` element. This code is intended to be used with the `fast_app` framework and served.
```python
from fasthtml.common import *
app,rt = fast_app()
@rt
def index(): return div("Hello, World!")
serve()
```
--------------------------------
### Print beginning of LLM context file
Source: https://llmstxt.org/intro.html
This code prints the first 300 characters of the generated LLM context string (`ctx`). This is useful for quickly inspecting the beginning of the XML output.
```python
print(ctx[:300])
```
--------------------------------
### Viewing and Saving Generated FastHTML Code in 'ed'
Source: https://llmstxt.org/ed-commonmark
Shows how to inspect the generated code within the 'ed' editor using the 'n' command for line count and 'p' command for printing the buffer content. It then demonstrates saving the generated FastHTML application to a Python file using the 'w' command and quitting 'ed' with 'q'.
```shell
* n
5
* p
from fasthtml.common import *
app,rt = fast_app()
@rt
def index(): return div("Hello, World!")
serve()
*w hello_world.py
5
*q
```
--------------------------------
### Access parsed title and summary
Source: https://llmstxt.org/intro.html
This code accesses and prints the `title` and `summary` attributes from the parsed `llms.txt` data structure. These attributes typically hold the main project title and a brief description.
```python
parsed.title,parsed.summary
```
--------------------------------
### Create LLM context file from llms.txt content
Source: https://llmstxt.org/intro.html
The `create_ctx` function generates an LLM context file in XML format from the provided 'llms.txt' content. This output is suitable for systems like Claude and is what the CLI tool uses internally.
```python
ctx = create_ctx(samp)
```
--------------------------------
### Generating FastHTML App with 'ed' AI Command
Source: https://llmstxt.org/ed-commonmark
Utilizes a hypothetical 'x' (eXecute AI) command in an LLM-enhanced 'ed' editor to generate a FastHTML web application. The command takes a natural language prompt, analyzes the loaded LLM context, and produces code that is written directly into the editor's buffer.
```shell
* x Create a simple FastHTML app which outputs 'Hello, World!', in a
.
Analyzing context and prompt...
Generating FastHTML app...
App written to buffer.
```
--------------------------------
### Convert llms.txt to XML context using CLI
Source: https://llmstxt.org/intro.html
This CLI command converts an 'llms.txt' file into an XML context file, saving the output to 'llms.md'. The `--optional True` flag can be used to include the 'Optional' section from the input file.
```sh
llms_txt2ctx llms.txt > llms.md
```
```sh
llms_txt2ctx --optional True llms.txt > llms.md
```
--------------------------------
### Access specific optional section content
Source: https://llmstxt.org/intro.html
This code demonstrates accessing a specific item within the 'Optional' section of the parsed `llms.txt` data. It retrieves the first item, likely a dictionary containing details about a link or resource.
```python
parsed.sections.Optional[0]
```
--------------------------------
### Import llms_txt module in Python
Source: https://llmstxt.org/intro.html
This Python statement imports all functions and classes from the `llms_txt` library, making them available for use in your script. This is the first step when using the library programmatically.
```python
from llms_txt import *
```
--------------------------------
### Read llms.txt content into a string
Source: https://llmstxt.org/intro.html
This Python code snippet reads the content of an 'llms-sample.txt' file into a string variable named `samp`. It utilizes the `Path` object from the `pathlib` module for file operations.
```python
samp = Path('llms-sample.txt').read_text()
```
--------------------------------
### Minimalist Python parser for llms.txt
Source: https://llmstxt.org/intro.html
This Python code provides a self-contained parser for 'llms.txt' files with no external dependencies beyond standard libraries. It uses regular expressions to extract the title, summary, info, and sections from the input text.
```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*\[(?P
[^\\]+)\]\((?P<url>[^\\)]+)\)(?::\\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
```
--------------------------------
### Parse llms.txt content into a data structure
Source: https://llmstxt.org/intro.html
The `parse_llms_file` function takes the content of an 'llms.txt' file as a string and returns a data structure representing its sections. The `optional=True` argument can be passed to include the optional section.
```python
parsed = parse_llms_file(samp)
list(parsed)
```
--------------------------------
### List sections from parsed llms.txt
Source: https://llmstxt.org/intro.html
This Python code snippet lists the top-level section titles found within the parsed `llms.txt` data. These usually correspond to the main headings in the input file.
```python
list(parsed.sections)
```
=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.