# Python module & CLI


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Given an `llms.txt` file, this provides a CLI and Python API to parse
the file and create an XML context file from it. The input file should
follow this format:

    # FastHTML

    > FastHTML is a python library which...

    When writing FastHTML apps remember to:

    - Thing to remember

    ## Docs

    - [Surreal](https://host/README.md): Tiny jQuery alternative with Locality of Behavior
    - [FastHTML quick start](https://host/quickstart.html.md): An overview of FastHTML features

    ## Examples

    - [Todo app](https://host/adv_app.py)

    ## Optional

    - [Starlette docs](https://host/starlette-sml.md): A subset of the Starlette docs

## Install

``` sh
pip install llms-txt
```

## How to use

### CLI

After installation, `llms_txt2ctx` is available in your terminal.

To get help for the CLI:

``` sh
llms_txt2ctx -h
```

To convert an `llms.txt` file to XML context and save to `llms.md`:

``` sh
llms_txt2ctx llms.txt > llms.md
```

Pass `--optional True` to add the ‘optional’ section of the input file.

### Python module

``` python
from llms_txt import *
```

``` python
samp = Path('llms-sample.txt').read_text()
```

Use `parse_llms_file` to create a data structure with the sections of an
llms.txt file (you can also add `optional=True` if needed):

``` python
parsed = parse_llms_file(samp)
list(parsed)
```

    ['title', 'summary', 'info', 'sections']

``` python
parsed.title,parsed.summary
```

    ('FastHTML',
     'FastHTML is a python library which brings together Starlette, Uvicorn, HTMX, and fastcore\'s `FT` "FastTags" into a library for creating server-rendered hypermedia applications.')

``` python
list(parsed.sections)
```

    ['Docs', 'Examples', 'Optional']

``` python
parsed.sections.Optional[0]
```

``` json
{ 'desc': 'A subset of the Starlette documentation useful for FastHTML '
          'development.',
  'title': 'Starlette full documentation',
  'url': 'https://gist.githubusercontent.com/jph00/809e4a4808d4510be0e3dc9565e9cbd3/raw/9b717589ca44cedc8aaf00b2b8cacef922964c0f/starlette-sml.md'}
```

Use `create_ctx` to create an LLM context file with XML sections,
suitable for systems such as Claude (this is what the CLI calls behind
the scenes).

``` python
ctx = create_ctx(samp)
```

``` python
print(ctx[:300])
```

    <project title="FastHTML" summary='FastHTML is a python library which brings together Starlette, Uvicorn, HTMX, and fastcore&#39;s `FT` "FastTags" into a library for creating server-rendered hypermedia applications.'>
    Remember:

    - Use `serve()` for running uvicorn (`if __name__ == "__main__"` is not

### Implementation and tests

To show how simple it is to parse `llms.txt` files, here’s a complete
parser in \<20 lines of code with no dependencies:

``` python
from pathlib import Path
import re,itertools

def chunked(it, chunk_sz):
    it = iter(it)
    return iter(lambda: list(itertools.islice(it, chunk_sz)), [])

def parse_llms_txt(txt):
    "Parse llms.txt file contents in `txt` to a `dict`"
    def _p(links):
        link_pat = '-\s*\[(?P<title>[^\]]+)\]\((?P<url>[^\)]+)\)(?::\s*(?P<desc>.*))?'
        return [re.search(link_pat, l).groupdict()
                for l in re.split(r'\n+', links.strip()) if l.strip()]

    start,*rest = re.split(fr'^##\s*(.*?$)', txt, flags=re.MULTILINE)
    sects = {k: _p(v) for k,v in dict(chunked(rest, 2)).items()}
    pat = '^#\s*(?P<title>.+?$)\n+(?:^>\s*(?P<summary>.+?$)$)?\n+(?P<info>.*)'
    d = re.search(pat, start.strip(), (re.MULTILINE|re.DOTALL)).groupdict()
    d['sections'] = sects
    return d
```

We have provided a test suite in `tests/test-parse.py` and confirmed
that this implementation passes all tests.