# 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](https://github.com/lfnovo/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

```bash
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.

**Podcast language + reliability — fixed via patched prompt templates (`prompts/podcast/`,
bind-mounted).** Two separate problems, one fix location:

1. *Language.* The episode profile's `language` field (`"de"` → resolved to `"German"`) **is**
   passed into both `podcast_creator` prompt templates as a `{{ language }}` variable
   (`nodes.py`, both `generate_outline_node` and `generate_transcript_node`), but the stock
   templates shipped in the image never reference it — so setting `language` had no effect and
   podcasts came out in English (the stock briefings and speaker backstories are English, which
   is the only language signal the model then sees). English text read aloud by the German-locked
   Chatterbox TTS is what produced the "English with a bad German accent" symptom.
2. *Reliability.* `qwen3.5:27b` is a thinking model. Podcast content is the **entire notebook**
   (`Notebook.get_context()` → every source's full text + all insights; for a whole book that's
   ~70k+ tokens), fed into *each* transcript-segment call. On long segments the `<think>` block
   eats the 5000-token response budget and the JSON gets truncated → `Invalid json output` →
   whole job fails. (Confirmed: `/no_think` cut a segment from 98s to 34s and left valid JSON.)

Fix — both handled in the two templates under `prompts/podcast/`, bind-mounted read-only over
the image's copies via `docker-compose.yml`:
- Prepended `/no_think` as the first line of both templates (Qwen honors it anywhere; harmless
  text for non-Qwen models) — disables thinking, freeing the token budget and ~3x speedup.
- Added a `{% if language %}CRITICAL LANGUAGE REQUIREMENT … in {{ language }}{% endif %}` block
  near the end of both templates, so the already-plumbed `language` field now actually forces the
  output language.

The bundled briefings and speaker backstory/personality were **also** translated to German (belt
and suspenders; reduces English drift from the source content), but the template change is what
makes `language` authoritative. **Editing these templates requires a container restart** to take
effect — they're bind-mounted, and the mount is the directory `prompts/podcast/` (not individual
files, which go stale on inode-replacing edits). If you switch off `qwen3.5:27b`, the `/no_think`
line becomes inert but harmless.

If you create your own episode profile, just set `language` — the template handles the rest.

### 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:

```bash
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](https://github.com/rhasspy/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:
  ```bash
  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.