Overhaul docs retrieval and web search quality

Replace the abandoned llms-txt-mcp/Chroma docs backend with an in-repo
MCP service: SQLite WAL + FTS5 + sentence-transformer embeddings,
transactional source replacement, persisted state across restarts,
singleflight refresh with conditional requests, hybrid lexical/semantic
ranking with exact-duplicate collapse, source/host filters, and
explicit-by-default content retrieval. Add docs_rebuild and a
docs-rebuild CLI command.

Add deterministic llms-full.txt snapshot generation for machine-local
menus with hash-validated provenance manifests; lifecycle commands
promote a local menu to its snapshot only when the manifest validates.
Switch public source profiles to content-bearing llms-full.txt feeds.

Improve web search: bounded provider fallback with per-attempt
diagnostics and cancellation, an optional Brave Search API provider,
strict SearXNG engine selection, capped link/media extraction, and a
real engine=browser renderer that routes every request through the
existing SSRF vetting while blocking WebSockets, non-GET traffic, and
private destinations.

Extend release checks with offline unit suites and isolated candidate
container tests for both images.

docker/docs/context_docs/embedder.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import asyncio
+import hashlib
+
+import numpy as np
+
+
+class SentenceTransformerEmbedder:
+    def __init__(self, model_name: str):
+        self.model_name = model_name
+        self.fingerprint = f"sentence-transformers:{model_name}"
+        self._model = None
+        self._lock = asyncio.Lock()
+
+    @property
+    def ready(self) -> bool:
+        return self._model is not None
+
+    async def ensure_ready(self) -> None:
+        if self._model is not None:
+            return
+        async with self._lock:
+            if self._model is None:
+                self._model = await asyncio.to_thread(self._load)
+
+    def _load(self):
+        from sentence_transformers import SentenceTransformer
+
+        return SentenceTransformer(self.model_name, device="cpu")
+
+    async def encode_documents(self, texts: list[str]) -> np.ndarray:
+        await self.ensure_ready()
+        return await asyncio.to_thread(self._encode, texts)
+
+    async def encode_query(self, text: str) -> np.ndarray:
+        vectors = await self.encode_documents([text])
+        return vectors[0]
+
+    def _encode(self, texts: list[str]) -> np.ndarray:
+        return np.asarray(
+            self._model.encode(
+                texts,
+                batch_size=32,
+                show_progress_bar=False,
+                normalize_embeddings=True,
+                convert_to_numpy=True,
+            ),
+            dtype=np.float32,
+        )
+
+
+class LexicalFallbackEmbedder:
+    """Deterministic fallback used only when a model cannot be loaded."""
+
+    fingerprint = "lexical-fallback-v1"
+    ready = True
+
+    async def ensure_ready(self) -> None:
+        return None
+
+    async def encode_documents(self, texts: list[str]) -> np.ndarray:
+        return np.asarray([self._encode(text) for text in texts], dtype=np.float32)
+
+    async def encode_query(self, text: str) -> np.ndarray:
+        return np.asarray(self._encode(text), dtype=np.float32)
+
+    @staticmethod
+    def _encode(text: str, dimensions: int = 384) -> np.ndarray:
+        vector = np.zeros(dimensions, dtype=np.float32)
+        for token in text.lower().split():
+            digest = hashlib.sha256(token.encode()).digest()
+            vector[int.from_bytes(digest[:4], "big") % dimensions] += 1.0
+        norm = np.linalg.norm(vector)
+        return vector / norm if norm else vector