Phase 5: AI provider system with local and cloud support

- Implement AIProvider base interface with chat() and is_available()
- Add LocalProvider connecting to bundled llama-server via OpenAI SDK
- Add OpenAIProvider for direct OpenAI API access
- Add AnthropicProvider for Anthropic Claude API
- Add LiteLLMProvider for multi-provider gateway
- Build AIProviderService with provider routing, auto-selection,
  and transcript context injection
- Add ai.chat IPC handler supporting chat, list_providers, set_provider,
  and configure actions
- Add ai_chat, ai_list_providers, ai_configure Tauri commands
- Build interactive AIChatPanel with message history, quick actions
  (Summarize, Action Items), and transcript context awareness
- Tests: 30 Python, 6 Rust, 0 Svelte errors

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

python/voice_to_notes/providers/litellm_provider.py

@@ -2,4 +2,41 @@
 
 from __future__ import annotations
 
-# TODO: Implement LiteLLM provider
+from typing import Any
+
+from voice_to_notes.providers.base import AIProvider
+
+
+class LiteLLMProvider(AIProvider):
+    """Routes through LiteLLM for access to 100+ LLM providers."""
+
+    def __init__(self, model: str = "gpt-4o-mini", **kwargs: Any) -> None:
+        self._model = model
+        self._extra_kwargs = kwargs
+
+    def chat(self, messages: list[dict[str, str]], **kwargs: Any) -> str:
+        try:
+            import litellm
+        except ImportError:
+            raise RuntimeError("litellm package is required. Install with: pip install litellm")
+
+        merged_kwargs = {**self._extra_kwargs, **kwargs}
+        response = litellm.completion(
+            model=merged_kwargs.get("model", self._model),
+            messages=messages,
+            temperature=merged_kwargs.get("temperature", 0.7),
+            max_tokens=merged_kwargs.get("max_tokens", 2048),
+        )
+        return response.choices[0].message.content or ""
+
+    def is_available(self) -> bool:
+        try:
+            import litellm  # noqa: F401
+
+            return True
+        except ImportError:
+            return False
+
+    @property
+    def name(self) -> str:
+        return "LiteLLM"