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>
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| app | ||
| README.md | ||
| requirements.txt | ||
OpenScribe server
Self-hosted FastAPI server: ingests recordings, transcribes them (faster-whisper), summarises them (Ollama), and serves the open API with exports. Everything runs on hardware you own.
Status: M0 scaffold. The API shape is live and browsable at
/docswith in-memory stubs. Transcription lands in M5, summaries in M6, real storage/DB alongside.
Run (dev)
cd server
python -m venv .venv
. .venv/Scripts/activate # Windows; or: . .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # edit as needed
uvicorn app.main:app --reload
- API docs (Swagger UI): http://localhost:8000/docs
- Health: http://localhost:8000/health
Self-hosted dependencies
For the AI features (M5/M6) you run, on your own kit:
- MinIO (or WebDAV / NAS) for object storage -
OPENSCRIBE_STORAGE_BACKEND=s3. - Ollama for summaries -
ollama serveandollama pull llama3.1. - faster-whisper downloads its model on first use; CPU works, CUDA is faster.
None of these are required for plain recording and transfer; they add transcription and summaries.
Layout
app/main.py FastAPI app + routes (mirrors ../api/openapi.yaml)
app/config.py Settings from env / .env
app/models.py Pydantic models (kept in sync with the OpenAPI schemas)
requirements.txt
.env.example
API
The contract is ../api/openapi.yaml. The device implements the LAN "device" paths; this
server implements ingest, transcript, summary and export.