feat(rag): index markdown KB — chunker, embed client, delta importer, Sources page

Phase 02 (story: import documents):

- fence-aware markdown chunker (heading sections, 200-char overlap,
  heading anchor on every chunk, 1200-char hard cap, fence blocks
  kept atomic and split under the cap)
- LLMClient over aipi (LiteLLM) reusing the openai client's httpx
  transport to send a clean {model, input} payload — the openai SDK
  injects encoding_format, which aipi's openai_like group rejects;
  token-budget batching + halving retry for the endpoint's
  ~1024-token per-request input cap
- two-phase per-file upsert importer: sha256 delta (unchanged skip),
  atomic commit, A9 exclusion walk, per-source prune, per-file error
  tolerance (rollback + log + continue, non-zero CLI exit), adaptive
  re-chunk at half target for URL-dense files the endpoint rejects
- scripts/import_docs CLI (repeatable --source, --prune, --limit,
  defaults ~/Homelab + ~/Deployments)
- GET /api/docs with per-doc chunk counts; Sources page wired to the
  real endpoint (stat cards, full-width a11y table, designed empty
  state, DOM-built rows — no innerHTML)
- tests: 63 passed (chunker/llm/importer units, docs API + importer
  integration), story E2E 3/3 (real endpoints, in-thread import);
  app/ coverage 98%
- real KB imported: 672 docs / 8969 chunks in ~3m, idempotent
  re-run (672 unchanged, 0 batches)
- harness: .agent/validate.sh now gates through uv (pytest +
  coverage >90% + ruff + pyright) instead of system python3

tests/fakes.py

@@ -0,0 +1,24 @@
+"""Shared test fakes (no network, deterministic)."""
+from __future__ import annotations
+
+from app.config import Settings
+
+
+class FakeEmbedder:
+    """Duck-typed stand-in for :class:`app.rag.llm.LLMClient` (see the
+    ``Embedder`` protocol in :mod:`app.rag.importer`).
+
+    Returns deterministic vectors of *dim* dimensions; records every call
+    so tests can assert batching behaviour.
+    """
+
+    def __init__(self, dim: int = 768) -> None:
+        self.dim = dim
+        self.settings = Settings(_env_file=None)  # pyright: ignore[reportCallIssue]
+        self.embed_batches = 0
+        self.calls: list[list[str]] = []
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        self.calls.append(list(texts))
+        self.embed_batches += 1
+        return [[0.01 * (i % 97) for i in range(self.dim)] for _ in texts]