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voice-to-notes/python/voice_to_notes/providers/local_provider.py

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Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
"""Local AI provider — bundled llama-server (OpenAI-compatible API)."""
from __future__ import annotations
import sys
from typing import Any
Phase 1 foundation: Tauri shell, Python sidecar, SQLite database Tauri v2 + Svelte + TypeScript frontend: - App shell with workspace layout (waveform, transcript, speakers, AI chat) - Placeholder components for all major UI areas - Typed stores (project, transcript, playback, AI) - TypeScript interfaces matching the database schema - Tauri bridge service with typed invoke wrappers - svelte-check passes with 0 errors Rust backend: - Tauri v2 app entry point with command registration - SQLite database layer (rusqlite with bundled SQLite) - Full schema: projects, media_files, speakers, segments, words, ai_outputs, annotations (with indexes) - Model structs with serde serialization - CRUD queries for projects, speakers, segments, words - Segment text editing preserves original text - Schema versioning for future migrations - 6 tests passing - Command stubs for project, transcribe, export, AI, settings, system - App state management Python sidecar: - JSON-line IPC protocol (stdin/stdout) - Message types: IPCMessage, progress, error, ready - Handler registry with routing and error handling - Ping/pong handler for connectivity testing - Service stubs: transcribe, diarize, pipeline, AI, export - Provider stubs: local (llama-server), OpenAI, Anthropic, LiteLLM - Hardware detection stubs - 14 tests passing, ruff clean Also adds: - Testing strategy document (docs/TESTING.md) - Validation script (scripts/validate.sh) - Updated .gitignore for Svelte, Rust, Python artifacts Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-26 15:16:06 -08:00
from voice_to_notes.providers.base import AIProvider
class LocalProvider(AIProvider):
"""Connects to bundled llama-server via its OpenAI-compatible API."""
def __init__(self, base_url: str = "http://localhost:8080", model: str = "local") -> None:
self._base_url = base_url.rstrip("/")
self._model = model
self._client: Any = None
def _ensure_client(self) -> Any:
if self._client is not None:
return self._client
try:
from openai import OpenAI
self._client = OpenAI(
base_url=f"{self._base_url}/v1",
api_key="not-needed", # llama-server doesn't require an API key
)
except ImportError:
raise RuntimeError(
"openai package is required for local AI. Install with: pip install openai"
)
return self._client
def chat(self, messages: list[dict[str, str]], **kwargs: Any) -> str:
client = self._ensure_client()
response = client.chat.completions.create(
model=self._model,
messages=messages,
temperature=kwargs.get("temperature", 0.7),
max_tokens=kwargs.get("max_tokens", 2048),
)
return response.choices[0].message.content or ""
def is_available(self) -> bool:
try:
import urllib.request
req = urllib.request.Request(f"{self._base_url}/health", method="GET")
with urllib.request.urlopen(req, timeout=2) as resp:
return resp.status == 200
except Exception:
return False
@property
def name(self) -> str:
return "Local (llama-server)"