diff --git a/README.md b/README.md index cb786ab..87776e6 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,7 @@ - [Jax](https://jax.readthedocs.io/en/latest/jax.html) - [Ray](https://docs.ray.io/en/latest/genindex.html) - [Langchain](https://api.python.langchain.com/en/latest/langchain_api_reference.html) +- [Hugging Face Transformers](https://huggingface.co/docs/transformers/index) ## Install Checkout the [Releases](https://github.com/lsgrep/mldocs/releases), download the latest `mldocs.alfredworkflow`, diff --git a/crawler/generate_ml_keywords.py b/crawler/generate_ml_keywords.py index 18be008..143ea70 100644 --- a/crawler/generate_ml_keywords.py +++ b/crawler/generate_ml_keywords.py @@ -1,9 +1,11 @@ import json import re from pathlib import Path +from typing import Dict, Any import requests import yaml +from bs4 import BeautifulSoup data_dir = f'{str(Path(__file__).resolve().parent.parent)}/data' @@ -73,23 +75,287 @@ def parse_generated_docs(link, pattern=None): doc_url = f'{base_url}/{href}' metadata = {'url': doc_url} data[k] = metadata + return data + + +def parse_huggingface_main_classes() -> Dict[str, Any]: + """Parse Hugging Face Transformers main API classes documentation. + + Returns: + Dictionary mapping class names to their documentation URLs + """ + data = {} + base_domain = 'https://huggingface.co' + + # Get main class pages from the API reference page + print('\nFetching main classes from API reference...') + resp = requests.get(f'{base_domain}/docs/transformers/main_classes/configuration') + soup = BeautifulSoup(resp.text, 'html.parser') + + # Find the sidebar navigation + main_class_pages = [] + for link in soup.select('a'): + href = link.get('href', '') + if '/main_classes/' in href: + page_name = href.split('/')[-1] + if page_name and page_name not in main_class_pages: + main_class_pages.append(page_name) + + # Remove duplicates and sort + main_class_pages = sorted(set(main_class_pages)) + + print(f'\nFound main class pages: {main_class_pages}') + print(f'Processing {len(main_class_pages)} main class pages...') + + # Process each main class page + for page in main_class_pages: + page_url = f'{base_domain}/docs/transformers/main_classes/{page}' + + try: + resp = requests.get(page_url) + soup = BeautifulSoup(resp.text, 'html.parser') + + # Find all class sections (h2 and h3 headers) + class_sections = soup.find_all(['h2', 'h3']) + current_class = None + + # Track links for summary + page_links = set() + page_classes = set() + + # Process all links first + for a in soup.find_all('a'): + href = a.get('href', '') + if href and '#transformers.' in href: + page_links.add(href) + + # Process class sections + for section in class_sections: + # Look for class name in the text + class_match = re.search(r'class transformers\.(\w+)', section.text) + if class_match: + current_class = class_match.group(1) + class_key = f'transformers.{current_class}' + page_classes.add(class_key) + + data[class_key] = { + 'url': f'{page_url}#transformers.{current_class}' + } + continue + + section_id = section.get('id', '') + if not section_id or section_id.startswith('_') or section_id.endswith('_'): + continue + + # If we're in a class context, this might be a method + if current_class: + method_key = f'transformers.{current_class}.{section_id}' + data[method_key] = { + 'url': f'{page_url}#transformers.{current_class}.{section_id}' + } + + # Print summary for this page + print(f'\n{page.upper()}:') + if page_classes: + print(f' Classes ({len(page_classes)}): {sorted(page_classes)}') + print(f' Total links: {len(page_links)}') + + except Exception as e: + print(f'Error processing {page_url}: {str(e)}') + continue + + return data +def parse_huggingface_docs(base_url: str, test_mode: bool = False) -> Dict[str, Any]: + """Parse Hugging Face Transformers documentation. + + Args: + base_url: The base URL of the Hugging Face documentation + + Returns: + Dictionary mapping function/class names to their documentation URLs + """ + data = {} + base_domain = 'https://huggingface.co' + + # Get the main page content + resp = requests.get(base_url) + soup = BeautifulSoup(resp.text, 'html.parser') + + # Find all model documentation links + model_links = [] + for link in soup.find_all('a'): + href = link.get('href') + if href and 'model_doc' in href: + # Handle both absolute and relative URLs + if href.startswith('http'): + full_url = href + elif href.startswith('//'): + full_url = f'https:{href}' + else: + # Handle relative paths + if href.startswith('/'): + full_url = f'{base_domain}{href}' + else: + # Construct URL relative to the docs base path + docs_base = '/'.join(base_url.split('/')[:-1]) + full_url = f'{docs_base}/{href}' + model_links.append(full_url) + + # For testing, only process ZoeDepth + if test_mode: + model_links = ['https://huggingface.co/docs/transformers/model_doc/zoedepth'] + + # Process each model's documentation + for model_url in model_links: + try: + print(f'Processing {model_url}') + print('Found model links:', len(model_links)) + model_resp = requests.get(model_url) + model_soup = BeautifulSoup(model_resp.text, 'html.parser') + + # Extract model name from URL + model_name = model_url.split('/')[-1].replace('-', '_') + + # Add the main model entry + model_key = f'transformers.{model_name}' + print(f'Adding model entry: {model_key}') + data[model_key] = { + 'url': model_url + } + + # Find all class definitions (h2 and h3 headers) + class_sections = model_soup.find_all(['h2', 'h3']) + current_class = None + + for section in class_sections: + # Look for the class name in the text + class_match = re.search(r'class transformers\.([\w]+)', section.text) + if class_match: + current_class = class_match.group(1) + class_key = f'transformers.{model_name}.{current_class}' + print(f'Found class: {class_key}') + + data[class_key] = { + 'url': f'{model_url}#transformers.{current_class.split(".")[-1]}' + } + continue + + section_id = section.get('id', '') + if not section_id or section_id.startswith('_') or section_id.endswith('_'): + continue + + # If we're in a class context, this might be a method + if current_class: + current_class = section_id + class_key = f'transformers.{model_name}.{current_class}' + print(f'Found class: {class_key}') + + # Get class description + desc = '' + next_p = section.find_next('p') + if next_p: + desc = next_p.text + + data[class_key] = { + 'url': f'{model_url}#{section_id}', + 'desc': desc + } + + # Find and add all methods in this class + method_sections = section.find_next(['h4', 'h5']) + while method_sections and method_sections.find_previous(['h2', 'h3']) == section: + method_id = method_sections.get('id', '') + if method_id and not method_id.startswith('_'): + method_key = f'{class_key}.{method_id}' + + # Get method description and parameters + desc = '' + params = [] + next_elem = method_sections.find_next(['p', 'ul']) + while next_elem and next_elem.name in ['p', 'ul']: + if next_elem.name == 'p': + desc += next_elem.text + ' ' + elif next_elem.name == 'ul': + for li in next_elem.find_all('li'): + params.append(li.text) + next_elem = next_elem.find_next(['p', 'ul']) + + data[method_key] = { + 'url': f'{model_url}#{method_id}', + 'desc': desc.strip(), + 'params': params + } + + method_sections = method_sections.find_next(['h4', 'h5']) + + # If we're in a class context, this might be a method + elif current_class: + method_key = f'transformers.{model_name}.{current_class}.{section_id}' + + data[method_key] = { + 'url': f'{model_url}#transformers.{current_class.split(".")[-1]}.{section_id}' + } + + except Exception as e: + print(f'Error processing {model_url}: {str(e)}') + continue + return data if __name__ == '__main__': - data = prepare_base_keywords() + # Load existing data if it exists + doc_file = f'{data_dir}/ml.json' + try: + with open(doc_file, 'r') as f: + data = json.load(f) + print(f'Loaded {len(data)} existing entries') + except FileNotFoundError: + data = {} + + # Add base keywords if not present + base_data = prepare_base_keywords() + for key, value in base_data.items(): + if key not in data: + data[key] = value + seed_file = f'{data_dir}/seed.yaml' seed = load_seed_file(seed_file) + + # Process TensorFlow docs for tensorflow_doc in seed['tensorflow']: print(f'processing: {tensorflow_doc["name"]}') crawled = parse_tf_docs(tensorflow_doc['url'], tensorflow_doc['prefix']) data.update(crawled) + + # Process generated docs for api_doc in seed['generated']: print(f'processing: {api_doc["name"]}') doc_url = api_doc['url'] - data.update(parse_generated_docs(doc_url)) + + # Special handling for Hugging Face Transformers + if api_doc['name'] == 'transformers': + print('Processing Hugging Face Transformers documentation...') + crawled = parse_huggingface_docs(doc_url, test_mode=False) + print(f'Crawled model data keys: {list(crawled.keys())}') + # Preserve Hugging Face model entries + for key, value in crawled.items(): + data[key] = value + + print('\nProcessing Hugging Face main API classes...') + main_classes = parse_huggingface_main_classes() + print('\nMain API Classes found:') + for key, value in main_classes.items(): + print(f' {key} -> {value["url"]}') + # Add main API class entries + for key, value in main_classes.items(): + data[key] = value + else: + data.update(parse_generated_docs(doc_url)) - doc_file = f'{data_dir}/ml.json' + print(f'Writing {len(data)} entries to {doc_file}') + print('Sample keys:', list(data.keys())[:5], '...') + print('Hugging Face keys:', [k for k in data.keys() if k.startswith('transformers.')]) with open(doc_file, 'w') as f: - json.dump(data, f) + json.dump(data, f, indent=2) diff --git a/data/ml.json b/data/ml.json index 3dcb1ae..a48b1d5 100644 --- a/data/ml.json +++ b/data/ml.json @@ -1 +1,69719 @@ -{"colab": {"url": "http://colab.research.google.com/", "desc": "Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more"}, "kaggle": {"url": "https://www.kaggle.com/", "desc": "Kaggle is the world's largest data science community with powerful tools and resources to help you achieve your data science goals."}, "?": {"url": "https://github.com/lsgrep/mldocs", "desc": "report mldocs bugs, or ask questions if you have any"}, "google dataset search": {"url": "https://datasetsearch.research.google.com/", "desc": "Google Dataset Search"}, "gds": {"url": "https://datasetsearch.research.google.com/", "desc": "Google Dataset Search"}, "papers with code": {"url": "https://paperswithcode.com/", "desc": "Papers With Code highlights trending ML research and the code to implement it."}, "tf.all_symbols": {"url": "https://www.tensorflow.org/api_docs/python/tf/all_symbols"}, "tf.AggregationMethod": {"url": "https://www.tensorflow.org/api_docs/python/tf/AggregationMethod"}, "tf.debugging.Assert": {"url": "https://www.tensorflow.org/api_docs/python/tf/debugging/Assert"}, "tf.CriticalSection": {"url": "https://www.tensorflow.org/api_docs/python/tf/CriticalSection"}, "tf.dtypes.DType": {"url": "https://www.tensorflow.org/api_docs/python/tf/dtypes/DType"}, "tf.DeviceSpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/DeviceSpec"}, "tf.GradientTape": {"url": "https://www.tensorflow.org/api_docs/python/tf/GradientTape"}, "tf.Graph": {"url": "https://www.tensorflow.org/api_docs/python/tf/Graph"}, "tf.IndexedSlices": {"url": "https://www.tensorflow.org/api_docs/python/tf/IndexedSlices"}, "tf.IndexedSlicesSpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/IndexedSlicesSpec"}, "tf.Module": {"url": "https://www.tensorflow.org/api_docs/python/tf/Module"}, "tf.Operation": {"url": "https://www.tensorflow.org/api_docs/python/tf/Operation"}, "tf.OptionalSpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/OptionalSpec"}, "tf.RaggedTensor": {"url": "https://www.tensorflow.org/api_docs/python/tf/RaggedTensor"}, "tf.RaggedTensorSpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/RaggedTensorSpec"}, "tf.RegisterGradient": {"url": "https://www.tensorflow.org/api_docs/python/tf/RegisterGradient"}, "tf.sparse.SparseTensor": {"url": "https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor"}, "tf.SparseTensorSpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/SparseTensorSpec"}, "tf.Tensor": {"url": "https://www.tensorflow.org/api_docs/python/tf/Tensor"}, "tf.TensorArray": {"url": "https://www.tensorflow.org/api_docs/python/tf/TensorArray"}, "tf.TensorArraySpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/TensorArraySpec"}, "tf.TensorShape": {"url": "https://www.tensorflow.org/api_docs/python/tf/TensorShape"}, "tf.TensorSpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/TensorSpec"}, "tf.TypeSpec": {"url": "https://www.tensorflow.org/api_docs/python/tf/TypeSpec"}, "tf.UnconnectedGradients": {"url": "https://www.tensorflow.org/api_docs/python/tf/UnconnectedGradients"}, "tf.Variable": {"url": "https://www.tensorflow.org/api_docs/python/tf/Variable"}, "tf.Variable.SaveSliceInfo": {"url": "https://www.tensorflow.org/api_docs/python/tf/Variable/SaveSliceInfo"}, "tf.VariableAggregation": {"url": "https://www.tensorflow.org/api_docs/python/tf/VariableAggregation"}, "tf.VariableSynchronization": {"url": "https://www.tensorflow.org/api_docs/python/tf/VariableSynchronization"}, "tf.math.abs": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/abs"}, "tf.math.acos": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/acos"}, "tf.math.acosh": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/acosh"}, "tf.math.add": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/add"}, "tf.math.add_n": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/add_n"}, "tf.approx_top_k": {"url": "https://www.tensorflow.org/api_docs/python/tf/approx_top_k"}, "tf.math.argmax": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/argmax"}, "tf.math.argmin": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/argmin"}, "tf.argsort": {"url": "https://www.tensorflow.org/api_docs/python/tf/argsort"}, "tf.dtypes.as_dtype": {"url": "https://www.tensorflow.org/api_docs/python/tf/dtypes/as_dtype"}, "tf.strings.as_string": {"url": "https://www.tensorflow.org/api_docs/python/tf/strings/as_string"}, "tf.math.asin": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/asin"}, "tf.math.asinh": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/asinh"}, "tf.debugging.assert_equal": {"url": "https://www.tensorflow.org/api_docs/python/tf/debugging/assert_equal"}, "tf.debugging.assert_greater": {"url": "https://www.tensorflow.org/api_docs/python/tf/debugging/assert_greater"}, "tf.debugging.assert_less": {"url": "https://www.tensorflow.org/api_docs/python/tf/debugging/assert_less"}, "tf.debugging.assert_rank": {"url": "https://www.tensorflow.org/api_docs/python/tf/debugging/assert_rank"}, "tf.math.atan": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/atan"}, "tf.math.atan2": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/atan2"}, "tf.math.atanh": {"url": "https://www.tensorflow.org/api_docs/python/tf/math/atanh"}, "tf.audio": {"url": "https://www.tensorflow.org/api_docs/python/tf/audio"}, "tf.audio.decode_wav": {"url": "https://www.tensorflow.org/api_docs/python/tf/audio/decode_wav"}, "tf.audio.encode_wav": {"url": "https://www.tensorflow.org/api_docs/python/tf/audio/encode_wav"}, "tf.autodiff": {"url": "https://www.tensorflow.org/api_docs/python/tf/autodiff"}, "tf.autodiff.ForwardAccumulator": {"url": "https://www.tensorflow.org/api_docs/python/tf/autodiff/ForwardAccumulator"}, "tf.autograph": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph"}, "tf.autograph.experimental": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/experimental"}, "tf.autograph.experimental.Feature": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/experimental/Feature"}, "tf.autograph.experimental.do_not_convert": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/experimental/do_not_convert"}, "tf.autograph.experimental.set_loop_options": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/experimental/set_loop_options"}, "tf.autograph.set_verbosity": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/set_verbosity"}, "tf.autograph.to_code": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/to_code"}, "tf.autograph.to_graph": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/to_graph"}, "tf.autograph.trace": {"url": "https://www.tensorflow.org/api_docs/python/tf/autograph/trace"}, "tf.batch_to_space": {"url": "https://www.tensorflow.org/api_docs/python/tf/batch_to_space"}, "tf.bitcast": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitcast"}, "tf.bitwise": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitwise"}, "tf.bitwise.bitwise_and": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitwise/bitwise_and"}, "tf.bitwise.bitwise_or": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitwise/bitwise_or"}, "tf.bitwise.bitwise_xor": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitwise/bitwise_xor"}, "tf.bitwise.invert": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitwise/invert"}, "tf.bitwise.left_shift": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitwise/left_shift"}, "tf.bitwise.right_shift": {"url": "https://www.tensorflow.org/api_docs/python/tf/bitwise/right_shift"}, "tf.boolean_mask": {"url": "https://www.tensorflow.org/api_docs/python/tf/boolean_mask"}, "tf.broadcast_dynamic_shape": {"url": "https://www.tensorflow.org/api_docs/python/tf/broadcast_dynamic_shape"}, "tf.broadcast_static_shape": {"url": "https://www.tensorflow.org/api_docs/python/tf/broadcast_static_shape"}, "tf.broadcast_to": {"url": "https://www.tensorflow.org/api_docs/python/tf/broadcast_to"}, "tf.case": {"url": "https://www.tensorflow.org/api_docs/python/tf/case"}, "tf.cast": {"url": "https://www.tensorflow.org/api_docs/python/tf/cast"}, "tf.clip_by_global_norm": {"url": "https://www.tensorflow.org/api_docs/python/tf/clip_by_global_norm"}, "tf.clip_by_norm": {"url": "https://www.tensorflow.org/api_docs/python/tf/clip_by_norm"}, "tf.clip_by_value": {"url": "https://www.tensorflow.org/api_docs/python/tf/clip_by_value"}, "tf.compat": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat"}, "tf.compat.as_bytes": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/as_bytes"}, "tf.compat.as_str": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/as_str"}, "tf.compat.as_str_any": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/as_str_any"}, "tf.compat.as_text": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/as_text"}, "tf.compat.dimension_at_index": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/dimension_at_index"}, "tf.compat.dimension_value": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/dimension_value"}, "tf.compat.forward_compatibility_horizon": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/forward_compatibility_horizon"}, "tf.compat.forward_compatible": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/forward_compatible"}, "tf.compat.path_to_str": {"url": "https://www.tensorflow.org/api_docs/python/tf/compat/path_to_str"}, "tf.dtypes.complex": {"url": "https://www.tensorflow.org/api_docs/python/tf/dtypes/complex"}, "tf.concat": {"url": "https://www.tensorflow.org/api_docs/python/tf/concat"}, "tf.cond": {"url": "https://www.tensorflow.org/api_docs/python/tf/cond"}, "tf.config": {"url": "https://www.tensorflow.org/api_docs/python/tf/config"}, "tf.config.LogicalDevice": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/LogicalDevice"}, "tf.config.LogicalDeviceConfiguration": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/LogicalDeviceConfiguration"}, "tf.config.PhysicalDevice": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/PhysicalDevice"}, "tf.config.experimental": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental"}, "tf.config.experimental.ClusterDeviceFilters": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/ClusterDeviceFilters"}, "tf.config.experimental.disable_mlir_bridge": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/disable_mlir_bridge"}, "tf.config.experimental.enable_mlir_bridge": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_mlir_bridge"}, "tf.config.experimental.enable_op_determinism": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_op_determinism"}, "tf.config.experimental.enable_tensor_float_32_execution": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/enable_tensor_float_32_execution"}, "tf.config.experimental.get_device_details": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details"}, "tf.config.experimental.get_device_policy": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_policy"}, "tf.config.experimental.get_memory_growth": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_memory_growth"}, "tf.config.experimental.get_memory_info": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_memory_info"}, "tf.config.experimental.get_memory_usage": {"url": "https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_memory_usage"}, 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