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>
66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
# SPDX-License-Identifier: GPL-3.0-only
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"""Summarisation providers.
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- OpenAICompatibleSummariser: any endpoint speaking the OpenAI /chat/completions API -
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OpenAI, Groq, Together, OpenRouter, LocalAI, vLLM, LM Studio, and Ollama's /v1 endpoint.
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- AnthropicSummariser: Claude via the official Anthropic SDK (Messages API). Anthropic does
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not expose an OpenAI-compatible endpoint, so it needs its own provider.
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"""
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from __future__ import annotations
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import httpx
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from ..models import Summary
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from .base import SUMMARY_SYSTEM, parse_summary, summary_user_prompt
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class OpenAICompatibleSummariser:
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"""Talks the OpenAI Chat Completions API. Works with any compatible base_url."""
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def __init__(self, base_url: str, api_key: str, model: str):
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self.base_url = base_url.rstrip("/")
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self.api_key = api_key
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self.model = model
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self.name = f"openai_compatible:{model}"
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def summarise(self, recording_id: str, transcript_text: str) -> Summary:
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headers = {"Authorization": f"Bearer {self.api_key}"} if self.api_key else {}
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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": SUMMARY_SYSTEM},
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{"role": "user", "content": summary_user_prompt(transcript_text)},
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],
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"temperature": 0.2,
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# Honoured by OpenAI/Groq/vLLM/etc.; ignored by servers that don't support it.
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"response_format": {"type": "json_object"},
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}
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resp = httpx.post(
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f"{self.base_url}/chat/completions", headers=headers, json=payload, timeout=120
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)
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resp.raise_for_status()
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content = resp.json()["choices"][0]["message"]["content"]
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return parse_summary(recording_id, self.name, content)
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class AnthropicSummariser:
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"""Claude via the official Anthropic SDK. Default model: claude-opus-4-8."""
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def __init__(self, api_key: str, model: str):
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import anthropic # imported lazily so the dep is only needed for this provider
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self._client = (
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anthropic.Anthropic(api_key=api_key) if api_key else anthropic.Anthropic()
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)
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self.model = model
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self.name = f"anthropic:{model}"
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def summarise(self, recording_id: str, transcript_text: str) -> Summary:
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resp = self._client.messages.create(
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model=self.model,
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max_tokens=2000,
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system=SUMMARY_SYSTEM,
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messages=[{"role": "user", "content": summary_user_prompt(transcript_text)}],
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)
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text = next((b.text for b in resp.content if b.type == "text"), "")
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return parse_summary(recording_id, self.name, text)
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