data/scraping_scripts/process_md_files.py

"""
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": "",
    },
}


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 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 __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)