open_notebook/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

What this repo is

This is a local Docker Compose deployment of Open Notebook (upstream image lfnovo/open_notebook), not a clone of its source. There is no application source code here — only deployment config (docker-compose.yml, .env, .gitignore). Application bugs/features belong upstream; this repo only concerns itself with how the stack is run locally.

Commands

docker compose up -d              # start (pulls images per pull_policy: always)
docker compose down                # stop and remove containers (data volumes persist)
docker compose logs -f open_notebook   # follow app logs
docker compose ps                  # container status

No build/lint/test suite exists in this repo — nothing here to run.

Architecture

Two services, defined in docker-compose.yml:

• surrealdb (surrealdb/surrealdb:v2) — the database, RocksDB-backed, bound to 127.0.0.1:8000 only (local debugging access, e.g. surreal sql). Credentials come from SURREAL_USER/SURREAL_PASSWORD (default root/root if unset in .env) and are shared with the open_notebook service so they stay in sync.
• open_notebook (lfnovo/open_notebook:v1-latest) — bundles the web UI (port 8502) and REST API (port 5055) in one image, both bound to 127.0.0.1 only. Connects to surrealdb over the internal compose network (ws://surrealdb:8000/rpc).

Data persists in ./surreal_data and ./notebook_data (bind mounts, gitignored).

AI providers

This deployment intentionally uses only two AI providers — no OpenAI/Anthropic/Google keys are wired in, even though such keys may exist in the host environment:

• Ollama, running natively on the host (not containerized) at 0.0.0.0:11434, restricted to GPU 1+2 (RTX 3090s) via CUDA_VISIBLE_DEVICES=1,2 in its systemd unit — GPU 0 (a T600 used for the desktop) is intentionally excluded. The open_notebook container reaches it via OLLAMA_BASE_URL=http://host.docker.internal:11434, which requires the extra_hosts: host.docker.internal:host-gateway entry in the compose file (Linux has no Docker-Desktop magic DNS for this).
• OpenRouter, the only cloud provider, configured via OPENROUTER_API_KEY (read from the host environment into .env).

Neither surrealdb nor open_notebook do local model inference themselves, so no GPU device reservation is needed in the compose file — all GPU usage happens inside the host's Ollama process.

Embedding model: use nomic-embed-text (274 MB, 2048 native context), not qwen3-embedding (a repurposed 8B-class LLM, ~13GB loaded). With the resident qwen3.5:27b chat model already using 17GB of GPU 1's 24GB, qwen3-embedding didn't fit and Ollama silently ran it 44-56% on CPU — a single embedding call took 39-54s, so any real document (many chunks, batched) blew past esperanto's 60s embedding timeout (ESPERANTO_EMBEDDING_TIMEOUT) and got stuck in an infinite retry loop that looked like "adding the source failed" even though extraction worked fine. Note: the credential's num_ctx field (meant to shrink Ollama's context/VRAM footprint) does not work for embeddings — esperanto's OllamaEmbeddingModel puts it at the top level of the /api/embed payload instead of nesting it under options, so Ollama silently ignores it. Don't rely on num_ctx to fix embedding VRAM contention; use a genuinely small embedding model instead.

A separate local llama.cpp server exists on this machine (managed via ~/llamacppctl-server_neu/llamacppctl/, container name llama_cpp_server) but is not wired into this stack. It can be added later as an additional OpenAI-compatible provider through the Open Notebook UI (Settings → AI Providers) if needed — note it competes for the same GPU 1/2 VRAM as Ollama, so don't run large models on both simultaneously.

Chat model context window: the Ollama credential used for qwen3.5:27b (credential:di8b31l16zikyyb6isyi) has num_ctx=98304 set explicitly. Without this, esperanto's OllamaLanguageModel silently defaults num_ctx to 8192 regardless of the model's real capability or OLLAMA_CONTEXT_LENGTH — any chat context (e.g. "full content" mode on a real document) beyond that gets truncated by Ollama, and because qwen3.5:27b is a thinking model, the truncated prompt frequently produces an empty final answer (the response is silently swallowed by clean_thinking_content) instead of a visible error — looks exactly like "chat gives no answer" from the UI. 98304 is the largest value that still keeps the whole model on GPU 1 (ollama ps shows 100% GPU); 131072 spills ~91% onto CPU and makes a single response take 5-6 minutes instead of ~1-2. Even at 98304, a full-book "Volltext" chat (~70k+ tokens of context) takes ~2-3 minutes — that's inherent to processing that much context on one consumer GPU, not a bug. For large documents, the default short/RAG context mode (only relevant chunks, not the whole document) stays fast; reserve "Volltext" for shorter sources. If qwen3.5:27b ever gets swapped for a different Ollama chat model, re-check this credential's num_ctx still makes sense for its VRAM footprint.

Text-to-speech / speech-to-text

Open Notebook's openai_compatible provider type talks to any endpoint implementing OpenAI's audio API shape (POST /audio/speech, POST /audio/transcriptions — see esperanto.providers.tts.openai_compatible / .stt.openai_compatible inside the app container for the exact contract). Since neither Ollama nor OpenRouter support TTS/STT, two thin wrapper servers in services/ expose the locally installed tools this way:

• services/tts_server.py — wraps chatterbox-tts (via ~/chatterbox-tts-cli/chatterbox_cli_v4.py), run with the chatterbox conda env (~/miniforge3/envs/chatterbox/bin/python).
• services/stt_server.py — wraps faster-whisper (large-v3 model), run with the base python3.

Both are plain background processes (nohup ... &), not systemd units — they don't survive a reboot; restart manually if needed:

cd ~/open-notebook/services
CUDA_VISIBLE_DEVICES=2 TTS_PORT=8901 nohup ~/miniforge3/envs/chatterbox/bin/python tts_server.py > logs/tts_server.log 2>&1 &
CUDA_VISIBLE_DEVICES=2 STT_PORT=8902 nohup python3 stt_server.py > logs/stt_server.log 2>&1 &

They're pinned to CUDA_VISIBLE_DEVICES=2 deliberately — GPU 1 tends to fill up with Ollama's resident models (OLLAMA_KEEP_ALIVE=-1 keeps them loaded), so TTS/STT get GPU 2 to themselves rather than risking an OOM race with Ollama.

Reachability from the open_notebook container requires two things, both already done: the extra_hosts: host.docker.internal:host-gateway entry in docker-compose.yml, and UFW rules allowing inbound 8901/tcp and 8902/tcp (same pattern as the existing 11434/tcp rule for Ollama — UFW defaults to deny-incoming, so any new host-side port a container needs to reach must be explicitly opened).

Registered in Open Notebook as openai_compatible credentials with base_url=http://host.docker.internal:8901 (TTS) / :8902 (STT), and set as default_text_to_speech_model / default_speech_to_text_model.

Voice cloning

tts_server.py resolves the incoming voice field via services/voices/*.wav (gitignored — personal voice recordings): <name>.wav becomes a selectable voice "<name>", cloned via chatterbox's audio_prompt_path. Unknown/omitted voices fall back to "default"; a bare language code (e.g. "en") still works and skips cloning (built-in voice for that language). All cloned voices are generated in German (CLONE_LANG in the script) since only German reference clips exist here — add entries to VOICE_LANG_OVERRIDES in the script if you add clips in other languages.

Three reference clips exist in services/voices/ (gitignored, not committed):

• default.wav — symlink to ~/chatterbox-tts-cli/my_voice_deutsch_60s.wav, the owner's own voice.
• male_thorsten.wav / female_kerstin.wav — synthesized with Piper using the CC0-licensed de_DE-thorsten-medium and de_DE-kerstin-low voice models (downloaded from huggingface.co/rhasspy/piper-voices). Deliberately not real people's recordings scraped from the internet — Piper's voices are explicitly released for this kind of downstream synthesis/cloning use, unlike e.g. Common Voice (consented for ASR training, not cloning) or random web audio (no consent at all). Regenerate with:

  piper --model de_DE-thorsten-medium.onnx --output_file male_thorsten.wav <<< "some German sentence"
  

Podcast speaker profiles are assigned across these three: business_panel (Marcus→male_thorsten, Elena→female_kerstin, Johny→default), tech_experts (Alex→male_thorsten, Jamie→female_kerstin), solo_expert/test_speaker_local (→female_kerstin/Anna). To add another distinct voice, drop a new <name>.wav (~10-30s, clean single-speaker audio, licensing-checked) into services/voices/, restart tts_server.py (voices are scanned once at startup), and set the speaker's voice_id to <name> via PUT /api/speaker-profiles/{id}.

Secrets

.env (gitignored) holds OPEN_NOTEBOOK_ENCRYPTION_KEY (encrypts provider credentials stored in SurrealDB), SURREAL_PASSWORD, and OPENROUTER_API_KEY. .env.example is the committed template. Provider API keys can also be entered directly in the Open Notebook UI after first startup — the project's own docs recommend this over env vars for better security since they get encrypted at rest.

Git

This directory is its own independent git repository (main branch) even though the parent home directory is also a git repo — this is intentional, not an accident to fix.