feat: port template upgrades (citations, eval, chunking, nomic prefixes)

mcp 2.x already on origin/main (#10). Does not change BM25-first
search_docs — n=6 is too small to flip the default.

- Numbered [1] citations via docs_mcp/format.py
- Eval P@1 + JSONL sidecar + eval.pvalue + eval.trace
- Heading-recursive chunker, keep chunk-0 and MAX_CHARS=4000
- Nomic prefixes at embed time only; stored text unprefixed

Closes #12

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__)
@@ -145,6 +146,17 @@ 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 +270,10 @@ def search_docs(
     """Search the HPE Morpheus Enterprise (Morpheus) 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,
@@ -296,7 +309,7 @@ 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]
@@ -326,7 +339,7 @@ def search_docs(
                 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)
+            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]
@@ -342,7 +355,7 @@ 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 +384,21 @@ 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()
@@ -1073,8 +1085,8 @@ 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}"