feat(ai): in-RAM semantic recall (RAG) for conversation context

Give the agent recall of things said beyond the verbatim window, without
breaking the RAM-only philosophy — nothing is persisted to disk.

- MemoryIndex: a capped, in-memory pool of embedded messages with pure-Python
  cosine search (no numpy). Retains far more than the rolling transcript so old
  lines can be surfaced on demand; oldest evicted past the cap to bound RAM.
- OllamaEmbedder: local embeddings via nomic-embed-text, on by default and
  independent of the chat provider (reuses the Ollama host when chat is Ollama).
- Bridge: captured room messages (live + backfilled) are embedded on a
  background worker so a slow embedder can't stall frame draining. On a /ai
  question the agent retrieves top-k relevant lines, drops weak (<min_score) and
  windowed-duplicate hits, and prepends them as a clearly-fenced "recalled
  context" preamble — kept at user role, never elevated to system, so untrusted
  room text informs without instructing. Falls back to recency-only if the
  embedder is unreachable.
- CLI: --no-rag, --embed-model, --embed-host, --rag-top-k.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

cmd_chat/agent/providers.py

@@ -72,6 +72,29 @@ class OllamaProvider:
         return [m.get("name", "") for m in r.json().get("models", [])]
 
 
+class OllamaEmbedder:
+    """Local text embeddings via Ollama (default ``nomic-embed-text``), used for
+    the agent's in-RAM semantic recall. Local + free, so it stays on by default
+    regardless of which provider answers chat. No key, nothing persisted."""
+
+    name = "ollama-embed"
+
+    def __init__(self, model: str = "nomic-embed-text", host: str | None = None,
+                 timeout: int = 60):
+        self.model = model
+        self.host = (host or os.environ.get("OLLAMA_HOST", "http://localhost:11434")).rstrip("/")
+        self.timeout = timeout
+
+    def embed(self, text: str) -> list[float]:
+        r = requests.post(
+            f"{self.host}/api/embeddings",
+            json={"model": self.model, "prompt": text},
+            timeout=self.timeout,
+        )
+        r.raise_for_status()
+        return r.json().get("embedding") or []
+
+
 class AnthropicProvider:
     """Anthropic Messages API. Cloud — opt-in. Needs ANTHROPIC_API_KEY."""