fix(rag): close audit gaps in the in-house engine

An adversarial audit of the piragi -> in-house swap surfaced nine confirmed
issues; this fixes all of them.

- Re-ingest now REPLACES a source's chunks instead of appending. Add
  VectorStore.delete_by_source and BaseIndexPlugin.replace_on_reingest (default
  True), called before add_chunks in both ingest paths. Without it every
  startup / periodic / manual reindex appended a fresh copy of each doc's
  chunks, growing the tables unbounded and crowding out distinct results.
  Conversations opt OUT (replace_on_reingest=False): their many messages share
  one source URI, so delete-by-source would wipe history.
- index_* now honor the plugin IngestResult. The explicit record endpoints
  (error / standard / decision / review / learning) raise on failure instead of
  writing a green tracking row for content that never persisted;
  conversation / journal indexing stays best-effort but skips the tracking row
  when the embed fails. index_message / index_entry return IngestResult.
- A deprecated index type (code) now returns 404 instead of a 500 leaked from
  _get_plugin's missing-plugin error: add OptimalService.is_index_registered
  and guard the stats / clear / refresh routes. The panel drops the dead 'Code'
  category, filter, badge, label, and mock data.
- Panel: getContext reads 'results' (matches SearchResponse) instead of a
  non-existent 'context' field; the reindex toast no longer reports phantom
  '0 code files'; the stats 'Updated' label uses the max timestamp across
  indexes rather than indexes[0]; ProactiveContextItem matches the wire shape.
- Drop the always-zero per-document chunk_count from the documents API.
- Remove dead RAG settings (hybrid_search, cross_encoder) the engine never
  consumed, and correct stale piragi / BM25 references in code, README, and
  CLAUDE.md. Delete the unused duplicate roboco/kb embedder package the swap
  shipped.

Adds tests for replace-on-reingest (incl. the conversations carve-out) and the
deprecated-index 404.

roboco/config.py

@@ -136,12 +136,6 @@ class Settings(BaseSettings):
         description="Use HyDE (hypothetical document embeddings). "
         "Makes one LLM call per query for better semantic matching.",
     )
-    rag_use_hybrid_search: bool = Field(
-        default=True, description="Use BM25 + vector hybrid search"
-    )
-    rag_use_cross_encoder: bool = Field(
-        default=True, description="Use neural reranking (slower but more accurate)"
-    )
     rag_auto_update_enabled: bool = Field(default=True)
     rag_auto_update_interval: int = Field(
         default=300, ge=60, description="Seconds between auto-updates"