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a80f6e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | import multiprocessing
import os
import re
import sys
from pathlib import Path
from typing import Iterable, Tuple
import nbformat
from nbconvert.exporters import MarkdownExporter
from nbconvert.preprocessors import Preprocessor
HIDE_IN_NB_MAGIC_OPEN = "<!-- HIDE_IN_NB"
HIDE_IN_NB_MAGIC_CLOSE = "HIDE_IN_NB -->"
class EscapePreprocessor(Preprocessor):
def preprocess_cell(self, cell, resources, index):
if cell.cell_type == "markdown":
# rewrite .ipynb links to .md
cell.source = re.sub(
r"\[([^\]]*)\]\((?![^\)]*//)([^)]*)\.ipynb\)",
r"[\1](\2.md)",
cell.source,
)
elif cell.cell_type == "code":
# escape ``` in code
cell.source = cell.source.replace("```", r"\`\`\`")
# escape ``` in output
# allow overriding title based on comment at beginning of cell
if cell.source.startswith("# title="):
lines = cell.source.split("\n")
title = lines[0].split("# title=")[1]
if title.startswith('"') and title.endswith('"'):
title = title[1:-1]
cell.metadata["title"] = title
cell.source = "\n".join(lines[1:])
if "outputs" in cell:
filter_out = set()
for i, output in enumerate(cell["outputs"]):
if "text" in output:
if not output["text"].strip():
filter_out.add(i)
continue
output["text"] = output["text"].replace("```", r"\`\`\`")
elif "data" in output:
for key, value in output["data"].items():
if isinstance(value, str):
output["data"][key] = value.replace("```", r"\`\`\`")
cell["outputs"] = [
output
for i, output in enumerate(cell["outputs"])
if i not in filter_out
]
return cell, resources
class ExtractAttachmentsPreprocessor(Preprocessor):
"""
Extracts all of the outputs from the notebook file. The extracted
outputs are returned in the 'resources' dictionary.
"""
def preprocess_cell(self, cell, resources, index):
"""
Apply a transformation on each cell,
Parameters
----------
cell : NotebookNode cell
Notebook cell being processed
resources : dictionary
Additional resources used in the conversion process. Allows
preprocessors to pass variables into the Jinja engine.
cell_index : int
Index of the cell being processed (see base.py)
"""
# Get files directory if it has been specified
# Make sure outputs key exists
if not isinstance(resources["outputs"], dict):
resources["outputs"] = {}
# Loop through all of the attachments in the cell
for name, attach in cell.get("attachments", {}).items():
for mime, data in attach.items():
if mime not in {
"image/png",
"image/jpeg",
"image/svg+xml",
"application/pdf",
}:
continue
# attachments are pre-rendered. Only replace markdown-formatted
# images with the following logic
attach_str = f"({name})"
if attach_str in cell.source:
data = f"(data:{mime};base64,{data})"
cell.source = cell.source.replace(attach_str, data)
return cell, resources
class CustomRegexRemovePreprocessor(Preprocessor):
def check_conditions(self, cell):
pattern = re.compile(r"(?s)(?:\s*\Z)|(?:.*#\s*\|\s*output:\s*false.*)")
rtn = not pattern.match(cell.source)
if not rtn:
return False
else:
return True
def preprocess(self, nb, resources):
nb.cells = [cell for cell in nb.cells if self.check_conditions(cell)]
return nb, resources
class UnHidePreprocessor(Preprocessor):
def preprocess_cell(self, cell, resources, index):
cell.source = cell.source.replace(HIDE_IN_NB_MAGIC_OPEN, "")
cell.source = cell.source.replace(HIDE_IN_NB_MAGIC_CLOSE, "")
return cell, resources
exporter = MarkdownExporter(
preprocessors=[
EscapePreprocessor,
ExtractAttachmentsPreprocessor,
CustomRegexRemovePreprocessor,
UnHidePreprocessor,
],
template_name="mdoutput",
extra_template_basedirs=["./scripts/notebook_convert_templates"],
)
def _process_path(tup: Tuple[Path, Path, Path]):
notebook_path, intermediate_docs_dir, output_docs_dir = tup
relative = notebook_path.relative_to(intermediate_docs_dir)
output_path = output_docs_dir / relative.parent / (relative.stem + ".md")
_convert_notebook(notebook_path, output_path, intermediate_docs_dir)
def _modify_frontmatter(
body: str, notebook_path: Path, intermediate_docs_dir: Path
) -> str:
# if frontmatter exists
rel_path = notebook_path.relative_to(intermediate_docs_dir).as_posix()
edit_url = (
f"https://github.com/langchain-ai/langchain/edit/master/docs/docs/{rel_path}"
)
frontmatter = {
"custom_edit_url": edit_url,
}
if re.match(r"^[\s\n]*---\n", body):
# frontmatter already present
for k, v in frontmatter.items():
# if key already exists, leave it
if re.match(f"{k}: ", body):
continue
else:
body = re.sub(r"^[\s\n]*---\n", f"---\n{k}: {v}\n", body, count=1)
return body
else:
insert = "\n".join([f"{k}: {v}" for k, v in frontmatter.items()])
return f"---\n{insert}\n---\n{body}"
def _convert_notebook(
notebook_path: Path, output_path: Path, intermediate_docs_dir: Path
) -> Path:
with open(notebook_path) as f:
nb = nbformat.read(f, as_version=4)
body, resources = exporter.from_notebook_node(nb)
body = _modify_frontmatter(body, notebook_path, intermediate_docs_dir)
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, "w") as f:
f.write(body)
return output_path
if __name__ == "__main__":
intermediate_docs_dir = Path(sys.argv[1])
output_docs_dir = Path(sys.argv[2])
source_paths_arg = os.environ.get("SOURCE_PATHS")
source_paths: Iterable[Path]
if source_paths_arg:
source_path_strs = re.split(r"\s+", source_paths_arg)
source_paths_stripped = [p.strip() for p in source_path_strs]
source_paths = [intermediate_docs_dir / p for p in source_paths_stripped if p]
else:
original_paths = list(intermediate_docs_dir.glob("**/*.ipynb"))
# exclude files that exist in output directory and are newer
relative_paths = [p.relative_to(intermediate_docs_dir) for p in original_paths]
out_paths = [
output_docs_dir / p.parent / (p.stem + ".md") for p in relative_paths
]
source_paths = [
p
for p, o in zip(original_paths, out_paths)
if not o.exists() or o.stat().st_mtime < p.stat().st_mtime
]
print(f"rebuilding {len(source_paths)}/{len(relative_paths)} notebooks")
with multiprocessing.Pool() as pool:
pool.map(
_process_path,
(
(notebook_path, intermediate_docs_dir, output_docs_dir)
for notebook_path in source_paths
),
)
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