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"""
Markdown Document Processor for Documentation Sources
This script processes Markdown (.md) and MDX (.mdx) files from various documentation sources
(such as Hugging Face Transformers, PEFT, TRL, LlamaIndex, and OpenAI Cookbook) and converts
them into a standardized JSONL format for further processing or indexing.
Key features:
1. Configurable for multiple documentation sources
2. Extracts titles, generates URLs, and counts tokens for each document
3. Supports inclusion/exclusion of specific directories and root files
4. Removes copyright headers from content
5. Generates a unique ID for each document
6. Determines if a whole document should be retrieved based on token count
7. Handles special cases like openai-cookbook repo by adding .ipynb extensions
8. Processes multiple sources in a single run
Usage:
python process_md_files.py <source1> <source2> ...
Where <source1>, <source2>, etc. are one or more of the predefined sources in SOURCE_CONFIGS
(e.g., 'transformers', 'llama_index', 'openai_cookbooks').
The script processes all Markdown files in the specified input directories (and their subdirectories),
applies the configured filters, and saves the results in JSONL files. Each line in the output
files represents a single document with metadata and content.
To add or modify sources, update the SOURCE_CONFIGS dictionary at the top of the script.
"""
# import argparse
# import json
# import logging
# import os
# import re
# import uuid
# from typing import Dict, List
# import tiktoken
# logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)
# # Configuration for different sources
# SOURCE_CONFIGS = {
# "transformers": {
# "base_url": "https://huggingface.co./docs/transformers/",
# "input_directory": "data/transformers_md_files",
# "output_file": "data/transformers_data.jsonl",
# "source_name": "transformers",
# "use_include_list": False,
# "included_dirs": [],
# "excluded_dirs": ["internal", "main_classes"],
# "excluded_root_files": [],
# "included_root_files": [],
# "url_extension": "",
# },
# "peft": {
# "base_url": "https://huggingface.co./docs/peft/",
# "input_directory": "data/peft_md_files",
# "output_file": "data/peft_data.jsonl",
# "source_name": "peft",
# "use_include_list": False,
# "included_dirs": [],
# "excluded_dirs": [],
# "excluded_root_files": [],
# "included_root_files": [],
# "url_extension": "",
# },
# "trl": {
# "base_url": "https://huggingface.co./docs/trl/",
# "input_directory": "data/trl_md_files",
# "output_file": "data/trl_data.jsonl",
# "source_name": "trl",
# "use_include_list": False,
# "included_dirs": [],
# "excluded_dirs": [],
# "excluded_root_files": [],
# "included_root_files": [],
# "url_extension": "",
# },
# "llama_index": {
# "base_url": "https://docs.llamaindex.ai/en/stable/",
# "input_directory": "data/llama_index_md_files",
# "output_file": "data/llama_index_data.jsonl",
# "source_name": "llama_index",
# "use_include_list": True,
# "included_dirs": [
# "getting_started",
# "understanding",
# "use_cases",
# "examples",
# "module_guides",
# "optimizing",
# ],
# "excluded_dirs": [],
# "excluded_root_files": [],
# "included_root_files": ["index.md"],
# "url_extension": "",
# },
# "openai_cookbooks": {
# "base_url": "https://github.com/openai/openai-cookbook/blob/main/examples/",
# "input_directory": "data/openai-cookbook_md_files",
# "output_file": "data/openai_cookbooks_data.jsonl",
# "source_name": "openai_cookbooks",
# "use_include_list": False,
# "included_dirs": [],
# "excluded_dirs": [],
# "excluded_root_files": [],
# "included_root_files": [],
# "url_extension": ".ipynb",
# },
# "langchain": {
# "base_url": "https://python.langchain.com/v0.2/docs/",
# "input_directory": "data/langchain_md_files",
# "output_file": "data/langchain_data.jsonl",
# "source_name": "langchain",
# "use_include_list": True,
# "included_dirs": ["how_to", "versions", "turorials", "integrations"],
# "excluded_dirs": [],
# "excluded_root_files": [],
# "included_root_files": ["security.md", "concepts.mdx", "introduction.mdx"],
# "url_extension": "",
# },
# "tai_blog": {
# "base_url": "",
# "input_directory": "",
# "output_file": "data/tai_blog_data.jsonl",
# "source_name": "tai_blog",
# "use_include_list": False,
# "included_dirs": [],
# "excluded_dirs": [],
# "excluded_root_files": [],
# "included_root_files": [],
# "url_extension": "",
# },
# }
# def extract_title(content: str):
# title_match = re.search(r"^#\s+(.+)$", content, re.MULTILINE)
# if title_match:
# return title_match.group(1).strip()
# lines = content.split("\n")
# for line in lines:
# if line.strip():
# return line.strip()
# return None
# def generate_url(file_path: str, config: Dict) -> str:
# path_without_extension = os.path.splitext(file_path)[0]
# path_with_forward_slashes = path_without_extension.replace("\\", "/")
# return config["base_url"] + path_with_forward_slashes + config["url_extension"]
# def should_include_file(file_path: str, config: Dict) -> bool:
# if os.path.dirname(file_path) == "":
# if config["use_include_list"]:
# return os.path.basename(file_path) in config["included_root_files"]
# else:
# return os.path.basename(file_path) not in config["excluded_root_files"]
# if config["use_include_list"]:
# return any(file_path.startswith(dir) for dir in config["included_dirs"])
# else:
# return not any(file_path.startswith(dir) for dir in config["excluded_dirs"])
# def num_tokens_from_string(string: str, encoding_name: str) -> int:
# encoding = tiktoken.get_encoding(encoding_name)
# num_tokens = len(
# encoding.encode(
# string, disallowed_special=(encoding.special_tokens_set - {"<|endoftext|>"})
# )
# )
# return num_tokens
# def remove_copyright_header(content: str) -> str:
# header_pattern = re.compile(r"<!--Copyright.*?-->\s*", re.DOTALL)
# cleaned_content = header_pattern.sub("", content, count=1)
# return cleaned_content.strip()
# def process_md_files(directory: str, config: Dict) -> List[Dict]:
# jsonl_data = []
# for root, _, files in os.walk(directory):
# for file in files:
# if file.endswith(".md") or file.endswith(".mdx"):
# file_path = os.path.join(root, file)
# relative_path = os.path.relpath(file_path, directory)
# if should_include_file(relative_path, config):
# with open(file_path, "r", encoding="utf-8") as f:
# content = f.read()
# title = extract_title(content)
# token_count = num_tokens_from_string(content, "cl100k_base")
# if token_count < 100 or token_count > 200_000:
# logger.info(
# f"Skipping {relative_path} due to token count {token_count}"
# )
# continue
# cleaned_content = remove_copyright_header(content)
# json_object = {
# "tokens": token_count,
# "doc_id": str(uuid.uuid4()),
# "name": (title if title else file),
# "url": generate_url(relative_path, config),
# "retrieve_doc": (token_count <= 8000),
# "source": config["source_name"],
# "content": cleaned_content,
# }
# jsonl_data.append(json_object)
# return jsonl_data
# def save_jsonl(data: List[Dict], output_file: str) -> None:
# with open(output_file, "w", encoding="utf-8") as f:
# for item in data:
# json.dump(item, f, ensure_ascii=False)
# f.write("\n")
# def combine_all_sources(sources: List[str]) -> None:
# all_data = []
# output_file = "data/all_sources_data.jsonl"
# for source in sources:
# if source not in SOURCE_CONFIGS:
# logger.error(f"Unknown source '{source}'. Skipping.")
# continue
# input_file = SOURCE_CONFIGS[source]["output_file"]
# logger.info(f"Processing source: {source}")
# with open(input_file, "r", encoding="utf-8") as f:
# for line in f:
# all_data.append(json.loads(line))
# logger.info(f"Total documents combined: {len(all_data)}")
# save_jsonl(all_data, output_file)
# logger.info(f"Combined data saved to {output_file}")
# def process_source(source: str) -> None:
# if source not in SOURCE_CONFIGS:
# logger.error(f"Unknown source '{source}'. Skipping.")
# return
# config = SOURCE_CONFIGS[source]
# logger.info(f"\n\nProcessing source: {source}")
# jsonl_data = process_md_files(config["input_directory"], config)
# save_jsonl(jsonl_data, config["output_file"])
# logger.info(
# f"Processed {len(jsonl_data)} files and saved to {config['output_file']}"
# )
# def main(sources: List[str]) -> None:
# for source in sources:
# process_source(source)
# if len(sources) > 1:
# # sources = [
# # "transformers",
# # "peft",
# # "trl",
# # "llama_index",
# # "langchain",
# # "openai_cookbooks",
# # "tai_blog",
# # ]
# combine_all_sources(sources)
# if __name__ == "__main__":
# parser = argparse.ArgumentParser(
# description="Process Markdown files from specified sources."
# )
# parser.add_argument(
# "sources",
# nargs="+",
# choices=SOURCE_CONFIGS.keys(),
# help="Specify one or more sources to process",
# )
# args = parser.parse_args()
# main(args.sources)
import argparse
import json
import logging
import os
import re
import uuid
from typing import Dict, List
import tiktoken
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Configuration for different sources
SOURCE_CONFIGS = {
"transformers": {
"base_url": "https://huggingface.co./docs/transformers/",
"input_directory": "data/transformers_md_files",
"output_file": "data/transformers_data.jsonl",
"source_name": "transformers",
"use_include_list": False,
"included_dirs": [],
"excluded_dirs": ["internal", "main_classes"],
"excluded_root_files": [],
"included_root_files": [],
"url_extension": "",
},
"peft": {
"base_url": "https://huggingface.co./docs/peft/",
"input_directory": "data/peft_md_files",
"output_file": "data/peft_data.jsonl",
"source_name": "peft",
"use_include_list": False,
"included_dirs": [],
"excluded_dirs": [],
"excluded_root_files": [],
"included_root_files": [],
"url_extension": "",
},
"trl": {
"base_url": "https://huggingface.co./docs/trl/",
"input_directory": "data/trl_md_files",
"output_file": "data/trl_data.jsonl",
"source_name": "trl",
"use_include_list": False,
"included_dirs": [],
"excluded_dirs": [],
"excluded_root_files": [],
"included_root_files": [],
"url_extension": "",
},
"llama_index": {
"base_url": "https://docs.llamaindex.ai/en/stable/",
"input_directory": "data/llama_index_md_files",
"output_file": "data/llama_index_data.jsonl",
"source_name": "llama_index",
"use_include_list": True,
"included_dirs": [
"getting_started",
"understanding",
"use_cases",
"examples",
"module_guides",
"optimizing",
],
"excluded_dirs": [],
"excluded_root_files": [],
"included_root_files": ["index.md"],
"url_extension": "",
},
"openai_cookbooks": {
"base_url": "https://github.com/openai/openai-cookbook/blob/main/examples/",
"input_directory": "data/openai-cookbook_md_files",
"output_file": "data/openai_cookbooks_data.jsonl",
"source_name": "openai_cookbooks",
"use_include_list": False,
"included_dirs": [],
"excluded_dirs": [],
"excluded_root_files": [],
"included_root_files": [],
"url_extension": ".ipynb",
},
"langchain": {
"base_url": "https://python.langchain.com/docs/",
"input_directory": "data/langchain_md_files",
"output_file": "data/langchain_data.jsonl",
"source_name": "langchain",
"use_include_list": True,
"included_dirs": ["how_to", "versions", "turorials", "integrations"],
"excluded_dirs": [],
"excluded_root_files": [],
"included_root_files": ["security.md", "concepts.mdx", "introduction.mdx"],
"url_extension": "",
},
"tai_blog": {
"base_url": "",
"input_directory": "",
"output_file": "data/tai_blog_data.jsonl",
"source_name": "tai_blog",
"use_include_list": False,
"included_dirs": [],
"excluded_dirs": [],
"excluded_root_files": [],
"included_root_files": [],
"url_extension": "",
},
"8-hour_primer": {
"base_url": "",
"input_directory": "data/8-hour_primer",
"output_file": "data/8-hour_primer_data.jsonl", # 8-hour Generative AI Primer
"source_name": "8-hour_primer",
"use_include_list": False,
"included_dirs": [],
"excluded_dirs": [],
"excluded_root_files": [],
"included_root_files": [],
"url_extension": "",
},
}
def extract_title(content: str):
title_match = re.search(r"^#\s+(.+)$", content, re.MULTILINE)
if title_match:
return title_match.group(1).strip()
lines = content.split("\n")
for line in lines:
if line.strip():
return line.strip()
return None
def generate_url(file_path: str, config: Dict) -> str:
"""
Return an empty string if base_url is empty;
otherwise return the constructed URL as before.
"""
if not config["base_url"]:
return ""
path_without_extension = os.path.splitext(file_path)[0]
path_with_forward_slashes = path_without_extension.replace("\\", "/")
return config["base_url"] + path_with_forward_slashes + config["url_extension"]
def should_include_file(file_path: str, config: Dict) -> bool:
if os.path.dirname(file_path) == "":
if config["use_include_list"]:
return os.path.basename(file_path) in config["included_root_files"]
else:
return os.path.basename(file_path) not in config["excluded_root_files"]
if config["use_include_list"]:
return any(file_path.startswith(dir) for dir in config["included_dirs"])
else:
return not any(file_path.startswith(dir) for dir in config["excluded_dirs"])
def num_tokens_from_string(string: str, encoding_name: str) -> int:
encoding = tiktoken.get_encoding(encoding_name)
num_tokens = len(encoding.encode(string, disallowed_special=()))
return num_tokens
def remove_copyright_header(content: str) -> str:
header_pattern = re.compile(r"<!--Copyright.*?-->\s*", re.DOTALL)
cleaned_content = header_pattern.sub("", content, count=1)
return cleaned_content.strip()
def process_md_files(directory: str, config: Dict) -> List[Dict]:
jsonl_data = []
for root, _, files in os.walk(directory):
for file in files:
if file.endswith(".md") or file.endswith(".mdx"):
file_path = os.path.join(root, file)
relative_path = os.path.relpath(file_path, directory)
if should_include_file(relative_path, config):
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
title = extract_title(content)
token_count = num_tokens_from_string(content, "cl100k_base")
# Skip very small or extremely large files
if token_count < 100 or token_count > 200_000:
logger.info(
f"Skipping {relative_path} due to token count {token_count}"
)
continue
cleaned_content = remove_copyright_header(content)
json_object = {
"tokens": token_count,
"doc_id": str(uuid.uuid4()),
"name": (title if title else file),
"url": generate_url(relative_path, config),
"retrieve_doc": (token_count <= 8000),
"source": config["source_name"],
"content": cleaned_content,
}
jsonl_data.append(json_object)
return jsonl_data
def save_jsonl(data: List[Dict], output_file: str) -> None:
with open(output_file, "w", encoding="utf-8") as f:
for item in data:
json.dump(item, f, ensure_ascii=False)
f.write("\n")
def combine_all_sources(sources: List[str]) -> None:
all_data = []
output_file = "data/all_sources_data.jsonl"
for source in sources:
if source not in SOURCE_CONFIGS:
logger.error(f"Unknown source '{source}'. Skipping.")
continue
input_file = SOURCE_CONFIGS[source]["output_file"]
logger.info(f"Processing source: {source}")
with open(input_file, "r", encoding="utf-8") as f:
for line in f:
all_data.append(json.loads(line))
logger.info(f"Total documents combined: {len(all_data)}")
save_jsonl(all_data, output_file)
logger.info(f"Combined data saved to {output_file}")
def process_source(source: str) -> None:
if source not in SOURCE_CONFIGS:
logger.error(f"Unknown source '{source}'. Skipping.")
return
config = SOURCE_CONFIGS[source]
logger.info(f"\n\nProcessing source: {source}")
jsonl_data = process_md_files(config["input_directory"], config)
save_jsonl(jsonl_data, config["output_file"])
logger.info(
f"Processed {len(jsonl_data)} files and saved to {config['output_file']}"
)
def main(sources: List[str]) -> None:
for source in sources:
process_source(source)
if len(sources) > 1:
combine_all_sources(sources)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Process Markdown files from specified sources."
)
parser.add_argument(
"sources",
nargs="+",
choices=SOURCE_CONFIGS.keys(),
help="Specify one or more sources to process",
)
args = parser.parse_args()
main(args.sources)
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