Metadata-Version: 2.5
Name: shidoshi
Version: 0.0.2
Summary: An opinionated way to augment Jupyter Lab for iterative work
Project-URL: Homepage, https://github.com/tdchaitanya/shidoshi
Project-URL: Repository, https://github.com/tdchaitanya/shidoshi
Project-URL: Issues, https://github.com/tdchaitanya/shidoshi/issues
Author-email: Chaitanya <tdchaitanya@gmail.com>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: ai-assistant,jupyter,jupyterlab,llm,openai
Classifier: Development Status :: 3 - Alpha
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.13
Requires-Dist: ipylab>=0.6
Requires-Dist: ipynbname>=2025.8.0.0
Requires-Dist: ipython>=8.39.0
Requires-Dist: openai>=2.41.1
Requires-Dist: trio>=0.25
Requires-Dist: typing-extensions>=4.0
Provides-Extra: notebook
Requires-Dist: ipywidgets>=8.0; extra == 'notebook'
Description-Content-Type: text/markdown

# shidoshi

[![PyPI](https://img.shields.io/pypi/v/shidoshi.svg)](https://pypi.org/project/shidoshi/)
[![CI](https://github.com/tdchaitanya/shidoshi/actions/workflows/test.yml/badge.svg)](https://github.com/tdchaitanya/shidoshi/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](LICENSE)

An opinionated way to augment Jupyter Lab for iterative work.

shidoshi adds `%ask` / `%%ask` magics to Jupyter that let you talk to an LLM
from inside a notebook — using the notebook itself, in order, as the
conversation history. No separate chat pane, no copy-pasting context: your
code cells, their outputs, and your notes are the context.

## Install

Requires Python ≥3.13 and JupyterLab. Set an API key before use:

```bash
export OPENAI_API_KEY=sk-...       # for the openai provider (default)
export OPENAI_BASE_URL=...         # optional, e.g. to point at a proxy
export OPENROUTER_API_KEY=...      # for the openrouter provider
```

### Installing across Jupyter environments

`%load_ext shidoshi` runs `import shidoshi` **inside the running kernel
process**. That means shidoshi has to be installed into whichever Python
environment the kernel you're using actually runs in. It ships a small
`shidoshi` command for setup, but the library itself is not a standalone
tool — so `uvx` / `uv tool install` (which run a tool in an isolated
subprocess, separate from any kernel) don't apply here.

- **Per-project venv with its own JupyterLab** (e.g. a `uv`-managed project):
  add shidoshi as a normal dependency of that project.

  ```bash
  uv add shidoshi
  # or: pip install shidoshi
  ```

- **One shared JupyterLab, many kernels** (each notebook's kernel points at a
  different project venv registered via `ipykernel install`): install
  shidoshi into *each* kernel's venv. Installing it only where JupyterLab
  itself lives will not make it importable from other kernels.

  ```bash
  # inside the venv backing a given kernel
  uv add shidoshi
  # or: pip install shidoshi
  ```

### Skipping `%load_ext` — the shidoshi kernel

To avoid typing `%load_ext shidoshi` in every notebook, register a kernel that
loads it for you:

```bash
shidoshi install-kernel --sys-prefix
```

Pick **Python 3 (shidoshi)** from the Jupyter kernel list and the magics are
already there. Nothing else changes: it is a stock Python kernel running this
environment's interpreter — your imports, variables, and debugger all work
exactly as before. The generated `kernel.json` just appends
`--IPKernelApp.extensions=shidoshi` to the normal `ipykernel_launcher`
command, with an absolute path to this environment's Python.

Useful flags:

| flag | effect |
| --- | --- |
| `--sys-prefix` | install into the active venv (best for a project venv) |
| `--user` | install into your per-user kernel directory |
| `--prefix PATH` | install into an explicit prefix |
| `--name` / `--display-name` | override the ids — use a distinct `--name` per environment if you register more than one |
| `--env KEY=VALUE` | set an environment variable for the kernel process (repeatable) |
| `--force` | replace an existing kernelspec of the same name (logos in it are kept) |

Installing is refused if a kernelspec of that name already exists, so it won't
quietly replace one you made by hand. Register one per environment with a
distinct `--name`.

Remove it with `jupyter kernelspec remove shidoshi`.

#### With uv

Add shidoshi to the project, then register the kernel from inside it. **Which
location flag you need depends on where JupyterLab itself runs from**, because
Jupyter only searches its own `sys.prefix`, your user directory, and the system
directory:

```bash
uv add shidoshi

# A: JupyterLab in an ephemeral env (uv's default suggestion).
#    Its sys.prefix is a uv cache dir, so --sys-prefix would be invisible.
uv run shidoshi install-kernel --user
uv run --with jupyter jupyter lab

# B: JupyterLab as a project dependency — sys.prefix *is* the project venv.
uv add --dev jupyterlab
uv run shidoshi install-kernel --sys-prefix
uv run jupyter lab
```

B keeps the kernel scoped to the project and disappears with the venv; A is
the one that works with `uv run --with jupyter`. If a freshly installed kernel
doesn't show up in the launcher, run `jupyter kernelspec list` **the same way
you start Lab** — that prints exactly the directories being searched.

Either way this replaces the kernel step in
[uv's Jupyter guide](https://docs.astral.sh/uv/guides/integration/jupyter/) —
you don't need `uv run ipython kernel install --env VIRTUAL_ENV ...` as well,
because `install-kernel` records `VIRTUAL_ENV` for you when it detects a venv.

That variable matters more than it looks. `uv pip install` resolves its target
from `VIRTUAL_ENV` (falling back to `CONDA_PREFIX`, then a base interpreter) —
never from the kernel that's running. So if you start Jupyter from a
conda-activated shell, a kernel without `VIRTUAL_ENV` will `import` from your
project venv while `!uv pip install` quietly installs into your conda base.
Pinning it keeps both views on the same environment.

Two related notes:

- `!uv add` was always safe — it finds the project by walking up for
  `pyproject.toml`, so it ignores `VIRTUAL_ENV` and targets the project venv
  either way.
- `%pip install` needs `uv venv --seed`; uv venvs have no `pip` in them by
  default. Prefer `!uv add`.

Pass `--env` to set anything else the kernel should launch with (repeatable),
including an override for `VIRTUAL_ENV`:

```bash
uv run shidoshi install-kernel --sys-prefix --env OPENAI_BASE_URL=http://127.0.0.1:18080/v1
```

Because the spec pins an absolute interpreter path, it can only ever start the
environment shidoshi is installed in. That is the advantage over the
`ipython_config.py` route below: `~/.ipython` is shared by *every* Python
environment under your `$HOME`, so putting the extension there makes every
kernel on the machine try to import shidoshi, including ones that don't have
it.

<details>
<summary>Auto-loading via ipython_config.py instead</summary>

Add to `~/.ipython/profile_default/ipython_config.py` (create it first with
`ipython profile create`):

```python
c.InteractiveShellApp.extensions = ["shidoshi"]
```

Only safe if shidoshi is installed in **every** environment you use for
Jupyter on that machine. To scope it, create a named profile
(`ipython profile create shidoshi`), put the `extensions` line in that
profile, and add `"--profile=shidoshi"` to the relevant kernel's `argv`.

</details>

## Quickstart

```
%load_ext shidoshi
```

(skip this line if you're on the **Python 3 (shidoshi)** kernel)

```
%%ask
What does the `history.build_history` function in this file do?
```

The response streams into the cell's output as Markdown.

## Magics reference

- **`%ask <prompt>`** — line magic for a one-line prompt.
  - Prefix with `model|` or `provider:model|` to override the configured
    default model for just this call, e.g. `%ask openrouter:openai/gpt-4o|summarize this`.
  - Add `--debug` anywhere on the line to also show the full request/response
    payload.
- **`%%ask [model]`** — cell magic; the whole cell body is the prompt
  (multi-line is fine, and it can reference images via Markdown
  `![]()`/`<img>` syntax or bare local file paths — they're inlined as
  base64). An optional model name on the magic line overrides the default
  for this call. Also supports `--debug`.
- **`%%skip`** — runs the cell normally, but the cell is left out of the
  context sent to the model entirely. Use it for scratch or exploratory
  cells you don't want the model to see.
- **`%%pin`** — runs the cell normally; its content and output are *always*
  included in context and are exempt from the auto-trim behavior below. Use
  it to protect a fact, constant, or definition you don't want dropped over
  a long session.

## How context is built

Every prior cell in the notebook — up to the one you're currently running,
and accounting for kernel restarts — is turned into conversation history
automatically:

- **Markdown cells** become background text/image context (treated as notes
  or reference material, not instructions).
- **Regular code cells** appear as fenced code plus their text/image
  outputs.
- **Prior `%ask` / `%%ask` cells** become real user/assistant turns. Their
  responses are reused from a per-cell cache rather than re-sent, so
  replaying history doesn't resend answers the model already produced.
- **`%%skip` cells** are dropped entirely.
- **`%%pin` cells** are always kept.

## Automatic context-length handling

If a request is rejected for exceeding the model's context window, shidoshi
automatically retries, dropping the oldest trimmable history units first
(markdown cells, then plain code cells, then whole ask+response pairs —
`%%pin` cells are never dropped), up to 20 times. A banner reports how many
cells were dropped so you know context shrank.

## Providers & tools

- **`openai`** (default) — uses the OpenAI Responses API.
- **`openrouter`** — uses OpenRouter's chat-completions API; select it with
  the `provider:model` prefix, e.g. `openrouter:anthropic/claude-3.5-sonnet`.

Every request currently has the built-in `web_search` tool attached, so the
model can search the web when it needs current information. (A `web_fetch`
tool also exists in the codebase but isn't wired into the magics yet — not
available today.)

## Debug mode

Add `--debug` to `%ask`/`%%ask` to render a collapsible, syntax-highlighted
panel showing exactly what was sent (system prompt, full message history,
tools) and every raw event streamed back — useful when the model's behavior
is surprising and you want to see the actual payload.

## Configuration

shidoshi reads TOML config, layered as defaults → `~/.shidoshi/config.toml`
→ `./.shidoshi/config.toml` (project config overrides user config):

```toml
default_model = "gpt-5.5"   # model used when none is specified
reasoning_effort = "low"    # OpenAI only
ask_color = "#eafbea"       # highlight color for %ask/%%ask cells
skip_color = "#ececec"      # highlight color for %%skip cells
```

## Development

```bash
uv sync
uv run pytest tests/unit tests/btp -v
```

Integration tests under `tests/integration/` require a live `OPENAI_API_KEY`
(or a proxy via `OPENAI_BASE_URL`) and are run with:

```bash
uv run pytest tests/integration/ -v -m integration
```

## License

Apache License 2.0 — see [LICENSE](LICENSE).
