Josh Knapp
d67625cd5a
- 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>
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
"""LiteLLM provider — multi-provider gateway."""
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from __future__ import annotations
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from typing import Any
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from voice_to_notes.providers.base import AIProvider
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class LiteLLMProvider(AIProvider):
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"""Routes through LiteLLM for access to 100+ LLM providers."""
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def __init__(self, model: str = "gpt-4o-mini", **kwargs: Any) -> None:
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self._model = model
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self._extra_kwargs = kwargs
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def chat(self, messages: list[dict[str, str]], **kwargs: Any) -> str:
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try:
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import litellm
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except ImportError:
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raise RuntimeError("litellm package is required. Install with: pip install litellm")
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merged_kwargs = {**self._extra_kwargs, **kwargs}
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response = litellm.completion(
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model=merged_kwargs.get("model", self._model),
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messages=messages,
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temperature=merged_kwargs.get("temperature", 0.7),
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max_tokens=merged_kwargs.get("max_tokens", 2048),
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)
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return response.choices[0].message.content or ""
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def is_available(self) -> bool:
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try:
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import litellm # noqa: F401
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return True
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except ImportError:
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return False
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@property
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def name(self) -> str:
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return "LiteLLM"
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