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/tests/fakes.py

@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+import hashlib
+from dataclasses import dataclass
+
+import numpy as np
+
+from context_docs.models import FetchResponse
+
+
+class FakeEmbedder:
+    fingerprint = "fake-embedder-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._vector(text) for text in texts], dtype=np.float32)
+
+    async def encode_query(self, text: str) -> np.ndarray:
+        return np.asarray(self._vector(text), dtype=np.float32)
+
+    @staticmethod
+    def _vector(text: str) -> list[float]:
+        lower = text.lower()
+        return [
+            float("api" in lower or "identifier" in lower),
+            float("persistence" in lower or "checkpoint" in lower),
+            float("background" in lower or "asynchronous" in lower),
+            0.25 + (int(hashlib.sha256(text.encode()).hexdigest()[:2], 16) / 1024),
+        ]
+
+
+@dataclass
+class FakeFetch:
+    status: int
+    body: str = ""
+    final_url: str | None = None
+    etag: str | None = None
+    last_modified: str | None = None
+
+
+class FakeFetcher:
+    def __init__(self, responses: list[FakeFetch]):
+        self.responses = list(responses)
+        self.calls = 0
+
+    async def fetch(self, source_url: str, state=None) -> FetchResponse:
+        self.calls += 1
+        response = self.responses.pop(0)
+        return FetchResponse(
+            status=response.status,
+            requested_url=source_url,
+            resolved_url=response.final_url or source_url,
+            body=response.body,
+            etag=response.etag,
+            last_modified=response.last_modified,
+        )