From 4ee15b4ff21a3dbb676a05404cc6163834789fb7 Mon Sep 17 00:00:00 2001 From: Khaled Sulayman Date: Wed, 18 Sep 2024 15:14:16 -0400 Subject: [PATCH 01/11] :sparkles: Add context-aware document chunking and PDF support Signed-off-by: Khaled Sulayman Co-authored-by: Aakanksha Duggal Co-authored-by: abhi1092 --- requirements.txt | 3 + src/instructlab/sdg/generate_data.py | 25 +- src/instructlab/sdg/pipeline.py | 1 - src/instructlab/sdg/utils/chunkers.py | 563 ++++++++++++++++++++++++++ src/instructlab/sdg/utils/chunking.py | 75 ---- src/instructlab/sdg/utils/taxonomy.py | 239 ++++++++--- 6 files changed, 757 insertions(+), 149 deletions(-) create mode 100644 src/instructlab/sdg/utils/chunkers.py delete mode 100644 src/instructlab/sdg/utils/chunking.py diff --git a/requirements.txt b/requirements.txt index 9d3e75f2..195dddfa 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,5 +10,8 @@ openai>=1.13.3,<2.0.0 # removed once that one is removed. # do not use 8.4.0 due to a bug in the library # https://github.com/instructlab/instructlab/issues/1389 +pypdf>=5.0.0 +tabulate>=0.9.0 tenacity>=8.3.0,!=8.4.0 +transformers>=4.44.2 xdg-base-dirs>=6.0.1 diff --git a/src/instructlab/sdg/generate_data.py b/src/instructlab/sdg/generate_data.py index d7bfd9f2..d123f33b 100644 --- a/src/instructlab/sdg/generate_data.py +++ b/src/instructlab/sdg/generate_data.py @@ -309,12 +309,16 @@ def generate_data( if not (taxonomy and os.path.exists(taxonomy)): raise GenerateException(f"Error: taxonomy ({taxonomy}) does not exist.") - leaf_nodes = read_taxonomy_leaf_nodes(taxonomy, taxonomy_base, yaml_rules) + date_suffix = datetime.now().replace(microsecond=0).isoformat().replace(":", "_") + document_output_dir = Path(output_dir) / f"documents-{date_suffix}" + + leaf_nodes = read_taxonomy_leaf_nodes( + taxonomy, taxonomy_base, yaml_rules, document_output_dir + ) if not leaf_nodes: raise GenerateException("Error: No new leaf nodes found in the taxonomy.") name = Path(model_name).stem # Just in case it is a file path - date_suffix = datetime.now().replace(microsecond=0).isoformat().replace(":", "_") output_file_messages = f"messages_{name}_{date_suffix}.jsonl" output_file_test = f"test_{name}_{date_suffix}.jsonl" output_file_train = f"train_{name}_{date_suffix}.jsonl" @@ -362,25 +366,26 @@ def generate_data( for leaf_node in leaf_nodes.values(): is_knowledge = False leaf_node_path = leaf_node[0]["taxonomy_path"].replace("->", "_") - samples = leaf_node_to_samples(leaf_node, server_ctx_size, chunk_word_count) + samples = leaf_node_to_samples( + leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name + ) if not samples: raise GenerateException("Error: No samples found in leaf node.") - - if samples[0].get("document"): + + if "document" in samples.column_names: pipe = knowledge_pipe is_knowledge = True - elif samples[0].get("seed_context"): + elif "seed_context" in samples.column_names: pipe = grounded_skills_pipe else: pipe = freeform_skills_pipe logger.debug("Samples: %s", samples) - ds = Dataset.from_list(samples) - logger.debug("Dataset: %s", ds) - new_generated_data = pipe.generate(ds, leaf_node_path) + + new_generated_data = pipe.generate(samples, leaf_node_path) if len(new_generated_data) == 0: empty_sdg_leaf_nodes.append(leaf_node_path) logger.warning("Empty dataset for qna node: %s", leaf_node_path) @@ -398,7 +403,7 @@ def generate_data( generate_eval_task_data( mmlu_bench_pipe, leaf_node_path, - ds, + samples, output_dir, date_suffix, ) diff --git a/src/instructlab/sdg/pipeline.py b/src/instructlab/sdg/pipeline.py index f349e5d5..20b03fb4 100644 --- a/src/instructlab/sdg/pipeline.py +++ b/src/instructlab/sdg/pipeline.py @@ -195,7 +195,6 @@ def _generate_single(self, dataset) -> Dataset: drop_duplicates_cols = block_prop.get("drop_duplicates", False) block = block_type(self.ctx, self, block_name, **block_config) logger.info("Running block: %s", block_name) - logger.info(dataset) # Execute the block and wrap errors with the block name/type dataset = block.generate(dataset) diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py new file mode 100644 index 00000000..6262c6df --- /dev/null +++ b/src/instructlab/sdg/utils/chunkers.py @@ -0,0 +1,563 @@ +import json +import logging +import re +import yaml +from abc import ABC, abstractmethod +from collections import defaultdict +from enum import Enum +from pathlib import Path +from typing import Iterable, List, Tuple, DefaultDict + +# Third Party +from datasets import Dataset, concatenate_datasets +from docling.datamodel.base_models import PipelineOptions +from docling.datamodel.document import ConvertedDocument, DocumentConversionInput +from docling.document_converter import ConversionStatus, DocumentConverter +from langchain_text_splitters import Language, RecursiveCharacterTextSplitter +from tabulate import tabulate +from transformers import AutoTokenizer + + +logger = logging.getLogger(__name__) +_DEFAULT_CHUNK_OVERLAP = 100 +DEFAULT_TAXONOMY_PATH = Path("~/.local/share/instructlab/taxonomy").expanduser() + + +class FileTypes(Enum): + MD = ".md" + PDF = ".pdf" + + +class ChunkerBase(ABC): + @abstractmethod + def chunk_documents(): + pass + + +class DocumentChunker: + """A factory chunker class that instantiates the applicable chunker + + Currently, only Markdown and PDF are supported. For Markdown, returns + TextSplitChunker, and for PDF, returns ContextAwareChunker""" + def __new__( + cls, + leaf_node = None, + output_dir: Path = None, + server_ctx_size=4096, + chunk_word_count=1024, + tokenizer_model_name: str = None, + ): + """Insantiate the appropriate chunker for the provided document + + Args: + leaf_node: a leaf node dict containing "documents", + "filepaths", and "taxonomy_path" keys + output_dir (Path): directory where artifacts should be stored + server_ctx_size (int): Context window size of server + chunk_word_count (int): Maximum number of words to chunk a document + tokenizer_model_name (str): name of huggingface model to get + tokenizer from + Returns: + TextSplitChunker | ContextAwareChunker: Object of the appropriate + chunker class for the provided filetype + """ + documents = leaf_node[0]["documents"] + assert type(documents) == list + filepaths = leaf_node[0]["filepaths"] + leaf_node_path = Path(leaf_node[0]["taxonomy_path"].replace("->", "/")) + + doc_dict = cls._split_docs_by_filetype(documents, filepaths) + if len(doc_dict.keys()) > 1: + raise ValueError(f"Received multiple document types") + + if FileTypes.MD in doc_dict: + return TextSplitChunker( + doc_dict[FileTypes.MD], + server_ctx_size, + chunk_word_count, + output_dir, + ) + + if FileTypes.PDF in doc_dict: + return ContextAwareChunker( + doc_dict[FileTypes.PDF], + filepaths, + DEFAULT_TAXONOMY_PATH / leaf_node_path / "qna.yaml", + output_dir, + tokenizer_model_name, + ) + + @staticmethod + def _split_docs_by_filetype(documents: List[str], filepaths: List[Path]) -> defaultdict[any, List]: + """Separate documents into lists based on their filetype. + + Currently, only Markdown and PDF are supported. + Args: + documents (List[str]): A list of the document contents as strings + filepaths (List[Path]): Corresponding document filepaths + Returns: + defaultdict: Dictionary with either ".md" or ".pdf" as a key. + Markdown items contain document contents, PDF items contain + paths to documents. + """ + doc_dict = defaultdict(list) + for doc, path in zip(documents, filepaths): + if path.suffix == ".md": + # append doc contents + doc_dict[FileTypes.MD].append(doc) + elif path.suffix == ".pdf": + # append doc paths + doc_dict[FileTypes.PDF].append(path) + else: + raise ValueError( + f"Received document of type .{path.suffix}, which is not a supported filetype" + ) + return doc_dict + + +class TextSplitChunker(ChunkerBase): + def __init__( + self, + document_contents: List | str, + server_ctx_size: int, + chunk_word_count: int, + output_dir: Path, + ): + self.document_contents = document_contents + self.server_ctx_size = server_ctx_size + self.chunk_word_count = chunk_word_count + self.output_dir = output_dir + + def chunk_documents(self) -> Dataset: + """Naively chunk markdown documents based on the word count provided by the user. + Returns: + List[str]: List of chunked documents. + """ + num_tokens_per_doc = self._num_tokens_from_words(self.chunk_word_count) + if num_tokens_per_doc > int(self.server_ctx_size - 1024): + raise ValueError( + "Error: {}".format( + str( + f"Given word count ({self.chunk_word_count}) per doc will exceed the server context window size ({self.server_ctx_size})" + ) + ) + ) + if self.document_contents == []: + return [] + + # Placeholder for params + content = [] + chunk_size = self._num_chars_from_tokens(num_tokens_per_doc) + chunk_overlap = _DEFAULT_CHUNK_OVERLAP + + # Using Markdown as default, document-specific chunking will be implemented in separate pr. + md_text_splitter = RecursiveCharacterTextSplitter.from_language( + language=Language.MARKDOWN, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + ) + + # Determine file type for heuristics, default with markdown + for doc in self.document_contents: + # Use regex to remove unnecessary dashes in front of pipe characters in a markdown table. + doc = re.sub(r"-{2,}\|", "-|", doc) + # Remove unnecessary spaces in front of pipe characters in a markdown table. + doc = re.sub(r"\ +\|", " |", doc) + temp = md_text_splitter.create_documents([doc]) + content.extend([item.page_content for item in temp]) + + return content + + @staticmethod + def _num_tokens_from_words(num_words) -> int: + return int(num_words * 1.3) # 1 word ~ 1.3 token + + @staticmethod + def _num_chars_from_tokens(num_tokens) -> int: + return int(num_tokens * 4) # 1 token ~ 4 English character + + +class ContextAwareChunker(ChunkerBase): + def __init__( + self, + document_paths, + filepaths, + leaf_node_path, + output_dir: Path, + tokenizer_model_name=None, + ): + self.document_paths = document_paths + self.filepaths = filepaths + self.leaf_node_path = leaf_node_path + self.output_dir = self._path_validator(output_dir) + if tokenizer_model_name is None: + self.tokenizer_model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1" + else: + self.tokenizer_model_name = tokenizer_model_name + self.qna_yaml = self._load_qna_yaml( + self._path_validator(leaf_node_path) if leaf_node_path else None + ) + + if tokenizer_model_name is None: + tokenizer_model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1" + self.tokenizer = self.create_tokenizer(tokenizer_model_name) + + def chunk_documents(self) -> Dataset: + """Semantically chunk PDF documents. + + Returns: + List: a list of chunks from the documents + """ + if self.document_paths == []: + return [] + + model_artifacts_path = DocumentConverter.download_models_hf() + converter = DocumentConverter(artifacts_path=model_artifacts_path) + inputs = DocumentConversionInput.from_paths(self.filepaths) + parsed_documents = converter.convert(inputs) + + docling_artifacts_path = self.export_documents(parsed_documents) + + docling_json_paths = list(docling_artifacts_path.glob("*.json")) + chunks = [] + for json_fp in docling_json_paths: + chunks.extend(self._process_parsed_docling_json(json_fp)) + + return chunks + + def _path_validator(self, path) -> Path: + """ + Validate the path and return a Path object. + Args: + path (str): Path to be validated. + Returns: + Path: Path object. + """ + if isinstance(path, str): + path = Path(path) + if not path.exists(): + raise FileNotFoundError(f"{path} does not exist.") + return path + + def _load_qna_yaml(self, qna_yaml_path: Path) -> dict: + """ + Load the qna YAML file. + Args: + qna_yaml_path (Path): Path to the knowledge qna YAML file. + Returns: + dict: Dictionary corresponding to knowledge qna YAML file. + """ + if qna_yaml_path: + with open(qna_yaml_path, "r", encoding="utf-8") as f: + return yaml.safe_load(f) + return {} + + def _process_parsed_docling_json(self, json_fp: Path) -> Dataset: + """ + Process the parsed docling json file and return a dataset. + Args: + json_fp (Path): Path to the parsed docling json file. + Returns: + List: a list of chunks built from the provided json file + """ + logger.info(f"Processing parsed docling json file: {json_fp}") + with open(json_fp, "r", encoding="utf-8") as f: + data = json.load(f) + + file_name = json_fp.stem + chunks = self.build_chunks_from_docling_json( + data, + max_token_per_chunk=500, + tokenizer=self.tokenizer, + ) + return self.fuse_texts(chunks, 200) + + def fuse_texts(self, text_list, short_length_threshold=100): + """ + Fuse short texts with preceding longer texts if their word count is below the threshold. + Args: + text_list (list): List of text chunks to process. + short_length_threshold (int): The word count threshold for determining short texts. + Returns: + list: List of fused texts. + """ + fused_texts = [] + previous_long_text = "" + + for text in text_list: + word_count = len(text.split()) + + if word_count <= short_length_threshold and previous_long_text: + # Append the short text to the last long text + fused_texts[-1] += "\n\n" + text + else: + # This is a long text, so add it to the list and remember it + fused_texts.append(text) + previous_long_text = text + + return fused_texts + + + def create_tokenizer(self, model_name: str): + """ + Create a tokenizer instance from a pre-trained model or a local directory. + + Args: + model_name (str): The name of the model or the path to the local directory. + + Returns: + AutoTokenizer: The tokenizer instance. + """ + try: + tokenizer = AutoTokenizer.from_pretrained(model_name) + logger.info(f"Successfully loaded tokenizer from: {model_name}") + return tokenizer + except Exception as e: + logger.error(f"Failed to load tokenizer from {model_name}: {str(e)}") + raise + + + def get_token_count(self, text, tokenizer): + """ + Get the number of tokens in a text using the provided tokenizer. + Args: + text (str): The text to tokenize. + tokenizer (AutoTokenizer): The tokenizer to use. + Returns: + int: Number of tokens. + """ + return len(tokenizer.tokenize(text)) + + + def add_heading_formatting(self, text): + """ + Add heading formatting to the text if the first part is short. + Args: + text (str): The input text to format. + Returns: + str: Formatted text with headings applied. + """ + text = text.split(".") + + # Change this from hardcoded to something more flexible + if len(text) > 1 and len(text[0].split(" ")) < 3: + text = f"**{text[0]}**" + ".".join(text[1:]) + else: + text = ".".join(text) + return text + + + def generate_table_from_parsed_rep(self, item): + """ + Generate the table from the parsed representation and return as a string. + Args: + item (dict): Parsed representation of a table. + Returns: + str: Formatted table as a string. + """ + caption = "" + if "text" in item: + caption = item["text"] + + data = item["data"] + + if len(data) <= 1 or len(data[0]) <= 1: + return "" + + table = [] + for i, row in enumerate(data): + trow = [] + for j, cell in enumerate(row): + trow.append(cell["text"]) + table.append(trow) + + table_text = tabulate(table, tablefmt="github") + if caption: + table_text += f"\nCaption: {caption}\n" + return table_text + + + def get_table(self, json_book, table_ref): + """ + Retrieve a table from a document based on a reference string. + Args: + json_book (dict): JSON representation of the document. + table_ref (str): Reference path to the table within the document. + Returns: + str: Formatted table string. + """ + parts = table_ref.split("/") + table_text = self.generate_table_from_parsed_rep(json_book[parts[1]][int(parts[2])]) + return table_text + + + def get_table_page_number(self, json_book, idx): + """ + Get the page number of a table or other document element. + Args: + json_book (dict): JSON representation of the document. + idx (int): Index of the element in the document. + Returns: + int: Page number of the element. + """ + prev_page_num, next_page_num = None, None + for book_element in json_book["main-text"][idx - 1 :: -1]: + if "prov" in book_element: + prev_page_num = book_element["prov"][0]["page"] + break + for book_element in json_book["main-text"][idx:]: + if "prov" in book_element: + next_page_num = book_element["prov"][0]["page"] + break + if prev_page_num is not None and next_page_num is not None: + if prev_page_num == next_page_num: + return prev_page_num + else: + return next_page_num + elif prev_page_num is not None: + return prev_page_num + elif next_page_num is not None: + return next_page_num + + + def build_chunks_from_docling_json(self, + json_book, + max_token_per_chunk, + tokenizer, + keep_same_page_thing_together=False, + chunking_criteria=None, + ): + """ + Build document chunks from a docling JSON representation. + Args: + json_book (dict): JSON document to process. + max_token_per_chunk (int): Maximum token count per chunk. + tokenizer (AutoTokenizer): Tokenizer instance to use. + keep_same_page_thing_together (bool): Whether to keep content on the same page together. + chunking_criteria (callable): Custom function for determining chunk breaks. + Returns: + list: List of document chunks. + """ + current_buffer = [] + document_chunks = [] + prev_page_number = None + book_title = None + + for idx, book_element in enumerate(json_book["main-text"]): + if book_element["type"] in [ + "page-footer", + "picture", + "reference", + "meta-data", + "figure", + "page-header", + ]: + continue + elif book_element["type"] == "footnote": + current_book_page_number = book_element["prov"][0]["page"] + elif book_element["type"] in [ + "subtitle-level-1", + "paragraph", + "table", + "title", + "equation", + ]: + if book_element["type"] == "table": + current_book_page_number = self.get_table_page_number(json_book, idx) + else: + current_book_page_number = book_element["prov"][0]["page"] + book_text = book_element["text"] + + if book_element["type"] == "subtitle-level-1": + if book_title is None: + book_title = book_text + book_text = f"# Title: **{book_text}**" + else: + book_text = f"## **{book_text}**" + + if book_element["type"] == "title": + book_text = f"# **{book_text}**" + if book_element["type"] == "page-header": + book_text = f"Page Header: **{book_text}**\n\n" + + if chunking_criteria is not None: + # custom break function that can be used to chunk document + if chunking_criteria(book_text): + document_chunks.append("\n\n".join(current_buffer)) + current_buffer = [] + elif ( + prev_page_number is not None + and prev_page_number != current_book_page_number + ) and keep_same_page_thing_together: + document_chunks.append("\n\n".join(current_buffer)) + current_buffer = [] + else: + if ( + self.get_token_count("\n\n".join(current_buffer), tokenizer) + >= max_token_per_chunk + and len(current_buffer) > 1 + ): + document_chunks.append("\n\n".join(current_buffer[:-1])) + + if ( + self.get_token_count(current_buffer[-1], tokenizer) + >= max_token_per_chunk + ): + document_chunks.append(current_buffer[-1]) + current_buffer = [] + else: + current_buffer = current_buffer[-1:] + + if book_element["type"] == "paragraph": + book_text = self.add_heading_formatting(book_text) + elif book_element["type"] == "table": + book_text = self.get_table(json_book, book_element["$ref"]) + + if "## References" in book_text or "## Acknowledgements" in book_text: + # For research papers we ignore everything after these sections + break + current_buffer.append(book_text) + + try: + prev_page_number = current_book_page_number + except Exception as e: + logger.error(f"Error processing book element: {book_element}, {str(e)}") + + if "\n\n".join(current_buffer) not in document_chunks: + document_chunks.append("\n\n".join(current_buffer)) + return document_chunks + + def export_documents(self, converted_docs: Iterable[ConvertedDocument]): + """Write converted documents to json files + + Check for successful conversions and write those to the docling artifacts directory. + Returns: + Path: path to directory with docling json artifacts + """ + docling_artifacts_path = self.output_dir / "docling-artifacts" + docling_artifacts_path.mkdir(parents=True, exist_ok=True) + + success_count = 0 + failure_count = 0 + + for doc in converted_docs: + if doc.status == ConversionStatus.SUCCESS: + success_count += 1 + doc_filename = doc.input.file.stem + + # Export Deep Search document JSON format: + with (docling_artifacts_path / f"{doc_filename}.json").open("w") as fp: + fp.write(json.dumps(doc.render_as_dict())) + + # Export Markdown format: + with (docling_artifacts_path / f"{doc_filename}.md").open("w") as fp: + fp.write(doc.render_as_markdown()) + else: + logger.info(f"Document {doc.input.file} failed to convert.") + failure_count += 1 + + logger.info( + f"Processed {success_count + failure_count} docs, of which {failure_count} failed" + ) + + return docling_artifacts_path diff --git a/src/instructlab/sdg/utils/chunking.py b/src/instructlab/sdg/utils/chunking.py deleted file mode 100644 index ab48d9b3..00000000 --- a/src/instructlab/sdg/utils/chunking.py +++ /dev/null @@ -1,75 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 - -# Standard -from typing import List -import logging -import re - -# Third Party -from langchain_text_splitters import Language, RecursiveCharacterTextSplitter - -_DEFAULT_CHUNK_OVERLAP = 100 - -logger = logging.getLogger(__name__) - - -def _num_tokens_from_words(num_words) -> int: - return int(num_words * 1.3) # 1 word ~ 1.3 token - - -def _num_chars_from_tokens(num_tokens) -> int: - return int(num_tokens * 4) # 1 token ~ 4 English character - - -def chunk_document(documents: List, server_ctx_size, chunk_word_count) -> List[str]: - """ - Iterates over the documents and splits them into chunks based on the word count provided by the user. - Args: - documents (list): List of documents retrieved from git (can also consist of a single document). - server_ctx_size (int): Context window size of server. - chunk_word_count (int): Maximum number of words to chunk a document. - Returns: - List[str]: List of chunked documents. - """ - - # Checks for input type error - if isinstance(documents, str): - documents = [documents] - logger.info( - "Converted single string into a list of string. Assumed the string passed in is the document. Normally, chunk_document() should take a list as input." - ) - elif not isinstance(documents, list): - raise TypeError( - "Expected: documents to be a list, but got {}".format(type(documents)) - ) - - no_tokens_per_doc = _num_tokens_from_words(chunk_word_count) - if no_tokens_per_doc > int(server_ctx_size - 1024): - raise ValueError( - "Error: {}".format( - str( - f"Given word count ({chunk_word_count}) per doc will exceed the server context window size ({server_ctx_size})" - ) - ) - ) - # Placeholder for params - content = [] - chunk_size = _num_chars_from_tokens(no_tokens_per_doc) - chunk_overlap = _DEFAULT_CHUNK_OVERLAP - - # Using Markdown as default, document-specific chunking will be implemented in separate pr. - text_splitter = RecursiveCharacterTextSplitter.from_language( - language=Language.MARKDOWN, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - ) - - # Determine file type for heuristics, default with markdown - for docs in documents: - # Use regex to remove unnecessary dashes in front of pipe characters in a markdown table. - docs = re.sub(r"-{2,}\|", "-|", docs) - # Remove unnecessary spaces in front of pipe characters in a markdown table. - docs = re.sub(r"\ +\|", " |", docs) - temp = text_splitter.create_documents([docs]) - content.extend([item.page_content for item in temp]) - return content diff --git a/src/instructlab/sdg/utils/taxonomy.py b/src/instructlab/sdg/utils/taxonomy.py index 9b472a48..8fc49d56 100644 --- a/src/instructlab/sdg/utils/taxonomy.py +++ b/src/instructlab/sdg/utils/taxonomy.py @@ -1,8 +1,9 @@ # SPDX-License-Identifier: Apache-2.0 # Standard +from datetime import datetime from pathlib import Path -from typing import Dict, List, Union +from typing import Dict, List, Tuple, Union import glob import logging import os @@ -10,6 +11,8 @@ import tempfile # Third Party +from datasets import Dataset +from docling_parse.docling_parse import pdf_parser from instructlab.schema.taxonomy import DEFAULT_TAXONOMY_FOLDERS as TAXONOMY_FOLDERS from instructlab.schema.taxonomy import ( TaxonomyMessageFormat, @@ -21,9 +24,13 @@ import yaml # Local -from . import chunking +from .chunkers import DocumentChunker + +# Initialize the pdf parser +parser = pdf_parser() logger = logging.getLogger(__name__) +DOC_FILEPATH = Path("~/.local/share/instructlab/documents").expanduser() def _is_taxonomy_file(fn: str) -> bool: @@ -106,43 +113,134 @@ def _get_taxonomy(repo="taxonomy"): def _get_documents( source: Dict[str, Union[str, List[str]]], skip_checkout: bool = False, -) -> List[str]: + document_output_dir: Path = None, +) -> Tuple[List[str], List[Path]]: """ - Retrieve the content of files from a Git repository. + Retrieve the content of files (Markdown and PDF) from a Git repository. Args: source (dict): Source info containing repository URL, commit hash, and list of file patterns. + skip_checkout (bool, optional): If True, skips checking out the specific commit. Defaults to False. + document_output_dir (Path, optional): Directory to clone the repository into. Defaults to current directory. Returns: - List[str]: List of document contents. - """ "" + Tuple[List[str], List[Path]]: + - List of document contents (Markdown as text and PDFs as extracted text). + - List of corresponding file paths. + + Raises: + SystemExit: If no valid documents are found. + OSError, GitCommandError, FileNotFoundError: For errors during Git operations or file access. + """ repo_url = source.get("repo") commit_hash = source.get("commit") file_patterns = source.get("patterns", []) - with tempfile.TemporaryDirectory() as temp_dir: - try: - repo = git.Repo.clone_from(repo_url, temp_dir) - if not skip_checkout: - repo.git.checkout(commit_hash) - file_contents = [] + try: + repo = git.Repo.clone_from(repo_url, document_output_dir) + + if not skip_checkout and commit_hash: + repo.git.checkout(commit_hash) - logger.debug("Processing files...") - for pattern in file_patterns: - for file_path in glob.glob(os.path.join(repo.working_dir, pattern)): - if os.path.isfile(file_path) and file_path.endswith(".md"): - with open(file_path, "r", encoding="utf-8") as file: - file_contents.append(file.read()) + file_contents = [] + filepaths = [] - if file_contents: - return file_contents - raise SystemExit("Couldn't find knowledge documents") - except (OSError, git.exc.GitCommandError, FileNotFoundError) as e: - raise e + logger.info("Processing files...") + for pattern in file_patterns: + # Use glob to find files matching the pattern + matched_files = glob.glob( + os.path.join(repo.working_dir, pattern), recursive=True + ) + logger.info(f"Pattern '{pattern}' matched {len(matched_files)} files.") + + for file_path in matched_files: + if os.path.isfile(file_path): + logger.info(f"Processing file: {file_path}") + try: + if file_path.lower().endswith(".md"): + # Process Markdown files + with open(file_path, "r", encoding="utf-8") as file: + content = file.read() + file_contents.append(content) + filepaths.append(Path(file_path)) + logger.info( + f"Appended Markdown content from {file_path}" + ) + + elif file_path.lower().endswith(".pdf"): + # Process PDF files using docling_parse's pdf_parser + doc_key = f"key_{os.path.basename(file_path)}" # Unique document key + logger.info(f"Loading PDF document from {file_path}") + + success = parser.load_document(doc_key, file_path) + if not success: + logger.warning( + f"Failed to load PDF document: {file_path}" + ) + continue + + num_pages = parser.number_of_pages(doc_key) + logger.info(f"PDF '{file_path}' has {num_pages} pages.") + + pdf_text = "" + + for page in range(num_pages): + try: + json_doc = parser.parse_pdf_from_key_on_page( + doc_key, page + ) + if "pages" not in json_doc or not json_doc["pages"]: + logger.warning( + f"Page {page + 1} could not be parsed in '{file_path}'" + ) + continue + + json_page = json_doc["pages"][0] + + # Extract text from cells + for cell in json_page.get("cells", []): + text = cell.get("content", {}).get( + "rnormalized", "" + ) + if text.strip(): # Only append non-empty text + pdf_text += text.strip() + "\n" + except Exception as page_error: + logger.warning( + f"Error parsing page {page + 1} of '{file_path}': {page_error}" + ) + continue + + if pdf_text: + file_contents.append(pdf_text) + filepaths.append(Path(file_path)) + + # Unload the document to free memory + parser.unload_document(doc_key) + logger.info(f"Unloaded PDF document: {file_path}") + + else: + logger.info(f"Skipping unsupported file type: {file_path}") + except Exception as file_error: + logger.error( + f"Error processing file '{file_path}': {file_error}" + ) + continue + else: + logger.info(f"Skipping non-file path: {file_path}") + + if file_contents: + return file_contents, filepaths + raise SystemExit("Couldn't find knowledge documents") + + except (OSError, git.exc.GitCommandError, FileNotFoundError) as e: + logger.error("Error retrieving documents: %s", str(e)) + raise e # pylint: disable=broad-exception-caught -def _read_taxonomy_file(file_path: str | Path, yamllint_config: str | None = None): +def _read_taxonomy_file( + file_path: str | Path, yamllint_config: str | None = None, document_output_dir: Path = Path() +): seed_instruction_data = [] parser = TaxonomyParser( @@ -163,8 +261,11 @@ def _read_taxonomy_file(file_path: str | Path, yamllint_config: str | None = Non task_description = contents.get("task_description", None) domain = contents.get("domain") documents = contents.get("document") + print(f"") if documents: - documents = _get_documents(source=documents) + document_contents, doc_filepaths = _get_documents( + source=documents, document_output_dir=document_output_dir + ) logger.debug("Content from git repo fetched") for seed_example in contents.get("seed_examples"): @@ -176,7 +277,8 @@ def _read_taxonomy_file(file_path: str | Path, yamllint_config: str | None = Non "questions_and_answers": question_answer_list, "context": context, "taxonomy_path": tax_path, - "document": documents, + "documents": document_contents, + "filepaths": doc_filepaths, "domain": domain, "document_outline": contents.get("document_outline"), } @@ -203,7 +305,10 @@ def _read_taxonomy_file(file_path: str | Path, yamllint_config: str | None = Non def read_taxonomy( - taxonomy: str | Path, taxonomy_base: str, yaml_rules: str | None = None + taxonomy: str | Path, + taxonomy_base: str, + yaml_rules: str | None = None, + document_output_dir: Path = Path(), ): yamllint_config = None # If no custom rules file, use default config if yaml_rules is not None: # user attempted to pass custom rules file @@ -218,7 +323,7 @@ def read_taxonomy( is_file = os.path.isfile(taxonomy) if is_file: # taxonomy is file seed_instruction_data, warnings, errors = _read_taxonomy_file( - taxonomy, yamllint_config + taxonomy, yamllint_config, document_output_dir ) if warnings: logger.warning( @@ -241,7 +346,9 @@ def read_taxonomy( logger.debug(f"* {e}") for f in taxonomy_files: file_path = os.path.join(taxonomy, f) - data, warnings, errors = _read_taxonomy_file(file_path, yamllint_config) + data, warnings, errors = _read_taxonomy_file( + file_path, yamllint_config, document_output_dir + ) total_warnings += warnings total_errors += errors if data: @@ -257,8 +364,10 @@ def read_taxonomy( return seed_instruction_data -def read_taxonomy_leaf_nodes(taxonomy, taxonomy_base, yaml_rules): - seed_instruction_data = read_taxonomy(taxonomy, taxonomy_base, yaml_rules) +def read_taxonomy_leaf_nodes(taxonomy, taxonomy_base, yaml_rules, document_output_dir): + seed_instruction_data = read_taxonomy( + taxonomy, taxonomy_base, yaml_rules, document_output_dir + ) # Transform into a more convenient format to feed into our updated SDG library leaf_nodes = {} @@ -270,43 +379,45 @@ def read_taxonomy_leaf_nodes(taxonomy, taxonomy_base, yaml_rules): return leaf_nodes -def _knowledge_leaf_node_to_samples(leaf_node, server_ctx_size, chunk_word_count): - samples = [] - # document is the same for the whole leaf node - chunks = ( - chunking.chunk_document( - documents=leaf_node[0]["document"], - server_ctx_size=server_ctx_size, - chunk_word_count=chunk_word_count, - ) - if leaf_node[0].get("document") - else [] - ) +def map_chunks_to_icls(chunks: List, leaf_node: Dict) -> Dataset: + chunked_dataset = [] # domain is the same for the whole leaf node domain = leaf_node[0].get("domain") - for chunk in chunks: - # pylint: disable=consider-using-enumerate for icl_ in leaf_node: - icl_query = { - f"icl_query_{idx+1}": val["question"] - for idx, val in enumerate(icl_["questions_and_answers"]) - } - icl_resp = { - f"icl_response_{idx+1}": val["answer"] - for idx, val in enumerate(icl_["questions_and_answers"]) - } - samples_row = { - "icl_document": icl_["context"], + record = { "document": chunk, - "document_outline": icl_["document_outline"], + "icl_document": icl_.get("context", ""), + "document_outline": icl_.get("document_outline", ""), "domain": domain, } - samples_row.update(icl_query) - samples_row.update(icl_resp) - samples.append(samples_row) + qna_pairs = icl_.get("questions_and_answers", []) + for i, qna in enumerate(qna_pairs): + record.update({ + f"icl_query_{i+1}": qna.get("question", ""), + f"icl_response_{i+1}": qna.get("answer", ""), + }) + + chunked_dataset.append(record) + + return Dataset.from_list(chunked_dataset) + + +def _knowledge_leaf_node_to_samples( + leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name +): + chunker = DocumentChunker( + leaf_node=leaf_node, + output_dir=document_output_dir, + server_ctx_size=server_ctx_size, + chunk_word_count=chunk_word_count, + tokenizer_model_name=model_name, + ) + chunks = chunker.chunk_documents() + + samples = map_chunks_to_icls(chunks, leaf_node) return samples @@ -322,14 +433,16 @@ def _skill_leaf_node_to_samples(leaf_node): samples[-1]["seed_question"] = leaf_node[i]["instruction"] samples[-1]["seed_response"] = leaf_node[i]["output"] - return samples + return Dataset.from_list(samples) -def leaf_node_to_samples(leaf_node, server_ctx_size, chunk_word_count): +def leaf_node_to_samples( + leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name +): if not leaf_node: return [] - if leaf_node[0].get("document"): + if leaf_node[0].get("documents"): return _knowledge_leaf_node_to_samples( - leaf_node, server_ctx_size, chunk_word_count + leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name ) return _skill_leaf_node_to_samples(leaf_node) From 88705d8b827acfdff42a59c423111efb11335b47 Mon Sep 17 00:00:00 2001 From: Khaled Sulayman Date: Tue, 5 Nov 2024 13:16:07 -0500 Subject: [PATCH 02/11] call markdown chunker after context-aware chunking Signed-off-by: Khaled Sulayman --- src/instructlab/sdg/utils/chunkers.py | 102 +++++++++++++++++--------- src/instructlab/sdg/utils/taxonomy.py | 4 +- 2 files changed, 68 insertions(+), 38 deletions(-) diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index 6262c6df..bd9f2b53 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -23,6 +23,13 @@ DEFAULT_TAXONOMY_PATH = Path("~/.local/share/instructlab/taxonomy").expanduser() +def _num_tokens_from_words(num_words) -> int: + return int(num_words * 1.3) # 1 word ~ 1.3 token + +def _num_chars_from_tokens(num_tokens) -> int: + return int(num_tokens * 4) # 1 token ~ 4 English character + + class FileTypes(Enum): MD = ".md" PDF = ".pdf" @@ -62,7 +69,17 @@ def __new__( chunker class for the provided filetype """ documents = leaf_node[0]["documents"] - assert type(documents) == list + + if isinstance(documents, str): + documents = [documents] + logger.info( + "Converted single string into a list of string. Assumed the string passed in is the document. Normally, chunk_document() should take a list as input." + ) + elif not isinstance(documents, list): + raise TypeError( + "Expected: documents to be a list, but got {}".format(type(documents)) + ) + filepaths = leaf_node[0]["filepaths"] leaf_node_path = Path(leaf_node[0]["taxonomy_path"].replace("->", "/")) @@ -84,6 +101,7 @@ def __new__( filepaths, DEFAULT_TAXONOMY_PATH / leaf_node_path / "qna.yaml", output_dir, + chunk_word_count, tokenizer_model_name, ) @@ -128,12 +146,12 @@ def __init__( self.chunk_word_count = chunk_word_count self.output_dir = output_dir - def chunk_documents(self) -> Dataset: + def chunk_documents(self) -> List: """Naively chunk markdown documents based on the word count provided by the user. Returns: List[str]: List of chunked documents. """ - num_tokens_per_doc = self._num_tokens_from_words(self.chunk_word_count) + num_tokens_per_doc = _num_tokens_from_words(self.chunk_word_count) if num_tokens_per_doc > int(self.server_ctx_size - 1024): raise ValueError( "Error: {}".format( @@ -145,36 +163,8 @@ def chunk_documents(self) -> Dataset: if self.document_contents == []: return [] - # Placeholder for params - content = [] - chunk_size = self._num_chars_from_tokens(num_tokens_per_doc) - chunk_overlap = _DEFAULT_CHUNK_OVERLAP - - # Using Markdown as default, document-specific chunking will be implemented in separate pr. - md_text_splitter = RecursiveCharacterTextSplitter.from_language( - language=Language.MARKDOWN, - chunk_size=chunk_size, - chunk_overlap=chunk_overlap, - ) - - # Determine file type for heuristics, default with markdown - for doc in self.document_contents: - # Use regex to remove unnecessary dashes in front of pipe characters in a markdown table. - doc = re.sub(r"-{2,}\|", "-|", doc) - # Remove unnecessary spaces in front of pipe characters in a markdown table. - doc = re.sub(r"\ +\|", " |", doc) - temp = md_text_splitter.create_documents([doc]) - content.extend([item.page_content for item in temp]) - - return content - - @staticmethod - def _num_tokens_from_words(num_words) -> int: - return int(num_words * 1.3) # 1 word ~ 1.3 token - - @staticmethod - def _num_chars_from_tokens(num_tokens) -> int: - return int(num_tokens * 4) # 1 token ~ 4 English character + chunk_size = _num_chars_from_tokens(num_tokens_per_doc) + return chunk_markdowns(self.document_contents, chunk_size) class ContextAwareChunker(ChunkerBase): @@ -184,12 +174,14 @@ def __init__( filepaths, leaf_node_path, output_dir: Path, + chunk_word_count: int, tokenizer_model_name=None, ): self.document_paths = document_paths self.filepaths = filepaths self.leaf_node_path = leaf_node_path self.output_dir = self._path_validator(output_dir) + self.chunk_word_count = chunk_word_count if tokenizer_model_name is None: self.tokenizer_model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1" else: @@ -202,7 +194,7 @@ def __init__( tokenizer_model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1" self.tokenizer = self.create_tokenizer(tokenizer_model_name) - def chunk_documents(self) -> Dataset: + def chunk_documents(self) -> List: """Semantically chunk PDF documents. Returns: @@ -270,9 +262,13 @@ def _process_parsed_docling_json(self, json_fp: Path) -> Dataset: max_token_per_chunk=500, tokenizer=self.tokenizer, ) - return self.fuse_texts(chunks, 200) + fused_texts = self.fuse_texts(chunks, 200) + + num_tokens_per_doc = _num_tokens_from_words(self.chunk_word_count) + chunk_size = _num_chars_from_tokens(num_tokens_per_doc) + return chunk_markdowns(fused_texts, chunk_size) - def fuse_texts(self, text_list, short_length_threshold=100): + def fuse_texts(self, text_list: List, short_length_threshold: int = 100): """ Fuse short texts with preceding longer texts if their word count is below the threshold. Args: @@ -561,3 +557,37 @@ def export_documents(self, converted_docs: Iterable[ConvertedDocument]): ) return docling_artifacts_path + + +def chunk_markdowns(documents: List | Dataset, chunk_size) -> Dataset: + """ + Iterates over the documents and splits them into chunks based on the word count provided by the user. + Args: + documents (list): List of documents retrieved from git (can also consist of a single document). + server_ctx_size (int): Context window size of server. + chunk_word_count (int): Maximum number of words to chunk a document. + Returns: + List[str]: List of chunked documents. + """ + + # Checks for input type error + content = [] + # chunk_size = _num_chars_from_tokens(no_tokens_per_doc) + chunk_overlap = _DEFAULT_CHUNK_OVERLAP + + # Using Markdown as default, document-specific chunking will be implemented in separate pr. + text_splitter = RecursiveCharacterTextSplitter.from_language( + language=Language.MARKDOWN, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + ) + + # Determine file type for heuristics, default with markdown + for docs in documents: + # Use regex to remove unnecessary dashes in front of pipe characters in a markdown table. + docs = re.sub(r"-{2,}\|", "-|", docs) + # Remove unnecessary spaces in front of pipe characters in a markdown table. + docs = re.sub(r"\ +\|", " |", docs) + temp = text_splitter.create_documents([docs]) + content.extend([item.page_content for item in temp]) + return content diff --git a/src/instructlab/sdg/utils/taxonomy.py b/src/instructlab/sdg/utils/taxonomy.py index 8fc49d56..30487807 100644 --- a/src/instructlab/sdg/utils/taxonomy.py +++ b/src/instructlab/sdg/utils/taxonomy.py @@ -308,7 +308,7 @@ def read_taxonomy( taxonomy: str | Path, taxonomy_base: str, yaml_rules: str | None = None, - document_output_dir: Path = Path(), + document_output_dir: Path | None = None, ): yamllint_config = None # If no custom rules file, use default config if yaml_rules is not None: # user attempted to pass custom rules file @@ -364,7 +364,7 @@ def read_taxonomy( return seed_instruction_data -def read_taxonomy_leaf_nodes(taxonomy, taxonomy_base, yaml_rules, document_output_dir): +def read_taxonomy_leaf_nodes(taxonomy, taxonomy_base, yaml_rules, document_output_dir=None): seed_instruction_data = read_taxonomy( taxonomy, taxonomy_base, yaml_rules, document_output_dir ) From 61e256c4de7319be42568b38b973bf6e4b24a0ce Mon Sep 17 00:00:00 2001 From: Khaled Sulayman Date: Tue, 5 Nov 2024 18:01:47 -0500 Subject: [PATCH 03/11] propagate taxonomy path to chunkers Signed-off-by: Khaled Sulayman --- src/instructlab/sdg/generate_data.py | 4 ++-- src/instructlab/sdg/utils/chunkers.py | 7 +++++-- src/instructlab/sdg/utils/taxonomy.py | 8 ++++---- 3 files changed, 11 insertions(+), 8 deletions(-) diff --git a/src/instructlab/sdg/generate_data.py b/src/instructlab/sdg/generate_data.py index d123f33b..ae0a80c5 100644 --- a/src/instructlab/sdg/generate_data.py +++ b/src/instructlab/sdg/generate_data.py @@ -268,7 +268,7 @@ def generate_data( model_name: Optional[str] = None, num_cpus: Optional[int] = None, num_instructions_to_generate: Optional[int] = 30, - taxonomy: Optional[str] = None, + taxonomy: Optional[str] = None, # TODO rename to taxonomy_path to match config taxonomy_base: Optional[str] = None, output_dir: Optional[str] = None, # TODO - not used and should be removed from the CLI @@ -367,7 +367,7 @@ def generate_data( is_knowledge = False leaf_node_path = leaf_node[0]["taxonomy_path"].replace("->", "_") samples = leaf_node_to_samples( - leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, taxonomy, server_ctx_size, chunk_word_count, document_output_dir, model_name ) if not samples: diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index bd9f2b53..d6e0df81 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -20,7 +20,6 @@ logger = logging.getLogger(__name__) _DEFAULT_CHUNK_OVERLAP = 100 -DEFAULT_TAXONOMY_PATH = Path("~/.local/share/instructlab/taxonomy").expanduser() def _num_tokens_from_words(num_words) -> int: @@ -49,6 +48,7 @@ class DocumentChunker: def __new__( cls, leaf_node = None, + taxonomy_path = None, output_dir: Path = None, server_ctx_size=4096, chunk_word_count=1024, @@ -70,6 +70,9 @@ def __new__( """ documents = leaf_node[0]["documents"] + if not isinstance(taxonomy_path, Path): + taxonomy_path = Path(taxonomy_path) + if isinstance(documents, str): documents = [documents] logger.info( @@ -99,7 +102,7 @@ def __new__( return ContextAwareChunker( doc_dict[FileTypes.PDF], filepaths, - DEFAULT_TAXONOMY_PATH / leaf_node_path / "qna.yaml", + taxonomy_path / leaf_node_path / "qna.yaml", output_dir, chunk_word_count, tokenizer_model_name, diff --git a/src/instructlab/sdg/utils/taxonomy.py b/src/instructlab/sdg/utils/taxonomy.py index 30487807..59994b53 100644 --- a/src/instructlab/sdg/utils/taxonomy.py +++ b/src/instructlab/sdg/utils/taxonomy.py @@ -30,7 +30,6 @@ parser = pdf_parser() logger = logging.getLogger(__name__) -DOC_FILEPATH = Path("~/.local/share/instructlab/documents").expanduser() def _is_taxonomy_file(fn: str) -> bool: @@ -406,10 +405,11 @@ def map_chunks_to_icls(chunks: List, leaf_node: Dict) -> Dataset: def _knowledge_leaf_node_to_samples( - leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, taxonomy_path, server_ctx_size, chunk_word_count, document_output_dir, model_name ): chunker = DocumentChunker( leaf_node=leaf_node, + taxonomy_path=taxonomy_path, output_dir=document_output_dir, server_ctx_size=server_ctx_size, chunk_word_count=chunk_word_count, @@ -437,12 +437,12 @@ def _skill_leaf_node_to_samples(leaf_node): def leaf_node_to_samples( - leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, taxonomy_path, server_ctx_size, chunk_word_count, document_output_dir, model_name ): if not leaf_node: return [] if leaf_node[0].get("documents"): return _knowledge_leaf_node_to_samples( - leaf_node, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, taxonomy_path, server_ctx_size, chunk_word_count, document_output_dir, model_name ) return _skill_leaf_node_to_samples(leaf_node) From 6be3bceea136f1d8e779be90b80e76e56cc2f80c Mon Sep 17 00:00:00 2001 From: Aakanksha Duggal Date: Tue, 5 Nov 2024 18:31:01 -0500 Subject: [PATCH 04/11] Simplify tokenizer initialization in ContextAwareChunker Signed-off-by: Aakanksha Duggal --- src/instructlab/sdg/utils/chunkers.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index d6e0df81..75bf59fc 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -178,23 +178,18 @@ def __init__( leaf_node_path, output_dir: Path, chunk_word_count: int, - tokenizer_model_name=None, + tokenizer_model_name="mistralai/Mixtral-8x7B-Instruct-v0.1", ): self.document_paths = document_paths self.filepaths = filepaths self.leaf_node_path = leaf_node_path self.output_dir = self._path_validator(output_dir) self.chunk_word_count = chunk_word_count - if tokenizer_model_name is None: - self.tokenizer_model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1" - else: - self.tokenizer_model_name = tokenizer_model_name + self.tokenizer_model_name = tokenizer_model_name self.qna_yaml = self._load_qna_yaml( self._path_validator(leaf_node_path) if leaf_node_path else None ) - if tokenizer_model_name is None: - tokenizer_model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1" self.tokenizer = self.create_tokenizer(tokenizer_model_name) def chunk_documents(self) -> List: From 4f47a7b974682a07406b1e285faaee93078bc8df Mon Sep 17 00:00:00 2001 From: Aakanksha Duggal Date: Tue, 5 Nov 2024 18:40:32 -0500 Subject: [PATCH 05/11] Use tokenizer for token count over word count in fuse_texts for consistency - Replaced word count with token count for short text threshold - Ensures consistency with token-based chunking approach Signed-off-by: Aakanksha Duggal --- src/instructlab/sdg/utils/chunkers.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index 75bf59fc..ccd7fdaf 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -265,13 +265,13 @@ def _process_parsed_docling_json(self, json_fp: Path) -> Dataset: num_tokens_per_doc = _num_tokens_from_words(self.chunk_word_count) chunk_size = _num_chars_from_tokens(num_tokens_per_doc) return chunk_markdowns(fused_texts, chunk_size) - + def fuse_texts(self, text_list: List, short_length_threshold: int = 100): """ - Fuse short texts with preceding longer texts if their word count is below the threshold. + Fuse short texts with preceding longer texts if their token count is below the threshold. Args: text_list (list): List of text chunks to process. - short_length_threshold (int): The word count threshold for determining short texts. + short_length_threshold (int): The token count threshold for determining short texts. Returns: list: List of fused texts. """ @@ -279,9 +279,9 @@ def fuse_texts(self, text_list: List, short_length_threshold: int = 100): previous_long_text = "" for text in text_list: - word_count = len(text.split()) + token_count = self.get_token_count(text, self.tokenizer) # Use tokenizer for token count - if word_count <= short_length_threshold and previous_long_text: + if token_count <= short_length_threshold and previous_long_text: # Append the short text to the last long text fused_texts[-1] += "\n\n" + text else: @@ -290,8 +290,7 @@ def fuse_texts(self, text_list: List, short_length_threshold: int = 100): previous_long_text = text return fused_texts - - + def create_tokenizer(self, model_name: str): """ Create a tokenizer instance from a pre-trained model or a local directory. From f627f252039d0454332a9ff72f836f50656494f0 Mon Sep 17 00:00:00 2001 From: Aakanksha Duggal Date: Tue, 5 Nov 2024 19:30:56 -0500 Subject: [PATCH 06/11] Refactor tokenizer initialization and update fuse_texts threshold for Mixtral model Signed-off-by: Aakanksha Duggal --- src/instructlab/sdg/utils/chunkers.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index ccd7fdaf..f5071999 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -185,7 +185,7 @@ def __init__( self.leaf_node_path = leaf_node_path self.output_dir = self._path_validator(output_dir) self.chunk_word_count = chunk_word_count - self.tokenizer_model_name = tokenizer_model_name + self.tokenizer_model_name = tokenizer_model_name if tokenizer_model_name is not None else "mistralai/Mixtral-8x7B-Instruct-v0.1" self.qna_yaml = self._load_qna_yaml( self._path_validator(leaf_node_path) if leaf_node_path else None ) @@ -266,12 +266,14 @@ def _process_parsed_docling_json(self, json_fp: Path) -> Dataset: chunk_size = _num_chars_from_tokens(num_tokens_per_doc) return chunk_markdowns(fused_texts, chunk_size) - def fuse_texts(self, text_list: List, short_length_threshold: int = 100): + def fuse_texts(self, text_list: List, short_length_threshold: int = 130): """ Fuse short texts with preceding longer texts if their token count is below the threshold. Args: text_list (list): List of text chunks to process. short_length_threshold (int): The token count threshold for determining short texts. + Default is 130, tuned specifically for the Mixtral tokenizer. + Update this value if changing the tokenizer model. Returns: list: List of fused texts. """ From 8257f5225ef88103ce333ca3939ad9b9e59704e5 Mon Sep 17 00:00:00 2001 From: Khaled Sulayman Date: Tue, 5 Nov 2024 19:26:04 -0500 Subject: [PATCH 07/11] update chunk building code Signed-off-by: Khaled Sulayman --- src/instructlab/sdg/utils/chunkers.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index f5071999..3f69e230 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -414,8 +414,8 @@ def get_table_page_number(self, json_book, idx): elif next_page_num is not None: return next_page_num - - def build_chunks_from_docling_json(self, + def build_chunks_from_docling_json( + self, json_book, max_token_per_chunk, tokenizer, @@ -456,7 +456,7 @@ def build_chunks_from_docling_json(self, "table", "title", "equation", - ]: + ]: # 'page-header', if book_element["type"] == "table": current_book_page_number = self.get_table_page_number(json_book, idx) else: @@ -492,12 +492,16 @@ def build_chunks_from_docling_json(self, >= max_token_per_chunk and len(current_buffer) > 1 ): + chunk_text = '\n\n'.join(current_buffer[:-1]) + print(f"Current chunk size {self.get_token_count(chunk_text, tokenizer)} and max is {max_token_per_chunk}") + document_chunks.append("\n\n".join(current_buffer[:-1])) if ( self.get_token_count(current_buffer[-1], tokenizer) >= max_token_per_chunk ): + print(f"This is too big a document to be left in the current buffer {self.get_token_count(current_buffer[-1], tokenizer)}") document_chunks.append(current_buffer[-1]) current_buffer = [] else: @@ -507,9 +511,8 @@ def build_chunks_from_docling_json(self, book_text = self.add_heading_formatting(book_text) elif book_element["type"] == "table": book_text = self.get_table(json_book, book_element["$ref"]) - if "## References" in book_text or "## Acknowledgements" in book_text: - # For research papers we ignore everything after these sections + # For research papers we ignore everything after this sections break current_buffer.append(book_text) @@ -522,6 +525,7 @@ def build_chunks_from_docling_json(self, document_chunks.append("\n\n".join(current_buffer)) return document_chunks + def export_documents(self, converted_docs: Iterable[ConvertedDocument]): """Write converted documents to json files From 1fe1f76bbe230820a4c00f3aca547dd375ae4f9c Mon Sep 17 00:00:00 2001 From: Ben Browning Date: Wed, 6 Nov 2024 08:15:08 -0500 Subject: [PATCH 08/11] Align torch requirement with CLI repo Relax the upper bound of the torch requirement to align with the CLI repo so that we stop force-downgrading torch to 2.3.x. Signed-off-by: Ben Browning --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 195dddfa..684539be 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,5 +13,6 @@ openai>=1.13.3,<2.0.0 pypdf>=5.0.0 tabulate>=0.9.0 tenacity>=8.3.0,!=8.4.0 -transformers>=4.44.2 +torch>=2.3.0,<2.5.0 +transformers>=4.41.2 xdg-base-dirs>=6.0.1 From 6851ad5eca5b8308b083c0c7c990d8a7d6ca1d81 Mon Sep 17 00:00:00 2001 From: Khaled Sulayman Date: Wed, 6 Nov 2024 01:00:19 -0500 Subject: [PATCH 09/11] Fix linting and formatting for context-aware chunking Signed-off-by: Khaled Sulayman --- src/instructlab/sdg/generate_data.py | 10 +- src/instructlab/sdg/utils/chunkers.py | 119 +++++++++-------- src/instructlab/sdg/utils/taxonomy.py | 66 ++++++---- tests/test_chunkers.py | 176 ++++++++++++++++++++++++++ 4 files changed, 291 insertions(+), 80 deletions(-) create mode 100644 tests/test_chunkers.py diff --git a/src/instructlab/sdg/generate_data.py b/src/instructlab/sdg/generate_data.py index ae0a80c5..f3e38e65 100644 --- a/src/instructlab/sdg/generate_data.py +++ b/src/instructlab/sdg/generate_data.py @@ -13,7 +13,6 @@ # Third Party # instructlab - All of these need to go away (other than sdg) - issue #6 -from datasets import Dataset from xdg_base_dirs import xdg_data_dirs, xdg_data_home import openai @@ -367,12 +366,17 @@ def generate_data( is_knowledge = False leaf_node_path = leaf_node[0]["taxonomy_path"].replace("->", "_") samples = leaf_node_to_samples( - leaf_node, taxonomy, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, + taxonomy, + server_ctx_size, + chunk_word_count, + document_output_dir, + model_name, ) if not samples: raise GenerateException("Error: No samples found in leaf node.") - + if "document" in samples.column_names: pipe = knowledge_pipe is_knowledge = True diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index 3f69e230..14b74969 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -1,22 +1,21 @@ -import json -import logging -import re -import yaml +# Standard from abc import ABC, abstractmethod from collections import defaultdict from enum import Enum from pathlib import Path -from typing import Iterable, List, Tuple, DefaultDict +from typing import DefaultDict, Iterable, List, Tuple +import json +import logging +import re # Third Party -from datasets import Dataset, concatenate_datasets -from docling.datamodel.base_models import PipelineOptions +from datasets import Dataset from docling.datamodel.document import ConvertedDocument, DocumentConversionInput from docling.document_converter import ConversionStatus, DocumentConverter from langchain_text_splitters import Language, RecursiveCharacterTextSplitter from tabulate import tabulate from transformers import AutoTokenizer - +import yaml logger = logging.getLogger(__name__) _DEFAULT_CHUNK_OVERLAP = 100 @@ -25,6 +24,7 @@ def _num_tokens_from_words(num_words) -> int: return int(num_words * 1.3) # 1 word ~ 1.3 token + def _num_chars_from_tokens(num_tokens) -> int: return int(num_tokens * 4) # 1 token ~ 4 English character @@ -36,26 +36,27 @@ class FileTypes(Enum): class ChunkerBase(ABC): @abstractmethod - def chunk_documents(): + def chunk_documents(self): pass class DocumentChunker: """A factory chunker class that instantiates the applicable chunker - + Currently, only Markdown and PDF are supported. For Markdown, returns TextSplitChunker, and for PDF, returns ContextAwareChunker""" + def __new__( cls, - leaf_node = None, - taxonomy_path = None, - output_dir: Path = None, + leaf_node, + taxonomy_path, + output_dir: Path, server_ctx_size=4096, chunk_word_count=1024, - tokenizer_model_name: str = None, + tokenizer_model_name: str | None = None, ): """Insantiate the appropriate chunker for the provided document - + Args: leaf_node: a leaf node dict containing "documents", "filepaths", and "taxonomy_path" keys @@ -63,7 +64,7 @@ def __new__( server_ctx_size (int): Context window size of server chunk_word_count (int): Maximum number of words to chunk a document tokenizer_model_name (str): name of huggingface model to get - tokenizer from + tokenizer from Returns: TextSplitChunker | ContextAwareChunker: Object of the appropriate chunker class for the provided filetype @@ -88,28 +89,32 @@ def __new__( doc_dict = cls._split_docs_by_filetype(documents, filepaths) if len(doc_dict.keys()) > 1: - raise ValueError(f"Received multiple document types") + raise ValueError("Received multiple document types") if FileTypes.MD in doc_dict: + doc_contents = [d for d, _ in doc_dict[FileTypes.MD]] return TextSplitChunker( - doc_dict[FileTypes.MD], + doc_contents, server_ctx_size, chunk_word_count, output_dir, ) if FileTypes.PDF in doc_dict: + doc_paths = [p for _, p in doc_dict[FileTypes.PDF]] return ContextAwareChunker( - doc_dict[FileTypes.PDF], + doc_paths, filepaths, taxonomy_path / leaf_node_path / "qna.yaml", - output_dir, + output_dir, chunk_word_count, tokenizer_model_name, ) @staticmethod - def _split_docs_by_filetype(documents: List[str], filepaths: List[Path]) -> defaultdict[any, List]: + def _split_docs_by_filetype( + documents: List[str], filepaths: List[Path] + ) -> DefaultDict[FileTypes, List[Tuple[str, Path]]]: """Separate documents into lists based on their filetype. Currently, only Markdown and PDF are supported. @@ -117,7 +122,7 @@ def _split_docs_by_filetype(documents: List[str], filepaths: List[Path]) -> defa documents (List[str]): A list of the document contents as strings filepaths (List[Path]): Corresponding document filepaths Returns: - defaultdict: Dictionary with either ".md" or ".pdf" as a key. + DefaultDict: Dictionary with either ".md" or ".pdf" as a key. Markdown items contain document contents, PDF items contain paths to documents. """ @@ -125,10 +130,10 @@ def _split_docs_by_filetype(documents: List[str], filepaths: List[Path]) -> defa for doc, path in zip(documents, filepaths): if path.suffix == ".md": # append doc contents - doc_dict[FileTypes.MD].append(doc) + doc_dict[FileTypes.MD].append((doc, path)) elif path.suffix == ".pdf": # append doc paths - doc_dict[FileTypes.PDF].append(path) + doc_dict[FileTypes.PDF].append((doc, path)) else: raise ValueError( f"Received document of type .{path.suffix}, which is not a supported filetype" @@ -170,7 +175,7 @@ def chunk_documents(self) -> List: return chunk_markdowns(self.document_contents, chunk_size) -class ContextAwareChunker(ChunkerBase): +class ContextAwareChunker(ChunkerBase): # pylint: disable=too-many-instance-attributes def __init__( self, document_paths, @@ -185,7 +190,11 @@ def __init__( self.leaf_node_path = leaf_node_path self.output_dir = self._path_validator(output_dir) self.chunk_word_count = chunk_word_count - self.tokenizer_model_name = tokenizer_model_name if tokenizer_model_name is not None else "mistralai/Mixtral-8x7B-Instruct-v0.1" + self.tokenizer_model_name = ( + tokenizer_model_name + if tokenizer_model_name is not None + else "mistralai/Mixtral-8x7B-Instruct-v0.1" + ) self.qna_yaml = self._load_qna_yaml( self._path_validator(leaf_node_path) if leaf_node_path else None ) @@ -229,7 +238,7 @@ def _path_validator(self, path) -> Path: raise FileNotFoundError(f"{path} does not exist.") return path - def _load_qna_yaml(self, qna_yaml_path: Path) -> dict: + def _load_qna_yaml(self, qna_yaml_path: Path | None) -> dict: """ Load the qna YAML file. Args: @@ -254,7 +263,6 @@ def _process_parsed_docling_json(self, json_fp: Path) -> Dataset: with open(json_fp, "r", encoding="utf-8") as f: data = json.load(f) - file_name = json_fp.stem chunks = self.build_chunks_from_docling_json( data, max_token_per_chunk=500, @@ -265,8 +273,10 @@ def _process_parsed_docling_json(self, json_fp: Path) -> Dataset: num_tokens_per_doc = _num_tokens_from_words(self.chunk_word_count) chunk_size = _num_chars_from_tokens(num_tokens_per_doc) return chunk_markdowns(fused_texts, chunk_size) - - def fuse_texts(self, text_list: List, short_length_threshold: int = 130): + + def fuse_texts( + self, text_list: List, short_length_threshold: int = 130 + ) -> List[str]: """ Fuse short texts with preceding longer texts if their token count is below the threshold. Args: @@ -277,11 +287,13 @@ def fuse_texts(self, text_list: List, short_length_threshold: int = 130): Returns: list: List of fused texts. """ - fused_texts = [] + fused_texts: List[str] = [] previous_long_text = "" for text in text_list: - token_count = self.get_token_count(text, self.tokenizer) # Use tokenizer for token count + token_count = self.get_token_count( + text, self.tokenizer + ) # Use tokenizer for token count if token_count <= short_length_threshold and previous_long_text: # Append the short text to the last long text @@ -292,7 +304,7 @@ def fuse_texts(self, text_list: List, short_length_threshold: int = 130): previous_long_text = text return fused_texts - + def create_tokenizer(self, model_name: str): """ Create a tokenizer instance from a pre-trained model or a local directory. @@ -311,7 +323,6 @@ def create_tokenizer(self, model_name: str): logger.error(f"Failed to load tokenizer from {model_name}: {str(e)}") raise - def get_token_count(self, text, tokenizer): """ Get the number of tokens in a text using the provided tokenizer. @@ -323,7 +334,6 @@ def get_token_count(self, text, tokenizer): """ return len(tokenizer.tokenize(text)) - def add_heading_formatting(self, text): """ Add heading formatting to the text if the first part is short. @@ -341,7 +351,6 @@ def add_heading_formatting(self, text): text = ".".join(text) return text - def generate_table_from_parsed_rep(self, item): """ Generate the table from the parsed representation and return as a string. @@ -360,9 +369,9 @@ def generate_table_from_parsed_rep(self, item): return "" table = [] - for i, row in enumerate(data): + for _, row in enumerate(data): trow = [] - for j, cell in enumerate(row): + for _, cell in enumerate(row): trow.append(cell["text"]) table.append(trow) @@ -371,7 +380,6 @@ def generate_table_from_parsed_rep(self, item): table_text += f"\nCaption: {caption}\n" return table_text - def get_table(self, json_book, table_ref): """ Retrieve a table from a document based on a reference string. @@ -382,10 +390,11 @@ def get_table(self, json_book, table_ref): str: Formatted table string. """ parts = table_ref.split("/") - table_text = self.generate_table_from_parsed_rep(json_book[parts[1]][int(parts[2])]) + table_text = self.generate_table_from_parsed_rep( + json_book[parts[1]][int(parts[2])] + ) return table_text - def get_table_page_number(self, json_book, idx): """ Get the page number of a table or other document element. @@ -407,11 +416,10 @@ def get_table_page_number(self, json_book, idx): if prev_page_num is not None and next_page_num is not None: if prev_page_num == next_page_num: return prev_page_num - else: - return next_page_num - elif prev_page_num is not None: + return next_page_num + if prev_page_num is not None: return prev_page_num - elif next_page_num is not None: + if next_page_num is not None: return next_page_num def build_chunks_from_docling_json( @@ -448,7 +456,7 @@ def build_chunks_from_docling_json( "page-header", ]: continue - elif book_element["type"] == "footnote": + if book_element["type"] == "footnote": current_book_page_number = book_element["prov"][0]["page"] elif book_element["type"] in [ "subtitle-level-1", @@ -458,7 +466,9 @@ def build_chunks_from_docling_json( "equation", ]: # 'page-header', if book_element["type"] == "table": - current_book_page_number = self.get_table_page_number(json_book, idx) + current_book_page_number = self.get_table_page_number( + json_book, idx + ) else: current_book_page_number = book_element["prov"][0]["page"] book_text = book_element["text"] @@ -492,8 +502,10 @@ def build_chunks_from_docling_json( >= max_token_per_chunk and len(current_buffer) > 1 ): - chunk_text = '\n\n'.join(current_buffer[:-1]) - print(f"Current chunk size {self.get_token_count(chunk_text, tokenizer)} and max is {max_token_per_chunk}") + chunk_text = "\n\n".join(current_buffer[:-1]) + print( + f"Current chunk size {self.get_token_count(chunk_text, tokenizer)} and max is {max_token_per_chunk}" + ) document_chunks.append("\n\n".join(current_buffer[:-1])) @@ -501,7 +513,9 @@ def build_chunks_from_docling_json( self.get_token_count(current_buffer[-1], tokenizer) >= max_token_per_chunk ): - print(f"This is too big a document to be left in the current buffer {self.get_token_count(current_buffer[-1], tokenizer)}") + print( + f"This is too big a document to be left in the current buffer {self.get_token_count(current_buffer[-1], tokenizer)}" + ) document_chunks.append(current_buffer[-1]) current_buffer = [] else: @@ -518,17 +532,16 @@ def build_chunks_from_docling_json( try: prev_page_number = current_book_page_number - except Exception as e: + except Exception as e: # pylint: disable=broad-exception-caught logger.error(f"Error processing book element: {book_element}, {str(e)}") if "\n\n".join(current_buffer) not in document_chunks: document_chunks.append("\n\n".join(current_buffer)) return document_chunks - def export_documents(self, converted_docs: Iterable[ConvertedDocument]): """Write converted documents to json files - + Check for successful conversions and write those to the docling artifacts directory. Returns: Path: path to directory with docling json artifacts diff --git a/src/instructlab/sdg/utils/taxonomy.py b/src/instructlab/sdg/utils/taxonomy.py index 59994b53..302f7e37 100644 --- a/src/instructlab/sdg/utils/taxonomy.py +++ b/src/instructlab/sdg/utils/taxonomy.py @@ -1,18 +1,16 @@ # SPDX-License-Identifier: Apache-2.0 # Standard -from datetime import datetime from pathlib import Path from typing import Dict, List, Tuple, Union import glob import logging import os import re -import tempfile # Third Party from datasets import Dataset -from docling_parse.docling_parse import pdf_parser +from docling_parse.docling_parse import pdf_parser # pylint: disable=no-name-in-module from instructlab.schema.taxonomy import DEFAULT_TAXONOMY_FOLDERS as TAXONOMY_FOLDERS from instructlab.schema.taxonomy import ( TaxonomyMessageFormat, @@ -27,7 +25,7 @@ from .chunkers import DocumentChunker # Initialize the pdf parser -parser = pdf_parser() +PDFParser = pdf_parser() logger = logging.getLogger(__name__) @@ -135,7 +133,7 @@ def _get_documents( commit_hash = source.get("commit") file_patterns = source.get("patterns", []) - try: + try: # pylint: disable=too-many-nested-blocks repo = git.Repo.clone_from(repo_url, document_output_dir) if not skip_checkout and commit_hash: @@ -171,21 +169,21 @@ def _get_documents( doc_key = f"key_{os.path.basename(file_path)}" # Unique document key logger.info(f"Loading PDF document from {file_path}") - success = parser.load_document(doc_key, file_path) + success = PDFParser.load_document(doc_key, file_path) if not success: logger.warning( f"Failed to load PDF document: {file_path}" ) continue - num_pages = parser.number_of_pages(doc_key) + num_pages = PDFParser.number_of_pages(doc_key) logger.info(f"PDF '{file_path}' has {num_pages} pages.") pdf_text = "" for page in range(num_pages): try: - json_doc = parser.parse_pdf_from_key_on_page( + json_doc = PDFParser.parse_pdf_from_key_on_page( doc_key, page ) if "pages" not in json_doc or not json_doc["pages"]: @@ -203,7 +201,7 @@ def _get_documents( ) if text.strip(): # Only append non-empty text pdf_text += text.strip() + "\n" - except Exception as page_error: + except Exception as page_error: # pylint: disable=broad-exception-caught logger.warning( f"Error parsing page {page + 1} of '{file_path}': {page_error}" ) @@ -214,12 +212,12 @@ def _get_documents( filepaths.append(Path(file_path)) # Unload the document to free memory - parser.unload_document(doc_key) + PDFParser.unload_document(doc_key) logger.info(f"Unloaded PDF document: {file_path}") else: logger.info(f"Skipping unsupported file type: {file_path}") - except Exception as file_error: + except Exception as file_error: # pylint: disable=broad-exception-caught logger.error( f"Error processing file '{file_path}': {file_error}" ) @@ -236,19 +234,20 @@ def _get_documents( raise e -# pylint: disable=broad-exception-caught def _read_taxonomy_file( - file_path: str | Path, yamllint_config: str | None = None, document_output_dir: Path = Path() + file_path: str | Path, + yamllint_config: str | None = None, + document_output_dir: Path = Path(), ): seed_instruction_data = [] - parser = TaxonomyParser( + taxonomy_parser = TaxonomyParser( schema_version=0, # Use version value in yaml message_format=TaxonomyMessageFormat.LOGGING, # Report warnings and errors to the logger yamllint_config=yamllint_config, yamllint_strict=True, # Report yamllint warnings as errors ) - taxonomy = parser.parse(file_path) + taxonomy = taxonomy_parser.parse(file_path) if taxonomy.warnings or taxonomy.errors: return seed_instruction_data, taxonomy.warnings, taxonomy.errors @@ -260,7 +259,7 @@ def _read_taxonomy_file( task_description = contents.get("task_description", None) domain = contents.get("domain") documents = contents.get("document") - print(f"") + document_contents, doc_filepaths = None, None if documents: document_contents, doc_filepaths = _get_documents( source=documents, document_output_dir=document_output_dir @@ -363,7 +362,9 @@ def read_taxonomy( return seed_instruction_data -def read_taxonomy_leaf_nodes(taxonomy, taxonomy_base, yaml_rules, document_output_dir=None): +def read_taxonomy_leaf_nodes( + taxonomy, taxonomy_base, yaml_rules, document_output_dir=None +): seed_instruction_data = read_taxonomy( taxonomy, taxonomy_base, yaml_rules, document_output_dir ) @@ -394,10 +395,12 @@ def map_chunks_to_icls(chunks: List, leaf_node: Dict) -> Dataset: qna_pairs = icl_.get("questions_and_answers", []) for i, qna in enumerate(qna_pairs): - record.update({ - f"icl_query_{i+1}": qna.get("question", ""), - f"icl_response_{i+1}": qna.get("answer", ""), - }) + record.update( + { + f"icl_query_{i+1}": qna.get("question", ""), + f"icl_response_{i+1}": qna.get("answer", ""), + } + ) chunked_dataset.append(record) @@ -405,7 +408,12 @@ def map_chunks_to_icls(chunks: List, leaf_node: Dict) -> Dataset: def _knowledge_leaf_node_to_samples( - leaf_node, taxonomy_path, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, + taxonomy_path, + server_ctx_size, + chunk_word_count, + document_output_dir, + model_name, ): chunker = DocumentChunker( leaf_node=leaf_node, @@ -437,12 +445,22 @@ def _skill_leaf_node_to_samples(leaf_node): def leaf_node_to_samples( - leaf_node, taxonomy_path, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, + taxonomy_path, + server_ctx_size, + chunk_word_count, + document_output_dir, + model_name, ): if not leaf_node: return [] if leaf_node[0].get("documents"): return _knowledge_leaf_node_to_samples( - leaf_node, taxonomy_path, server_ctx_size, chunk_word_count, document_output_dir, model_name + leaf_node, + taxonomy_path, + server_ctx_size, + chunk_word_count, + document_output_dir, + model_name, ) return _skill_leaf_node_to_samples(leaf_node) diff --git a/tests/test_chunkers.py b/tests/test_chunkers.py new file mode 100644 index 00000000..b7821bd6 --- /dev/null +++ b/tests/test_chunkers.py @@ -0,0 +1,176 @@ +# SPDX-License-Identifier: Apache-2.0 + +# Standard +from pathlib import Path + +# Third Party +from docling.datamodel.base_models import PipelineOptions +from docling.datamodel.document import ConvertedDocument, DocumentConversionInput +from docling.document_converter import ConversionStatus, DocumentConverter +import pytest + +# First Party +from instructlab.sdg.utils.chunkers import ( + ContextAwareChunker, + DocumentChunker, + FileTypes, + TextSplitChunker, +) + +# Local +from .testdata import testdata + + +# def read_document_contents(document_path: Path): +# # TODO +# if document_path.suffix == ".md": +# pass +# if document_path.suffix == ".pdf": +# pass + + +def build_leaf_node(document_paths: str | list): + # TODO maybe check for directory + if not isinstance(document_paths, list): + document_paths = [document_paths] + + return [ + { + "taxonomy_path": "", # TODO + "filepaths": document_paths, + "documents": [read_document_contents(d) for d in document_paths], + } + ] + + +@pytest.fixture +def documents_dir(): + return Path(__file__) / "testdata" / "sample_documents" + + +@pytest.mark.parametrize( + "filepaths, chunker_type", + [ + ([Path("document.md")], TextSplitChunker), + ([Path("document.pdf")], ContextAwareChunker), + ], +) +def test_chunker_factory(filepaths, chunker_type): + """Test that the DocumentChunker factory class returns the proper Chunker type""" + leaf_node = [ + { + "documents": ["Lorem ipsum"], + "taxonomy_path": "sample/path", + "filepaths": filepaths, + } + ] + chunker = DocumentChunker(leaf_node=leaf_node) + assert isinstance(chunker, chunker_type) + + +def test_chunker_factory_unsupported_filetype(): + """Test that the DocumentChunker factory class fails when provided an unsupported document""" + leaf_node = [ + { + "documents": ["Lorem ipsum"], + "taxonomy_path": "sample/path", + "filepaths": [Path("document.jpg")], + } + ] + with pytest.raises(ValueError): + _ = DocumentChunker(leaf_node=leaf_node) + + +# class TestTextSplitChunker(): +# @pytest.fixture +# def chunker(): +# pass + +# def test_chunk_documents(): +# pass + +# def test_chunk_docs_wc_exceeds_ctx_window(self): +# with pytest.raises(ValueError) as exc: +# chunking.chunk_document( +# documents=testdata.documents, +# chunk_word_count=1000, +# server_ctx_size=1034, +# ) +# assert ( +# "Given word count (1000) per doc will exceed the server context window size (1034)" +# in str(exc.value) +# ) + +# def test_chunk_docs_long_lines(self): +# # TODO see if this is applicable to context-aware +# chunk_words = 50 +# chunks = chunking.chunk_document( +# documents=testdata.long_line_documents, +# chunk_word_count=chunk_words, +# server_ctx_size=4096, +# ) +# max_tokens = chunking._num_tokens_from_words(chunk_words) +# max_chars = chunking._num_chars_from_tokens(max_tokens) +# max_chars += chunking._DEFAULT_CHUNK_OVERLAP # add in the chunk overlap +# max_chars += 50 # and a bit extra for some really long words +# for chunk in chunks: +# assert len(chunk) <= max_chars + +# def test_chunk_docs_chunk_overlap_error(self): +# # TODO check if applicable to context-aware +# with pytest.raises(ValueError) as exc: +# chunking.chunk_document( +# documents=testdata.documents, +# chunk_word_count=5, +# server_ctx_size=1034, +# ) +# assert ( +# "Got a larger chunk overlap (100) than chunk size (24), should be smaller" +# in str(exc.value) +# ) + + +# class TestContextAwareChunker(): +# @pytest.fixture +# def chunker(documents_dir): +# pass + +# def test_chunk_documents(): +# pass + +# def test_path_validator(): +# pass + +# def test_load_qna_yaml(): +# pass + +# def test_process_parsed_docling_json(): +# pass + +# def test_fuse_texts(): +# pass + +# def test_create_tokenizer(): +# pass + +# def test_get_token_count(): +# pass + +# def test_add_heading_formatting(): +# pass + +# def test_generate_table_from_parsed_rep(): +# pass + +# def test_get_table(): +# pass + +# def test_get_table_page_number(): +# pass + +# def test_build_chunks_from_docling_json(): +# pass + +# def test_export_document(): +# pass + From e7b166618ee492608b88a75b9707d2622997f13a Mon Sep 17 00:00:00 2001 From: Khaled Sulayman Date: Thu, 31 Oct 2024 19:51:48 -0400 Subject: [PATCH 10/11] Update testing for Document Chunker classes Signed-off-by: Khaled Sulayman Co-authored-by: Aakanksha Duggal --- requirements.txt | 4 +- src/instructlab/sdg/utils/chunkers.py | 4 +- tests/test_chunkers.py | 158 ++++--------------- tests/test_chunking.py | 52 ------ tests/testdata/sample_documents/moo_deng.pdf | Bin 0 -> 863677 bytes tests/testdata/sample_documents/qna.yaml | 2 + 6 files changed, 37 insertions(+), 183 deletions(-) delete mode 100644 tests/test_chunking.py create mode 100644 tests/testdata/sample_documents/moo_deng.pdf create mode 100644 tests/testdata/sample_documents/qna.yaml diff --git a/requirements.txt b/requirements.txt index 684539be..0f48ca93 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,16 +1,16 @@ # SPDX-License-Identifier: Apache-2.0 click>=8.1.7,<9.0.0 datasets>=2.18.0,<3.0.0 +docling>=1.15.0,<2.0.0 GitPython>=3.1.42,<4.0.0 httpx>=0.25.0,<1.0.0 instructlab-schema>=0.4.0 langchain-text-splitters -openai>=1.13.3,<2.0.0 # Note: this dependency goes along with langchain-text-splitters and may be # removed once that one is removed. # do not use 8.4.0 due to a bug in the library # https://github.com/instructlab/instructlab/issues/1389 -pypdf>=5.0.0 +openai>=1.13.3,<2.0.0 tabulate>=0.9.0 tenacity>=8.3.0,!=8.4.0 torch>=2.3.0,<2.5.0 diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index 14b74969..5e46216d 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -3,7 +3,7 @@ from collections import defaultdict from enum import Enum from pathlib import Path -from typing import DefaultDict, Iterable, List, Tuple +from typing import DefaultDict, Iterable, List, Tuple, Optional import json import logging import re @@ -50,7 +50,7 @@ def __new__( cls, leaf_node, taxonomy_path, - output_dir: Path, + output_dir: Optional[Path], server_ctx_size=4096, chunk_word_count=1024, tokenizer_model_name: str | None = None, diff --git a/tests/test_chunkers.py b/tests/test_chunkers.py index b7821bd6..2270a8a8 100644 --- a/tests/test_chunkers.py +++ b/tests/test_chunkers.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: Apache-2.0 # Standard +import tempfile from pathlib import Path # Third Party @@ -21,31 +22,9 @@ from .testdata import testdata -# def read_document_contents(document_path: Path): -# # TODO -# if document_path.suffix == ".md": -# pass -# if document_path.suffix == ".pdf": -# pass - - -def build_leaf_node(document_paths: str | list): - # TODO maybe check for directory - if not isinstance(document_paths, list): - document_paths = [document_paths] - - return [ - { - "taxonomy_path": "", # TODO - "filepaths": document_paths, - "documents": [read_document_contents(d) for d in document_paths], - } - ] - - @pytest.fixture def documents_dir(): - return Path(__file__) / "testdata" / "sample_documents" + return Path(__file__).parent / "testdata" / "sample_documents" @pytest.mark.parametrize( @@ -55,122 +34,47 @@ def documents_dir(): ([Path("document.pdf")], ContextAwareChunker), ], ) -def test_chunker_factory(filepaths, chunker_type): +def test_chunker_factory(filepaths, chunker_type, documents_dir): """Test that the DocumentChunker factory class returns the proper Chunker type""" leaf_node = [ { "documents": ["Lorem ipsum"], - "taxonomy_path": "sample/path", + "taxonomy_path": "", "filepaths": filepaths, } ] - chunker = DocumentChunker(leaf_node=leaf_node) - assert isinstance(chunker, chunker_type) - - -def test_chunker_factory_unsupported_filetype(): + with tempfile.TemporaryDirectory() as temp_dir: +<<<<<<< HEAD + chunker = DocumentChunker(leaf_node=leaf_node, taxonomy_path=documents_dir, output_dir=temp_dir, tokenizer_model_name="mistralai/Mixtral-8x7B-Instruct-v0.1") +======= + chunker = DocumentChunker( + leaf_node=leaf_node, + taxonomy_path=documents_dir, + output_dir=temp_dir, + tokenizer_model_name="instructlab/merlinite-7b-lab", + ) +>>>>>>> eaf7434 (wip) + assert isinstance(chunker, chunker_type) + + +def test_chunker_factory_unsupported_filetype(documents_dir): """Test that the DocumentChunker factory class fails when provided an unsupported document""" leaf_node = [ { "documents": ["Lorem ipsum"], - "taxonomy_path": "sample/path", + "taxonomy_path": "", "filepaths": [Path("document.jpg")], } ] with pytest.raises(ValueError): - _ = DocumentChunker(leaf_node=leaf_node) - - -# class TestTextSplitChunker(): -# @pytest.fixture -# def chunker(): -# pass - -# def test_chunk_documents(): -# pass - -# def test_chunk_docs_wc_exceeds_ctx_window(self): -# with pytest.raises(ValueError) as exc: -# chunking.chunk_document( -# documents=testdata.documents, -# chunk_word_count=1000, -# server_ctx_size=1034, -# ) -# assert ( -# "Given word count (1000) per doc will exceed the server context window size (1034)" -# in str(exc.value) -# ) - -# def test_chunk_docs_long_lines(self): -# # TODO see if this is applicable to context-aware -# chunk_words = 50 -# chunks = chunking.chunk_document( -# documents=testdata.long_line_documents, -# chunk_word_count=chunk_words, -# server_ctx_size=4096, -# ) -# max_tokens = chunking._num_tokens_from_words(chunk_words) -# max_chars = chunking._num_chars_from_tokens(max_tokens) -# max_chars += chunking._DEFAULT_CHUNK_OVERLAP # add in the chunk overlap -# max_chars += 50 # and a bit extra for some really long words -# for chunk in chunks: -# assert len(chunk) <= max_chars - -# def test_chunk_docs_chunk_overlap_error(self): -# # TODO check if applicable to context-aware -# with pytest.raises(ValueError) as exc: -# chunking.chunk_document( -# documents=testdata.documents, -# chunk_word_count=5, -# server_ctx_size=1034, -# ) -# assert ( -# "Got a larger chunk overlap (100) than chunk size (24), should be smaller" -# in str(exc.value) -# ) - - -# class TestContextAwareChunker(): -# @pytest.fixture -# def chunker(documents_dir): -# pass - -# def test_chunk_documents(): -# pass - -# def test_path_validator(): -# pass - -# def test_load_qna_yaml(): -# pass - -# def test_process_parsed_docling_json(): -# pass - -# def test_fuse_texts(): -# pass - -# def test_create_tokenizer(): -# pass - -# def test_get_token_count(): -# pass - -# def test_add_heading_formatting(): -# pass - -# def test_generate_table_from_parsed_rep(): -# pass - -# def test_get_table(): -# pass - -# def test_get_table_page_number(): -# pass - -# def test_build_chunks_from_docling_json(): -# pass - -# def test_export_document(): -# pass - + with tempfile.TemporaryDirectory() as temp_dir: +<<<<<<< HEAD + _ = DocumentChunker(leaf_node=leaf_node, taxonomy_path=documents_dir, output_dir=temp_dir, tokenizer_model_name="mistralai/Mixtral-8x7B-Instruct-v0.1") +======= + _ = DocumentChunker( + leaf_node=leaf_node, + taxonomy_path=documents_dir, + output_dir=temp_dir, + tokenizer_model_name="instructlab/merlinite-7b-lab", + ) +>>>>>>> eaf7434 (wip) diff --git a/tests/test_chunking.py b/tests/test_chunking.py deleted file mode 100644 index dae1f61f..00000000 --- a/tests/test_chunking.py +++ /dev/null @@ -1,52 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 - -# Third Party -import pytest - -# First Party -from instructlab.sdg.utils import chunking - -# Local -from .testdata import testdata - - -class TestChunking: - """Test collection in instructlab.utils.chunking.""" - - def test_chunk_docs_wc_exceeds_ctx_window(self): - with pytest.raises(ValueError) as exc: - chunking.chunk_document( - documents=testdata.documents, - chunk_word_count=1000, - server_ctx_size=1034, - ) - assert ( - "Given word count (1000) per doc will exceed the server context window size (1034)" - in str(exc.value) - ) - - def test_chunk_docs_chunk_overlap_error(self): - with pytest.raises(ValueError) as exc: - chunking.chunk_document( - documents=testdata.documents, - chunk_word_count=5, - server_ctx_size=1034, - ) - assert ( - "Got a larger chunk overlap (100) than chunk size (24), should be smaller" - in str(exc.value) - ) - - def test_chunk_docs_long_lines(self): - chunk_words = 50 - chunks = chunking.chunk_document( - documents=testdata.long_line_documents, - chunk_word_count=chunk_words, - server_ctx_size=4096, - ) - max_tokens = chunking._num_tokens_from_words(chunk_words) - max_chars = chunking._num_chars_from_tokens(max_tokens) - max_chars += chunking._DEFAULT_CHUNK_OVERLAP # add in the chunk overlap - max_chars += 50 # and a bit extra for some really long words - for chunk in chunks: - assert len(chunk) <= max_chars diff --git a/tests/testdata/sample_documents/moo_deng.pdf b/tests/testdata/sample_documents/moo_deng.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4c5fc0e53ccd9d00513994ca170123922af5ec0a GIT binary 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z3^qAzUU(2xPz;F+u=_w_3Wbws{)fZvirpVh!>80NZmUp2PV863;0}U)Vm3VklHr{2xXCN5mfRI0!GG;S_rwU~Py`q;WJ3 z(h$4Z>qbKeW$OV0{(voKhKuYkX<;hQwl~-cA$}50s@U^@hyo5_alKBHiSX3idi1hr?+ zjpRWB$l?XIA3ysrH&;t|$LlJ#WC=V2w)YVT4 Date: Wed, 6 Nov 2024 23:13:19 -0500 Subject: [PATCH 11/11] Change prints in build_chunks_from_docling_json to debug messages Signed-off-by: Khaled Sulayman --- src/instructlab/sdg/utils/chunkers.py | 12 ++++++------ tests/test_chunkers.py | 10 +--------- 2 files changed, 7 insertions(+), 15 deletions(-) diff --git a/src/instructlab/sdg/utils/chunkers.py b/src/instructlab/sdg/utils/chunkers.py index 5e46216d..97cb9084 100644 --- a/src/instructlab/sdg/utils/chunkers.py +++ b/src/instructlab/sdg/utils/chunkers.py @@ -3,7 +3,7 @@ from collections import defaultdict from enum import Enum from pathlib import Path -from typing import DefaultDict, Iterable, List, Tuple, Optional +from typing import DefaultDict, Iterable, List, Optional, Tuple import json import logging import re @@ -50,7 +50,7 @@ def __new__( cls, leaf_node, taxonomy_path, - output_dir: Optional[Path], + output_dir: Path, server_ctx_size=4096, chunk_word_count=1024, tokenizer_model_name: str | None = None, @@ -238,7 +238,7 @@ def _path_validator(self, path) -> Path: raise FileNotFoundError(f"{path} does not exist.") return path - def _load_qna_yaml(self, qna_yaml_path: Path | None) -> dict: + def _load_qna_yaml(self, qna_yaml_path: Optional[Path]) -> dict: """ Load the qna YAML file. Args: @@ -503,7 +503,7 @@ def build_chunks_from_docling_json( and len(current_buffer) > 1 ): chunk_text = "\n\n".join(current_buffer[:-1]) - print( + logger.debug( f"Current chunk size {self.get_token_count(chunk_text, tokenizer)} and max is {max_token_per_chunk}" ) @@ -513,8 +513,8 @@ def build_chunks_from_docling_json( self.get_token_count(current_buffer[-1], tokenizer) >= max_token_per_chunk ): - print( - f"This is too big a document to be left in the current buffer {self.get_token_count(current_buffer[-1], tokenizer)}" + logger.debug( + f"The following text was dropped from the document because it was too long to fit into a single context for synthetic data generation: {current_buffer[-1]}" ) document_chunks.append(current_buffer[-1]) current_buffer = [] diff --git a/tests/test_chunkers.py b/tests/test_chunkers.py index 2270a8a8..7d2923d9 100644 --- a/tests/test_chunkers.py +++ b/tests/test_chunkers.py @@ -1,8 +1,8 @@ # SPDX-License-Identifier: Apache-2.0 # Standard -import tempfile from pathlib import Path +import tempfile # Third Party from docling.datamodel.base_models import PipelineOptions @@ -44,16 +44,12 @@ def test_chunker_factory(filepaths, chunker_type, documents_dir): } ] with tempfile.TemporaryDirectory() as temp_dir: -<<<<<<< HEAD - chunker = DocumentChunker(leaf_node=leaf_node, taxonomy_path=documents_dir, output_dir=temp_dir, tokenizer_model_name="mistralai/Mixtral-8x7B-Instruct-v0.1") -======= chunker = DocumentChunker( leaf_node=leaf_node, taxonomy_path=documents_dir, output_dir=temp_dir, tokenizer_model_name="instructlab/merlinite-7b-lab", ) ->>>>>>> eaf7434 (wip) assert isinstance(chunker, chunker_type) @@ -68,13 +64,9 @@ def test_chunker_factory_unsupported_filetype(documents_dir): ] with pytest.raises(ValueError): with tempfile.TemporaryDirectory() as temp_dir: -<<<<<<< HEAD - _ = DocumentChunker(leaf_node=leaf_node, taxonomy_path=documents_dir, output_dir=temp_dir, tokenizer_model_name="mistralai/Mixtral-8x7B-Instruct-v0.1") -======= _ = DocumentChunker( leaf_node=leaf_node, taxonomy_path=documents_dir, output_dir=temp_dir, tokenizer_model_name="instructlab/merlinite-7b-lab", ) ->>>>>>> eaf7434 (wip)