feat(navi): semantic query router for intelligent tool selection - Phase H2b

Add centroid-based query classifier that routes Aurora queries to the
appropriate handler (nav_route, nav_reverse_geocode, direct_answer,
rag_search) before the RAG pipeline runs. Uses TEI embeddings against
pre-computed route centroids from 38 example queries.

- query_router.py: standalone module with lazy centroid init
- query_router_test.py: 7-query test suite (all passing)
- Corresponding recon_rag_tool.py v4.2.0 deployed to Open WebUI DB

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

lib/query_router.py

@@ -0,0 +1,161 @@
+"""Semantic query router for Aurora.
+
+Classifies user queries into routes (nav_route, nav_reverse_geocode,
+direct_answer, rag_search) by comparing query embeddings against
+pre-computed route centroids from example queries.
+
+TEI endpoint: http://100.64.0.14:8090/embed (cortex via Tailscale)
+"""
+
+import math
+import threading
+import requests
+
+# ── Route examples ────────────────────────────────────────────────────────────
+ROUTE_EXAMPLES = {
+    "nav_route": [
+        "how do I get to Boise",
+        "directions to Twin Falls",
+        "how do I get from Buhl to Boise",
+        "drive from Jerome to Sun Valley",
+        "route from Boise to McCall",
+        "what's the fastest way to Sun Valley",
+        "how far is it to Twin Falls",
+        "take me to Shoshone",
+        "navigate to the airport",
+        "how do I drive to Salt Lake City",
+        "walking directions to the park",
+        "bike route to downtown",
+    ],
+    "nav_reverse_geocode": [
+        "what town is at 42.5, -114.7",
+        "where am I right now",
+        "what is at coordinates 43.6, -116.2",
+        "what location is 42.574, -114.607",
+        "where is this place 44.0, -114.3",
+        "what city is near 42.7, -114.5",
+        "reverse geocode 43.0, -115.0",
+        "what's at this location 42.9, -114.8",
+    ],
+    "direct_answer": [
+        "hello",
+        "hey aurora",
+        "good morning",
+        "thanks",
+        "thank you",
+        "what's your name",
+        "who are you",
+        "tell me a joke",
+        "how are you",
+        "hi there",
+    ],
+    "rag_search": [
+        "what does the survival manual say about water",
+        "how to purify water in the field",
+        "how to treat a gunshot wound",
+        "what is the ranger handbook chapter on patrolling",
+        "field manual water purification",
+        "how to build a shelter in the wilderness",
+        "tactical combat casualty care procedures",
+        "what does FM 21-76 say about fire starting",
+    ],
+}
+
+# ── Module-level cache ────────────────────────────────────────────────────────
+_ROUTE_CENTROIDS: dict | None = None
+_LOCK = threading.Lock()
+
+
+def _embed_batch(texts: list[str], tei_url: str) -> list[list[float]]:
+    """Embed a batch of texts via TEI."""
+    resp = requests.post(tei_url, json={"inputs": texts}, timeout=30)
+    resp.raise_for_status()
+    return resp.json()
+
+
+def _compute_centroid(vectors: list[list[float]]) -> list[float]:
+    """Element-wise mean of vectors."""
+    n = len(vectors)
+    dim = len(vectors[0])
+    centroid = [0.0] * dim
+    for vec in vectors:
+        for i in range(dim):
+            centroid[i] += vec[i]
+    for i in range(dim):
+        centroid[i] /= n
+    return centroid
+
+
+def _cosine_similarity(a: list[float], b: list[float]) -> float:
+    """Cosine similarity between two vectors (pure Python)."""
+    dot = 0.0
+    norm_a = 0.0
+    norm_b = 0.0
+    for i in range(len(a)):
+        dot += a[i] * b[i]
+        norm_a += a[i] * a[i]
+        norm_b += b[i] * b[i]
+    denom = math.sqrt(norm_a) * math.sqrt(norm_b)
+    if denom == 0:
+        return 0.0
+    return dot / denom
+
+
+def _ensure_centroids(tei_url: str) -> dict[str, list[float]]:
+    """Lazy-init: embed all examples in one batch, compute centroids, cache."""
+    global _ROUTE_CENTROIDS
+    if _ROUTE_CENTROIDS is not None:
+        return _ROUTE_CENTROIDS
+
+    with _LOCK:
+        if _ROUTE_CENTROIDS is not None:
+            return _ROUTE_CENTROIDS
+
+        # Flatten all examples into one batch
+        all_texts = []
+        route_ranges: dict[str, tuple[int, int]] = {}
+        offset = 0
+        for route, examples in ROUTE_EXAMPLES.items():
+            route_ranges[route] = (offset, offset + len(examples))
+            all_texts.extend(examples)
+            offset += len(examples)
+
+        all_vectors = _embed_batch(all_texts, tei_url)
+
+        centroids = {}
+        for route, (start, end) in route_ranges.items():
+            centroids[route] = _compute_centroid(all_vectors[start:end])
+
+        _ROUTE_CENTROIDS = centroids
+        return _ROUTE_CENTROIDS
+
+
+def classify(
+    query: str,
+    tei_url: str = "http://100.64.0.14:8090/embed",
+    threshold: float = 0.45,
+) -> tuple[str, float]:
+    """Classify a query into a route.
+
+    Returns (route_name, confidence). If no route exceeds the threshold,
+    returns ("rag_search", best_score) as the safe default.
+    """
+    centroids = _ensure_centroids(tei_url)
+
+    # Embed the query
+    vecs = _embed_batch([query], tei_url)
+    query_vec = vecs[0]
+
+    # Compare against all centroids
+    best_route = "rag_search"
+    best_score = 0.0
+    for route, centroid in centroids.items():
+        sim = _cosine_similarity(query_vec, centroid)
+        if sim > best_score:
+            best_score = sim
+            best_route = route
+
+    if best_score < threshold:
+        return ("rag_search", best_score)
+
+    return (best_route, best_score)