feat(kb/search): LLM-powered query expansion in /kb/search

Single-shot search ranks the source whose title literally contains the
query terms way above everything else, so "מהי תקנה 37" returns 6 hits
from the תקנה 37 circular + 2 from the law and zero from ספר הליקויים
— even though the latter is the canonical medical reference for that
regulation. The ask agent already worked around this by issuing several
search_insurance_kb calls with different phrasings; that intelligence is
now also available to the public /kb/search endpoint.

Implementation:
- _retrieve_rrf extracts vector + lexical retrieval (RRF fused, no
  rerank) into a reusable async helper. Each candidate now carries
  chunk_id so callers can dedup when merging multiple sub-searches.
- _expand_query asks the configured Claude model (via ai-gateway) for
  up to 3 alternative phrasings, with a 12s timeout. On any failure the
  caller silently falls back to single-query mode.
- search() gains an `expand: bool` parameter. When true, runs the
  original query plus all variants through _retrieve_rrf in parallel,
  merges by chunk_id keeping the best RRF score, and reranks the union
  against the ORIGINAL query so the final order reflects what the user
  asked, not the variants.
- /kb/search defaults to expand=true.

Cost per call: 1 extra LLM round-trip (~1-2s on Sonnet) and 3 extra
Voyage embeds (~150ms each, parallel). Latency budget grows from ~700ms
to ~3s, acceptable for a search the user explicitly triggered.

Refs Task Master #1

api/routes/kb_public.py

@@ -37,6 +37,10 @@ class SearchRequest(BaseModel):
     query: str = Field(..., min_length=1, max_length=500)
     kind: Literal["any", "law", "regulation", "circular"] = "any"
     top_k: int = Field(8, ge=1, le=15)
+    # Multi-query expansion: ask the LLM for alternative phrasings, retrieve
+    # for each, merge, then rerank against the original query. Recovers
+    # documents that don't share the user's exact wording.
+    expand: bool = True
 
 
 @router.post("/search")
@@ -47,6 +51,7 @@ async def search(body: SearchRequest, request: Request):
         query=body.query,
         kind=body.kind,
         top_k=body.top_k,
+        expand=body.expand,
     )
     # Dates come back as datetime.date; serialize as ISO strings for the UI.
     serialized = []

api/services/kb/search.py

@@ -1,12 +1,22 @@
 """Hybrid search: vector + full-text, fused with Reciprocal Rank Fusion,
-and finally reranked with a Voyage cross-encoder for precision."""
+and finally reranked with a Voyage cross-encoder for precision.
+
+`search()` supports an `expand=True` mode that asks the LLM for query
+variants, retrieves candidates for each, merges them, and reranks the
+union against the original query. Costs one extra LLM call + one Voyage
+embed per variant, but recovers documents that don't share the user's
+exact phrasing — e.g. ספר הליקויים when the user types "מהי תקנה 37".
+"""
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import os
 from typing import Literal
 
+from openai import AsyncOpenAI
+
 from api.services.kb import voyage
 from api.services.kb.db import get_pool
 
@@ -17,17 +27,20 @@ _CANDIDATES_PER_SIDE = 30
 # Number of fused RRF candidates fed to the reranker. Higher = better
 # recall before the final cut; capped at ~30 to keep latency reasonable.
 _RERANK_POOL = 30
+# How many alternative phrasings to ask the LLM to generate when expand=True.
+_EXPANSION_VARIANTS = 3
+_EXPANSION_TIMEOUT_S = 12.0
 
 
-async def search(
+async def _retrieve_rrf(
     query: str,
-    kind: Literal["law", "regulation", "circular", "any"] = "any",
-    top_k: int = 8,
+    kind: Literal["law", "regulation", "circular", "any"],
 ) -> list[dict]:
-    query = (query or "").strip()
-    if not query:
-        return []
+    """Run vector + lexical retrieval, fuse with RRF. No rerank, no top_k cut.
 
+    Each candidate dict carries `chunk_id` (kb_chunk.id) so callers can
+    deduplicate when merging results from multiple queries.
+    """
     [vec] = await voyage.embed([query], input_type="query")
 
     kind_filter = ""
@@ -36,7 +49,6 @@ async def search(
         kind_filter = "AND s.kind = $4"
         params.append(kind)
 
-    # Two CTEs — vector rank and lexical rank — fused via RRF.
     sql = f"""
     WITH vec AS (
       SELECT c.id, ROW_NUMBER() OVER (ORDER BY c.embedding <=> $1::vector) AS rank
@@ -67,6 +79,7 @@ async def search(
       ) u GROUP BY id
     )
     SELECT
+      c.id AS chunk_id,
       s.id AS source_id, s.kind, s.title, s.identifier, s.source_url,
       s.published_at, s.effective_at, s.original_path,
       c.heading_path, c.section_ref, c.content, c.page_number,
@@ -81,33 +94,123 @@ async def search(
     pool = await get_pool()
     async with pool.acquire() as conn:
         rows = await conn.fetch(sql, *params)
+    return [dict(r) for r in rows]
 
-    candidates = [dict(r) for r in rows]
-    logger.info(
-        "[kb.search] query=%r kind=%s RRF_candidates=%d",
-        query[:80], kind, len(candidates),
-    )
-    if not candidates:
+
+_EXPANSION_PROMPT = (
+    "אתה עוזר חיפוש בבסיס ידע משפטי של הביטוח הלאומי בישראל "
+    "(החוק, התקנות, ספרי המבחנים והליקויים, וחוזרי המוסד). "
+    "המשתמש שואל שאלה. החזר עד {n} ניסוחים חלופיים שיעזרו לאחזר "
+    "את אותו מידע ממסמכים משפטיים בעברית — תרגומים מסגנון שאלתי "
+    "לסגנון מסמך, מילים נרדפות (למשל 'תקנה 37' → 'בדיקה מחדש', "
+    "'נכות' → 'נכה'/'דרגת נכות'/'מוגבלות'), והוספת מונחים רפואיים/משפטיים "
+    "שלא מופיעים במפורש בשאלה. החזר ניסוח אחד לשורה, ללא מספור או הקדמה. "
+    "אל תחזור על הניסוח המקורי."
+)
+
+
+async def _expand_query(query: str) -> list[str]:
+    """Ask the LLM for alternative phrasings. Empty list on failure (caller
+    just runs the original query)."""
+    base = (os.environ.get("AI_GATEWAY_URL") or "http://localhost:3000").rstrip("/")
+    api_key = os.environ.get("AI_GATEWAY_API_KEY", "")
+    if not api_key:
         return []
+    client = AsyncOpenAI(base_url=f"{base}/v1", api_key=api_key)
+    try:
+        resp = await asyncio.wait_for(
+            client.chat.completions.create(
+                model=os.environ.get("CLAUDE_MODEL", "sonnet"),
+                messages=[
+                    {"role": "system", "content": _EXPANSION_PROMPT.format(n=_EXPANSION_VARIANTS)},
+                    {"role": "user", "content": query},
+                ],
+                max_tokens=256,
+                temperature=0.3,
+            ),
+            timeout=_EXPANSION_TIMEOUT_S,
+        )
+    except (asyncio.TimeoutError, Exception) as e:
+        logger.warning("[kb.search] query expansion failed: %s", e)
+        return []
+    text = (resp.choices[0].message.content or "").strip()
+    variants: list[str] = []
+    for line in text.splitlines():
+        v = line.strip(" -•·*\t").strip()
+        if not v or v == query or v in variants:
+            continue
+        variants.append(v)
+        if len(variants) >= _EXPANSION_VARIANTS:
+            break
+    return variants
 
+
+async def _rerank_or_truncate(
+    query: str, candidates: list[dict], top_k: int,
+) -> list[dict]:
+    """Cross-encoder rerank against `query`; fall back to RRF order on error."""
     rerank_disabled = os.environ.get("KB_RERANK_DISABLED", "").lower() in ("1", "true")
     if rerank_disabled or len(candidates) <= top_k:
         return candidates[:top_k]
-
-    # Cross-encoder rerank for precision. On failure fall back to the RRF order
-    # rather than returning nothing — degraded precision is better than outage.
     texts = [c["content"] for c in candidates]
     try:
         ranked = await voyage.rerank(query=query, documents=texts, top_k=top_k)
     except voyage.VoyageError as e:
         logger.warning("[kb.search] rerank failed, falling back to RRF: %s", e)
         return candidates[:top_k]
-
     out: list[dict] = []
     for r in ranked:
         i = r["index"]
         if 0 <= i < len(candidates):
-            item = {**candidates[i], "rerank_score": r["relevance_score"]}
-            out.append(item)
+            out.append({**candidates[i], "rerank_score": r["relevance_score"]})
     logger.info("[kb.search] rerank kept top %d / %d", len(out), len(candidates))
     return out
+
+
+async def search(
+    query: str,
+    kind: Literal["law", "regulation", "circular", "any"] = "any",
+    top_k: int = 8,
+    expand: bool = False,
+) -> list[dict]:
+    query = (query or "").strip()
+    if not query:
+        return []
+
+    if not expand:
+        candidates = await _retrieve_rrf(query, kind)
+        logger.info(
+            "[kb.search] query=%r kind=%s RRF_candidates=%d",
+            query[:80], kind, len(candidates),
+        )
+        if not candidates:
+            return []
+        return await _rerank_or_truncate(query, candidates, top_k)
+
+    # Multi-query expansion: variants run in parallel with the original.
+    variants = await _expand_query(query)
+    queries = [query] + variants
+    logger.info("[kb.search] expand=true variants=%d", len(variants))
+
+    results = await asyncio.gather(
+        *[_retrieve_rrf(q, kind) for q in queries],
+        return_exceptions=True,
+    )
+    merged: dict[int, dict] = {}
+    for r in results:
+        if isinstance(r, Exception):
+            logger.warning("[kb.search] sub-retrieval failed: %s", r)
+            continue
+        for c in r:
+            cid = c["chunk_id"]
+            if cid not in merged or c["score"] > merged[cid]["score"]:
+                merged[cid] = c
+
+    candidates = sorted(merged.values(), key=lambda x: -x["score"])[:_RERANK_POOL]
+    logger.info(
+        "[kb.search] merged candidates=%d (from %d queries)",
+        len(candidates), len(queries),
+    )
+    if not candidates:
+        return []
+    return await _rerank_or_truncate(query, candidates, top_k)