts.
+`search_docs` therefore defaults to dense; rerank is opt-in via
+`RERANK_ENABLED`.
+
+Dense P@1 miss: `create a user account` (MRR 0.250).

eval/retrievers.py

@@ -51,7 +51,9 @@ class DenseRetriever:
         self.pool = pool
 
     def retrieve(self, query: str, k: int = 10) -> list[tuple[str, str]]:
-        res = self.col.query(query_texts=[query], n_results=self.pool)
+        from rag.embeddings import EMBED_QUERY_PREFIX, embed_texts
+        qvec = embed_texts([query], prefix=EMBED_QUERY_PREFIX)[0]
+        res = self.col.query(query_embeddings=[qvec], n_results=self.pool)
         ids = (res.get("ids") or [[]])[0]
         return _collapse_to_pages(ids, k)
 

eval/run_eval.py

@@ -34,6 +34,12 @@ def load_queries(path: Path) -> list[dict]:
         return [json.loads(line) for line in fh if line.strip()]
 
 
+def p_at_1(retrieved: list[tuple[str, str]], expected: list[tuple[str, str]]) -> float:
+    if not retrieved or not expected:
+        return 0.0
+    return 1.0 if retrieved[0] in set(expected) else 0.0
+
+
 def reciprocal_rank(retrieved: list[tuple[str, str]], expected: list[tuple[str, str]]) -> float:
     expected_set = set(expected)
     for i, page in enumerate(retrieved, start=1):
@@ -65,8 +71,12 @@ def main() -> int:
     p = argparse.ArgumentParser()
     p.add_argument("--queries", type=Path, default=Path("eval/queries.jsonl"))
     p.add_argument("--k", type=int, default=5)
+    p.add_argument("--ks", default="1,5,10,20", help="comma-separated k-curve")
     p.add_argument("--output", type=Path, default=Path("eval/results/baseline.md"))
+    p.add_argument("--compare", type=Path, default=None)
     args = p.parse_args()
+    ks = sorted({int(x) for x in args.ks.split(",") if x.strip()}) or [args.k]
+    max_k = max(ks + [args.k])
 
     if not args.queries.exists():
         print(f"queries file not found: {args.queries}")
@@ -109,34 +119,39 @@ def main() -> int:
     rows: dict[str, dict[str, float]] = {}
     per_query: list[dict] = []
     for r in retrievers:
-        mrr_sum = recall_sum = ndcg_sum = 0.0
+        mrr_sum = recall_sum = ndcg_sum = p1_sum = 0.0
         elapsed_sum = 0.0
         for q in queries:
             expected = [(e["bundle_id"], e["page_id"]) for e in q["expected"]]
             t0 = time.time()
-            retrieved = r.retrieve(q["query"], k=max(args.k, 10))
+            retrieved = r.retrieve(q["query"], k=max(max_k, 10))
             elapsed = time.time() - t0
             mrr = reciprocal_rank(retrieved, expected)
+            p1 = p_at_1(retrieved, expected)
             recall = recall_at_k(retrieved, expected, args.k)
             ndcg = ndcg_at_k(retrieved, expected, args.k)
             mrr_sum += mrr
+            p1_sum += p1
             recall_sum += recall
             ndcg_sum += ndcg
             elapsed_sum += elapsed
             per_query.append({
                 "retriever": r.name, "query": q["query"],
-                "mrr": mrr, "recall@k": recall, "ndcg@k": ndcg,
+                "mrr": mrr, "p_at_1": int(p1), "recall@k": recall, "ndcg@k": ndcg,
                 "top1": list(retrieved[0]) if retrieved else None,
+                "ranked": [list(p) for p in retrieved],
                 "elapsed_s": round(elapsed, 3),
             })
         n = len(queries)
         rows[r.name] = {
+            "P@1": p1_sum / n,
             "MRR": mrr_sum / n,
             f"Recall@{args.k}": recall_sum / n,
             f"nDCG@{args.k}": ndcg_sum / n,
             "avg_latency_s": elapsed_sum / n,
         }
-        print(f"  {r.name}: MRR={rows[r.name]['MRR']:.3f}  "
+        print(f"  {r.name}: P@1={rows[r.name]['P@1']:.3f}  "
+              f"MRR={rows[r.name]['MRR']:.3f}  "
               f"Recall@{args.k}={rows[r.name][f'Recall@{args.k}']:.3f}  "
               f"nDCG@{args.k}={rows[r.name][f'nDCG@{args.k}']:.3f}  "
               f"avg={rows[r.name]['avg_latency_s']*1000:.0f}ms")
@@ -144,10 +159,10 @@ def main() -> int:
     args.output.parent.mkdir(parents=True, exist_ok=True)
     md = [f"# Retrieval eval — k={args.k}", "",
           f"_{len(queries)} hand-curated queries, generated {time.strftime('%Y-%m-%d %H:%M:%S')}_", "",
-          "| Retriever | MRR | Recall@{k} | nDCG@{k} | avg latency |".replace("{k}", str(args.k)),
-          "| --- | ---: | ---: | ---: | ---: |"]
+          "| Retriever | P@1 | MRR | Recall@{k} | nDCG@{k} | avg latency |".replace("{k}", str(args.k)),
+          "| --- | ---: | ---: | ---: | ---: | ---: |"]
     for name, m in rows.items():
-        md.append(f"| `{name}` | {m['MRR']:.3f} | {m[f'Recall@{args.k}']:.3f} "
+        md.append(f"| `{name}` | {m['P@1']:.3f} | {m['MRR']:.3f} | {m[f'Recall@{args.k}']:.3f} "
                   f"| {m[f'nDCG@{args.k}']:.3f} | {m['avg_latency_s']*1000:.0f}ms |")
     md += ["", "## Per-query results", "",
            "| Retriever | Query | MRR | top-1 |", "| --- | --- | ---: | --- |"]
@@ -155,7 +170,22 @@ def main() -> int:
         top1 = f"`{r['top1'][0]}/{r['top1'][1][:24]}...`" if r["top1"] else "—"
         md.append(f"| `{r['retriever']}` | {r['query'][:60]} | {r['mrr']:.3f} | {top1} |")
     args.output.write_text("\n".join(md) + "\n")
+    sidecar = args.output.with_suffix(".jsonl")
+    with open(sidecar, "w") as fh:
+        for r in per_query:
+            fh.write(json.dumps({
+                "query": r["query"],
+                "retriever": r["retriever"],
+                "ranked": r.get("ranked") or [],
+                "rr": r["mrr"],
+                "p_at_1": r["p_at_1"],
+            }) + "\n")
     print(f"wrote {args.output}")
+    print(f"wrote {sidecar}")
+    if args.compare:
+        from eval.pvalue import compare, load_sidecar, render
+        print()
+        print(render(compare(load_sidecar(sidecar), load_sidecar(args.compare))), end="")
     return 0
 
 

eval/test_metrics.py

@@ -0,0 +1,84 @@
+"""Stdlib tests for eval metrics + the permutation test.
+
+Must not open Chroma — the template has no corpus. Run with:
+
+    python -m unittest eval.test_metrics
+"""
+from __future__ import annotations
+
+import math
+import unittest
+
+from eval.pvalue import compare, paired_permutation
+from eval.run_eval import ndcg_at_k, p_at_1, recall_at_k, reciprocal_rank
+
+
+A, B, C, X, Y = ("b", "a"), ("b", "b"), ("b", "c"), ("b", "x"), ("b", "y")
+
+
+class MetricTests(unittest.TestCase):
+    def test_reciprocal_rank(self) -> None:
+        # Q1: expected at rank 1
+        self.assertEqual(reciprocal_rank([A, B, C], [A]), 1.0)
+        # Q2: expected at rank 2
+        self.assertEqual(reciprocal_rank([B, A], [A]), 0.5)
+        # Q3: miss
+        self.assertEqual(reciprocal_rank([X, Y], [A]), 0.0)
+
+    def test_p_at_1(self) -> None:
+        self.assertEqual(p_at_1([A, B], [A]), 1.0)
+        self.assertEqual(p_at_1([B, A], [A]), 0.0)
+        self.assertEqual(p_at_1([], [A]), 0.0)
+        self.assertEqual(p_at_1([A], []), 0.0)
+
+    def test_recall_at_k(self) -> None:
+        self.assertEqual(recall_at_k([A, B, C], [A], 1), 1.0)
+        self.assertEqual(recall_at_k([B, A], [A], 1), 0.0)
+        self.assertEqual(recall_at_k([B, A], [A], 2), 1.0)
+        self.assertEqual(recall_at_k([X, Y], [A], 5), 0.0)
+        self.assertEqual(recall_at_k([A, B], [A, C], 1), 0.5)
+
+    def test_ndcg_at_k(self) -> None:
+        self.assertEqual(ndcg_at_k([A], [A], 1), 1.0)
+        # expected at rank 2: dcg = 1/log2(3), idcg = 1
+        self.assertAlmostEqual(
+            ndcg_at_k([B, A], [A], 2),
+            (1.0 / math.log2(3)) / 1.0,
+        )
+        self.assertEqual(ndcg_at_k([X, Y], [A], 5), 0.0)
+
+
+class PermutationTests(unittest.TestCase):
+    def test_identical_lists_not_significant(self) -> None:
+        scores = [0.5, 1.0, 0.0, 1.0, 0.5]
+        report = paired_permutation(scores, list(scores), n_resamples=2000, seed=0)
+        self.assertEqual(report["Diff(A-B)"], 0.0)
+        self.assertEqual(report["p_value"], 1.0)
+        self.assertFalse(report["significant"])
+
+    def test_large_paired_difference_is_significant(self) -> None:
+        a = [1.0] * 20
+        b = [0.0] * 20
+        report = paired_permutation(a, b, n_resamples=5000, seed=0)
+        self.assertGreater(report["Diff(A-B)"], 0.9)
+        self.assertLess(report["p_value"], 0.05)
+        self.assertTrue(report["significant"])
+
+    def test_compare_joins_on_retriever_and_query(self) -> None:
+        rows_a = (
+            [{"retriever": "dense", "query": f"q{i}", "rr": 1.0, "p_at_1": 1} for i in range(20)]
+            + [{"retriever": "bm25", "query": "q0", "rr": 0.0, "p_at_1": 0}]
+        )
+        rows_b = (
+            [{"retriever": "dense", "query": f"q{i}", "rr": 0.0, "p_at_1": 0} for i in range(20)]
+            + [{"retriever": "bm25", "query": "q0", "rr": 0.0, "p_at_1": 0}]
+        )
+        reports = compare(rows_a, rows_b, metric="rr", n_resamples=2000, seed=0)
+        by_name = {r["retriever"]: r for r in reports}
+        self.assertIn("dense", by_name)
+        self.assertTrue(by_name["dense"]["significant"])
+        self.assertFalse(by_name["bm25"]["significant"])
+
+
+if __name__ == "__main__":
+    unittest.main()

eval/trace.py

@@ -0,0 +1,114 @@
+"""Page-level miss dump using this clone's Dense/BM25 retrievers.
+
+    python -m eval.trace --queries eval/queries.jsonl
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from eval.run_eval import load_queries, p_at_1
+
+
+def classify_top1_source(top1, dense_pages, bm25_pages) -> str:
+    if top1 is None:
+        return "neither"
+    in_d, in_b = top1 in dense_pages, top1 in bm25_pages
+    if in_d and in_b:
+        return "both"
+    if in_d:
+        return "dense_only"
+    if in_b:
+        return "bm25_only"
+    return "neither"
+
+
+def first_ranks(pages: list[tuple[str, str]]) -> dict[str, int]:
+    out: dict[str, int] = {}
+    for i, (bid, pid) in enumerate(pages, start=1):
+        key = f"{bid}/{pid}"
+        if key not in out:
+            out[key] = i
+    return out
+
+
+def render_misses(rows: list[dict]) -> str:
+    misses = [r for r in rows if not r.get("hit")]
+    if not misses:
+        return "# Eval misses\n\n_(none)_\n"
+    lines = [f"# Eval misses ({len(misses)})", ""]
+    for row in misses:
+        lines += [
+            f"## {row['query']}",
+            "",
+            f"- expected: `{row['expected']}`",
+            f"- top-5: `{row['ranked_pages'][:5]}`",
+            f"- top1_source: `{row['top1_source']}`",
+            "",
+        ]
+    return "\n".join(lines)
+
+
+def main() -> int:
+    p = argparse.ArgumentParser()
+    p.add_argument("--queries", type=Path, default=Path("eval/queries.jsonl"))
+    p.add_argument("--trace-out", type=Path, default=Path("eval/results/trace.jsonl"))
+    p.add_argument("--misses-out", type=Path, default=Path("eval/results/misses.md"))
+    args = p.parse_args()
+    if not args.queries.exists():
+        print(f"queries file not found: {args.queries}")
+        return 1
+    try:
+        import os
+        import chromadb
+        from chromadb.config import Settings
+        from rag.embeddings import embedding_function
+        from rag.bm25 import BM25Index
+        from eval.retrievers import BM25Retriever, DenseRetriever
+        product = os.environ.get("PRODUCT_NAME", "hvm")
+        root = Path(__file__).resolve().parent.parent
+        col = chromadb.PersistentClient(
+            path=str(root / "chroma"),
+            settings=Settings(anonymized_telemetry=False),
+        ).get_collection(f"{product}_docs", embedding_function=embedding_function())
+        bm = BM25Index(str(root / "bm25" / f"{product}_docs.db"))
+        dense_r, bm25_r = DenseRetriever(col), BM25Retriever(bm)
+    except Exception as e:
+        args.trace_out.parent.mkdir(parents=True, exist_ok=True)
+        args.trace_out.write_text("")
+        args.misses_out.write_text("# Eval misses\n\nno index\n")
+        print(f"no index ({e}); wrote empty trace")
+        return 0
+
+    rows = []
+    for q in load_queries(args.queries):
+        expected = [(e["bundle_id"], e["page_id"]) for e in q["expected"]]
+        dense_pages = dense_r.retrieve(q["query"], k=50)
+        bm25_pages = bm25_r.retrieve(q["query"], k=50)
+        ranked = dense_pages or bm25_pages  # HVM default retrieval is dense-first
+        top1 = ranked[0] if ranked else None
+        p1 = p_at_1(ranked, expected)
+        rows.append({
+            "query": q["query"],
+            "expected": [list(p) for p in expected],
+            "hit": bool(p1),
+            "p_at_1": int(p1),
+            "dense_rank": first_ranks(dense_pages),
+            "bm25_rank": first_ranks(bm25_pages),
+            "top1": list(top1) if top1 else None,
+            "top1_source": classify_top1_source(top1, set(dense_pages), set(bm25_pages)),
+            "ranked_pages": [list(p) for p in ranked],
+        })
+    args.trace_out.parent.mkdir(parents=True, exist_ok=True)
+    with open(args.trace_out, "w") as fh:
+        for row in rows:
+            fh.write(json.dumps(row) + "\n")
+    args.misses_out.write_text(render_misses(rows))
+    print(f"wrote {args.trace_out} ({len(rows)} queries)")
+    print(f"wrote {args.misses_out}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

rag/chunk.py

@@ -1,24 +1,12 @@
-"""Markdown chunker — paragraph-aware, ~400-600 token target.
+"""Markdown chunker — heading-recursive, ~400-600 token target.
 
-Adjust the chunking strategy per product if your page format differs
-significantly from prose. The output shape (id, text, metadata) is
-fixed by the downstream Chroma + BM25 indexing in rag/index.py — don't
-change that.
+Chunk by semantic section (ATX headings), not raw page/length. A
+synthetic chunk 0 (title + first paragraph + optional keyword bag) is
+always emitted first — dense retrieval lands on it. Do not drop it.
 
-The key knob you'll tune per product is chunk-0. Dense retrieval lands
-on chunk 0 first for most queries. Make it a synthetic chunk built
-from:
-
-  - the page title (as natural-language H1)
-  - a 1-sentence task description (you'll have to generate this — for
-    pages that already have a "## Overview" or "## Introduction" the
-    first sentence usually works)
-  - a keyword bag of important terms (filenames, API names, error
-    codes — the rare technical tokens that BM25 lights up on)
-
-Without a rich chunk 0, dense retrieval gets dominated by the much
-larger prose body, and short pages (script examples, reference cards)
-get buried.
+No chonkie / langchain dependency. The output shape (id, text, metadata)
+is fixed by rag/index.py — don't change that. `heading_path` is optional
+metadata (e.g. "Install > Linux") and is not required for indexing.
 """
 from __future__ import annotations
 
@@ -26,17 +14,12 @@ import re
 from typing import Iterator
 
 
-# Approximate token estimate from char count. Tunable — set per
-# embedder if the default 4 chars/token is wrong.
 CHARS_PER_TOKEN = 4
 TARGET_TOKENS = 500
 TARGET_CHARS = TARGET_TOKENS * CHARS_PER_TOKEN
-# Hard cap: nomic-embed-text's context is 2048 tokens. Anything larger
-# 400s the entire embed batch. 6000 chars works for prose but markdown
-# tables with lots of `|` separators tokenize ~1.4× denser; a 5839-char
-# table chunk from the HVM qualification matrix tokenized past 2048 and
-# crashed the rebuild. 4000 chars stays under 2048 tokens even for
-# dense table content while leaving headroom for the query side.
+# nomic-embed-text context is 2048 tokens. Markdown tables with lots of
+# `|` tokenize ~1.4× denser than prose; 4000 chars stays under 2048 even
+# for qualification-matrix chunks (a 5839-char table crashed a rebuild).
 MAX_CHARS = 4000
 
 
@@ -97,6 +80,158 @@ def split_paragraphs(md: str) -> list[str]:
     return [b for b in blocks if b]
 
 
+def _heading_level(block: str) -> int:
+    first = block.lstrip().split("\n", 1)[0].strip()
+    m = re.match(r"^(#{1,6})\s+\S", first)
+    return len(m.group(1)) if m else 0
+
+
+def _heading_title(block: str) -> str:
+    first = block.split("\n", 1)[0].strip()
+    return re.sub(r"^#{1,6}\s+", "", first).strip()
+
+
+def _is_fence(block: str) -> bool:
+    return block.lstrip().startswith("```")
+
+
+def _joined_len(blocks: list[str]) -> int:
+    if not blocks:
+        return 0
+    return sum(len(b) for b in blocks) + 2 * (len(blocks) - 1)
+
+
+def _split_len(text: str) -> list[str]:
+    size = TARGET_CHARS
+    return [text[i:i + size] for i in range(0, len(text), size)] or [text]
+
+
+def _hard_wrap(text: str) -> list[str]:
+    """Last-resort split. Never used on fenced code blocks."""
+    if len(text) <= TARGET_CHARS:
+        return [text]
+    parts = re.split(r"\n\s*\n", text)
+    if len(parts) <= 1:
+        return _split_len(text)
+    packed: list[str] = []
+    buf: list[str] = []
+    n = 0
+    for part in parts:
+        if len(part) > TARGET_CHARS:
+            if buf:
+                packed.append("\n\n".join(buf))
+                buf, n = [], 0
+            packed.extend(_split_len(part))
+            continue
+        if n + len(part) > TARGET_CHARS and buf:
+            packed.append("\n\n".join(buf))
+            buf, n = [], 0
+        buf.append(part)
+        n += len(part)
+    if buf:
+        packed.append("\n\n".join(buf))
+    return packed
+
+
+def _pack_paragraphs(blocks: list[str], path: str) -> list[tuple[str, str]]:
+    out: list[tuple[str, str]] = []
+    buf: list[str] = []
+    buf_chars = 0
+
+    def flush() -> None:
+        nonlocal buf, buf_chars
+        if buf:
+            out.append(("\n\n".join(buf), path))
+            buf, buf_chars = [], 0
+
+    for p in blocks:
+        if _is_fence(p) and len(p) > TARGET_CHARS:
+            flush()
+            out.append((p, path))  # never slice a fence
+            continue
+        if len(p) > TARGET_CHARS:
+            flush()
+            out.extend((piece, path) for piece in _hard_wrap(p))
+            continue
+        if buf_chars + len(p) > TARGET_CHARS and buf:
+            flush()
+        buf.append(p)
+        buf_chars += len(p)
+    flush()
+    return out
+
+
+def _split_at_level(blocks: list[str], level: int) -> list[tuple[list[str], list[str]]]:
+    """Split into (path_suffix, blocks) groups starting at `level` headings.
+
+    path_suffix is [] for preamble, [title] for a section headed at `level`.
+    """
+    groups: list[tuple[list[str], list[str]]] = []
+    preamble: list[str] = []
+    current_path: list[str] = []
+    current: list[str] = []
+    for b in blocks:
+        lv = _heading_level(b)
+        if lv == level:
+            if current:
+                groups.append((current_path, current))
+            elif preamble:
+                groups.append(([], preamble))
+                preamble = []
+            current_path = [_heading_title(b)]
+            current = [b]
+        elif current:
+            current.append(b)
+        else:
+            preamble.append(b)
+    if current:
+        if preamble and not groups:
+            groups.append(([], preamble))
+            preamble = []
+        groups.append((current_path, current))
+    elif preamble:
+        groups.append(([], preamble))
+    return groups
+
+
+def _pack_recursive(blocks: list[str], path: list[str]) -> list[tuple[str, str]]:
+    if not blocks:
+        return []
+    path_s = " > ".join(path)
+    if _joined_len(blocks) <= TARGET_CHARS:
+        return [("\n\n".join(blocks), path_s)]
+
+    levels = [_heading_level(b) for b in blocks]
+    heading_levels = [lv for lv in levels if lv > 0]
+    if not heading_levels:
+        return _pack_paragraphs(blocks, path_s)
+
+    split_lv = min(heading_levels)
+    groups = _split_at_level(blocks, split_lv)
+    if len(groups) <= 1:
+        # Can't split at this heading level — descend or pack paragraphs.
+        if levels[0] == split_lv:
+            title = _heading_title(blocks[0])
+            rest = blocks[1:]
+            if not rest:
+                return _pack_paragraphs(blocks, path_s)
+            packed = _pack_recursive(rest, path + [title])
+            if not packed:
+                return [(blocks[0], " > ".join(path + [title]))]
+            glued = blocks[0] + "\n\n" + packed[0][0]
+            if len(glued) <= TARGET_CHARS:
+                packed[0] = (glued, packed[0][1])
+            else:
+                packed.insert(0, (blocks[0], packed[0][1]))
+            return packed
+        return _pack_paragraphs(blocks, path_s)
+
+    out: list[tuple[str, str]] = []
+    for suffix, group in groups:
+        out.extend(_pack_recursive(group, path + suffix))
+    return out
+
+
 def chunks_from_page(
     text: str,
     page_id: str,
@@ -112,7 +247,6 @@ def chunks_from_page(
     if not paragraphs:
         return
 
-    # ----- Chunk 0: synthetic anchor for dense retrieval ---------
     title = metadata.get("title") or page_id
     first_para = next((p for p in paragraphs if not p.startswith("#")), "")
     chunk0_body = (
@@ -127,28 +261,15 @@ def chunks_from_page(
         "metadata": {**metadata, "ordinal": 0},
     }
 
-    # ----- Body chunks: pack paragraphs up to TARGET_CHARS -------
     ordinal = 1
-
-    def emit(buf: list[str]) -> Iterator[dict]:
-        nonlocal ordinal
-        merged = "\n\n".join(buf)
-        for piece in _hard_split(merged):
+    for body, heading_path in _pack_recursive(paragraphs, []):
+        for piece in _hard_split(body):
+            meta = {**metadata, "ordinal": ordinal}
+            if heading_path:
+                meta["heading_path"] = heading_path
             yield {
                 "id":       f"{metadata['bundle_id']}::{page_id}::{ordinal}",
                 "text":     piece,
-                "metadata": {**metadata, "ordinal": ordinal},
+                "metadata": meta,
             }
             ordinal += 1
-
-    buf: list[str] = []
-    buf_chars = 0
-    for p in paragraphs:
-        if buf_chars + len(p) > TARGET_CHARS and buf:
-            yield from emit(buf)
-            buf = []
-            buf_chars = 0
-        buf.append(p)
-        buf_chars += len(p)
-    if buf:
-        yield from emit(buf)

rag/embeddings.py

@@ -41,6 +41,21 @@ def _resolve_urls() -> list[str]:
 OLLAMA_URLS = _resolve_urls()
 EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-embed-text")
 EMBED_DIM = int(os.environ.get("EMBED_DIM", "768"))
+# nomic asymmetric prefixes — embed time only. Empty string disables.
+EMBED_QUERY_PREFIX = os.environ.get("EMBED_QUERY_PREFIX", "search_query: ")
+EMBED_DOC_PREFIX = os.environ.get("EMBED_DOC_PREFIX", "search_document: ")
+
+
+def apply_prefix(text: str, prefix: str) -> str:
+    if not prefix:
+        return text
+    return prefix + text
+
+
+def embed_texts(texts: list[str], *, prefix: str, ef: Any | None = None) -> list[list[float]]:
+    """Embed texts after applying prefix. Callers store the original strings."""
+    fn = ef if ef is not None else embedding_function()
+    return list(fn([apply_prefix(t, prefix) for t in texts]))
 
 
 class OllamaEmbeddings(EmbeddingFunction):

rag/index.py

@@ -18,7 +18,7 @@ import chromadb
 from chromadb.config import Settings
 
 from .chunk import chunks_from_page
-from .embeddings import embedding_function
+from .embeddings import EMBED_DOC_PREFIX, embed_texts, embedding_function
 
 log = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO, format="%(asctime)s  %(message)s")
@@ -78,9 +78,12 @@ def upsert_to_chroma(records: list[dict]) -> int:
     total = 0
     for i in range(0, len(records), BATCH):
         chunk = records[i:i + BATCH]
+        texts = [r["text"] for r in chunk]
+        vectors = embed_texts(texts, prefix=EMBED_DOC_PREFIX, ef=embedding_function())
         col.upsert(
             ids=[r["id"] for r in chunk],
-            documents=[r["text"] for r in chunk],
+            documents=texts,
+            embeddings=vectors,
             metadatas=[r["metadata"] for r in chunk],
         )
         total += len(chunk)

rag/test_chunk.py

@@ -0,0 +1,71 @@
+"""Heading-recursive chunker tests. No corpus, no Chroma."""
+from __future__ import annotations
+
+import unittest
+
+import rag.chunk as chunk
+
+
+META = {"bundle_id": "Admin.10.0", "title": "Admin Guide"}
+
+
+def _bodies(text: str) -> list[str]:
+    return [c["text"] for c in chunk.chunks_from_page(text, "Page", META)]
+
+
+class ChunkTests(unittest.TestCase):
+    def setUp(self) -> None:
+        self._saved = chunk.TARGET_CHARS
+
+    def tearDown(self) -> None:
+        chunk.TARGET_CHARS = self._saved
+
+    def test_chunk0_always_has_title_on_short_page(self) -> None:
+        page = "# Install\n\nJust one short paragraph about installing."
+        chunks = list(chunk.chunks_from_page(page, "Install", META))
+        self.assertGreaterEqual(len(chunks), 1)
+        self.assertEqual(chunks[0]["metadata"]["ordinal"], 0)
+        self.assertIn("# Admin Guide", chunks[0]["text"])
+
+    def test_sibling_h2_sections_do_not_mix(self) -> None:
+        chunk.TARGET_CHARS = 80
+        page = (
+            "# A\n\n"
+            "intro text for A\n\n"
+            "## A.1\n\n"
+            "alpha content lives here only\n\n"
+            "## A.2\n\n"
+            "beta content lives here only\n"
+        )
+        bodies = _bodies(page)
+        self.assertTrue(any("alpha content" in b and "beta content" not in b for b in bodies[1:]),
+                        bodies)
+        self.assertTrue(any("beta content" in b and "alpha content" not in b for b in bodies[1:]),
+                        bodies)
+
+    def test_giant_section_splits_on_paragraphs(self) -> None:
+        chunk.TARGET_CHARS = 40
+        paras = [f"Paragraph number {i} with enough words." for i in range(8)]
+        page = "# Giant\n\n" + "\n\n".join(paras)
+        bodies = _bodies(page)
+        # skip chunk 0
+        for b in bodies[1:]:
+            # may exceed by at most one paragraph (the one that filled the buf)
+            self.assertLessEqual(len(b), chunk.TARGET_CHARS + len(paras[0]) + 2, b)
+
+    def test_fenced_code_never_sliced(self) -> None:
+        chunk.TARGET_CHARS = 30
+        fence = "```\n" + ("x" * 80) + "\n```"
+        page = "# Code\n\nBefore.\n\n" + fence + "\n\nAfter."
+        bodies = _bodies(page)
+        joined = "\n".join(bodies)
+        self.assertIn(fence, joined)
+        # no body chunk should contain a half-fence
+        for b in bodies:
+            if "```" in b:
+                self.assertTrue(b.strip().startswith("```") or "```\n" in b)
+                self.assertGreaterEqual(b.count("```"), 2, b)
+
+
+if __name__ == "__main__":
+    unittest.main()

rag/test_embeddings.py

@@ -0,0 +1,41 @@
+"""Prefix helpers — no Ollama, no Chroma."""
+from __future__ import annotations
+
+import unittest
+
+from rag.embeddings import apply_prefix, embed_texts
+
+
+class FakeEmbedder:
+    def __init__(self) -> None:
+        self.seen: list[str] = []
+
+    def __call__(self, texts: list[str]) -> list[list[float]]:
+        self.seen.extend(texts)
+        return [[0.0, 1.0] for _ in texts]
+
+
+class PrefixTests(unittest.TestCase):
+    def test_index_sends_doc_prefix_keeps_raw_text(self) -> None:
+        fake = FakeEmbedder()
+        raw = ["hello"]
+        vecs = embed_texts(raw, prefix="search_document: ", ef=fake)
+        self.assertEqual(fake.seen, ["search_document: hello"])
+        self.assertEqual(raw, ["hello"])  # caller storage unchanged
+        self.assertEqual(len(vecs), 1)
+
+    def test_query_sends_query_prefix(self) -> None:
+        fake = FakeEmbedder()
+        embed_texts(["q"], prefix="search_query: ", ef=fake)
+        self.assertEqual(fake.seen, ["search_query: q"])
+        self.assertNotIn("q", fake.seen)
+
+    def test_empty_prefix_is_raw(self) -> None:
+        self.assertEqual(apply_prefix("hello", ""), "hello")
+        fake = FakeEmbedder()
+        embed_texts(["hello"], prefix="", ef=fake)
+        self.assertEqual(fake.seen, ["hello"])
+
+
+if __name__ == "__main__":
+    unittest.main()