Laurence
51321aa7c5
Lets the owner point transcription and summarisation at any AI: an open-standard
endpoint (OpenAI-compatible / local faster-whisper / Ollama) or a commercial API
(OpenAI, Anthropic, Gemini). Config-driven, self-hostable, no lock-in.
What changed:
- server/app/providers/: provider layer.
- base.py: Transcriber/Summariser protocols + shared summary prompt + tolerant JSON
parser (uniform Summary shape across providers).
- summary.py: OpenAICompatibleSummariser (any /chat/completions - OpenAI, Groq,
OpenRouter, LocalAI, LM Studio, vLLM, Ollama /v1) and AnthropicSummariser (Claude
via the official anthropic SDK; Messages API has no OpenAI-compatible endpoint).
- transcription.py: OpenAICompatibleTranscriber (/audio/transcriptions - OpenAI,
Groq, self-hosted whisper server) and LocalWhisperTranscriber (faster-whisper,
execution wired in M5).
- factory.py: builds the configured providers with per-provider defaults
(anthropic -> claude-opus-4-8, openai_compatible -> gpt-4o-mini, ollama -> llama3.1).
- config.py + .env.example: transcription_provider / llm_provider selectors + base_url,
key, model settings; local faster-whisper and Ollama kept as the self-hosted defaults.
- main.py: /health now reports the resolved provider names (no secrets).
- requirements.txt: httpx drives all HTTP providers; anthropic + faster-whisper are
optional, only for their respective providers.
- docs/ai-providers.md: config recipes for OpenAI, Groq, Anthropic, Gemini, LocalAI,
LM Studio, Ollama, self-hosted whisper.
- state/: DECISIONS, ARCHITECTURE, TODO updated.
Why:
- The user asked to connect the device to any open standard AI or commercial one; this
is also the core differentiator vs Plaud's locked cloud.
Notes:
- Anthropic provider uses the official SDK and defaults to claude-opus-4-8 (per the
claude-api guidance). AI deps are optional per chosen provider. Modules byte-compile
cleanly; end-to-end wiring into the ingest pipeline lands with M5.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
# SPDX-License-Identifier: GPL-3.0-only
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"""Provider interfaces plus the shared summary prompt and a tolerant JSON parser.
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Keeping the prompt and parser here means every LLM provider produces the same Summary
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shape regardless of whether it is OpenAI-compatible, Anthropic, or a local model.
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"""
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from __future__ import annotations
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import json
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import re
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from typing import Protocol
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from ..models import Summary, Transcript
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SUMMARY_SYSTEM = (
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"You are a meeting-notes assistant. Given a transcript, produce a concise overview, "
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"the key points, and any action items. Respond with ONLY a JSON object of the form "
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'{"overview": string, "key_points": [string], "action_items": [string]} and nothing '
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"else."
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)
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def summary_user_prompt(transcript_text: str) -> str:
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return f"Transcript:\n\n{transcript_text}\n\nProduce the JSON summary now."
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def parse_summary(recording_id: str, model: str, raw: str) -> Summary:
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"""Parse an LLM reply into a Summary, tolerating prose or code-fences around the JSON."""
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data: dict = {}
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match = re.search(r"\{.*\}", raw, re.DOTALL)
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if match:
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try:
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data = json.loads(match.group(0))
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except json.JSONDecodeError:
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data = {}
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return Summary(
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recording_id=recording_id,
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model=model,
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overview=str(data.get("overview") or raw.strip()[:1000]),
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key_points=[str(x) for x in (data.get("key_points") or [])],
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action_items=[str(x) for x in (data.get("action_items") or [])],
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)
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class Transcriber(Protocol):
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name: str
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def transcribe(self, audio_path: str, language: str | None = None) -> Transcript: ...
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class Summariser(Protocol):
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name: str
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def summarise(self, recording_id: str, transcript_text: str) -> Summary: ...
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