Add Phase 3: LLM integration with Ollama streaming and preflight checks

Wire the REPL to a local Ollama instance via streaming HTTP (SSE).
LLMClient handles async streaming chat, StreamHandler renders live
Markdown via Rich and accumulates tool call fragments. Startup now
runs a preflight check that verifies Ollama is reachable and the
configured model is pulled, exiting with a clear message on failure.
Also adds .gitignore and updates config to use qwen3.5.

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

app/main.py

@@ -55,6 +55,12 @@ def parse_args() -> argparse.Namespace:
     return parser.parse_args()
 
 
+async def _preflight(config: AppConfig) -> None:
+    """Check that Ollama is reachable and the configured model is available."""
+    async with LLMClient(config.llm) as client:
+        await client.preflight_check()
+
+
 async def _run_repl(
     ctx: SessionContext,
     config: AppConfig,
@@ -171,6 +177,18 @@ def main() -> None:
     if args.verbose:
         print_info("Verbose mode enabled")
 
+    # Preflight: check Ollama is reachable and model exists
+    try:
+        asyncio.run(_preflight(config))
+    except LLMConnectionError as e:
+        print_error(str(e))
+        sys.exit(1)
+    except LLMError as e:
+        print_error(str(e))
+        sys.exit(1)
+
+    print_success("Ollama connected, model ready.")
+
     # Create session and start REPL
     ctx = SessionContext(config)
     logger.info("startup_complete")