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

README.md

@@ -77,6 +77,10 @@ uv run python -m scripts.import_docs --source ~/SomeOtherDocs
 - Only **`*.md`** files are indexed. Directories like `.venv`,
   `node_modules`, `.git`, `__pycache__`, `.pytest_cache`, `dist`, `build`
   are skipped (see `.agent/PLAN.md` anchor A9).
+- Every file is logged on its own line (`import: added|updated|unchanged|
+  pruned …`), and the run ends with a one-line summary (`import: summary
+  files=… added=… updated=… unchanged=… pruned=… chunks=… embed_batches=…`)
+  so the counts are greppable in logs.
 - Unchanged files are **not re-embedded** — only new/changed ones, so
   refreshes are cheap.
 - To sanity-check the LLM backend (models + embedding dimension) after any
@@ -194,6 +198,14 @@ served locally (no CDN), `BOR_ENVIRONMENT=production`.
 ## Troubleshooting
 
 - **`401` from aipi** — set `BOR_LLM_API_KEY` (or `$AIPI_KEY`).
+- **`litellm.UnsupportedParamsError … encoding_format` from aipi** — the
+  aipi proxy (litellm `openai_like`) rejects the `encoding_format` parameter
+  that the `openai` SDK injects into every embeddings request. The app
+  already works around this by POSTing a minimal `{model, input}` payload
+  through the openai client's own httpx transport (`app/rag/llm.py` →
+  `LLMClient._embed_batch`). If you see this, you are likely calling the
+  endpoint with a different client — drop the parameter (or set
+  `litellm.drop_params = True` on the proxy).
 - **Embedding dimension mismatch** — aipi changed models; run
   `uv run python -m scripts.llm_probe`, update `BOR_EMBEDDING_DIM`, then
   drop + recreate the chunks table (new migration or manual `TRUNCATE