From 8bf28187daba7d3a8b2525d1f18cd66095e0a209 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Tue, 16 Dec 2025 14:38:35 -0800 Subject: [PATCH 01/23] Delete NoopTextSplitter --- .../graphrag/index/text_splitting/text_splitting.py | 10 +--------- .../indexing/text_splitting/test_text_splitting.py | 8 -------- 2 files changed, 1 insertion(+), 17 deletions(-) diff --git a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py index caa9153ef1..6bbff8c936 100644 --- a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py +++ b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py @@ -1,7 +1,7 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -"""A module containing the 'Tokenizer', 'TextSplitter', 'NoopTextSplitter' and 'TokenTextSplitter' models.""" +"""A module containing the 'Tokenizer', 'TextSplitter', and 'TokenTextSplitter' models.""" import logging from abc import ABC, abstractmethod @@ -72,14 +72,6 @@ def split_text(self, text: str | list[str]) -> Iterable[str]: """Split text method definition.""" -class NoopTextSplitter(TextSplitter): - """Noop text splitter class definition.""" - - def split_text(self, text: str | list[str]) -> Iterable[str]: - """Split text method definition.""" - return [text] if isinstance(text, str) else text - - class TokenTextSplitter(TextSplitter): """Token text splitter class definition.""" diff --git a/tests/unit/indexing/text_splitting/test_text_splitting.py b/tests/unit/indexing/text_splitting/test_text_splitting.py index eb5e72d649..2ab6350a89 100644 --- a/tests/unit/indexing/text_splitting/test_text_splitting.py +++ b/tests/unit/indexing/text_splitting/test_text_splitting.py @@ -7,7 +7,6 @@ import pytest import tiktoken from graphrag.index.text_splitting.text_splitting import ( - NoopTextSplitter, TokenChunkerOptions, TokenTextSplitter, split_multiple_texts_on_tokens, @@ -15,13 +14,6 @@ ) -def test_noop_text_splitter() -> None: - splitter = NoopTextSplitter() - - assert list(splitter.split_text("some text")) == ["some text"] - assert list(splitter.split_text(["some", "text"])) == ["some", "text"] - - class MockTokenizer: def encode(self, text): return [ord(char) for char in text] From bdc2485433e07d92fb00d5725831efa5be0a22bf Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Tue, 16 Dec 2025 14:43:54 -0800 Subject: [PATCH 02/23] Delete unused check_token_limit --- .../index/text_splitting/check_token_limit.py | 15 --------------- 1 file changed, 15 deletions(-) delete mode 100644 packages/graphrag/graphrag/index/text_splitting/check_token_limit.py diff --git a/packages/graphrag/graphrag/index/text_splitting/check_token_limit.py b/packages/graphrag/graphrag/index/text_splitting/check_token_limit.py deleted file mode 100644 index 7b6a139e02..0000000000 --- a/packages/graphrag/graphrag/index/text_splitting/check_token_limit.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""Token limit method definition.""" - -from graphrag.index.text_splitting.text_splitting import TokenTextSplitter - - -def check_token_limit(text, max_token): - """Check token limit.""" - text_splitter = TokenTextSplitter(chunk_size=max_token, chunk_overlap=0) - docs = text_splitter.split_text(text) - if len(docs) > 1: - return 0 - return 1 From 9e8c900dd4d2ca01e5a85efd61926b18106f8ee1 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Wed, 17 Dec 2025 15:15:35 -0800 Subject: [PATCH 03/23] Add base chunking factory and migrate workflow to use it --- packages/graphrag/graphrag/api/prompt_tune.py | 9 - .../chunk_text => chunking}/__init__.py | 2 +- .../chunk_text => chunking}/bootstrap.py | 0 .../graphrag/graphrag/chunking/chunker.py | 123 ++++++++++++ packages/graphrag/graphrag/cli/prompt_tune.py | 2 - .../index/operations/chunk_text/chunk_text.py | 140 -------------- .../index/operations/chunk_text/strategies.py | 52 ----- .../index/operations/chunk_text/typing.py | 27 --- .../index/text_splitting/text_splitting.py | 105 ++-------- .../index/workflows/create_base_text_units.py | 76 +++++--- .../graphrag/prompt_tune/loader/input.py | 10 +- .../operations/chunk_text/test_chunk_text.py | 180 ------------------ .../{test_strategies.py => test_chunker.py} | 76 +++----- .../text_splitting/test_text_splitting.py | 54 ++---- 14 files changed, 230 insertions(+), 626 deletions(-) rename packages/graphrag/graphrag/{index/operations/chunk_text => chunking}/__init__.py (60%) rename packages/graphrag/graphrag/{index/operations/chunk_text => chunking}/bootstrap.py (100%) create mode 100644 packages/graphrag/graphrag/chunking/chunker.py delete mode 100644 packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py delete mode 100644 packages/graphrag/graphrag/index/operations/chunk_text/strategies.py delete mode 100644 packages/graphrag/graphrag/index/operations/chunk_text/typing.py delete mode 100644 tests/unit/indexing/operations/chunk_text/test_chunk_text.py rename tests/unit/indexing/operations/chunk_text/{test_strategies.py => test_chunker.py} (50%) diff --git a/packages/graphrag/graphrag/api/prompt_tune.py b/packages/graphrag/graphrag/api/prompt_tune.py index 43fb0a5590..e051102377 100644 --- a/packages/graphrag/graphrag/api/prompt_tune.py +++ b/packages/graphrag/graphrag/api/prompt_tune.py @@ -12,13 +12,10 @@ """ import logging -from typing import Annotated -import annotated_types from pydantic import PositiveInt, validate_call from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks -from graphrag.config.defaults import graphrag_config_defaults from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.language_model.manager import ModelManager from graphrag.logger.standard_logging import init_loggers @@ -55,10 +52,6 @@ @validate_call(config={"arbitrary_types_allowed": True}) async def generate_indexing_prompts( config: GraphRagConfig, - chunk_size: PositiveInt = graphrag_config_defaults.chunks.size, - overlap: Annotated[ - int, annotated_types.Gt(-1) - ] = graphrag_config_defaults.chunks.overlap, limit: PositiveInt = 15, selection_method: DocSelectionType = DocSelectionType.RANDOM, domain: str | None = None, @@ -100,8 +93,6 @@ async def generate_indexing_prompts( limit=limit, select_method=selection_method, logger=logger, - chunk_size=chunk_size, - overlap=overlap, n_subset_max=n_subset_max, k=k, ) diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/__init__.py b/packages/graphrag/graphrag/chunking/__init__.py similarity index 60% rename from packages/graphrag/graphrag/index/operations/chunk_text/__init__.py rename to packages/graphrag/graphrag/chunking/__init__.py index 1e000e6aa7..6610d3a82f 100644 --- a/packages/graphrag/graphrag/index/operations/chunk_text/__init__.py +++ b/packages/graphrag/graphrag/chunking/__init__.py @@ -1,4 +1,4 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -"""The Indexing Engine text chunk package root.""" +"""System-level chunking package.""" diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/bootstrap.py b/packages/graphrag/graphrag/chunking/bootstrap.py similarity index 100% rename from packages/graphrag/graphrag/index/operations/chunk_text/bootstrap.py rename to packages/graphrag/graphrag/chunking/bootstrap.py diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag/graphrag/chunking/chunker.py new file mode 100644 index 0000000000..49e48f57f7 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/chunker.py @@ -0,0 +1,123 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing the 'Chunker' class.""" + +from abc import ABC, abstractmethod +from collections.abc import Callable +from typing import Any + +import nltk +from graphrag_common.factory.factory import Factory, ServiceScope + +from graphrag.chunking.bootstrap import bootstrap +from graphrag.config.enums import ChunkStrategyType +from graphrag.config.models.chunking_config import ChunkingConfig +from graphrag.index.text_splitting.text_splitting import ( + split_single_text_on_tokens, +) +from graphrag.tokenizer.get_tokenizer import get_tokenizer + + +class Chunker(ABC): + """Abstract base class for text chunkers.""" + + @abstractmethod + def __init__(self, **kwargs: Any) -> None: + """Create a chunker instance.""" + + @abstractmethod + def chunk(self, text: str) -> list[str]: + """Chunk method definition.""" + + +class TokenChunker(Chunker): + """A chunker that splits text into token-based chunks.""" + + def __init__( + self, + size: int, + overlap: int, + encoding_model: str, + **kwargs: Any, + ) -> None: + """Create a token chunker instance.""" + self._size = size + self._overlap = overlap + self._encoding_model = encoding_model + + def chunk(self, text: str) -> list[str]: + """Chunk the text into token-based chunks.""" + tokenizer = get_tokenizer(encoding_model=self._encoding_model) + return split_single_text_on_tokens( + text, + chunk_overlap=self._overlap, + tokens_per_chunk=self._size, + encode=tokenizer.encode, + decode=tokenizer.decode, + ) + + +class SentenceChunker(Chunker): + """A chunker that splits text into sentence-based chunks.""" + + def __init__(self, **kwargs: Any) -> None: + """Create a sentence chunker instance.""" + bootstrap() + + def chunk(self, text: str) -> list[str]: + """Chunk the text into sentence-based chunks.""" + return nltk.sent_tokenize(text) + + +class ChunkerFactory(Factory[Chunker]): + """Factory for creating Chunker instances.""" + + +chunker_factory = ChunkerFactory() + + +def register_chunker( + chunker_type: str, + chunker_initializer: Callable[..., Chunker], + scope: ServiceScope = "transient", +) -> None: + """Register a custom chunker implementation. + + Args + ---- + - chunker_type: str + The chunker id to register. + - chunker_initializer: Callable[..., Chunker] + The chunker initializer to register. + """ + chunker_factory.register(chunker_type, chunker_initializer, scope) + + +def create_chunker(config: ChunkingConfig) -> Chunker: + """Create a chunker implementation based on the given configuration. + + Args + ---- + - config: ChunkingConfig + The chunker configuration to use. + + Returns + ------- + Chunker + The created chunker implementation. + """ + config_model = config.model_dump() + chunker_strategy = config.strategy + + if chunker_strategy not in chunker_factory: + match chunker_strategy: + case ChunkStrategyType.tokens: + chunker_factory.register(ChunkStrategyType.tokens, TokenChunker) + case ChunkStrategyType.sentence: + chunker_factory.register(ChunkStrategyType.sentence, SentenceChunker) + case _: + msg = f"ChunkingConfig.strategy '{chunker_strategy}' is not registered in the ChunkerFactory. Registered types: {', '.join(chunker_factory.keys())}." + raise ValueError(msg) + + return chunker_factory.create(chunker_strategy, init_args=config_model) diff --git a/packages/graphrag/graphrag/cli/prompt_tune.py b/packages/graphrag/graphrag/cli/prompt_tune.py index 2ba02e0ead..0c80092770 100644 --- a/packages/graphrag/graphrag/cli/prompt_tune.py +++ b/packages/graphrag/graphrag/cli/prompt_tune.py @@ -81,8 +81,6 @@ async def prompt_tune( prompts = await api.generate_indexing_prompts( config=graph_config, - chunk_size=chunk_size, - overlap=overlap, limit=limit, selection_method=selection_method, domain=domain, diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py b/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py deleted file mode 100644 index fc50370e28..0000000000 --- a/packages/graphrag/graphrag/index/operations/chunk_text/chunk_text.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing _get_num_total, chunk, run_strategy and load_strategy methods definitions.""" - -from typing import Any, cast - -import pandas as pd - -from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks -from graphrag.config.models.chunking_config import ChunkingConfig, ChunkStrategyType -from graphrag.index.operations.chunk_text.typing import ( - ChunkInput, - ChunkStrategy, -) -from graphrag.logger.progress import ProgressTicker, progress_ticker - - -def chunk_text( - input: pd.DataFrame, - column: str, - size: int, - overlap: int, - encoding_model: str, - strategy: ChunkStrategyType, - callbacks: WorkflowCallbacks, -) -> pd.Series: - """ - Chunk a piece of text into smaller pieces. - - ## Usage - ```yaml - args: - column: # The name of the column containing the text to chunk, this can either be a column with text, or a column with a list[tuple[doc_id, str]] - strategy: # The strategy to use to chunk the text, see below for more details - ``` - - ## Strategies - The text chunk verb uses a strategy to chunk the text. The strategy is an object which defines the strategy to use. The following strategies are available: - - ### tokens - This strategy uses the [tokens] library to chunk a piece of text. The strategy config is as follows: - - ```yaml - strategy: tokens - size: 1200 # Optional, The chunk size to use, default: 1200 - overlap: 100 # Optional, The chunk overlap to use, default: 100 - ``` - - ### sentence - This strategy uses the nltk library to chunk a piece of text into sentences. The strategy config is as follows: - - ```yaml - strategy: sentence - ``` - """ - strategy_exec = load_strategy(strategy) - - num_total = _get_num_total(input, column) - tick = progress_ticker(callbacks.progress, num_total) - - # collapse the config back to a single object to support "polymorphic" function call - config = ChunkingConfig(size=size, overlap=overlap, encoding_model=encoding_model) - - return cast( - "pd.Series", - input.apply( - cast( - "Any", - lambda x: run_strategy( - strategy_exec, - x[column], - config, - tick, - ), - ), - axis=1, - ), - ) - - -def run_strategy( - strategy_exec: ChunkStrategy, - input: ChunkInput, - config: ChunkingConfig, - tick: ProgressTicker, -) -> list[str | tuple[list[str] | None, str, int]]: - """Run strategy method definition.""" - if isinstance(input, str): - return [item.text_chunk for item in strategy_exec([input], config, tick)] - - # We can work with both just a list of text content - # or a list of tuples of (document_id, text content) - # text_to_chunk = ''' - texts = [item if isinstance(item, str) else item[1] for item in input] - - strategy_results = strategy_exec(texts, config, tick) - - results = [] - for strategy_result in strategy_results: - doc_indices = strategy_result.source_doc_indices - if isinstance(input[doc_indices[0]], str): - results.append(strategy_result.text_chunk) - else: - doc_ids = [input[doc_idx][0] for doc_idx in doc_indices] - results.append(( - doc_ids, - strategy_result.text_chunk, - strategy_result.n_tokens, - )) - return results - - -def load_strategy(strategy: ChunkStrategyType) -> ChunkStrategy: - """Load strategy method definition.""" - match strategy: - case ChunkStrategyType.tokens: - from graphrag.index.operations.chunk_text.strategies import run_tokens - - return run_tokens - case ChunkStrategyType.sentence: - # NLTK - from graphrag.index.operations.chunk_text.bootstrap import bootstrap - from graphrag.index.operations.chunk_text.strategies import run_sentences - - bootstrap() - return run_sentences - case _: - msg = f"Unknown strategy: {strategy}" - raise ValueError(msg) - - -def _get_num_total(output: pd.DataFrame, column: str) -> int: - num_total = 0 - for row in output[column]: - if isinstance(row, str): - num_total += 1 - else: - num_total += len(row) - return num_total diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py b/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py deleted file mode 100644 index 9d48786e7f..0000000000 --- a/packages/graphrag/graphrag/index/operations/chunk_text/strategies.py +++ /dev/null @@ -1,52 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing chunk strategies.""" - -from collections.abc import Iterable - -import nltk - -from graphrag.config.models.chunking_config import ChunkingConfig -from graphrag.index.operations.chunk_text.typing import TextChunk -from graphrag.index.text_splitting.text_splitting import ( - TokenChunkerOptions, - split_multiple_texts_on_tokens, -) -from graphrag.logger.progress import ProgressTicker -from graphrag.tokenizer.get_tokenizer import get_tokenizer - - -def run_tokens( - input: list[str], - config: ChunkingConfig, - tick: ProgressTicker, -) -> Iterable[TextChunk]: - """Chunks text into chunks based on encoding tokens.""" - tokens_per_chunk = config.size - chunk_overlap = config.overlap - tokenizer = get_tokenizer(encoding_model=config.encoding_model) - return split_multiple_texts_on_tokens( - input, - TokenChunkerOptions( - chunk_overlap=chunk_overlap, - tokens_per_chunk=tokens_per_chunk, - encode=tokenizer.encode, - decode=tokenizer.decode, - ), - tick, - ) - - -def run_sentences( - input: list[str], _config: ChunkingConfig, tick: ProgressTicker -) -> Iterable[TextChunk]: - """Chunks text into multiple parts by sentence.""" - for doc_idx, text in enumerate(input): - sentences = nltk.sent_tokenize(text) - for sentence in sentences: - yield TextChunk( - text_chunk=sentence, - source_doc_indices=[doc_idx], - ) - tick(1) diff --git a/packages/graphrag/graphrag/index/operations/chunk_text/typing.py b/packages/graphrag/graphrag/index/operations/chunk_text/typing.py deleted file mode 100644 index bf58ef5ec1..0000000000 --- a/packages/graphrag/graphrag/index/operations/chunk_text/typing.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'TextChunk' model.""" - -from collections.abc import Callable, Iterable -from dataclasses import dataclass - -from graphrag.config.models.chunking_config import ChunkingConfig -from graphrag.logger.progress import ProgressTicker - - -@dataclass -class TextChunk: - """Text chunk class definition.""" - - text_chunk: str - source_doc_indices: list[int] - n_tokens: int | None = None - - -ChunkInput = str | list[str] | list[tuple[str, str]] -"""Input to a chunking strategy. Can be a string, a list of strings, or a list of tuples of (id, text).""" - -ChunkStrategy = Callable[ - [list[str], ChunkingConfig, ProgressTicker], Iterable[TextChunk] -] diff --git a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py index 6bbff8c936..b8bc62020e 100644 --- a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py +++ b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py @@ -1,18 +1,15 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -"""A module containing the 'Tokenizer', 'TextSplitter', and 'TokenTextSplitter' models.""" +"""A module containing the 'TokenTextSplitter' class.""" import logging -from abc import ABC, abstractmethod -from collections.abc import Callable, Iterable -from dataclasses import dataclass -from typing import Any, cast +from abc import ABC +from collections.abc import Callable +from typing import cast import pandas as pd -from graphrag.index.operations.chunk_text.typing import TextChunk -from graphrag.logger.progress import ProgressTicker from graphrag.tokenizer.get_tokenizer import get_tokenizer from graphrag.tokenizer.tokenizer import Tokenizer @@ -24,22 +21,8 @@ logger = logging.getLogger(__name__) -@dataclass(frozen=True) -class TokenChunkerOptions: - """TokenChunkerOptions data class.""" - - chunk_overlap: int - """Overlap in tokens between chunks""" - tokens_per_chunk: int - """Maximum number of tokens per chunk""" - decode: DecodeFn - """ Function to decode a list of token ids to a string""" - encode: EncodeFn - """ Function to encode a string to a list of token ids""" - - -class TextSplitter(ABC): - """Text splitter class definition.""" +class TokenTextSplitter(ABC): + """Token text splitter class definition.""" _chunk_size: int _chunk_overlap: int @@ -58,6 +41,7 @@ def __init__( keep_separator: bool = False, add_start_index: bool = False, strip_whitespace: bool = True, + tokenizer: Tokenizer | None = None, ): """Init method definition.""" self._chunk_size = chunk_size @@ -66,22 +50,6 @@ def __init__( self._keep_separator = keep_separator self._add_start_index = add_start_index self._strip_whitespace = strip_whitespace - - @abstractmethod - def split_text(self, text: str | list[str]) -> Iterable[str]: - """Split text method definition.""" - - -class TokenTextSplitter(TextSplitter): - """Token text splitter class definition.""" - - def __init__( - self, - tokenizer: Tokenizer | None = None, - **kwargs: Any, - ): - """Init method definition.""" - super().__init__(**kwargs) self._tokenizer = tokenizer or get_tokenizer() def num_tokens(self, text: str) -> int: @@ -98,68 +66,37 @@ def split_text(self, text: str | list[str]) -> list[str]: msg = f"Attempting to split a non-string value, actual is {type(text)}" raise TypeError(msg) - token_chunker_options = TokenChunkerOptions( + return split_single_text_on_tokens( + text, chunk_overlap=self._chunk_overlap, tokens_per_chunk=self._chunk_size, decode=self._tokenizer.decode, encode=self._tokenizer.encode, ) - return split_single_text_on_tokens(text=text, tokenizer=token_chunker_options) - -def split_single_text_on_tokens(text: str, tokenizer: TokenChunkerOptions) -> list[str]: +def split_single_text_on_tokens( + text: str, + tokens_per_chunk: int, + chunk_overlap: int, + encode: EncodeFn, + decode: DecodeFn, +) -> list[str]: """Split a single text and return chunks using the tokenizer.""" result = [] - input_ids = tokenizer.encode(text) + input_ids = encode(text) start_idx = 0 - cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) + cur_idx = min(start_idx + tokens_per_chunk, len(input_ids)) chunk_ids = input_ids[start_idx:cur_idx] while start_idx < len(input_ids): - chunk_text = tokenizer.decode(list(chunk_ids)) + chunk_text = decode(list(chunk_ids)) result.append(chunk_text) # Append chunked text as string if cur_idx == len(input_ids): break - start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap - cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) - chunk_ids = input_ids[start_idx:cur_idx] - - return result - - -# Adapted from - https://github.com/langchain-ai/langchain/blob/77b359edf5df0d37ef0d539f678cf64f5557cb54/libs/langchain/langchain/text_splitter.py#L471 -# So we could have better control over the chunking process -def split_multiple_texts_on_tokens( - texts: list[str], tokenizer: TokenChunkerOptions, tick: ProgressTicker -) -> list[TextChunk]: - """Split multiple texts and return chunks with metadata using the tokenizer.""" - result = [] - mapped_ids = [] - - for source_doc_idx, text in enumerate(texts): - encoded = tokenizer.encode(text) - if tick: - tick(1) # Track progress if tick callback is provided - mapped_ids.append((source_doc_idx, encoded)) - - input_ids = [ - (source_doc_idx, id) for source_doc_idx, ids in mapped_ids for id in ids - ] - - start_idx = 0 - cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) - chunk_ids = input_ids[start_idx:cur_idx] - - while start_idx < len(input_ids): - chunk_text = tokenizer.decode([id for _, id in chunk_ids]) - doc_indices = list({doc_idx for doc_idx, _ in chunk_ids}) - result.append(TextChunk(chunk_text, doc_indices, len(chunk_ids))) - if cur_idx == len(input_ids): - break - start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap - cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) + start_idx += tokens_per_chunk - chunk_overlap + cur_idx = min(start_idx + tokens_per_chunk, len(input_ids)) chunk_ids = input_ids[start_idx:cur_idx] return result diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index b80cf6d9f4..c62e80739a 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -10,12 +10,13 @@ import pandas as pd from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks -from graphrag.config.models.chunking_config import ChunkStrategyType +from graphrag.chunking.chunker import Chunker, create_chunker +from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.config.models.graph_rag_config import GraphRagConfig -from graphrag.index.operations.chunk_text.chunk_text import chunk_text from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput from graphrag.index.utils.hashing import gen_sha512_hash +from graphrag.logger.progress import progress_ticker from graphrag.tokenizer.get_tokenizer import get_tokenizer from graphrag.utils.storage import load_table_from_storage, write_table_to_storage @@ -30,18 +31,7 @@ async def run_workflow( logger.info("Workflow started: create_base_text_units") documents = await load_table_from_storage("documents", context.output_storage) - chunks = config.chunks - - output = create_base_text_units( - documents, - context.callbacks, - chunks.size, - chunks.overlap, - chunks.encoding_model, - strategy=chunks.strategy, - prepend_metadata=chunks.prepend_metadata, - chunk_size_includes_metadata=chunks.chunk_size_includes_metadata, - ) + output = create_base_text_units(documents, context.callbacks, config.chunks) await write_table_to_storage(output, "text_units", context.output_storage) @@ -52,19 +42,21 @@ async def run_workflow( def create_base_text_units( documents: pd.DataFrame, callbacks: WorkflowCallbacks, - size: int, - overlap: int, - encoding_model: str, - strategy: ChunkStrategyType, - prepend_metadata: bool, - chunk_size_includes_metadata: bool, + chunks_config: ChunkingConfig, ) -> pd.DataFrame: """All the steps to transform base text_units.""" documents.sort_values(by=["id"], ascending=[True], inplace=True) + size = chunks_config.size + encoding_model = chunks_config.encoding_model + prepend_metadata = chunks_config.prepend_metadata + chunk_size_includes_metadata = chunks_config.chunk_size_includes_metadata + tokenizer = get_tokenizer(encoding_model=encoding_model) + num_total = _get_num_total(documents, "text") + tick = progress_ticker(callbacks.progress, num_total) - def chunker(row: pd.Series) -> Any: + def chunker_fn(row: pd.Series) -> Any: line_delimiter = ".\n" metadata_str = "" metadata_tokens = 0 @@ -85,16 +77,14 @@ def chunker(row: pd.Series) -> Any: message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk." raise ValueError(message) - chunked = chunk_text( + chunks_config.size = size - metadata_tokens + chunker = create_chunker(chunks_config) + + chunked = _chunk_text( pd.DataFrame([row]).reset_index(drop=True), column="text", - size=size - metadata_tokens, - overlap=overlap, - encoding_model=encoding_model, - strategy=strategy, - callbacks=callbacks, + chunker=chunker, )[0] - if prepend_metadata: for index, chunk in enumerate(chunked): if isinstance(chunk, str): @@ -105,6 +95,7 @@ def chunker(row: pd.Series) -> Any: ) row["chunks"] = chunked + tick() return row # Track progress of row-wise apply operation @@ -113,7 +104,7 @@ def chunker(row: pd.Series) -> Any: def chunker_with_logging(row: pd.Series, row_index: int) -> Any: """Add logging to chunker execution.""" - result = chunker(row) + result = chunker_fn(row) logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return result @@ -142,3 +133,30 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: return cast( "pd.DataFrame", text_units[text_units["text"].notna()].reset_index(drop=True) ) + + +def _get_num_total(output: pd.DataFrame, column: str) -> int: + num_total = 0 + for row in output[column]: + if isinstance(row, str): + num_total += 1 + else: + num_total += len(row) + return num_total + + +def _chunk_text( + input: pd.DataFrame, + column: str, + chunker: Chunker, +) -> pd.Series: + return cast( + "pd.Series", + input.apply( + cast( + "Any", + lambda x: chunker.chunk(x[column]), + ), + axis=1, + ), + ) diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py index 556f1dd615..c749d3b91a 100644 --- a/packages/graphrag/graphrag/prompt_tune/loader/input.py +++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py @@ -46,8 +46,6 @@ async def load_docs_in_chunks( select_method: DocSelectionType, limit: int, logger: logging.Logger, - chunk_size: int, - overlap: int, n_subset_max: int = N_SUBSET_MAX, k: int = K, ) -> list[str]: @@ -69,16 +67,10 @@ async def load_docs_in_chunks( {"storage": input_storage, "config": config.input}, ) dataset = await input_reader.read_files() - chunk_config = config.chunks chunks_df = create_base_text_units( documents=dataset, callbacks=NoopWorkflowCallbacks(), - size=chunk_size, - overlap=overlap, - encoding_model=chunk_config.encoding_model, - strategy=chunk_config.strategy, - prepend_metadata=chunk_config.prepend_metadata, - chunk_size_includes_metadata=chunk_config.chunk_size_includes_metadata, + chunks_config=config.chunks, ) # Depending on the select method, build the dataset diff --git a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py b/tests/unit/indexing/operations/chunk_text/test_chunk_text.py deleted file mode 100644 index 33e5b7ccde..0000000000 --- a/tests/unit/indexing/operations/chunk_text/test_chunk_text.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - - -from unittest import mock -from unittest.mock import ANY, Mock - -import pandas as pd -import pytest -from graphrag.config.enums import ChunkStrategyType -from graphrag.index.operations.chunk_text.chunk_text import ( - _get_num_total, - chunk_text, - load_strategy, - run_strategy, -) -from graphrag.index.operations.chunk_text.typing import ( - TextChunk, -) - - -def test_get_num_total_default(): - output = pd.DataFrame({"column": ["a", "b", "c"]}) - - total = _get_num_total(output, "column") - assert total == 3 - - -def test_get_num_total_array(): - output = pd.DataFrame({"column": [["a", "b", "c"], ["x", "y"]]}) - - total = _get_num_total(output, "column") - assert total == 5 - - -def test_load_strategy_tokens(): - strategy_type = ChunkStrategyType.tokens - - strategy_loaded = load_strategy(strategy_type) - - assert strategy_loaded.__name__ == "run_tokens" - - -def test_load_strategy_sentence(): - strategy_type = ChunkStrategyType.sentence - - strategy_loaded = load_strategy(strategy_type) - - assert strategy_loaded.__name__ == "run_sentences" - - -def test_load_strategy_none(): - strategy_type = ChunkStrategyType - - with pytest.raises( - ValueError, match="Unknown strategy: " - ): - load_strategy(strategy_type) # type: ignore - - -def test_run_strategy_str(): - input = "text test for run strategy" - config = Mock() - tick = Mock() - strategy_mocked = Mock() - - strategy_mocked.return_value = [ - TextChunk( - text_chunk="text test for run strategy", - source_doc_indices=[0], - ) - ] - - runned = run_strategy(strategy_mocked, input, config, tick) - assert runned == ["text test for run strategy"] - - -def test_run_strategy_arr_str(): - input = ["text test for run strategy", "use for strategy"] - config = Mock() - tick = Mock() - strategy_mocked = Mock() - - strategy_mocked.return_value = [ - TextChunk( - text_chunk="text test for run strategy", source_doc_indices=[0], n_tokens=5 - ), - TextChunk(text_chunk="use for strategy", source_doc_indices=[1], n_tokens=3), - ] - - expected = [ - "text test for run strategy", - "use for strategy", - ] - - runned = run_strategy(strategy_mocked, input, config, tick) - assert runned == expected - - -def test_run_strategy_arr_tuple(): - input = [("text test for run strategy", "3"), ("use for strategy", "5")] - config = Mock() - tick = Mock() - strategy_mocked = Mock() - - strategy_mocked.return_value = [ - TextChunk( - text_chunk="text test for run strategy", source_doc_indices=[0], n_tokens=5 - ), - TextChunk(text_chunk="use for strategy", source_doc_indices=[1], n_tokens=3), - ] - - expected = [ - ( - ["text test for run strategy"], - "text test for run strategy", - 5, - ), - ( - ["use for strategy"], - "use for strategy", - 3, - ), - ] - - runned = run_strategy(strategy_mocked, input, config, tick) - assert runned == expected - - -def test_run_strategy_arr_tuple_same_doc(): - input = [("text test for run strategy", "3"), ("use for strategy", "5")] - config = Mock() - tick = Mock() - strategy_mocked = Mock() - - strategy_mocked.return_value = [ - TextChunk( - text_chunk="text test for run strategy", source_doc_indices=[0], n_tokens=5 - ), - TextChunk(text_chunk="use for strategy", source_doc_indices=[0], n_tokens=3), - ] - - expected = [ - ( - ["text test for run strategy"], - "text test for run strategy", - 5, - ), - ( - ["text test for run strategy"], - "use for strategy", - 3, - ), - ] - - runned = run_strategy(strategy_mocked, input, config, tick) - assert runned == expected - - -@mock.patch("graphrag.index.operations.chunk_text.chunk_text.load_strategy") -@mock.patch("graphrag.index.operations.chunk_text.chunk_text.run_strategy") -@mock.patch("graphrag.index.operations.chunk_text.chunk_text.progress_ticker") -def test_chunk_text(mock_progress_ticker, mock_run_strategy, mock_load_strategy): - input_data = pd.DataFrame({"name": ["The Shining"]}) - column = "name" - size = 10 - overlap = 2 - encoding_model = "model" - strategy = ChunkStrategyType.sentence - callbacks = Mock() - callbacks.progress = Mock() - - mock_load_strategy.return_value = Mock() - mock_progress_ticker.return_value = Mock() - - chunk_text(input_data, column, size, overlap, encoding_model, strategy, callbacks) - - mock_run_strategy.assert_called_with( - mock_load_strategy(), "The Shining", ANY, mock_progress_ticker.return_value - ) diff --git a/tests/unit/indexing/operations/chunk_text/test_strategies.py b/tests/unit/indexing/operations/chunk_text/test_chunker.py similarity index 50% rename from tests/unit/indexing/operations/chunk_text/test_strategies.py rename to tests/unit/indexing/operations/chunk_text/test_chunker.py index 3eae0d74d9..06ecb8b5a9 100644 --- a/tests/unit/indexing/operations/chunk_text/test_strategies.py +++ b/tests/unit/indexing/operations/chunk_text/test_chunker.py @@ -3,13 +3,10 @@ from unittest.mock import Mock, patch +from graphrag.chunking.bootstrap import bootstrap +from graphrag.chunking.chunker import create_chunker +from graphrag.config.enums import ChunkStrategyType from graphrag.config.models.chunking_config import ChunkingConfig -from graphrag.index.operations.chunk_text.bootstrap import bootstrap -from graphrag.index.operations.chunk_text.strategies import ( - run_sentences, - run_tokens, -) -from graphrag.index.operations.chunk_text.typing import TextChunk from graphrag.tokenizer.get_tokenizer import get_tokenizer @@ -19,33 +16,28 @@ def setup_method(self, method): def test_basic_functionality(self): """Test basic sentence splitting without metadata""" - input = ["This is a test. Another sentence."] - tick = Mock() - chunks = list(run_sentences(input, ChunkingConfig(), tick)) + input = "This is a test. Another sentence." + chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.sentence)) + chunks = chunker.chunk(input) assert len(chunks) == 2 - assert chunks[0].text_chunk == "This is a test." - assert chunks[1].text_chunk == "Another sentence." - assert all(c.source_doc_indices == [0] for c in chunks) - tick.assert_called_once_with(1) + assert chunks[0] == "This is a test." + assert chunks[1] == "Another sentence." def test_multiple_documents(self): """Test processing multiple input documents""" input = ["First. Document.", "Second. Doc."] - tick = Mock() - chunks = list(run_sentences(input, ChunkingConfig(), tick)) - + chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.sentence)) + chunks = [chunk for doc in input for chunk in chunker.chunk(doc)] assert len(chunks) == 4 - assert chunks[0].source_doc_indices == [0] - assert chunks[2].source_doc_indices == [1] - assert tick.call_count == 2 def test_mixed_whitespace_handling(self): """Test input with irregular whitespace""" - input = [" Sentence with spaces. Another one! "] - chunks = list(run_sentences(input, ChunkingConfig(), Mock())) - assert chunks[0].text_chunk == " Sentence with spaces." - assert chunks[1].text_chunk == "Another one!" + input = " Sentence with spaces. Another one! " + chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.sentence)) + chunks = chunker.chunk(input) + assert chunks[0] == " Sentence with spaces." + assert chunks[1] == "Another one!" class TestRunTokens: @@ -56,38 +48,18 @@ def test_basic_functionality(self, mock_get_encoding): mock_encoder.decode.side_effect = lambda x: bytes(x).decode() mock_get_encoding.return_value = mock_encoder - # Input and config - input = [ - "Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to." - ] - config = ChunkingConfig(size=5, overlap=1, encoding_model="fake-encoding") - tick = Mock() - - # Run the function - chunks = list(run_tokens(input, config, tick)) - - # Verify output - assert len(chunks) > 0 - assert all(isinstance(chunk, TextChunk) for chunk in chunks) - tick.assert_called_once_with(1) - - @patch("tiktoken.get_encoding") - def test_non_string_input(self, mock_get_encoding): - """Test handling of non-string input (e.g., numbers).""" - mock_encoder = Mock() - mock_encoder.encode.side_effect = lambda x: list(str(x).encode()) - mock_encoder.decode.side_effect = lambda x: bytes(x).decode() - mock_get_encoding.return_value = mock_encoder - - input = [123] # Non-string input - config = ChunkingConfig(size=5, overlap=1, encoding_model="fake-encoding") - tick = Mock() + input = "Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to." + config = ChunkingConfig( + size=5, + overlap=1, + encoding_model="fake-encoding", + strategy=ChunkStrategyType.tokens, + ) - chunks = list(run_tokens(input, config, tick)) # type: ignore + chunker = create_chunker(config) + chunks = chunker.chunk(input) - # Verify non-string input is handled assert len(chunks) > 0 - assert "123" in chunks[0].text_chunk @patch("tiktoken.get_encoding") diff --git a/tests/unit/indexing/text_splitting/test_text_splitting.py b/tests/unit/indexing/text_splitting/test_text_splitting.py index 2ab6350a89..166170c00c 100644 --- a/tests/unit/indexing/text_splitting/test_text_splitting.py +++ b/tests/unit/indexing/text_splitting/test_text_splitting.py @@ -2,14 +2,11 @@ # Licensed under the MIT License from unittest import mock -from unittest.mock import MagicMock import pytest import tiktoken from graphrag.index.text_splitting.text_splitting import ( - TokenChunkerOptions, TokenTextSplitter, - split_multiple_texts_on_tokens, split_single_text_on_tokens, ) @@ -55,32 +52,21 @@ def test_split_text_large_input(mock_split): @mock.patch("graphrag.index.text_splitting.text_splitting.split_single_text_on_tokens") -@mock.patch("graphrag.index.text_splitting.text_splitting.TokenChunkerOptions") -def test_token_text_splitter(mock_tokenizer, mock_split_text): - text = "chunk1 chunk2 chunk3" +def test_token_text_splitter(mock_split_text): expected_chunks = ["chunk1", "chunk2", "chunk3"] - mocked_tokenizer = MagicMock() - mock_tokenizer.return_value = mocked_tokenizer mock_split_text.return_value = expected_chunks - splitter = TokenTextSplitter() + splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2) splitter.split_text(["chunk1", "chunk2", "chunk3"]) - mock_split_text.assert_called_once_with(text=text, tokenizer=mocked_tokenizer) + mock_split_text.assert_called_once() def test_split_single_text_on_tokens(): text = "This is a test text, meaning to be taken seriously by this test only." mocked_tokenizer = MockTokenizer() - tokenizer = TokenChunkerOptions( - chunk_overlap=5, - tokens_per_chunk=10, - decode=mocked_tokenizer.decode, - encode=lambda text: mocked_tokenizer.encode(text), - ) - expected_splits = [ "This is a ", "is a test ", @@ -97,27 +83,14 @@ def test_split_single_text_on_tokens(): "est only.", ] - result = split_single_text_on_tokens(text=text, tokenizer=tokenizer) - assert result == expected_splits - - -def test_split_multiple_texts_on_tokens(): - texts = [ - "This is a test text, meaning to be taken seriously by this test only.", - "This is th second text, meaning to be taken seriously by this test only.", - ] - - mocked_tokenizer = MockTokenizer() - mock_tick = MagicMock() - tokenizer = TokenChunkerOptions( + result = split_single_text_on_tokens( + text=text, chunk_overlap=5, tokens_per_chunk=10, decode=mocked_tokenizer.decode, encode=lambda text: mocked_tokenizer.encode(text), ) - - split_multiple_texts_on_tokens(texts, tokenizer, tick=mock_tick) - mock_tick.assert_called() + assert result == expected_splits def test_split_single_text_on_tokens_no_overlap(): @@ -132,13 +105,6 @@ def encode(text: str) -> list[int]: def decode(tokens: list[int]) -> str: return enc.decode(tokens) - tokenizer = TokenChunkerOptions( - chunk_overlap=1, - tokens_per_chunk=2, - decode=decode, - encode=lambda text: encode(text), - ) - expected_splits = [ "This is", " is a", @@ -157,5 +123,11 @@ def decode(tokens: list[int]) -> str: " only.", ] - result = split_single_text_on_tokens(text=text, tokenizer=tokenizer) + result = split_single_text_on_tokens( + text=text, + chunk_overlap=1, + tokens_per_chunk=2, + decode=decode, + encode=lambda text: encode(text), + ) assert result == expected_splits From 461291706f70d7cf997b7dfe65d76b504845aafd Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Wed, 17 Dec 2025 16:12:25 -0800 Subject: [PATCH 04/23] Split apart chunker module --- .../graphrag/graphrag/chunking/chunker.py | 104 ------------------ .../graphrag/chunking/chunker_factory.py | 69 ++++++++++++ .../graphrag/chunking/sentence_chunker.py | 23 ++++ .../graphrag/chunking/token_chunker.py | 39 +++++++ .../index/workflows/create_base_text_units.py | 3 +- .../operations/chunk_text/test_chunker.py | 2 +- 6 files changed, 134 insertions(+), 106 deletions(-) create mode 100644 packages/graphrag/graphrag/chunking/chunker_factory.py create mode 100644 packages/graphrag/graphrag/chunking/sentence_chunker.py create mode 100644 packages/graphrag/graphrag/chunking/token_chunker.py diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag/graphrag/chunking/chunker.py index 49e48f57f7..b35f1fbd79 100644 --- a/packages/graphrag/graphrag/chunking/chunker.py +++ b/packages/graphrag/graphrag/chunking/chunker.py @@ -4,20 +4,8 @@ """A module containing the 'Chunker' class.""" from abc import ABC, abstractmethod -from collections.abc import Callable from typing import Any -import nltk -from graphrag_common.factory.factory import Factory, ServiceScope - -from graphrag.chunking.bootstrap import bootstrap -from graphrag.config.enums import ChunkStrategyType -from graphrag.config.models.chunking_config import ChunkingConfig -from graphrag.index.text_splitting.text_splitting import ( - split_single_text_on_tokens, -) -from graphrag.tokenizer.get_tokenizer import get_tokenizer - class Chunker(ABC): """Abstract base class for text chunkers.""" @@ -29,95 +17,3 @@ def __init__(self, **kwargs: Any) -> None: @abstractmethod def chunk(self, text: str) -> list[str]: """Chunk method definition.""" - - -class TokenChunker(Chunker): - """A chunker that splits text into token-based chunks.""" - - def __init__( - self, - size: int, - overlap: int, - encoding_model: str, - **kwargs: Any, - ) -> None: - """Create a token chunker instance.""" - self._size = size - self._overlap = overlap - self._encoding_model = encoding_model - - def chunk(self, text: str) -> list[str]: - """Chunk the text into token-based chunks.""" - tokenizer = get_tokenizer(encoding_model=self._encoding_model) - return split_single_text_on_tokens( - text, - chunk_overlap=self._overlap, - tokens_per_chunk=self._size, - encode=tokenizer.encode, - decode=tokenizer.decode, - ) - - -class SentenceChunker(Chunker): - """A chunker that splits text into sentence-based chunks.""" - - def __init__(self, **kwargs: Any) -> None: - """Create a sentence chunker instance.""" - bootstrap() - - def chunk(self, text: str) -> list[str]: - """Chunk the text into sentence-based chunks.""" - return nltk.sent_tokenize(text) - - -class ChunkerFactory(Factory[Chunker]): - """Factory for creating Chunker instances.""" - - -chunker_factory = ChunkerFactory() - - -def register_chunker( - chunker_type: str, - chunker_initializer: Callable[..., Chunker], - scope: ServiceScope = "transient", -) -> None: - """Register a custom chunker implementation. - - Args - ---- - - chunker_type: str - The chunker id to register. - - chunker_initializer: Callable[..., Chunker] - The chunker initializer to register. - """ - chunker_factory.register(chunker_type, chunker_initializer, scope) - - -def create_chunker(config: ChunkingConfig) -> Chunker: - """Create a chunker implementation based on the given configuration. - - Args - ---- - - config: ChunkingConfig - The chunker configuration to use. - - Returns - ------- - Chunker - The created chunker implementation. - """ - config_model = config.model_dump() - chunker_strategy = config.strategy - - if chunker_strategy not in chunker_factory: - match chunker_strategy: - case ChunkStrategyType.tokens: - chunker_factory.register(ChunkStrategyType.tokens, TokenChunker) - case ChunkStrategyType.sentence: - chunker_factory.register(ChunkStrategyType.sentence, SentenceChunker) - case _: - msg = f"ChunkingConfig.strategy '{chunker_strategy}' is not registered in the ChunkerFactory. Registered types: {', '.join(chunker_factory.keys())}." - raise ValueError(msg) - - return chunker_factory.create(chunker_strategy, init_args=config_model) diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag/graphrag/chunking/chunker_factory.py new file mode 100644 index 0000000000..05960a3bf5 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/chunker_factory.py @@ -0,0 +1,69 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'ChunkerFactory', 'register_chunker', and 'create_chunker'.""" + +from collections.abc import Callable + +from graphrag_common.factory.factory import Factory, ServiceScope + +from graphrag.chunking.chunker import Chunker +from graphrag.config.enums import ChunkStrategyType +from graphrag.config.models.chunking_config import ChunkingConfig + + +class ChunkerFactory(Factory[Chunker]): + """Factory for creating Chunker instances.""" + + +chunker_factory = ChunkerFactory() + + +def register_chunker( + chunker_type: str, + chunker_initializer: Callable[..., Chunker], + scope: ServiceScope = "transient", +) -> None: + """Register a custom chunker implementation. + + Args + ---- + - chunker_type: str + The chunker id to register. + - chunker_initializer: Callable[..., Chunker] + The chunker initializer to register. + """ + chunker_factory.register(chunker_type, chunker_initializer, scope) + + +def create_chunker(config: ChunkingConfig) -> Chunker: + """Create a chunker implementation based on the given configuration. + + Args + ---- + - config: ChunkingConfig + The chunker configuration to use. + + Returns + ------- + Chunker + The created chunker implementation. + """ + config_model = config.model_dump() + chunker_strategy = config.strategy + + if chunker_strategy not in chunker_factory: + match chunker_strategy: + case ChunkStrategyType.tokens: + from graphrag.chunking.token_chunker import TokenChunker + + chunker_factory.register(ChunkStrategyType.tokens, TokenChunker) + case ChunkStrategyType.sentence: + from graphrag.chunking.sentence_chunker import SentenceChunker + + chunker_factory.register(ChunkStrategyType.sentence, SentenceChunker) + case _: + msg = f"ChunkingConfig.strategy '{chunker_strategy}' is not registered in the ChunkerFactory. Registered types: {', '.join(chunker_factory.keys())}." + raise ValueError(msg) + + return chunker_factory.create(chunker_strategy, init_args=config_model) diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag/graphrag/chunking/sentence_chunker.py new file mode 100644 index 0000000000..140fba9d51 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/sentence_chunker.py @@ -0,0 +1,23 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'SentenceChunker' class.""" + +from typing import Any + +import nltk + +from graphrag.chunking.bootstrap import bootstrap +from graphrag.chunking.chunker import Chunker + + +class SentenceChunker(Chunker): + """A chunker that splits text into sentence-based chunks.""" + + def __init__(self, **kwargs: Any) -> None: + """Create a sentence chunker instance.""" + bootstrap() + + def chunk(self, text: str) -> list[str]: + """Chunk the text into sentence-based chunks.""" + return nltk.sent_tokenize(text) diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py new file mode 100644 index 0000000000..ad155c8af6 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -0,0 +1,39 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'TokenChunker' class.""" + +from typing import Any + +from graphrag.chunking.chunker import Chunker +from graphrag.index.text_splitting.text_splitting import ( + split_single_text_on_tokens, +) +from graphrag.tokenizer.get_tokenizer import get_tokenizer + + +class TokenChunker(Chunker): + """A chunker that splits text into token-based chunks.""" + + def __init__( + self, + size: int, + overlap: int, + encoding_model: str, + **kwargs: Any, + ) -> None: + """Create a token chunker instance.""" + self._size = size + self._overlap = overlap + self._encoding_model = encoding_model + + def chunk(self, text: str) -> list[str]: + """Chunk the text into token-based chunks.""" + tokenizer = get_tokenizer(encoding_model=self._encoding_model) + return split_single_text_on_tokens( + text, + chunk_overlap=self._overlap, + tokens_per_chunk=self._size, + encode=tokenizer.encode, + decode=tokenizer.decode, + ) diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index c62e80739a..5d9df2f806 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -10,7 +10,8 @@ import pandas as pd from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks -from graphrag.chunking.chunker import Chunker, create_chunker +from graphrag.chunking.chunker import Chunker +from graphrag.chunking.chunker_factory import create_chunker from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.typing.context import PipelineRunContext diff --git a/tests/unit/indexing/operations/chunk_text/test_chunker.py b/tests/unit/indexing/operations/chunk_text/test_chunker.py index 06ecb8b5a9..c979d51887 100644 --- a/tests/unit/indexing/operations/chunk_text/test_chunker.py +++ b/tests/unit/indexing/operations/chunk_text/test_chunker.py @@ -4,7 +4,7 @@ from unittest.mock import Mock, patch from graphrag.chunking.bootstrap import bootstrap -from graphrag.chunking.chunker import create_chunker +from graphrag.chunking.chunker_factory import create_chunker from graphrag.config.enums import ChunkStrategyType from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.tokenizer.get_tokenizer import get_tokenizer From b63f747d44d0b26c8c4e2afb312846884dd7db8c Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Thu, 18 Dec 2025 11:58:53 -0800 Subject: [PATCH 05/23] Co-locate chunking/splitting --- .../graphrag/chunking/token_chunker.py | 18 ++++---- .../token_text_splitter.py} | 45 +++++++------------ .../operations/embed_text/run_embed_text.py | 2 +- .../operations => chunking}/__init__.py | 0 .../chunk_text => chunking}/test_chunker.py | 0 .../test_text_splitting.py | 39 +++++++--------- .../operations/chunk_text/__init__.py | 2 - .../unit/indexing/text_splitting/__init__.py | 2 - 8 files changed, 39 insertions(+), 69 deletions(-) rename packages/graphrag/graphrag/{index/text_splitting/text_splitting.py => chunking/token_text_splitter.py} (58%) rename tests/unit/{indexing/operations => chunking}/__init__.py (100%) rename tests/unit/{indexing/operations/chunk_text => chunking}/test_chunker.py (100%) rename tests/unit/{indexing/text_splitting => chunking}/test_text_splitting.py (71%) delete mode 100644 tests/unit/indexing/operations/chunk_text/__init__.py delete mode 100644 tests/unit/indexing/text_splitting/__init__.py diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index ad155c8af6..79f870a157 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -6,8 +6,8 @@ from typing import Any from graphrag.chunking.chunker import Chunker -from graphrag.index.text_splitting.text_splitting import ( - split_single_text_on_tokens, +from graphrag.chunking.token_text_splitter import ( + TokenTextSplitter, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer @@ -26,14 +26,12 @@ def __init__( self._size = size self._overlap = overlap self._encoding_model = encoding_model + self._text_splitter = TokenTextSplitter( + chunk_size=size, + chunk_overlap=overlap, + tokenizer=get_tokenizer(encoding_model=encoding_model), + ) def chunk(self, text: str) -> list[str]: """Chunk the text into token-based chunks.""" - tokenizer = get_tokenizer(encoding_model=self._encoding_model) - return split_single_text_on_tokens( - text, - chunk_overlap=self._overlap, - tokens_per_chunk=self._size, - encode=tokenizer.encode, - decode=tokenizer.decode, - ) + return self._text_splitter.split_text(text) diff --git a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py b/packages/graphrag/graphrag/chunking/token_text_splitter.py similarity index 58% rename from packages/graphrag/graphrag/index/text_splitting/text_splitting.py rename to packages/graphrag/graphrag/chunking/token_text_splitter.py index 7be6c538ae..9491c5625b 100644 --- a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py +++ b/packages/graphrag/graphrag/chunking/token_text_splitter.py @@ -1,7 +1,7 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -"""A module containing 'TokenTextSplitter' class and 'split_single_text_on_tokens' function.""" +"""A module containing 'TokenTextSplitter' class and 'split_text_on_tokens' function.""" import logging from abc import ABC @@ -10,7 +10,6 @@ import pandas as pd -from graphrag.tokenizer.get_tokenizer import get_tokenizer from graphrag.tokenizer.tokenizer import Tokenizer EncodedText = list[int] @@ -26,47 +25,33 @@ class TokenTextSplitter(ABC): _chunk_size: int _chunk_overlap: int - _length_function: LengthFn - _keep_separator: bool - _add_start_index: bool - _strip_whitespace: bool def __init__( self, + tokenizer: Tokenizer, # based on OpenAI embedding chunk size limits # https://devblogs.microsoft.com/azure-sql/embedding-models-and-dimensions-optimizing-the-performance-resource-usage-ratio/ chunk_size: int = 8191, chunk_overlap: int = 100, - length_function: LengthFn = len, - keep_separator: bool = False, - add_start_index: bool = False, - strip_whitespace: bool = True, - tokenizer: Tokenizer | None = None, ): """Init method definition.""" self._chunk_size = chunk_size self._chunk_overlap = chunk_overlap - self._length_function = length_function - self._keep_separator = keep_separator - self._add_start_index = add_start_index - self._strip_whitespace = strip_whitespace - self._tokenizer = tokenizer or get_tokenizer() + self._tokenizer = tokenizer def num_tokens(self, text: str) -> int: """Return the number of tokens in a string.""" return self._tokenizer.num_tokens(text) - def split_text(self, text: str | list[str]) -> list[str]: + def split_text(self, text: str) -> list[str]: """Split text method.""" - if isinstance(text, list): - text = " ".join(text) - elif cast("bool", pd.isna(text)) or text == "": + if cast("bool", pd.isna(text)) or text == "": return [] if not isinstance(text, str): msg = f"Attempting to split a non-string value, actual is {type(text)}" raise TypeError(msg) - return split_single_text_on_tokens( + return split_text_on_tokens( text, chunk_overlap=self._chunk_overlap, tokens_per_chunk=self._chunk_size, @@ -75,7 +60,7 @@ def split_text(self, text: str | list[str]) -> list[str]: ) -def split_single_text_on_tokens( +def split_text_on_tokens( text: str, tokens_per_chunk: int, chunk_overlap: int, @@ -84,19 +69,19 @@ def split_single_text_on_tokens( ) -> list[str]: """Split a single text and return chunks using the tokenizer.""" result = [] - input_ids = encode(text) + input_tokens = encode(text) start_idx = 0 - cur_idx = min(start_idx + tokens_per_chunk, len(input_ids)) - chunk_ids = input_ids[start_idx:cur_idx] + cur_idx = min(start_idx + tokens_per_chunk, len(input_tokens)) + chunk_tokens = input_tokens[start_idx:cur_idx] - while start_idx < len(input_ids): - chunk_text = decode(list(chunk_ids)) + while start_idx < len(input_tokens): + chunk_text = decode(list(chunk_tokens)) result.append(chunk_text) # Append chunked text as string - if cur_idx == len(input_ids): + if cur_idx == len(input_tokens): break start_idx += tokens_per_chunk - chunk_overlap - cur_idx = min(start_idx + tokens_per_chunk, len(input_ids)) - chunk_ids = input_ids[start_idx:cur_idx] + cur_idx = min(start_idx + tokens_per_chunk, len(input_tokens)) + chunk_tokens = input_tokens[start_idx:cur_idx] return result diff --git a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py index cb8b2016f5..f489cb5776 100644 --- a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py +++ b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py @@ -10,7 +10,7 @@ import numpy as np from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks -from graphrag.index.text_splitting.text_splitting import TokenTextSplitter +from graphrag.chunking.token_text_splitter import TokenTextSplitter from graphrag.index.utils.is_null import is_null from graphrag.language_model.protocol.base import EmbeddingModel from graphrag.logger.progress import ProgressTicker, progress_ticker diff --git a/tests/unit/indexing/operations/__init__.py b/tests/unit/chunking/__init__.py similarity index 100% rename from tests/unit/indexing/operations/__init__.py rename to tests/unit/chunking/__init__.py diff --git a/tests/unit/indexing/operations/chunk_text/test_chunker.py b/tests/unit/chunking/test_chunker.py similarity index 100% rename from tests/unit/indexing/operations/chunk_text/test_chunker.py rename to tests/unit/chunking/test_chunker.py diff --git a/tests/unit/indexing/text_splitting/test_text_splitting.py b/tests/unit/chunking/test_text_splitting.py similarity index 71% rename from tests/unit/indexing/text_splitting/test_text_splitting.py rename to tests/unit/chunking/test_text_splitting.py index 166170c00c..08293a9463 100644 --- a/tests/unit/indexing/text_splitting/test_text_splitting.py +++ b/tests/unit/chunking/test_text_splitting.py @@ -5,10 +5,11 @@ import pytest import tiktoken -from graphrag.index.text_splitting.text_splitting import ( +from graphrag.chunking.token_text_splitter import ( TokenTextSplitter, - split_single_text_on_tokens, + split_text_on_tokens, ) +from graphrag.tokenizer.get_tokenizer import get_tokenizer class MockTokenizer: @@ -19,31 +20,34 @@ def decode(self, token_ids): return "".join(chr(id) for id in token_ids) +tokenizer = get_tokenizer() + + def test_split_text_str_empty(): - splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2) + splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) result = splitter.split_text("") assert result == [] def test_split_text_str_bool(): - splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2) + splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) result = splitter.split_text(None) # type: ignore assert result == [] def test_split_text_str_int(): - splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2) + splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) with pytest.raises(TypeError): splitter.split_text(123) # type: ignore -@mock.patch("graphrag.index.text_splitting.text_splitting.split_single_text_on_tokens") +@mock.patch("graphrag.chunking.token_text_splitter.split_text_on_tokens") def test_split_text_large_input(mock_split): large_text = "a" * 10_000 mock_split.return_value = ["chunk"] * 2_000 - splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2) + splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) result = splitter.split_text(large_text) @@ -51,20 +55,7 @@ def test_split_text_large_input(mock_split): mock_split.assert_called_once() -@mock.patch("graphrag.index.text_splitting.text_splitting.split_single_text_on_tokens") -def test_token_text_splitter(mock_split_text): - expected_chunks = ["chunk1", "chunk2", "chunk3"] - - mock_split_text.return_value = expected_chunks - - splitter = TokenTextSplitter(chunk_size=5, chunk_overlap=2) - - splitter.split_text(["chunk1", "chunk2", "chunk3"]) - - mock_split_text.assert_called_once() - - -def test_split_single_text_on_tokens(): +def test_split_text_on_tokens(): text = "This is a test text, meaning to be taken seriously by this test only." mocked_tokenizer = MockTokenizer() expected_splits = [ @@ -83,7 +74,7 @@ def test_split_single_text_on_tokens(): "est only.", ] - result = split_single_text_on_tokens( + result = split_text_on_tokens( text=text, chunk_overlap=5, tokens_per_chunk=10, @@ -93,7 +84,7 @@ def test_split_single_text_on_tokens(): assert result == expected_splits -def test_split_single_text_on_tokens_no_overlap(): +def test_split_text_on_tokens_no_overlap(): text = "This is a test text, meaning to be taken seriously by this test only." enc = tiktoken.get_encoding("cl100k_base") @@ -123,7 +114,7 @@ def decode(tokens: list[int]) -> str: " only.", ] - result = split_single_text_on_tokens( + result = split_text_on_tokens( text=text, chunk_overlap=1, tokens_per_chunk=2, diff --git a/tests/unit/indexing/operations/chunk_text/__init__.py b/tests/unit/indexing/operations/chunk_text/__init__.py deleted file mode 100644 index 0a3e38adfb..0000000000 --- a/tests/unit/indexing/operations/chunk_text/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License diff --git a/tests/unit/indexing/text_splitting/__init__.py b/tests/unit/indexing/text_splitting/__init__.py deleted file mode 100644 index 0a3e38adfb..0000000000 --- a/tests/unit/indexing/text_splitting/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License From a20dbdb795e8044451e3638943fdb68ec487253d Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Thu, 18 Dec 2025 13:22:18 -0800 Subject: [PATCH 06/23] Collapse token splitting functionality into one class/function --- .../graphrag/chunking/token_chunker.py | 49 +++++-- .../graphrag/chunking/token_text_splitter.py | 87 ------------ .../operations/embed_text/run_embed_text.py | 36 +++-- tests/unit/chunking/test_chunker.py | 86 ++++++++++++ tests/unit/chunking/test_text_splitting.py | 124 ------------------ 5 files changed, 142 insertions(+), 240 deletions(-) delete mode 100644 packages/graphrag/graphrag/chunking/token_text_splitter.py delete mode 100644 tests/unit/chunking/test_text_splitting.py diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index 79f870a157..7d26842650 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -3,14 +3,16 @@ """A module containing 'TokenChunker' class.""" +from collections.abc import Callable from typing import Any from graphrag.chunking.chunker import Chunker -from graphrag.chunking.token_text_splitter import ( - TokenTextSplitter, -) from graphrag.tokenizer.get_tokenizer import get_tokenizer +EncodedText = list[int] +DecodeFn = Callable[[EncodedText], str] +EncodeFn = Callable[[str], EncodedText] + class TokenChunker(Chunker): """A chunker that splits text into token-based chunks.""" @@ -26,12 +28,41 @@ def __init__( self._size = size self._overlap = overlap self._encoding_model = encoding_model - self._text_splitter = TokenTextSplitter( - chunk_size=size, - chunk_overlap=overlap, - tokenizer=get_tokenizer(encoding_model=encoding_model), - ) + self._tokenizer = get_tokenizer(encoding_model=encoding_model) def chunk(self, text: str) -> list[str]: """Chunk the text into token-based chunks.""" - return self._text_splitter.split_text(text) + return split_text_on_tokens( + text, + chunk_size=self._size, + chunk_overlap=self._overlap, + encode=self._tokenizer.encode, + decode=self._tokenizer.decode, + ) + + +def split_text_on_tokens( + text: str, + chunk_size: int, + chunk_overlap: int, + encode: EncodeFn, + decode: DecodeFn, +) -> list[str]: + """Split a single text and return chunks using the tokenizer.""" + result = [] + input_tokens = encode(text) + + start_idx = 0 + cur_idx = min(start_idx + chunk_size, len(input_tokens)) + chunk_tokens = input_tokens[start_idx:cur_idx] + + while start_idx < len(input_tokens): + chunk_text = decode(list(chunk_tokens)) + result.append(chunk_text) # Append chunked text as string + if cur_idx == len(input_tokens): + break + start_idx += chunk_size - chunk_overlap + cur_idx = min(start_idx + chunk_size, len(input_tokens)) + chunk_tokens = input_tokens[start_idx:cur_idx] + + return result diff --git a/packages/graphrag/graphrag/chunking/token_text_splitter.py b/packages/graphrag/graphrag/chunking/token_text_splitter.py deleted file mode 100644 index 9491c5625b..0000000000 --- a/packages/graphrag/graphrag/chunking/token_text_splitter.py +++ /dev/null @@ -1,87 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'TokenTextSplitter' class and 'split_text_on_tokens' function.""" - -import logging -from abc import ABC -from collections.abc import Callable -from typing import cast - -import pandas as pd - -from graphrag.tokenizer.tokenizer import Tokenizer - -EncodedText = list[int] -DecodeFn = Callable[[EncodedText], str] -EncodeFn = Callable[[str], EncodedText] -LengthFn = Callable[[str], int] - -logger = logging.getLogger(__name__) - - -class TokenTextSplitter(ABC): - """Token text splitter class definition.""" - - _chunk_size: int - _chunk_overlap: int - - def __init__( - self, - tokenizer: Tokenizer, - # based on OpenAI embedding chunk size limits - # https://devblogs.microsoft.com/azure-sql/embedding-models-and-dimensions-optimizing-the-performance-resource-usage-ratio/ - chunk_size: int = 8191, - chunk_overlap: int = 100, - ): - """Init method definition.""" - self._chunk_size = chunk_size - self._chunk_overlap = chunk_overlap - self._tokenizer = tokenizer - - def num_tokens(self, text: str) -> int: - """Return the number of tokens in a string.""" - return self._tokenizer.num_tokens(text) - - def split_text(self, text: str) -> list[str]: - """Split text method.""" - if cast("bool", pd.isna(text)) or text == "": - return [] - if not isinstance(text, str): - msg = f"Attempting to split a non-string value, actual is {type(text)}" - raise TypeError(msg) - - return split_text_on_tokens( - text, - chunk_overlap=self._chunk_overlap, - tokens_per_chunk=self._chunk_size, - decode=self._tokenizer.decode, - encode=self._tokenizer.encode, - ) - - -def split_text_on_tokens( - text: str, - tokens_per_chunk: int, - chunk_overlap: int, - encode: EncodeFn, - decode: DecodeFn, -) -> list[str]: - """Split a single text and return chunks using the tokenizer.""" - result = [] - input_tokens = encode(text) - - start_idx = 0 - cur_idx = min(start_idx + tokens_per_chunk, len(input_tokens)) - chunk_tokens = input_tokens[start_idx:cur_idx] - - while start_idx < len(input_tokens): - chunk_text = decode(list(chunk_tokens)) - result.append(chunk_text) # Append chunked text as string - if cur_idx == len(input_tokens): - break - start_idx += tokens_per_chunk - chunk_overlap - cur_idx = min(start_idx + tokens_per_chunk, len(input_tokens)) - chunk_tokens = input_tokens[start_idx:cur_idx] - - return result diff --git a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py index f489cb5776..188d19ffcc 100644 --- a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py +++ b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py @@ -10,7 +10,7 @@ import numpy as np from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks -from graphrag.chunking.token_text_splitter import TokenTextSplitter +from graphrag.chunking.token_chunker import split_text_on_tokens from graphrag.index.utils.is_null import is_null from graphrag.language_model.protocol.base import EmbeddingModel from graphrag.logger.progress import ProgressTicker, progress_ticker @@ -39,17 +39,15 @@ async def run_embed_text( if is_null(input): return TextEmbeddingResult(embeddings=None) - splitter = _get_splitter(tokenizer, batch_max_tokens) - semaphore: asyncio.Semaphore = asyncio.Semaphore(num_threads) # Break up the input texts. The sizes here indicate how many snippets are in each input text - texts, input_sizes = _prepare_embed_texts(input, splitter) + texts, input_sizes = _prepare_embed_texts(input, tokenizer, batch_max_tokens) text_batches = _create_text_batches( texts, + tokenizer, batch_size, batch_max_tokens, - splitter, ) logger.info( "embedding %d inputs via %d snippets using %d batches. max_batch_size=%d, batch_max_tokens=%d", @@ -72,13 +70,6 @@ async def run_embed_text( return TextEmbeddingResult(embeddings=embeddings) -def _get_splitter(tokenizer: Tokenizer, batch_max_tokens: int) -> TokenTextSplitter: - return TokenTextSplitter( - tokenizer=tokenizer, - chunk_size=batch_max_tokens, - ) - - async def _execute( model: EmbeddingModel, chunks: list[list[str]], @@ -100,9 +91,9 @@ async def embed(chunk: list[str]): def _create_text_batches( texts: list[str], + tokenizer: Tokenizer, max_batch_size: int, max_batch_tokens: int, - splitter: TokenTextSplitter, ) -> list[list[str]]: """Create batches of texts to embed.""" # https://learn.microsoft.com/en-us/azure/ai-services/openai/reference @@ -112,7 +103,7 @@ def _create_text_batches( current_batch_tokens = 0 for text in texts: - token_count = splitter.num_tokens(text) + token_count = tokenizer.num_tokens(text) if ( len(current_batch) >= max_batch_size or current_batch_tokens + token_count > max_batch_tokens @@ -131,18 +122,23 @@ def _create_text_batches( def _prepare_embed_texts( - input: list[str], splitter: TokenTextSplitter + input: list[str], + tokenizer: Tokenizer, + batch_max_tokens: int = 8191, + chunk_overlap: int = 100, ) -> tuple[list[str], list[int]]: sizes: list[int] = [] snippets: list[str] = [] for text in input: - # Split the input text and filter out any empty content - split_texts = splitter.split_text(text) - if split_texts is None: - continue + split_texts = split_text_on_tokens( + text, + chunk_size=batch_max_tokens, + chunk_overlap=chunk_overlap, + encode=tokenizer.encode, + decode=tokenizer.decode, + ) split_texts = [text for text in split_texts if len(text) > 0] - sizes.append(len(split_texts)) snippets.extend(split_texts) diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index c979d51887..a7405e13a0 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -5,6 +5,9 @@ from graphrag.chunking.bootstrap import bootstrap from graphrag.chunking.chunker_factory import create_chunker +from graphrag.chunking.token_chunker import ( + split_text_on_tokens, +) from graphrag.config.enums import ChunkStrategyType from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.tokenizer.get_tokenizer import get_tokenizer @@ -62,6 +65,89 @@ def test_basic_functionality(self, mock_get_encoding): assert len(chunks) > 0 +class MockTokenizer: + def encode(self, text): + return [ord(char) for char in text] + + def decode(self, token_ids): + return "".join(chr(id) for id in token_ids) + + +tokenizer = get_tokenizer() + + +def test_split_text_str_empty(): + result = split_text_on_tokens( + "", + chunk_size=5, + chunk_overlap=2, + encode=tokenizer.encode, + decode=tokenizer.decode, + ) + assert result == [] + + +def test_split_text_on_tokens(): + text = "This is a test text, meaning to be taken seriously by this test only." + mocked_tokenizer = MockTokenizer() + expected_splits = [ + "This is a ", + "is a test ", + "test text,", + "text, mean", + " meaning t", + "ing to be ", + "o be taken", + "taken seri", # cspell:disable-line + " seriously", + "ously by t", # cspell:disable-line + " by this t", + "his test o", + "est only.", + ] + + result = split_text_on_tokens( + text=text, + chunk_overlap=5, + chunk_size=10, + decode=mocked_tokenizer.decode, + encode=lambda text: mocked_tokenizer.encode(text), + ) + assert result == expected_splits + + +def test_split_text_on_tokens_no_overlap(): + text = "This is a test text, meaning to be taken seriously by this test only." + tok = get_tokenizer(encoding_model="cl100k_base") + + expected_splits = [ + "This is", + " is a", + " a test", + " test text", + " text,", + ", meaning", + " meaning to", + " to be", + " be taken", # cspell:disable-line + " taken seriously", # cspell:disable-line + " seriously by", + " by this", # cspell:disable-line + " this test", + " test only", + " only.", + ] + + result = split_text_on_tokens( + text=text, + chunk_size=2, + chunk_overlap=1, + decode=tok.decode, + encode=tok.encode, + ) + assert result == expected_splits + + @patch("tiktoken.get_encoding") def test_get_encoding_fn_encode(mock_get_encoding): # Create a mock encoding object with encode and decode methods diff --git a/tests/unit/chunking/test_text_splitting.py b/tests/unit/chunking/test_text_splitting.py deleted file mode 100644 index 08293a9463..0000000000 --- a/tests/unit/chunking/test_text_splitting.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -from unittest import mock - -import pytest -import tiktoken -from graphrag.chunking.token_text_splitter import ( - TokenTextSplitter, - split_text_on_tokens, -) -from graphrag.tokenizer.get_tokenizer import get_tokenizer - - -class MockTokenizer: - def encode(self, text): - return [ord(char) for char in text] - - def decode(self, token_ids): - return "".join(chr(id) for id in token_ids) - - -tokenizer = get_tokenizer() - - -def test_split_text_str_empty(): - splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) - result = splitter.split_text("") - - assert result == [] - - -def test_split_text_str_bool(): - splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) - result = splitter.split_text(None) # type: ignore - - assert result == [] - - -def test_split_text_str_int(): - splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) - with pytest.raises(TypeError): - splitter.split_text(123) # type: ignore - - -@mock.patch("graphrag.chunking.token_text_splitter.split_text_on_tokens") -def test_split_text_large_input(mock_split): - large_text = "a" * 10_000 - mock_split.return_value = ["chunk"] * 2_000 - splitter = TokenTextSplitter(tokenizer=tokenizer, chunk_size=5, chunk_overlap=2) - - result = splitter.split_text(large_text) - - assert len(result) == 2_000, "Large input was not split correctly" - mock_split.assert_called_once() - - -def test_split_text_on_tokens(): - text = "This is a test text, meaning to be taken seriously by this test only." - mocked_tokenizer = MockTokenizer() - expected_splits = [ - "This is a ", - "is a test ", - "test text,", - "text, mean", - " meaning t", - "ing to be ", - "o be taken", - "taken seri", # cspell:disable-line - " seriously", - "ously by t", # cspell:disable-line - " by this t", - "his test o", - "est only.", - ] - - result = split_text_on_tokens( - text=text, - chunk_overlap=5, - tokens_per_chunk=10, - decode=mocked_tokenizer.decode, - encode=lambda text: mocked_tokenizer.encode(text), - ) - assert result == expected_splits - - -def test_split_text_on_tokens_no_overlap(): - text = "This is a test text, meaning to be taken seriously by this test only." - enc = tiktoken.get_encoding("cl100k_base") - - def encode(text: str) -> list[int]: - if not isinstance(text, str): - text = f"{text}" - return enc.encode(text) - - def decode(tokens: list[int]) -> str: - return enc.decode(tokens) - - expected_splits = [ - "This is", - " is a", - " a test", - " test text", - " text,", - ", meaning", - " meaning to", - " to be", - " be taken", # cspell:disable-line - " taken seriously", # cspell:disable-line - " seriously by", - " by this", # cspell:disable-line - " this test", - " test only", - " only.", - ] - - result = split_text_on_tokens( - text=text, - chunk_overlap=1, - tokens_per_chunk=2, - decode=decode, - encode=lambda text: encode(text), - ) - assert result == expected_splits From e5c1aa7d5205aedbef888ca79ca53678d314de7b Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Thu, 18 Dec 2025 13:46:22 -0800 Subject: [PATCH 07/23] Restore create_base_text_units parameterization --- .../graphrag/chunking/chunker_factory.py | 11 ++++-- .../graphrag/chunking/token_chunker.py | 5 ++- .../index/workflows/create_base_text_units.py | 37 +++++++++++++------ .../graphrag/prompt_tune/loader/input.py | 6 ++- tests/unit/chunking/test_chunker.py | 24 ++++++------ 5 files changed, 53 insertions(+), 30 deletions(-) diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag/graphrag/chunking/chunker_factory.py index 05960a3bf5..0ec789ab31 100644 --- a/packages/graphrag/graphrag/chunking/chunker_factory.py +++ b/packages/graphrag/graphrag/chunking/chunker_factory.py @@ -10,6 +10,7 @@ from graphrag.chunking.chunker import Chunker from graphrag.config.enums import ChunkStrategyType from graphrag.config.models.chunking_config import ChunkingConfig +from graphrag.tokenizer.tokenizer import Tokenizer class ChunkerFactory(Factory[Chunker]): @@ -36,7 +37,9 @@ def register_chunker( chunker_factory.register(chunker_type, chunker_initializer, scope) -def create_chunker(config: ChunkingConfig) -> Chunker: +def create_chunker( + config: ChunkingConfig, tokenizer: Tokenizer | None = None +) -> Chunker: """Create a chunker implementation based on the given configuration. Args @@ -50,6 +53,8 @@ def create_chunker(config: ChunkingConfig) -> Chunker: The created chunker implementation. """ config_model = config.model_dump() + if tokenizer is not None: + config_model["tokenizer"] = tokenizer chunker_strategy = config.strategy if chunker_strategy not in chunker_factory: @@ -57,11 +62,11 @@ def create_chunker(config: ChunkingConfig) -> Chunker: case ChunkStrategyType.tokens: from graphrag.chunking.token_chunker import TokenChunker - chunker_factory.register(ChunkStrategyType.tokens, TokenChunker) + register_chunker(ChunkStrategyType.tokens, TokenChunker) case ChunkStrategyType.sentence: from graphrag.chunking.sentence_chunker import SentenceChunker - chunker_factory.register(ChunkStrategyType.sentence, SentenceChunker) + register_chunker(ChunkStrategyType.sentence, SentenceChunker) case _: msg = f"ChunkingConfig.strategy '{chunker_strategy}' is not registered in the ChunkerFactory. Registered types: {', '.join(chunker_factory.keys())}." raise ValueError(msg) diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index 7d26842650..b0a82a1ab4 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -7,7 +7,7 @@ from typing import Any from graphrag.chunking.chunker import Chunker -from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer EncodedText = list[int] DecodeFn = Callable[[EncodedText], str] @@ -22,13 +22,14 @@ def __init__( size: int, overlap: int, encoding_model: str, + tokenizer: Tokenizer, **kwargs: Any, ) -> None: """Create a token chunker instance.""" self._size = size self._overlap = overlap self._encoding_model = encoding_model - self._tokenizer = get_tokenizer(encoding_model=encoding_model) + self._tokenizer = tokenizer def chunk(self, text: str) -> list[str]: """Chunk the text into token-based chunks.""" diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 5d9df2f806..f6d36709f5 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -19,6 +19,7 @@ from graphrag.index.utils.hashing import gen_sha512_hash from graphrag.logger.progress import progress_ticker from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import load_table_from_storage, write_table_to_storage logger = logging.getLogger(__name__) @@ -32,7 +33,18 @@ async def run_workflow( logger.info("Workflow started: create_base_text_units") documents = await load_table_from_storage("documents", context.output_storage) - output = create_base_text_units(documents, context.callbacks, config.chunks) + chunking_config = config.chunks + tokenizer = get_tokenizer(encoding_model=chunking_config.encoding_model) + + output = create_base_text_units( + documents, + context.callbacks, + tokenizer=tokenizer, + chunk_size=chunking_config.size, + chunk_overlap=chunking_config.overlap, + prepend_metadata=chunking_config.prepend_metadata, + chunk_size_includes_metadata=chunking_config.chunk_size_includes_metadata, + ) await write_table_to_storage(output, "text_units", context.output_storage) @@ -43,17 +55,15 @@ async def run_workflow( def create_base_text_units( documents: pd.DataFrame, callbacks: WorkflowCallbacks, - chunks_config: ChunkingConfig, + tokenizer: Tokenizer, + chunk_size: int, + chunk_overlap: int, + prepend_metadata: bool, + chunk_size_includes_metadata: bool, ) -> pd.DataFrame: """All the steps to transform base text_units.""" documents.sort_values(by=["id"], ascending=[True], inplace=True) - size = chunks_config.size - encoding_model = chunks_config.encoding_model - prepend_metadata = chunks_config.prepend_metadata - chunk_size_includes_metadata = chunks_config.chunk_size_includes_metadata - - tokenizer = get_tokenizer(encoding_model=encoding_model) num_total = _get_num_total(documents, "text") tick = progress_ticker(callbacks.progress, num_total) @@ -74,12 +84,17 @@ def chunker_fn(row: pd.Series) -> Any: if chunk_size_includes_metadata: metadata_tokens = len(tokenizer.encode(metadata_str)) - if metadata_tokens >= size: + if metadata_tokens >= chunk_size: message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk." raise ValueError(message) - chunks_config.size = size - metadata_tokens - chunker = create_chunker(chunks_config) + chunker = create_chunker( + ChunkingConfig( + size=chunk_size - metadata_tokens, + overlap=chunk_overlap, + ), + tokenizer=tokenizer, + ) chunked = _chunk_text( pd.DataFrame([row]).reset_index(drop=True), diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py index c749d3b91a..a01c3c3af9 100644 --- a/packages/graphrag/graphrag/prompt_tune/loader/input.py +++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py @@ -70,7 +70,11 @@ async def load_docs_in_chunks( chunks_df = create_base_text_units( documents=dataset, callbacks=NoopWorkflowCallbacks(), - chunks_config=config.chunks, + tokenizer=tokenizer, + chunk_size=config.chunks.size, + chunk_overlap=config.chunks.overlap, + prepend_metadata=config.chunks.prepend_metadata, + chunk_size_includes_metadata=config.chunks.chunk_size_includes_metadata, ) # Depending on the select method, build the dataset diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index a7405e13a0..5ff46918cb 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -13,6 +13,16 @@ from graphrag.tokenizer.get_tokenizer import get_tokenizer +class MockTokenizer: + def encode(self, text): + return [ord(char) for char in text] + + def decode(self, token_ids): + return "".join(chr(id) for id in token_ids) + + +tokenizer = get_tokenizer() + class TestRunSentences: def setup_method(self, method): bootstrap() @@ -59,23 +69,11 @@ def test_basic_functionality(self, mock_get_encoding): strategy=ChunkStrategyType.tokens, ) - chunker = create_chunker(config) + chunker = create_chunker(config, tokenizer=tokenizer) chunks = chunker.chunk(input) assert len(chunks) > 0 - -class MockTokenizer: - def encode(self, text): - return [ord(char) for char in text] - - def decode(self, token_ids): - return "".join(chr(id) for id in token_ids) - - -tokenizer = get_tokenizer() - - def test_split_text_str_empty(): result = split_text_on_tokens( "", From b7c06730d78c4b9f6d4e99fc5a68aa9dc492520e Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Thu, 18 Dec 2025 13:56:32 -0800 Subject: [PATCH 08/23] Move Tokenizer base class to common package --- .../graphrag_common/types/__init__.py | 8 ++ .../graphrag_common/types}/tokenizer.py | 0 .../graphrag/chunking/chunker_factory.py | 2 +- .../graphrag/chunking/token_chunker.py | 3 +- .../index/operations/embed_text/embed_text.py | 2 +- .../operations/embed_text/run_embed_text.py | 2 +- .../build_mixed_context.py | 2 +- .../graph_context/context_builder.py | 2 +- .../graph_context/sort_context.py | 2 +- .../summarize_communities.py | 2 +- .../text_unit_context/context_builder.py | 2 +- .../text_unit_context/sort_context.py | 2 +- .../index/text_splitting/text_splitting.py | 102 ++++++++++++++++++ .../index/workflows/create_base_text_units.py | 2 +- .../workflows/create_community_reports.py | 2 +- .../create_community_reports_text.py | 2 +- .../workflows/generate_text_embeddings.py | 2 +- .../generator/extract_graph_prompt.py | 3 +- .../context_builder/community_context.py | 2 +- .../context_builder/conversation_history.py | 2 +- .../dynamic_community_selection.py | 3 +- .../query/context_builder/local_context.py | 2 +- .../query/context_builder/rate_relevancy.py | 2 +- .../query/context_builder/source_context.py | 2 +- .../graphrag/graphrag/query/llm/text_utils.py | 2 +- .../graphrag/query/question_gen/base.py | 3 +- .../graphrag/query/question_gen/local_gen.py | 3 +- .../graphrag/query/structured_search/base.py | 2 +- .../basic_search/basic_context.py | 2 +- .../structured_search/basic_search/search.py | 3 +- .../drift_search/drift_context.py | 2 +- .../structured_search/drift_search/primer.py | 2 +- .../structured_search/drift_search/search.py | 2 +- .../global_search/community_context.py | 3 +- .../structured_search/global_search/search.py | 2 +- .../local_search/mixed_context.py | 2 +- .../structured_search/local_search/search.py | 3 +- .../graphrag/tokenizer/get_tokenizer.py | 3 +- .../graphrag/tokenizer/litellm_tokenizer.py | 3 +- .../graphrag/tokenizer/tiktoken_tokenizer.py | 3 +- tests/unit/chunking/test_chunker.py | 2 + 41 files changed, 158 insertions(+), 39 deletions(-) create mode 100644 packages/graphrag-common/graphrag_common/types/__init__.py rename packages/{graphrag/graphrag/tokenizer => graphrag-common/graphrag_common/types}/tokenizer.py (100%) create mode 100644 packages/graphrag/graphrag/index/text_splitting/text_splitting.py diff --git a/packages/graphrag-common/graphrag_common/types/__init__.py b/packages/graphrag-common/graphrag_common/types/__init__.py new file mode 100644 index 0000000000..f897ef3dd1 --- /dev/null +++ b/packages/graphrag-common/graphrag_common/types/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""The GraphRAG types module: contains commonly-reused types across the monorepo packages.""" + +from graphrag_common.types.tokenizer import Tokenizer + +__all__ = ["Tokenizer"] diff --git a/packages/graphrag/graphrag/tokenizer/tokenizer.py b/packages/graphrag-common/graphrag_common/types/tokenizer.py similarity index 100% rename from packages/graphrag/graphrag/tokenizer/tokenizer.py rename to packages/graphrag-common/graphrag_common/types/tokenizer.py diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag/graphrag/chunking/chunker_factory.py index 0ec789ab31..7ee4b36498 100644 --- a/packages/graphrag/graphrag/chunking/chunker_factory.py +++ b/packages/graphrag/graphrag/chunking/chunker_factory.py @@ -6,11 +6,11 @@ from collections.abc import Callable from graphrag_common.factory.factory import Factory, ServiceScope +from graphrag_common.types.tokenizer import Tokenizer from graphrag.chunking.chunker import Chunker from graphrag.config.enums import ChunkStrategyType from graphrag.config.models.chunking_config import ChunkingConfig -from graphrag.tokenizer.tokenizer import Tokenizer class ChunkerFactory(Factory[Chunker]): diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index b0a82a1ab4..7bf5f84916 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -6,8 +6,9 @@ from collections.abc import Callable from typing import Any +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.chunking.chunker import Chunker -from graphrag.tokenizer.tokenizer import Tokenizer EncodedText = list[int] DecodeFn = Callable[[EncodedText], str] diff --git a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py index 08f941f908..58aae657ee 100644 --- a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py +++ b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py @@ -7,11 +7,11 @@ import numpy as np import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.index.operations.embed_text.run_embed_text import run_embed_text from graphrag.language_model.protocol.base import EmbeddingModel -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore, VectorStoreDocument logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py index 188d19ffcc..3891ca6cb9 100644 --- a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py +++ b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py @@ -8,13 +8,13 @@ from dataclasses import dataclass import numpy as np +from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.token_chunker import split_text_on_tokens from graphrag.index.utils.is_null import is_null from graphrag.language_model.protocol.base import EmbeddingModel from graphrag.logger.progress import ProgressTicker, progress_ticker -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py index 6ae59a40c8..a519a21343 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py @@ -4,12 +4,12 @@ """A module containing build_mixed_context method definition.""" import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.index.operations.summarize_communities.graph_context.sort_context import ( sort_context, ) -from graphrag.tokenizer.tokenizer import Tokenizer def build_mixed_context( diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py index 34d281b3f8..4b1ba4c201 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py @@ -7,6 +7,7 @@ from typing import cast import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks @@ -30,7 +31,6 @@ where_column_equals, ) from graphrag.logger.progress import progress_iterable -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py index 3b7863f2f8..1630b9f96b 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py @@ -3,9 +3,9 @@ """Sort context by degree in descending order.""" import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas -from graphrag.tokenizer.tokenizer import Tokenizer def sort_context( diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py b/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py index 709a4e3b5a..51044b4260 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py @@ -7,6 +7,7 @@ from collections.abc import Callable import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks @@ -26,7 +27,6 @@ from graphrag.index.utils.derive_from_rows import derive_from_rows from graphrag.language_model.protocol.base import ChatModel from graphrag.logger.progress import progress_ticker -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py index 8d6e0ae2e0..d2b11c6a36 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py @@ -7,6 +7,7 @@ from typing import cast import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.index.operations.summarize_communities.build_mixed_context import ( @@ -18,7 +19,6 @@ from graphrag.index.operations.summarize_communities.text_unit_context.sort_context import ( sort_context, ) -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py index 7bad931b59..f06ecad34d 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py @@ -6,9 +6,9 @@ import logging import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py new file mode 100644 index 0000000000..52c428786a --- /dev/null +++ b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py @@ -0,0 +1,102 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'TokenTextSplitter' class and 'split_single_text_on_tokens' function.""" + +import logging +from abc import ABC +from collections.abc import Callable +from typing import cast + +import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer + +from graphrag.tokenizer.get_tokenizer import get_tokenizer + +EncodedText = list[int] +DecodeFn = Callable[[EncodedText], str] +EncodeFn = Callable[[str], EncodedText] +LengthFn = Callable[[str], int] + +logger = logging.getLogger(__name__) + + +class TokenTextSplitter(ABC): + """Token text splitter class definition.""" + + _chunk_size: int + _chunk_overlap: int + _length_function: LengthFn + _keep_separator: bool + _add_start_index: bool + _strip_whitespace: bool + + def __init__( + self, + # based on OpenAI embedding chunk size limits + # https://devblogs.microsoft.com/azure-sql/embedding-models-and-dimensions-optimizing-the-performance-resource-usage-ratio/ + chunk_size: int = 8191, + chunk_overlap: int = 100, + length_function: LengthFn = len, + keep_separator: bool = False, + add_start_index: bool = False, + strip_whitespace: bool = True, + tokenizer: Tokenizer | None = None, + ): + """Init method definition.""" + self._chunk_size = chunk_size + self._chunk_overlap = chunk_overlap + self._length_function = length_function + self._keep_separator = keep_separator + self._add_start_index = add_start_index + self._strip_whitespace = strip_whitespace + self._tokenizer = tokenizer or get_tokenizer() + + def num_tokens(self, text: str) -> int: + """Return the number of tokens in a string.""" + return self._tokenizer.num_tokens(text) + + def split_text(self, text: str | list[str]) -> list[str]: + """Split text method.""" + if isinstance(text, list): + text = " ".join(text) + elif cast("bool", pd.isna(text)) or text == "": + return [] + if not isinstance(text, str): + msg = f"Attempting to split a non-string value, actual is {type(text)}" + raise TypeError(msg) + + return split_single_text_on_tokens( + text, + chunk_overlap=self._chunk_overlap, + tokens_per_chunk=self._chunk_size, + decode=self._tokenizer.decode, + encode=self._tokenizer.encode, + ) + + +def split_single_text_on_tokens( + text: str, + tokens_per_chunk: int, + chunk_overlap: int, + encode: EncodeFn, + decode: DecodeFn, +) -> list[str]: + """Split a single text and return chunks using the tokenizer.""" + result = [] + input_ids = encode(text) + + start_idx = 0 + cur_idx = min(start_idx + tokens_per_chunk, len(input_ids)) + chunk_ids = input_ids[start_idx:cur_idx] + + while start_idx < len(input_ids): + chunk_text = decode(list(chunk_ids)) + result.append(chunk_text) # Append chunked text as string + if cur_idx == len(input_ids): + break + start_idx += tokens_per_chunk - chunk_overlap + cur_idx = min(start_idx + tokens_per_chunk, len(input_ids)) + chunk_ids = input_ids[start_idx:cur_idx] + + return result diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index f6d36709f5..4219998cc5 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -8,6 +8,7 @@ from typing import Any, cast import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.chunker import Chunker @@ -19,7 +20,6 @@ from graphrag.index.utils.hashing import gen_sha512_hash from graphrag.logger.progress import progress_ticker from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import load_table_from_storage, write_table_to_storage logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/workflows/create_community_reports.py b/packages/graphrag/graphrag/index/workflows/create_community_reports.py index 981e47227e..f0320d3d10 100644 --- a/packages/graphrag/graphrag/index/workflows/create_community_reports.py +++ b/packages/graphrag/graphrag/index/workflows/create_community_reports.py @@ -6,6 +6,7 @@ import logging import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks @@ -29,7 +30,6 @@ from graphrag.language_model.manager import ModelManager from graphrag.language_model.protocol.base import ChatModel from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import ( load_table_from_storage, storage_has_table, diff --git a/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py b/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py index ac269df8bc..c0100b8645 100644 --- a/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py +++ b/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py @@ -6,6 +6,7 @@ import logging import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.config.enums import AsyncType @@ -28,7 +29,6 @@ from graphrag.language_model.manager import ModelManager from graphrag.language_model.protocol.base import ChatModel from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import load_table_from_storage, write_table_to_storage logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py index ef24d3e348..879a445565 100644 --- a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py +++ b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py @@ -6,6 +6,7 @@ import logging import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.config.embeddings import ( @@ -23,7 +24,6 @@ from graphrag.language_model.manager import ModelManager from graphrag.language_model.protocol.base import EmbeddingModel from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import ( load_table_from_storage, write_table_to_storage, diff --git a/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py b/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py index db0f87cf97..6c0720c1c9 100644 --- a/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py +++ b/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py @@ -5,6 +5,8 @@ from pathlib import Path +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.prompt_tune.template.extract_graph import ( EXAMPLE_EXTRACTION_TEMPLATE, GRAPH_EXTRACTION_JSON_PROMPT, @@ -13,7 +15,6 @@ UNTYPED_GRAPH_EXTRACTION_PROMPT, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer EXTRACT_GRAPH_FILENAME = "extract_graph.txt" diff --git a/packages/graphrag/graphrag/query/context_builder/community_context.py b/packages/graphrag/graphrag/query/context_builder/community_context.py index 4917be0186..6d92052c23 100644 --- a/packages/graphrag/graphrag/query/context_builder/community_context.py +++ b/packages/graphrag/graphrag/query/context_builder/community_context.py @@ -8,11 +8,11 @@ from typing import Any, cast import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.community_report import CommunityReport from graphrag.data_model.entity import Entity from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/context_builder/conversation_history.py b/packages/graphrag/graphrag/query/context_builder/conversation_history.py index c20998121c..bfeba28074 100644 --- a/packages/graphrag/graphrag/query/context_builder/conversation_history.py +++ b/packages/graphrag/graphrag/query/context_builder/conversation_history.py @@ -7,9 +7,9 @@ from enum import Enum import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer """ Enum for conversation roles diff --git a/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py b/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py index 904478f738..3de6d99d6b 100644 --- a/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py +++ b/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py @@ -10,12 +10,13 @@ from time import time from typing import Any +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.data_model.community import Community from graphrag.data_model.community_report import CommunityReport from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.rate_prompt import RATE_QUERY from graphrag.query.context_builder.rate_relevancy import rate_relevancy -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/context_builder/local_context.py b/packages/graphrag/graphrag/query/context_builder/local_context.py index a2d8a54533..b803262931 100644 --- a/packages/graphrag/graphrag/query/context_builder/local_context.py +++ b/packages/graphrag/graphrag/query/context_builder/local_context.py @@ -7,6 +7,7 @@ from typing import Any, cast import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.covariate import Covariate from graphrag.data_model.entity import Entity @@ -24,7 +25,6 @@ to_relationship_dataframe, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer def build_entity_context( diff --git a/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py b/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py index 6fa128999f..9084b8ef04 100644 --- a/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py +++ b/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py @@ -9,11 +9,11 @@ from typing import Any import numpy as np +from graphrag_common.types.tokenizer import Tokenizer from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.rate_prompt import RATE_QUERY from graphrag.query.llm.text_utils import try_parse_json_object -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/context_builder/source_context.py b/packages/graphrag/graphrag/query/context_builder/source_context.py index eaf308f629..68f5e5ecda 100644 --- a/packages/graphrag/graphrag/query/context_builder/source_context.py +++ b/packages/graphrag/graphrag/query/context_builder/source_context.py @@ -7,11 +7,11 @@ from typing import Any, cast import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.relationship import Relationship from graphrag.data_model.text_unit import TextUnit from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer """ Contain util functions to build text unit context for the search's system prompt diff --git a/packages/graphrag/graphrag/query/llm/text_utils.py b/packages/graphrag/graphrag/query/llm/text_utils.py index ddd6abe1ef..ccae99b894 100644 --- a/packages/graphrag/graphrag/query/llm/text_utils.py +++ b/packages/graphrag/graphrag/query/llm/text_utils.py @@ -9,10 +9,10 @@ from collections.abc import Iterator from itertools import islice +from graphrag_common.types.tokenizer import Tokenizer from json_repair import repair_json from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/question_gen/base.py b/packages/graphrag/graphrag/query/question_gen/base.py index 2195aee52a..3231cac281 100644 --- a/packages/graphrag/graphrag/query/question_gen/base.py +++ b/packages/graphrag/graphrag/query/question_gen/base.py @@ -7,13 +7,14 @@ from dataclasses import dataclass from typing import Any +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.builders import ( GlobalContextBuilder, LocalContextBuilder, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer @dataclass diff --git a/packages/graphrag/graphrag/query/question_gen/local_gen.py b/packages/graphrag/graphrag/query/question_gen/local_gen.py index 82795e9cf3..1e21bf14e8 100644 --- a/packages/graphrag/graphrag/query/question_gen/local_gen.py +++ b/packages/graphrag/graphrag/query/question_gen/local_gen.py @@ -7,6 +7,8 @@ import time from typing import Any, cast +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.callbacks.llm_callbacks import BaseLLMCallback from graphrag.language_model.protocol.base import ChatModel from graphrag.prompts.query.question_gen_system_prompt import QUESTION_SYSTEM_PROMPT @@ -18,7 +20,6 @@ ConversationHistory, ) from graphrag.query.question_gen.base import BaseQuestionGen, QuestionResult -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/base.py b/packages/graphrag/graphrag/query/structured_search/base.py index 753b419f69..22f6a5d969 100644 --- a/packages/graphrag/graphrag/query/structured_search/base.py +++ b/packages/graphrag/graphrag/query/structured_search/base.py @@ -9,6 +9,7 @@ from typing import Any, Generic, TypeVar import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.builders import ( @@ -21,7 +22,6 @@ ConversationHistory, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer @dataclass diff --git a/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py b/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py index b7390017fc..1fb0591016 100644 --- a/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py +++ b/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py @@ -7,6 +7,7 @@ from typing import cast import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.text_unit import TextUnit from graphrag.language_model.protocol.base import EmbeddingModel @@ -16,7 +17,6 @@ ) from graphrag.query.context_builder.conversation_history import ConversationHistory from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/basic_search/search.py b/packages/graphrag/graphrag/query/structured_search/basic_search/search.py index ce5f656845..418b7ffe34 100644 --- a/packages/graphrag/graphrag/query/structured_search/basic_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/basic_search/search.py @@ -8,6 +8,8 @@ from collections.abc import AsyncGenerator from typing import Any +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.callbacks.query_callbacks import QueryCallbacks from graphrag.language_model.protocol.base import ChatModel from graphrag.prompts.query.basic_search_system_prompt import ( @@ -16,7 +18,6 @@ from graphrag.query.context_builder.builders import BasicContextBuilder from graphrag.query.context_builder.conversation_history import ConversationHistory from graphrag.query.structured_search.base import BaseSearch, SearchResult -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) """ diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py index 4b4325ae2e..d217b714a4 100644 --- a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py +++ b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py @@ -9,6 +9,7 @@ import numpy as np import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.config.models.drift_search_config import DRIFTSearchConfig from graphrag.data_model.community_report import CommunityReport @@ -28,7 +29,6 @@ LocalSearchMixedContext, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py b/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py index 2a7f145711..0fc17a69f5 100644 --- a/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py +++ b/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py @@ -10,6 +10,7 @@ import numpy as np import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from tqdm.asyncio import tqdm_asyncio from graphrag.config.models.drift_search_config import DRIFTSearchConfig @@ -20,7 +21,6 @@ ) from graphrag.query.structured_search.base import SearchResult from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/search.py b/packages/graphrag/graphrag/query/structured_search/drift_search/search.py index 64a8e52b43..8e26ab1191 100644 --- a/packages/graphrag/graphrag/query/structured_search/drift_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/drift_search/search.py @@ -8,6 +8,7 @@ from collections.abc import AsyncGenerator from typing import Any +from graphrag_common.types.tokenizer import Tokenizer from tqdm.asyncio import tqdm_asyncio from graphrag.callbacks.query_callbacks import QueryCallbacks @@ -26,7 +27,6 @@ from graphrag.query.structured_search.drift_search.state import QueryState from graphrag.query.structured_search.local_search.search import LocalSearch from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py b/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py index 1709aab1a8..fa05e17f5f 100644 --- a/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py +++ b/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py @@ -5,6 +5,8 @@ from typing import Any +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.data_model.community import Community from graphrag.data_model.community_report import CommunityReport from graphrag.data_model.entity import Entity @@ -20,7 +22,6 @@ ) from graphrag.query.structured_search.base import GlobalContextBuilder from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer class GlobalCommunityContext(GlobalContextBuilder): diff --git a/packages/graphrag/graphrag/query/structured_search/global_search/search.py b/packages/graphrag/graphrag/query/structured_search/global_search/search.py index 86b95d0088..e201194403 100644 --- a/packages/graphrag/graphrag/query/structured_search/global_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/global_search/search.py @@ -12,6 +12,7 @@ from typing import Any import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.query_callbacks import QueryCallbacks from graphrag.language_model.protocol.base import ChatModel @@ -31,7 +32,6 @@ ) from graphrag.query.llm.text_utils import try_parse_json_object from graphrag.query.structured_search.base import BaseSearch, SearchResult -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py b/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py index b91272d164..981fc333d9 100644 --- a/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py +++ b/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py @@ -7,6 +7,7 @@ from typing import Any import pandas as pd +from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.community_report import CommunityReport from graphrag.data_model.covariate import Covariate @@ -41,7 +42,6 @@ from graphrag.query.input.retrieval.text_units import get_candidate_text_units from graphrag.query.structured_search.base import LocalContextBuilder from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/local_search/search.py b/packages/graphrag/graphrag/query/structured_search/local_search/search.py index fdd72949da..b0db4097fa 100644 --- a/packages/graphrag/graphrag/query/structured_search/local_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/local_search/search.py @@ -8,6 +8,8 @@ from collections.abc import AsyncGenerator from typing import Any +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.callbacks.query_callbacks import QueryCallbacks from graphrag.language_model.protocol.base import ChatModel from graphrag.prompts.query.local_search_system_prompt import ( @@ -18,7 +20,6 @@ ConversationHistory, ) from graphrag.query.structured_search.base import BaseSearch, SearchResult -from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py index 5d1ef40f0c..34a10feba2 100644 --- a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py +++ b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py @@ -3,11 +3,12 @@ """Get Tokenizer.""" +from graphrag_common.types.tokenizer import Tokenizer + from graphrag.config.defaults import ENCODING_MODEL from graphrag.config.models.language_model_config import LanguageModelConfig from graphrag.tokenizer.litellm_tokenizer import LitellmTokenizer from graphrag.tokenizer.tiktoken_tokenizer import TiktokenTokenizer -from graphrag.tokenizer.tokenizer import Tokenizer def get_tokenizer( diff --git a/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py b/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py index 1a85f56086..a486c77d6a 100644 --- a/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py +++ b/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py @@ -3,10 +3,9 @@ """LiteLLM Tokenizer.""" +from graphrag_common.types.tokenizer import Tokenizer from litellm import decode, encode # type: ignore -from graphrag.tokenizer.tokenizer import Tokenizer - class LitellmTokenizer(Tokenizer): """LiteLLM Tokenizer.""" diff --git a/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py b/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py index fa6c6e9b43..b069dc8e48 100644 --- a/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py +++ b/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py @@ -4,8 +4,7 @@ """Tiktoken Tokenizer.""" import tiktoken - -from graphrag.tokenizer.tokenizer import Tokenizer +from graphrag_common.types.tokenizer import Tokenizer class TiktokenTokenizer(Tokenizer): diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 5ff46918cb..be9ed25448 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -23,6 +23,7 @@ def decode(self, token_ids): tokenizer = get_tokenizer() + class TestRunSentences: def setup_method(self, method): bootstrap() @@ -74,6 +75,7 @@ def test_basic_functionality(self, mock_get_encoding): assert len(chunks) > 0 + def test_split_text_str_empty(): result = split_text_on_tokens( "", From 896a48ce1ec5f45d3f88c152d5d00155046cd2d4 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Thu, 18 Dec 2025 16:38:34 -0800 Subject: [PATCH 09/23] Move pre-pending into chunkers --- .../{bootstrap.py => bootstrap_nltk.py} | 0 .../graphrag/chunking/chunk_strategy_type.py | 17 +++ .../graphrag/graphrag/chunking/chunker.py | 2 +- .../graphrag/chunking/chunker_factory.py | 4 +- .../models => chunking}/chunking_config.py | 10 +- .../graphrag/chunking/sentence_chunker.py | 23 +++- .../graphrag/chunking/token_chunker.py | 35 +++++- packages/graphrag/graphrag/config/defaults.py | 2 +- packages/graphrag/graphrag/config/enums.py | 11 -- .../config/models/graph_rag_config.py | 2 +- .../index/workflows/create_base_text_units.py | 101 ++---------------- .../graphrag/prompt_tune/loader/input.py | 7 +- .../graphrag/tokenizer/get_tokenizer.py | 4 +- tests/unit/chunking/test_chunker.py | 6 +- tests/unit/config/utils.py | 2 +- 15 files changed, 97 insertions(+), 129 deletions(-) rename packages/graphrag/graphrag/chunking/{bootstrap.py => bootstrap_nltk.py} (100%) create mode 100644 packages/graphrag/graphrag/chunking/chunk_strategy_type.py rename packages/graphrag/graphrag/{config/models => chunking}/chunking_config.py (88%) diff --git a/packages/graphrag/graphrag/chunking/bootstrap.py b/packages/graphrag/graphrag/chunking/bootstrap_nltk.py similarity index 100% rename from packages/graphrag/graphrag/chunking/bootstrap.py rename to packages/graphrag/graphrag/chunking/bootstrap_nltk.py diff --git a/packages/graphrag/graphrag/chunking/chunk_strategy_type.py b/packages/graphrag/graphrag/chunking/chunk_strategy_type.py new file mode 100644 index 0000000000..e6ab40b9b6 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/chunk_strategy_type.py @@ -0,0 +1,17 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""Chunk strategy type enumeration.""" + +from enum import Enum + + +class ChunkStrategyType(str, Enum): + """ChunkStrategy class definition.""" + + tokens = "tokens" + sentence = "sentence" + + def __repr__(self): + """Get a string representation.""" + return f'"{self.value}"' diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag/graphrag/chunking/chunker.py index b35f1fbd79..491a01ab0f 100644 --- a/packages/graphrag/graphrag/chunking/chunker.py +++ b/packages/graphrag/graphrag/chunking/chunker.py @@ -15,5 +15,5 @@ def __init__(self, **kwargs: Any) -> None: """Create a chunker instance.""" @abstractmethod - def chunk(self, text: str) -> list[str]: + def chunk(self, text: str, metadata: str | dict | None = None) -> list[str]: """Chunk method definition.""" diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag/graphrag/chunking/chunker_factory.py index 7ee4b36498..d7bdc834c3 100644 --- a/packages/graphrag/graphrag/chunking/chunker_factory.py +++ b/packages/graphrag/graphrag/chunking/chunker_factory.py @@ -8,9 +8,9 @@ from graphrag_common.factory.factory import Factory, ServiceScope from graphrag_common.types.tokenizer import Tokenizer +from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.chunking.chunker import Chunker -from graphrag.config.enums import ChunkStrategyType -from graphrag.config.models.chunking_config import ChunkingConfig +from graphrag.chunking.chunking_config import ChunkingConfig class ChunkerFactory(Factory[Chunker]): diff --git a/packages/graphrag/graphrag/config/models/chunking_config.py b/packages/graphrag/graphrag/chunking/chunking_config.py similarity index 88% rename from packages/graphrag/graphrag/config/models/chunking_config.py rename to packages/graphrag/graphrag/chunking/chunking_config.py index 4d1214241c..77b8080f77 100644 --- a/packages/graphrag/graphrag/config/models/chunking_config.py +++ b/packages/graphrag/graphrag/chunking/chunking_config.py @@ -5,13 +5,17 @@ from pydantic import BaseModel, Field +from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.config.defaults import graphrag_config_defaults -from graphrag.config.enums import ChunkStrategyType class ChunkingConfig(BaseModel): """Configuration section for chunking.""" + strategy: str = Field( + description="The chunking strategy to use.", + default=ChunkStrategyType.tokens, + ) size: int = Field( description="The chunk size to use.", default=graphrag_config_defaults.chunks.size, @@ -20,10 +24,6 @@ class ChunkingConfig(BaseModel): description="The chunk overlap to use.", default=graphrag_config_defaults.chunks.overlap, ) - strategy: ChunkStrategyType = Field( - description="The chunking strategy to use.", - default=graphrag_config_defaults.chunks.strategy, - ) encoding_model: str = Field( description="The encoding model to use.", default=graphrag_config_defaults.chunks.encoding_model, diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag/graphrag/chunking/sentence_chunker.py index 140fba9d51..af75fcd1c0 100644 --- a/packages/graphrag/graphrag/chunking/sentence_chunker.py +++ b/packages/graphrag/graphrag/chunking/sentence_chunker.py @@ -3,21 +3,36 @@ """A module containing 'SentenceChunker' class.""" +import json from typing import Any import nltk -from graphrag.chunking.bootstrap import bootstrap +from graphrag.chunking.bootstrap_nltk import bootstrap from graphrag.chunking.chunker import Chunker class SentenceChunker(Chunker): """A chunker that splits text into sentence-based chunks.""" - def __init__(self, **kwargs: Any) -> None: + def __init__(self, prepend_metadata: bool, **kwargs: Any) -> None: """Create a sentence chunker instance.""" + self._prepend_metadata = prepend_metadata bootstrap() - def chunk(self, text: str) -> list[str]: + def chunk(self, text: str, metadata: str | dict | None = None) -> list[str]: """Chunk the text into sentence-based chunks.""" - return nltk.sent_tokenize(text) + chunks = nltk.sent_tokenize(text) + + if self._prepend_metadata and metadata is not None: + line_delimiter = ".\n" + metadata_str = "" + if isinstance(metadata, str): + metadata = json.loads(metadata) + if isinstance(metadata, dict): + metadata_str = ( + line_delimiter.join(f"{k}: {v}" for k, v in metadata.items()) + + line_delimiter + ) + chunks = [metadata_str + chunk for chunk in chunks] + return chunks diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index 7bf5f84916..41848fd043 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -3,6 +3,7 @@ """A module containing 'TokenChunker' class.""" +import json from collections.abc import Callable from typing import Any @@ -23,6 +24,8 @@ def __init__( size: int, overlap: int, encoding_model: str, + prepend_metadata: bool, + chunk_size_includes_metadata: bool, tokenizer: Tokenizer, **kwargs: Any, ) -> None: @@ -30,18 +33,44 @@ def __init__( self._size = size self._overlap = overlap self._encoding_model = encoding_model + self._prepend_metadata = prepend_metadata + self._chunk_size_includes_metadata = chunk_size_includes_metadata self._tokenizer = tokenizer - def chunk(self, text: str) -> list[str]: + def chunk(self, text: str, metadata: str | dict | None = None) -> list[str]: """Chunk the text into token-based chunks.""" - return split_text_on_tokens( + line_delimiter = ".\n" + metadata_str = "" + metadata_tokens = 0 + + if self._prepend_metadata and metadata is not None: + if isinstance(metadata, str): + metadata = json.loads(metadata) + if isinstance(metadata, dict): + metadata_str = ( + line_delimiter.join(f"{k}: {v}" for k, v in metadata.items()) + + line_delimiter + ) + + if self._chunk_size_includes_metadata: + metadata_tokens = len(self._tokenizer.encode(metadata_str)) + if metadata_tokens >= self._size: + message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk." + raise ValueError(message) + + chunks = split_text_on_tokens( text, - chunk_size=self._size, + chunk_size=self._size - metadata_tokens, chunk_overlap=self._overlap, encode=self._tokenizer.encode, decode=self._tokenizer.decode, ) + if self._prepend_metadata: + chunks = [metadata_str + chunk for chunk in chunks] + + return chunks + def split_text_on_tokens( text: str, diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py index e330c82449..f7e904d7af 100644 --- a/packages/graphrag/graphrag/config/defaults.py +++ b/packages/graphrag/graphrag/config/defaults.py @@ -10,11 +10,11 @@ from graphrag_cache import CacheType from graphrag_storage import StorageType +from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.config.embeddings import default_embeddings from graphrag.config.enums import ( AsyncType, AuthType, - ChunkStrategyType, InputFileType, ModelType, NounPhraseExtractorType, diff --git a/packages/graphrag/graphrag/config/enums.py b/packages/graphrag/graphrag/config/enums.py index 4835763afc..855f062f27 100644 --- a/packages/graphrag/graphrag/config/enums.py +++ b/packages/graphrag/graphrag/config/enums.py @@ -112,17 +112,6 @@ class AsyncType(str, Enum): Threaded = "threaded" -class ChunkStrategyType(str, Enum): - """ChunkStrategy class definition.""" - - tokens = "tokens" - sentence = "sentence" - - def __repr__(self): - """Get a string representation.""" - return f'"{self.value}"' - - class SearchMethod(Enum): """The type of search to run.""" diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py index 162fe12567..a0cfb34f29 100644 --- a/packages/graphrag/graphrag/config/models/graph_rag_config.py +++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py @@ -12,10 +12,10 @@ from pydantic import BaseModel, Field, model_validator import graphrag.config.defaults as defs +from graphrag.chunking.chunking_config import ChunkingConfig from graphrag.config.defaults import graphrag_config_defaults from graphrag.config.enums import VectorStoreType from graphrag.config.models.basic_search_config import BasicSearchConfig -from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.config.models.cluster_graph_config import ClusterGraphConfig from graphrag.config.models.community_reports_config import CommunityReportsConfig from graphrag.config.models.drift_search_config import DRIFTSearchConfig diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 4219998cc5..94d2bf0f4c 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -3,7 +3,6 @@ """A module containing run_workflow method definition.""" -import json import logging from typing import Any, cast @@ -13,7 +12,6 @@ from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.chunker import Chunker from graphrag.chunking.chunker_factory import create_chunker -from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput @@ -33,17 +31,13 @@ async def run_workflow( logger.info("Workflow started: create_base_text_units") documents = await load_table_from_storage("documents", context.output_storage) - chunking_config = config.chunks - tokenizer = get_tokenizer(encoding_model=chunking_config.encoding_model) - + tokenizer = get_tokenizer(encoding_model=config.chunks.encoding_model) + chunker = create_chunker(config.chunks, tokenizer) output = create_base_text_units( documents, context.callbacks, tokenizer=tokenizer, - chunk_size=chunking_config.size, - chunk_overlap=chunking_config.overlap, - prepend_metadata=chunking_config.prepend_metadata, - chunk_size_includes_metadata=chunking_config.chunk_size_includes_metadata, + chunker=chunker, ) await write_table_to_storage(output, "text_units", context.output_storage) @@ -56,73 +50,22 @@ def create_base_text_units( documents: pd.DataFrame, callbacks: WorkflowCallbacks, tokenizer: Tokenizer, - chunk_size: int, - chunk_overlap: int, - prepend_metadata: bool, - chunk_size_includes_metadata: bool, + chunker: Chunker, ) -> pd.DataFrame: """All the steps to transform base text_units.""" documents.sort_values(by=["id"], ascending=[True], inplace=True) - num_total = _get_num_total(documents, "text") - tick = progress_ticker(callbacks.progress, num_total) - - def chunker_fn(row: pd.Series) -> Any: - line_delimiter = ".\n" - metadata_str = "" - metadata_tokens = 0 - - if prepend_metadata and "metadata" in row: - metadata = row["metadata"] - if isinstance(metadata, str): - metadata = json.loads(metadata) - if isinstance(metadata, dict): - metadata_str = ( - line_delimiter.join(f"{k}: {v}" for k, v in metadata.items()) - + line_delimiter - ) - - if chunk_size_includes_metadata: - metadata_tokens = len(tokenizer.encode(metadata_str)) - if metadata_tokens >= chunk_size: - message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk." - raise ValueError(message) - - chunker = create_chunker( - ChunkingConfig( - size=chunk_size - metadata_tokens, - overlap=chunk_overlap, - ), - tokenizer=tokenizer, - ) - - chunked = _chunk_text( - pd.DataFrame([row]).reset_index(drop=True), - column="text", - chunker=chunker, - )[0] - if prepend_metadata: - for index, chunk in enumerate(chunked): - if isinstance(chunk, str): - chunked[index] = metadata_str + chunk - else: - chunked[index] = ( - (chunk[0], metadata_str + chunk[1], chunk[2]) if chunk else None - ) - - row["chunks"] = chunked - tick() - return row + total_rows = len(documents) + tick = progress_ticker(callbacks.progress, total_rows) # Track progress of row-wise apply operation - total_rows = len(documents) logger.info("Starting chunking process for %d documents", total_rows) def chunker_with_logging(row: pd.Series, row_index: int) -> Any: - """Add logging to chunker execution.""" - result = chunker_fn(row) + row["chunks"] = chunker.chunk(row["text"], row.get("metadata")) + tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) - return result + return row text_units = documents.apply( lambda row: chunker_with_logging(row, row.name), axis=1 @@ -150,29 +93,3 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: "pd.DataFrame", text_units[text_units["text"].notna()].reset_index(drop=True) ) - -def _get_num_total(output: pd.DataFrame, column: str) -> int: - num_total = 0 - for row in output[column]: - if isinstance(row, str): - num_total += 1 - else: - num_total += len(row) - return num_total - - -def _chunk_text( - input: pd.DataFrame, - column: str, - chunker: Chunker, -) -> pd.Series: - return cast( - "pd.Series", - input.apply( - cast( - "Any", - lambda x: chunker.chunk(x[column]), - ), - axis=1, - ), - ) diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py index a01c3c3af9..5f06df8a53 100644 --- a/packages/graphrag/graphrag/prompt_tune/loader/input.py +++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py @@ -12,6 +12,7 @@ from graphrag_storage import create_storage from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks +from graphrag.chunking.chunker_factory import create_chunker from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.input.factory import InputReaderFactory from graphrag.index.operations.embed_text.run_embed_text import ( @@ -61,6 +62,7 @@ async def load_docs_in_chunks( cache=NoopCache(), ) tokenizer = get_tokenizer(embeddings_llm_settings) + chunker = create_chunker(config.chunks, tokenizer) input_storage = create_storage(config.input.storage) input_reader = InputReaderFactory().create( config.input.file_type, @@ -71,10 +73,7 @@ async def load_docs_in_chunks( documents=dataset, callbacks=NoopWorkflowCallbacks(), tokenizer=tokenizer, - chunk_size=config.chunks.size, - chunk_overlap=config.chunks.overlap, - prepend_metadata=config.chunks.prepend_metadata, - chunk_size_includes_metadata=config.chunks.chunk_size_includes_metadata, + chunker=chunker, ) # Depending on the select method, build the dataset diff --git a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py index 34a10feba2..fcfd29ae2a 100644 --- a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py +++ b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py @@ -13,7 +13,7 @@ def get_tokenizer( model_config: LanguageModelConfig | None = None, - encoding_model: str = ENCODING_MODEL, + encoding_model: str | None = None, ) -> Tokenizer: """ Get the tokenizer for the given model configuration or fallback to a tiktoken based tokenizer. @@ -39,4 +39,6 @@ def get_tokenizer( return LitellmTokenizer(model_name=model_config.model) + if encoding_model is None: + encoding_model = ENCODING_MODEL return TiktokenTokenizer(encoding_name=encoding_model) diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index be9ed25448..bb4add8e63 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -3,13 +3,13 @@ from unittest.mock import Mock, patch -from graphrag.chunking.bootstrap import bootstrap +from graphrag.chunking.bootstrap_nltk import bootstrap +from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.chunking.chunker_factory import create_chunker +from graphrag.chunking.chunking_config import ChunkingConfig from graphrag.chunking.token_chunker import ( split_text_on_tokens, ) -from graphrag.config.enums import ChunkStrategyType -from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.tokenizer.get_tokenizer import get_tokenizer diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py index 1315907e59..f46b29a37c 100644 --- a/tests/unit/config/utils.py +++ b/tests/unit/config/utils.py @@ -4,8 +4,8 @@ from dataclasses import asdict import graphrag.config.defaults as defs +from graphrag.chunking.chunking_config import ChunkingConfig from graphrag.config.models.basic_search_config import BasicSearchConfig -from graphrag.config.models.chunking_config import ChunkingConfig from graphrag.config.models.cluster_graph_config import ClusterGraphConfig from graphrag.config.models.community_reports_config import CommunityReportsConfig from graphrag.config.models.drift_search_config import DRIFTSearchConfig From 9aa94dfd868c9eded45db9486f2815e160af3e90 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Thu, 18 Dec 2025 17:15:04 -0800 Subject: [PATCH 10/23] Streamline config --- .../graphrag/chunking/chunk_strategy_type.py | 12 +++----- .../graphrag/chunking/chunker_factory.py | 8 +++--- .../graphrag/chunking/chunking_config.py | 28 ++++++++++--------- .../graphrag/chunking/sentence_chunker.py | 2 +- .../graphrag/chunking/token_chunker.py | 4 +-- packages/graphrag/graphrag/config/defaults.py | 2 +- .../graphrag/graphrag/config/init_content.py | 2 ++ .../index/workflows/create_base_text_units.py | 1 - tests/unit/chunking/test_chunker.py | 8 +++--- 9 files changed, 33 insertions(+), 34 deletions(-) diff --git a/packages/graphrag/graphrag/chunking/chunk_strategy_type.py b/packages/graphrag/graphrag/chunking/chunk_strategy_type.py index e6ab40b9b6..df3c9801e2 100644 --- a/packages/graphrag/graphrag/chunking/chunk_strategy_type.py +++ b/packages/graphrag/graphrag/chunking/chunk_strategy_type.py @@ -3,15 +3,11 @@ """Chunk strategy type enumeration.""" -from enum import Enum +from enum import StrEnum -class ChunkStrategyType(str, Enum): +class ChunkStrategyType(StrEnum): """ChunkStrategy class definition.""" - tokens = "tokens" - sentence = "sentence" - - def __repr__(self): - """Get a string representation.""" - return f'"{self.value}"' + Tokens = "tokens" + Sentence = "sentence" diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag/graphrag/chunking/chunker_factory.py index d7bdc834c3..5986f73e5f 100644 --- a/packages/graphrag/graphrag/chunking/chunker_factory.py +++ b/packages/graphrag/graphrag/chunking/chunker_factory.py @@ -59,14 +59,14 @@ def create_chunker( if chunker_strategy not in chunker_factory: match chunker_strategy: - case ChunkStrategyType.tokens: + case ChunkStrategyType.Tokens: from graphrag.chunking.token_chunker import TokenChunker - register_chunker(ChunkStrategyType.tokens, TokenChunker) - case ChunkStrategyType.sentence: + register_chunker(ChunkStrategyType.Tokens, TokenChunker) + case ChunkStrategyType.Sentence: from graphrag.chunking.sentence_chunker import SentenceChunker - register_chunker(ChunkStrategyType.sentence, SentenceChunker) + register_chunker(ChunkStrategyType.Sentence, SentenceChunker) case _: msg = f"ChunkingConfig.strategy '{chunker_strategy}' is not registered in the ChunkerFactory. Registered types: {', '.join(chunker_factory.keys())}." raise ValueError(msg) diff --git a/packages/graphrag/graphrag/chunking/chunking_config.py b/packages/graphrag/graphrag/chunking/chunking_config.py index 77b8080f77..a111f5d2ac 100644 --- a/packages/graphrag/graphrag/chunking/chunking_config.py +++ b/packages/graphrag/graphrag/chunking/chunking_config.py @@ -3,36 +3,38 @@ """Parameterization settings for the default configuration.""" -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from graphrag.chunking.chunk_strategy_type import ChunkStrategyType -from graphrag.config.defaults import graphrag_config_defaults class ChunkingConfig(BaseModel): """Configuration section for chunking.""" + model_config = ConfigDict(extra="allow") + """Allow extra fields to support custom cache implementations.""" + strategy: str = Field( description="The chunking strategy to use.", - default=ChunkStrategyType.tokens, + default=ChunkStrategyType.Tokens, ) - size: int = Field( + size: int | None = Field( description="The chunk size to use.", - default=graphrag_config_defaults.chunks.size, + default=None, ) - overlap: int = Field( + overlap: int | None = Field( description="The chunk overlap to use.", - default=graphrag_config_defaults.chunks.overlap, + default=None, ) - encoding_model: str = Field( + encoding_model: str | None = Field( description="The encoding model to use.", - default=graphrag_config_defaults.chunks.encoding_model, + default=None, ) - prepend_metadata: bool = Field( + prepend_metadata: bool | None = Field( description="Prepend metadata into each chunk.", - default=graphrag_config_defaults.chunks.prepend_metadata, + default=None, ) - chunk_size_includes_metadata: bool = Field( + chunk_size_includes_metadata: bool | None = Field( description="Count metadata in max tokens.", - default=graphrag_config_defaults.chunks.chunk_size_includes_metadata, + default=None, ) diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag/graphrag/chunking/sentence_chunker.py index af75fcd1c0..bc68e4b6e0 100644 --- a/packages/graphrag/graphrag/chunking/sentence_chunker.py +++ b/packages/graphrag/graphrag/chunking/sentence_chunker.py @@ -15,7 +15,7 @@ class SentenceChunker(Chunker): """A chunker that splits text into sentence-based chunks.""" - def __init__(self, prepend_metadata: bool, **kwargs: Any) -> None: + def __init__(self, prepend_metadata: bool = False, **kwargs: Any) -> None: """Create a sentence chunker instance.""" self._prepend_metadata = prepend_metadata bootstrap() diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index 41848fd043..d688b23d4a 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -24,9 +24,9 @@ def __init__( size: int, overlap: int, encoding_model: str, - prepend_metadata: bool, - chunk_size_includes_metadata: bool, tokenizer: Tokenizer, + prepend_metadata: bool = False, + chunk_size_includes_metadata: bool = False, **kwargs: Any, ) -> None: """Create a token chunker instance.""" diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py index f7e904d7af..e3f1f33845 100644 --- a/packages/graphrag/graphrag/config/defaults.py +++ b/packages/graphrag/graphrag/config/defaults.py @@ -60,9 +60,9 @@ class BasicSearchDefaults: class ChunksDefaults: """Default values for chunks.""" + strategy: str = ChunkStrategyType.Tokens size: int = 1200 overlap: int = 100 - strategy: ClassVar[ChunkStrategyType] = ChunkStrategyType.tokens encoding_model: str = ENCODING_MODEL prepend_metadata: bool = False chunk_size_includes_metadata: bool = False diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py index 4567406820..8d2c85af0a 100644 --- a/packages/graphrag/graphrag/config/init_content.py +++ b/packages/graphrag/graphrag/config/init_content.py @@ -55,8 +55,10 @@ file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json] chunks: + strategy: {graphrag_config_defaults.chunks.strategy} size: {graphrag_config_defaults.chunks.size} overlap: {graphrag_config_defaults.chunks.overlap} + encoding_model: {graphrag_config_defaults.chunks.encoding_model} ### Output/storage settings ### ## If blob storage is specified in the following four sections, diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 94d2bf0f4c..04371a293e 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -92,4 +92,3 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: return cast( "pd.DataFrame", text_units[text_units["text"].notna()].reset_index(drop=True) ) - diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index bb4add8e63..2041294d95 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -31,7 +31,7 @@ def setup_method(self, method): def test_basic_functionality(self): """Test basic sentence splitting without metadata""" input = "This is a test. Another sentence." - chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.sentence)) + chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) assert len(chunks) == 2 @@ -41,14 +41,14 @@ def test_basic_functionality(self): def test_multiple_documents(self): """Test processing multiple input documents""" input = ["First. Document.", "Second. Doc."] - chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.sentence)) + chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = [chunk for doc in input for chunk in chunker.chunk(doc)] assert len(chunks) == 4 def test_mixed_whitespace_handling(self): """Test input with irregular whitespace""" input = " Sentence with spaces. Another one! " - chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.sentence)) + chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) assert chunks[0] == " Sentence with spaces." assert chunks[1] == "Another one!" @@ -67,7 +67,7 @@ def test_basic_functionality(self, mock_get_encoding): size=5, overlap=1, encoding_model="fake-encoding", - strategy=ChunkStrategyType.tokens, + strategy=ChunkStrategyType.Tokens, ) chunker = create_chunker(config, tokenizer=tokenizer) From eb22d7a61b30e2181cae957d2432a26c3e10591d Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Thu, 18 Dec 2025 17:20:04 -0800 Subject: [PATCH 11/23] Fix defaults construction --- .../graphrag/graphrag/config/models/graph_rag_config.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py index a0cfb34f29..9b80f7e520 100644 --- a/packages/graphrag/graphrag/config/models/graph_rag_config.py +++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py @@ -127,7 +127,14 @@ def _validate_input_base_dir(self) -> None: chunks: ChunkingConfig = Field( description="The chunking configuration to use.", - default=ChunkingConfig(), + default=ChunkingConfig( + strategy=graphrag_config_defaults.chunks.strategy, + size=graphrag_config_defaults.chunks.size, + overlap=graphrag_config_defaults.chunks.overlap, + encoding_model=graphrag_config_defaults.chunks.encoding_model, + prepend_metadata=graphrag_config_defaults.chunks.prepend_metadata, + chunk_size_includes_metadata=graphrag_config_defaults.chunks.chunk_size_includes_metadata, + ), ) """The chunking configuration to use.""" From 780a03827c37b7a8edfe4a2a14a8380c551c9b8a Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Fri, 19 Dec 2025 16:36:30 -0800 Subject: [PATCH 12/23] Add prepending tests --- .../graphrag/graphrag/chunking/chunker.py | 2 +- .../graphrag/chunking/sentence_chunker.py | 13 +--- .../graphrag/chunking/token_chunker.py | 15 +--- .../index/workflows/create_base_text_units.py | 6 +- tests/unit/chunking/test_chunker.py | 72 ++++++++----------- 5 files changed, 42 insertions(+), 66 deletions(-) diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag/graphrag/chunking/chunker.py index 491a01ab0f..b455c62fd1 100644 --- a/packages/graphrag/graphrag/chunking/chunker.py +++ b/packages/graphrag/graphrag/chunking/chunker.py @@ -15,5 +15,5 @@ def __init__(self, **kwargs: Any) -> None: """Create a chunker instance.""" @abstractmethod - def chunk(self, text: str, metadata: str | dict | None = None) -> list[str]: + def chunk(self, text: str, metadata: dict | None = None) -> list[str]: """Chunk method definition.""" diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag/graphrag/chunking/sentence_chunker.py index bc68e4b6e0..441693a441 100644 --- a/packages/graphrag/graphrag/chunking/sentence_chunker.py +++ b/packages/graphrag/graphrag/chunking/sentence_chunker.py @@ -3,7 +3,6 @@ """A module containing 'SentenceChunker' class.""" -import json from typing import Any import nltk @@ -20,19 +19,11 @@ def __init__(self, prepend_metadata: bool = False, **kwargs: Any) -> None: self._prepend_metadata = prepend_metadata bootstrap() - def chunk(self, text: str, metadata: str | dict | None = None) -> list[str]: + def chunk(self, text: str, metadata: dict | None = None) -> list[str]: """Chunk the text into sentence-based chunks.""" chunks = nltk.sent_tokenize(text) if self._prepend_metadata and metadata is not None: - line_delimiter = ".\n" - metadata_str = "" - if isinstance(metadata, str): - metadata = json.loads(metadata) - if isinstance(metadata, dict): - metadata_str = ( - line_delimiter.join(f"{k}: {v}" for k, v in metadata.items()) - + line_delimiter - ) + metadata_str = ".\n".join(f"{k}: {v}" for k, v in metadata.items()) + ".\n" chunks = [metadata_str + chunk for chunk in chunks] return chunks diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index d688b23d4a..770fb2a115 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -3,7 +3,6 @@ """A module containing 'TokenChunker' class.""" -import json from collections.abc import Callable from typing import Any @@ -23,7 +22,6 @@ def __init__( self, size: int, overlap: int, - encoding_model: str, tokenizer: Tokenizer, prepend_metadata: bool = False, chunk_size_includes_metadata: bool = False, @@ -32,25 +30,18 @@ def __init__( """Create a token chunker instance.""" self._size = size self._overlap = overlap - self._encoding_model = encoding_model self._prepend_metadata = prepend_metadata self._chunk_size_includes_metadata = chunk_size_includes_metadata self._tokenizer = tokenizer - def chunk(self, text: str, metadata: str | dict | None = None) -> list[str]: + def chunk(self, text: str, metadata: dict | None = None) -> list[str]: """Chunk the text into token-based chunks.""" - line_delimiter = ".\n" + # we have to create and measure the metadata first to account for the length when chunking metadata_str = "" metadata_tokens = 0 if self._prepend_metadata and metadata is not None: - if isinstance(metadata, str): - metadata = json.loads(metadata) - if isinstance(metadata, dict): - metadata_str = ( - line_delimiter.join(f"{k}: {v}" for k, v in metadata.items()) - + line_delimiter - ) + metadata_str = ".\n".join(f"{k}: {v}" for k, v in metadata.items()) + ".\n" if self._chunk_size_includes_metadata: metadata_tokens = len(self._tokenizer.encode(metadata_str)) diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 04371a293e..c757ea0d7e 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -3,6 +3,7 @@ """A module containing run_workflow method definition.""" +import json import logging from typing import Any, cast @@ -62,7 +63,10 @@ def create_base_text_units( logger.info("Starting chunking process for %d documents", total_rows) def chunker_with_logging(row: pd.Series, row_index: int) -> Any: - row["chunks"] = chunker.chunk(row["text"], row.get("metadata")) + metadata = row.get("metadata") + if (metadata is not None) and isinstance(metadata, str): + metadata = json.loads(metadata) + row["chunks"] = chunker.chunk(row["text"], metadata=metadata) tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 2041294d95..69ba45541c 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -11,14 +11,15 @@ split_text_on_tokens, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag_common.types.tokenizer import Tokenizer -class MockTokenizer: - def encode(self, text): +class MockTokenizer(Tokenizer): + def encode(self, text) -> list[int]: return [ord(char) for char in text] - def decode(self, token_ids): - return "".join(chr(id) for id in token_ids) + def decode(self, tokens) -> str: + return "".join(chr(id) for id in tokens) tokenizer = get_tokenizer() @@ -53,6 +54,18 @@ def test_mixed_whitespace_handling(self): assert chunks[0] == " Sentence with spaces." assert chunks[1] == "Another one!" + def test_prepend_metadata(self): + """Test prepending metadata to chunks""" + input = "This is a test. Another sentence." + config = ChunkingConfig( + strategy=ChunkStrategyType.Sentence, prepend_metadata=True + ) + chunker = create_chunker(config) + chunks = chunker.chunk(input, metadata={"message": "hello"}) + + assert chunks[0] == "message: hello.\nThis is a test." + assert chunks[1] == "message: hello.\nAnother sentence." + class TestRunTokens: @patch("tiktoken.get_encoding") @@ -75,6 +88,20 @@ def test_basic_functionality(self, mock_get_encoding): assert len(chunks) > 0 + def test_prepend_metadata(self): + """Test prepending metadata to chunks""" + mocked_tokenizer = MockTokenizer() + input = "This is a test." + config = ChunkingConfig( + strategy=ChunkStrategyType.Tokens, size=5, overlap=0, prepend_metadata=True + ) + chunker = create_chunker(config, tokenizer=mocked_tokenizer) + chunks = chunker.chunk(input, metadata={"message": "hello"}) + + assert chunks[0] == "message: hello.\nThis " + assert chunks[1] == "message: hello.\nis a " + assert chunks[2] == "message: hello.\ntest." + def test_split_text_str_empty(): result = split_text_on_tokens( @@ -146,40 +173,3 @@ def test_split_text_on_tokens_no_overlap(): encode=tok.encode, ) assert result == expected_splits - - -@patch("tiktoken.get_encoding") -def test_get_encoding_fn_encode(mock_get_encoding): - # Create a mock encoding object with encode and decode methods - mock_encoding = Mock() - mock_encoding.encode = Mock(return_value=[1, 2, 3]) - mock_encoding.decode = Mock(return_value="decoded text") - - # Configure the mock_get_encoding to return the mock encoding object - mock_get_encoding.return_value = mock_encoding - - # Call the function to get encode and decode functions - tokenizer = get_tokenizer(encoding_model="mock_encoding") - - # Test the encode function - encoded_text = tokenizer.encode("test text") - assert encoded_text == [1, 2, 3] - mock_encoding.encode.assert_called_once_with("test text") - - -@patch("tiktoken.get_encoding") -def test_get_encoding_fn_decode(mock_get_encoding): - # Create a mock encoding object with encode and decode methods - mock_encoding = Mock() - mock_encoding.encode = Mock(return_value=[1, 2, 3]) - mock_encoding.decode = Mock(return_value="decoded text") - - # Configure the mock_get_encoding to return the mock encoding object - mock_get_encoding.return_value = mock_encoding - - # Call the function to get encode and decode functions - tokenizer = get_tokenizer(encoding_model="mock_encoding") - - decoded_text = tokenizer.decode([1, 2, 3]) - assert decoded_text == "decoded text" - mock_encoding.decode.assert_called_once_with([1, 2, 3]) From 026474a073bf7e144ed8eea9bef1e8cb2c88ef90 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Fri, 19 Dec 2025 17:39:42 -0800 Subject: [PATCH 13/23] Remove chunk_size_includes_metadata config --- docs/config/yaml.md | 3 +- docs/index/inputs.md | 52 +------------------ .../graphrag/graphrag/chunking/chunker.py | 6 ++- .../graphrag/chunking/chunking_config.py | 4 -- .../graphrag/chunking/chunking_document.py | 15 ++++++ .../graphrag/chunking/sentence_chunker.py | 6 ++- .../chunking/text_chunking_document.py | 20 +++++++ .../graphrag/chunking/token_chunker.py | 24 +++------ packages/graphrag/graphrag/config/defaults.py | 1 - .../config/models/graph_rag_config.py | 1 - .../index/workflows/create_base_text_units.py | 4 +- tests/unit/chunking/test_chunker.py | 21 ++++---- tests/unit/config/utils.py | 1 - tests/verbs/test_create_base_text_units.py | 19 ------- 14 files changed, 67 insertions(+), 110 deletions(-) create mode 100644 packages/graphrag/graphrag/chunking/chunking_document.py create mode 100644 packages/graphrag/graphrag/chunking/text_chunking_document.py diff --git a/docs/config/yaml.md b/docs/config/yaml.md index 768abef463..10973fd211 100644 --- a/docs/config/yaml.md +++ b/docs/config/yaml.md @@ -100,12 +100,11 @@ These settings configure how we parse documents into text chunks. This is necess #### Fields +- `strategy` **str**[tokens|sentences] - How to chunk the text. - `size` **int** - The max chunk size in tokens. - `overlap` **int** - The chunk overlap in tokens. -- `strategy` **str**[tokens|sentences] - How to chunk the text. - `encoding_model` **str** - The text encoding model to use for splitting on token boundaries. - `prepend_metadata` **bool** - Determines if metadata values should be added at the beginning of each chunk. Default=`False`. -- `chunk_size_includes_metadata` **bool** - Specifies whether the chunk size calculation should include metadata tokens. Default=`False`. ## Outputs and Storage diff --git a/docs/index/inputs.md b/docs/index/inputs.md index b62bceacfa..7f34370a70 100644 --- a/docs/index/inputs.md +++ b/docs/index/inputs.md @@ -82,10 +82,9 @@ As described above, when documents are imported you can specify a list of `metad ### Chunking Config -Next, the `chunks` block needs to instruct the chunker how to handle this metadata when creating text units. By default, it is ignored. We have two settings to include it: +Next, the `chunks` block needs to instruct the chunker how to handle this metadata when creating text units. By default, it is ignored. We have the following setting to include it: - `prepend_metadata`. This instructs the importer to copy the contents of the `metadata` column for each row into the start of every single text chunk. This metadata is copied as key: value pairs on new lines. -- `chunk_size_includes_metadata`: This tells the chunker how to compute the chunk size when metadata is included. By default, we create the text units using your specified `chunk_size` *and then* prepend the metadata. This means that the final text unit lengths may be longer than your configured `chunk_size`, and it will vary based on the length of the metadata for each document. When this setting is `True`, we will compute the raw text using the remainder after measuring the metadata length so that the resulting text units always comply with your configured `chunk_size`. ### Examples @@ -124,7 +123,6 @@ chunks: size: 100 overlap: 0 prepend_metadata: true - chunk_size_includes_metadata: false ``` Documents DataFrame @@ -162,54 +160,6 @@ US to lift most federal COVID-19 vaccine mandates,WASHINGTON (AP) The Biden admi NY lawmakers begin debating budget 1 month after due date,ALBANY, N.Y. (AP) New York lawmakers began voting Monday on a $229 billion state budget due a month ago that would raise the minimum wage, crack down on illicit pot shops and ban gas stoves and furnaces in new buildings. Negotiations among Gov. Kathy Hochul and her fellow Democrats in control of the Legislature dragged on past the April 1 budget deadline, largely because of disagreements over changes to the bail law and other policy proposals included in the spending plan. Floor debates on some budget bills began Monday. State Senate Majority Leader Andrea Stewart-Cousins said she expected voting to be wrapped up Tuesday for a budget she said contains "significant wins" for New Yorkers. "I would have liked to have done this sooner. I think we would all agree to that," Cousins told reporters before voting began. "This has been a very policy-laden budget and a lot of the policies had to parsed through." Hochul was able to push through a change to the bail law that will eliminate the standard that requires judges to prescribe the "least restrictive" means to ensure defendants return to court. Hochul said judges needed the extra discretion. Some liberal lawmakers argued that it would undercut the sweeping bail reforms approved in 2019 and result in more people with low incomes and people of color in pretrial detention. Here are some other policy provisions that will be included in the budget, according to state officials. The minimum wage would be raised to $17 in New York City and some of its suburbs and $16 in the rest of the state by 2026. That's up from $15 in the city and $14.20 upstate. --- - -settings.yaml - -```yaml -input: - file_type: csv - title_column: headline - text_column: article - metadata: [headline] - -chunks: - size: 50 - overlap: 5 - prepend_metadata: true - chunk_size_includes_metadata: true -``` - -Documents DataFrame - -| id | title | text | creation_date | metadata | -| --------------------- | --------------------------------------------------------- | ------------------------ | ----------------------------- | --------------------------------------------------------------------------- | -| (generated from text) | US to lift most federal COVID-19 vaccine mandates | (article column content) | (create date of articles.csv) | { "headline": "US to lift most federal COVID-19 vaccine mandates" } | -| (generated from text) | NY lawmakers begin debating budget 1 month after due date | (article column content) | (create date of articles.csv) | { "headline": "NY lawmakers begin debating budget 1 month after due date" } | - -Raw Text Chunks - -| content | length | -| ------- | ------: | -| title: US to lift most federal COVID-19 vaccine mandates
WASHINGTON (AP) The Biden administration will end most of the last remaining federal COVID-19 vaccine requirements next week when the national public health emergency for the coronavirus ends, the White House said Monday. Vaccine requirements for federal workers and federal contractors, | 50 | -| title: US to lift most federal COVID-19 vaccine mandates
federal workers and federal contractors as well as foreign air travelers to the U.S., will end May 11. The government is also beginning the process of lifting shot requirements for Head Start educators, healthcare workers, and noncitizens at U.S. land borders. | 50 | -| title: US to lift most federal COVID-19 vaccine mandates
noncitizens at U.S. land borders. The requirements are among the last vestiges of some of the more coercive measures taken by the federal government to promote vaccination as the deadly virus raged, and their end marks the latest display of how | 50 | -| title: US to lift most federal COVID-19 vaccine mandates
the latest display of how President Joe Biden's administration is moving to treat COVID-19 as a routine, endemic illness. "While I believe that these vaccine mandates had a tremendous beneficial impact, we are now at a point where we think that | 50 | -| title: US to lift most federal COVID-19 vaccine mandates
point where we think that it makes a lot of sense to pull these requirements down," White House COVID-19 coordinator Dr. Ashish Jha told The Associated Press on Monday. | 38 | -| title: NY lawmakers begin debating budget 1 month after due date
ALBANY, N.Y. (AP) New York lawmakers began voting Monday on a $229 billion state budget due a month ago that would raise the minimum wage, crack down on illicit pot shops and ban gas stoves and furnaces in new | 50 | -| title: NY lawmakers begin debating budget 1 month after due date
stoves and furnaces in new buildings. Negotiations among Gov. Kathy Hochul and her fellow Democrats in control of the Legislature dragged on past the April 1 budget deadline, largely because of disagreements over changes to the bail law and | 50 | -| title: NY lawmakers begin debating budget 1 month after due date
to the bail law and other policy proposals included in the spending plan. Floor debates on some budget bills began Monday. State Senate Majority Leader Andrea Stewart-Cousins said she expected voting to be wrapped up Tuesday for a budget | 50 | -|title: NY lawmakers begin debating budget 1 month after due date
up Tuesday for a budget she said contains "significant wins" for New Yorkers. "I would have liked to have done this sooner. I think we would all agree to that," Cousins told reporters before voting began. "This has been | 50 | -| title: NY lawmakers begin debating budget 1 month after due date
voting began. "This has been a very policy-laden budget and a lot of the policies had to parsed through." Hochul was able to push through a change to the bail law that will eliminate the standard that requires judges | 50 | -| title: NY lawmakers begin debating budget 1 month after due date
the standard that requires judges to prescribe the "least restrictive" means to ensure defendants return to court. Hochul said judges needed the extra discretion. Some liberal lawmakers argued that it would undercut the sweeping bail reforms approved in 2019 | 50 | -| title: NY lawmakers begin debating budget 1 month after due date
bail reforms approved in 2019 and result in more people with low incomes and people of color in pretrial detention. Here are some other policy provisions that will be included in the budget, according to state officials. The minimum | 50 | -| title: NY lawmakers begin debating budget 1 month after due date
to state officials. The minimum wage would be raised to $17 in be raised to $17 in New York City and some of its suburbs and $16 in the rest of the state by 2026. That's up from $15 | 50 | -| title: NY lawmakers begin debating budget 1 month after due date
2026. That's up from $15 in the city and $14.20 upstate. | 22 | - - -In this example we can see that the two input documents were parsed into fourteen output text chunks. The title (headline) of each document is prepended and included in the computed chunk size, so each chunk matches the configured chunk size (except the last one for each document). We've also configured some overlap in these text chunks, so the last five tokens are shared. Why would you use overlap in your text chunks? Consider that when you are splitting documents based on tokens, it is highly likely that sentences or even related concepts will be split into separate chunks. Each text chunk is processed separately by the language model, so this may result in incomplete "ideas" at the boundaries of the chunk. Overlap ensures that these split concepts are fully contained in at least one of the chunks. - - #### JSON files This final example uses a JSON file for each of the same two articles. In this example we'll set the object fields to read, but we will not add metadata to the text chunks. diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag/graphrag/chunking/chunker.py index b455c62fd1..5c4808ae67 100644 --- a/packages/graphrag/graphrag/chunking/chunker.py +++ b/packages/graphrag/graphrag/chunking/chunker.py @@ -6,6 +6,8 @@ from abc import ABC, abstractmethod from typing import Any +from graphrag.chunking.chunking_document import ChunkingDocument + class Chunker(ABC): """Abstract base class for text chunkers.""" @@ -15,5 +17,7 @@ def __init__(self, **kwargs: Any) -> None: """Create a chunker instance.""" @abstractmethod - def chunk(self, text: str, metadata: dict | None = None) -> list[str]: + def chunk( + self, document: ChunkingDocument, metadata: dict | None = None + ) -> list[str]: """Chunk method definition.""" diff --git a/packages/graphrag/graphrag/chunking/chunking_config.py b/packages/graphrag/graphrag/chunking/chunking_config.py index a111f5d2ac..437ed3fb5a 100644 --- a/packages/graphrag/graphrag/chunking/chunking_config.py +++ b/packages/graphrag/graphrag/chunking/chunking_config.py @@ -34,7 +34,3 @@ class ChunkingConfig(BaseModel): description="Prepend metadata into each chunk.", default=None, ) - chunk_size_includes_metadata: bool | None = Field( - description="Count metadata in max tokens.", - default=None, - ) diff --git a/packages/graphrag/graphrag/chunking/chunking_document.py b/packages/graphrag/graphrag/chunking/chunking_document.py new file mode 100644 index 0000000000..1907b1ea50 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/chunking_document.py @@ -0,0 +1,15 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing the 'Chunker' class.""" + +from abc import ABC, abstractmethod +from typing import Any + + +class ChunkingDocument(ABC): + """Abstract base class for documents that need to be chunked. If you want to use a text-based chunker, ensure __str__ is implemented.""" + + @abstractmethod + def __init__(self, **kwargs: Any) -> None: + """Create a chunking document instance.""" diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag/graphrag/chunking/sentence_chunker.py index 441693a441..5e801d27da 100644 --- a/packages/graphrag/graphrag/chunking/sentence_chunker.py +++ b/packages/graphrag/graphrag/chunking/sentence_chunker.py @@ -9,6 +9,7 @@ from graphrag.chunking.bootstrap_nltk import bootstrap from graphrag.chunking.chunker import Chunker +from graphrag.chunking.chunking_document import ChunkingDocument class SentenceChunker(Chunker): @@ -19,8 +20,11 @@ def __init__(self, prepend_metadata: bool = False, **kwargs: Any) -> None: self._prepend_metadata = prepend_metadata bootstrap() - def chunk(self, text: str, metadata: dict | None = None) -> list[str]: + def chunk( + self, document: ChunkingDocument, metadata: dict | None = None + ) -> list[str]: """Chunk the text into sentence-based chunks.""" + text = str(document) chunks = nltk.sent_tokenize(text) if self._prepend_metadata and metadata is not None: diff --git a/packages/graphrag/graphrag/chunking/text_chunking_document.py b/packages/graphrag/graphrag/chunking/text_chunking_document.py new file mode 100644 index 0000000000..f725052b31 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/text_chunking_document.py @@ -0,0 +1,20 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing the 'Chunker' class.""" + +from typing import Any + +from graphrag.chunking.chunking_document import ChunkingDocument + + +class TextChunkingDocument(ChunkingDocument): + """Represents a basic text document for chunking.""" + + def __init__(self, text: str, **kwargs: Any) -> None: + """Create a chunking document instance.""" + self._text = text + + def __str__(self) -> str: + """Get the text of the document.""" + return self._text diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index 770fb2a115..b2dfaa0e41 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -9,6 +9,7 @@ from graphrag_common.types.tokenizer import Tokenizer from graphrag.chunking.chunker import Chunker +from graphrag.chunking.chunking_document import ChunkingDocument EncodedText = list[int] DecodeFn = Callable[[EncodedText], str] @@ -24,40 +25,31 @@ def __init__( overlap: int, tokenizer: Tokenizer, prepend_metadata: bool = False, - chunk_size_includes_metadata: bool = False, **kwargs: Any, ) -> None: """Create a token chunker instance.""" self._size = size self._overlap = overlap self._prepend_metadata = prepend_metadata - self._chunk_size_includes_metadata = chunk_size_includes_metadata self._tokenizer = tokenizer - def chunk(self, text: str, metadata: dict | None = None) -> list[str]: + def chunk( + self, document: ChunkingDocument, metadata: dict | None = None + ) -> list[str]: """Chunk the text into token-based chunks.""" # we have to create and measure the metadata first to account for the length when chunking - metadata_str = "" - metadata_tokens = 0 - - if self._prepend_metadata and metadata is not None: - metadata_str = ".\n".join(f"{k}: {v}" for k, v in metadata.items()) + ".\n" - - if self._chunk_size_includes_metadata: - metadata_tokens = len(self._tokenizer.encode(metadata_str)) - if metadata_tokens >= self._size: - message = "Metadata tokens exceeds the maximum tokens per chunk. Please increase the tokens per chunk." - raise ValueError(message) + text = str(document) chunks = split_text_on_tokens( text, - chunk_size=self._size - metadata_tokens, + chunk_size=self._size, chunk_overlap=self._overlap, encode=self._tokenizer.encode, decode=self._tokenizer.decode, ) - if self._prepend_metadata: + if self._prepend_metadata and metadata is not None: + metadata_str = ".\n".join(f"{k}: {v}" for k, v in metadata.items()) + ".\n" chunks = [metadata_str + chunk for chunk in chunks] return chunks diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py index e3f1f33845..85ca07330d 100644 --- a/packages/graphrag/graphrag/config/defaults.py +++ b/packages/graphrag/graphrag/config/defaults.py @@ -65,7 +65,6 @@ class ChunksDefaults: overlap: int = 100 encoding_model: str = ENCODING_MODEL prepend_metadata: bool = False - chunk_size_includes_metadata: bool = False @dataclass diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py index 9b80f7e520..b303672192 100644 --- a/packages/graphrag/graphrag/config/models/graph_rag_config.py +++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py @@ -133,7 +133,6 @@ def _validate_input_base_dir(self) -> None: overlap=graphrag_config_defaults.chunks.overlap, encoding_model=graphrag_config_defaults.chunks.encoding_model, prepend_metadata=graphrag_config_defaults.chunks.prepend_metadata, - chunk_size_includes_metadata=graphrag_config_defaults.chunks.chunk_size_includes_metadata, ), ) """The chunking configuration to use.""" diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index c757ea0d7e..9eb0b7f0be 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -13,6 +13,7 @@ from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.chunker import Chunker from graphrag.chunking.chunker_factory import create_chunker +from graphrag.chunking.text_chunking_document import TextChunkingDocument from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput @@ -66,7 +67,8 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: metadata = row.get("metadata") if (metadata is not None) and isinstance(metadata, str): metadata = json.loads(metadata) - row["chunks"] = chunker.chunk(row["text"], metadata=metadata) + document = TextChunkingDocument(text=row["text"]) + row["chunks"] = chunker.chunk(document, metadata=metadata) tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 69ba45541c..9819f1faf5 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -7,6 +7,7 @@ from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.chunking.chunker_factory import create_chunker from graphrag.chunking.chunking_config import ChunkingConfig +from graphrag.chunking.text_chunking_document import TextChunkingDocument from graphrag.chunking.token_chunker import ( split_text_on_tokens, ) @@ -31,7 +32,7 @@ def setup_method(self, method): def test_basic_functionality(self): """Test basic sentence splitting without metadata""" - input = "This is a test. Another sentence." + input = TextChunkingDocument(text="This is a test. Another sentence.") chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) @@ -39,16 +40,10 @@ def test_basic_functionality(self): assert chunks[0] == "This is a test." assert chunks[1] == "Another sentence." - def test_multiple_documents(self): - """Test processing multiple input documents""" - input = ["First. Document.", "Second. Doc."] - chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) - chunks = [chunk for doc in input for chunk in chunker.chunk(doc)] - assert len(chunks) == 4 - def test_mixed_whitespace_handling(self): """Test input with irregular whitespace""" - input = " Sentence with spaces. Another one! " + + input = TextChunkingDocument(text=" Sentence with spaces. Another one! ") chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) assert chunks[0] == " Sentence with spaces." @@ -56,7 +51,7 @@ def test_mixed_whitespace_handling(self): def test_prepend_metadata(self): """Test prepending metadata to chunks""" - input = "This is a test. Another sentence." + input = TextChunkingDocument(text="This is a test. Another sentence.") config = ChunkingConfig( strategy=ChunkStrategyType.Sentence, prepend_metadata=True ) @@ -75,7 +70,9 @@ def test_basic_functionality(self, mock_get_encoding): mock_encoder.decode.side_effect = lambda x: bytes(x).decode() mock_get_encoding.return_value = mock_encoder - input = "Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to." + input = TextChunkingDocument( + text="Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to." + ) config = ChunkingConfig( size=5, overlap=1, @@ -91,7 +88,7 @@ def test_basic_functionality(self, mock_get_encoding): def test_prepend_metadata(self): """Test prepending metadata to chunks""" mocked_tokenizer = MockTokenizer() - input = "This is a test." + input = TextChunkingDocument(text="This is a test.") config = ChunkingConfig( strategy=ChunkStrategyType.Tokens, size=5, overlap=0, prepend_metadata=True ) diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py index f46b29a37c..8e3613714e 100644 --- a/tests/unit/config/utils.py +++ b/tests/unit/config/utils.py @@ -167,7 +167,6 @@ def assert_chunking_configs(actual: ChunkingConfig, expected: ChunkingConfig) -> assert actual.strategy == expected.strategy assert actual.encoding_model == expected.encoding_model assert actual.prepend_metadata == expected.prepend_metadata - assert actual.chunk_size_includes_metadata == expected.chunk_size_includes_metadata def assert_snapshots_configs( diff --git a/tests/verbs/test_create_base_text_units.py b/tests/verbs/test_create_base_text_units.py index 73b72fc2cb..e13bb569d1 100644 --- a/tests/verbs/test_create_base_text_units.py +++ b/tests/verbs/test_create_base_text_units.py @@ -43,22 +43,3 @@ async def test_create_base_text_units_metadata(): actual = await load_table_from_storage("text_units", context.output_storage) compare_outputs(actual, expected, ["text", "document_id", "n_tokens"]) - - -async def test_create_base_text_units_metadata_included_in_chunk(): - expected = load_test_table("text_units_metadata_included_chunk") - - context = await create_test_context() - - config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG) # type: ignore - config.input.metadata = ["title"] - config.chunks.prepend_metadata = True - config.chunks.chunk_size_includes_metadata = True - - await update_document_metadata(config.input.metadata, context) - - await run_workflow(config, context) - - actual = await load_table_from_storage("text_units", context.output_storage) - # only check the columns from the base workflow - our expected table is the final and will have more - compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"]) From c8dbb029f41a09e660fc93ec21e8ded59655e735 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 10:17:27 -0800 Subject: [PATCH 14/23] Revert ChunkingDocument interface --- .../graphrag/graphrag/chunking/chunker.py | 8 ++------ .../graphrag/chunking/chunking_document.py | 15 -------------- .../graphrag/chunking/sentence_chunker.py | 6 +----- .../chunking/text_chunking_document.py | 20 ------------------- .../graphrag/chunking/token_chunker.py | 8 +------- .../index/workflows/create_base_text_units.py | 4 +--- tests/unit/chunking/test_chunker.py | 14 ++++++------- 7 files changed, 11 insertions(+), 64 deletions(-) delete mode 100644 packages/graphrag/graphrag/chunking/chunking_document.py delete mode 100644 packages/graphrag/graphrag/chunking/text_chunking_document.py diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag/graphrag/chunking/chunker.py index 5c4808ae67..06fdff300b 100644 --- a/packages/graphrag/graphrag/chunking/chunker.py +++ b/packages/graphrag/graphrag/chunking/chunker.py @@ -6,18 +6,14 @@ from abc import ABC, abstractmethod from typing import Any -from graphrag.chunking.chunking_document import ChunkingDocument - class Chunker(ABC): - """Abstract base class for text chunkers.""" + """Abstract base class for document chunkers.""" @abstractmethod def __init__(self, **kwargs: Any) -> None: """Create a chunker instance.""" @abstractmethod - def chunk( - self, document: ChunkingDocument, metadata: dict | None = None - ) -> list[str]: + def chunk(self, text: str, metadata: dict | None = None) -> list[str]: """Chunk method definition.""" diff --git a/packages/graphrag/graphrag/chunking/chunking_document.py b/packages/graphrag/graphrag/chunking/chunking_document.py deleted file mode 100644 index 1907b1ea50..0000000000 --- a/packages/graphrag/graphrag/chunking/chunking_document.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing the 'Chunker' class.""" - -from abc import ABC, abstractmethod -from typing import Any - - -class ChunkingDocument(ABC): - """Abstract base class for documents that need to be chunked. If you want to use a text-based chunker, ensure __str__ is implemented.""" - - @abstractmethod - def __init__(self, **kwargs: Any) -> None: - """Create a chunking document instance.""" diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag/graphrag/chunking/sentence_chunker.py index 5e801d27da..441693a441 100644 --- a/packages/graphrag/graphrag/chunking/sentence_chunker.py +++ b/packages/graphrag/graphrag/chunking/sentence_chunker.py @@ -9,7 +9,6 @@ from graphrag.chunking.bootstrap_nltk import bootstrap from graphrag.chunking.chunker import Chunker -from graphrag.chunking.chunking_document import ChunkingDocument class SentenceChunker(Chunker): @@ -20,11 +19,8 @@ def __init__(self, prepend_metadata: bool = False, **kwargs: Any) -> None: self._prepend_metadata = prepend_metadata bootstrap() - def chunk( - self, document: ChunkingDocument, metadata: dict | None = None - ) -> list[str]: + def chunk(self, text: str, metadata: dict | None = None) -> list[str]: """Chunk the text into sentence-based chunks.""" - text = str(document) chunks = nltk.sent_tokenize(text) if self._prepend_metadata and metadata is not None: diff --git a/packages/graphrag/graphrag/chunking/text_chunking_document.py b/packages/graphrag/graphrag/chunking/text_chunking_document.py deleted file mode 100644 index f725052b31..0000000000 --- a/packages/graphrag/graphrag/chunking/text_chunking_document.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing the 'Chunker' class.""" - -from typing import Any - -from graphrag.chunking.chunking_document import ChunkingDocument - - -class TextChunkingDocument(ChunkingDocument): - """Represents a basic text document for chunking.""" - - def __init__(self, text: str, **kwargs: Any) -> None: - """Create a chunking document instance.""" - self._text = text - - def __str__(self) -> str: - """Get the text of the document.""" - return self._text diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index b2dfaa0e41..a07daa9fe5 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -9,7 +9,6 @@ from graphrag_common.types.tokenizer import Tokenizer from graphrag.chunking.chunker import Chunker -from graphrag.chunking.chunking_document import ChunkingDocument EncodedText = list[int] DecodeFn = Callable[[EncodedText], str] @@ -33,13 +32,8 @@ def __init__( self._prepend_metadata = prepend_metadata self._tokenizer = tokenizer - def chunk( - self, document: ChunkingDocument, metadata: dict | None = None - ) -> list[str]: + def chunk(self, text: str, metadata: dict | None = None) -> list[str]: """Chunk the text into token-based chunks.""" - # we have to create and measure the metadata first to account for the length when chunking - text = str(document) - chunks = split_text_on_tokens( text, chunk_size=self._size, diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 9eb0b7f0be..c757ea0d7e 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -13,7 +13,6 @@ from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.chunker import Chunker from graphrag.chunking.chunker_factory import create_chunker -from graphrag.chunking.text_chunking_document import TextChunkingDocument from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput @@ -67,8 +66,7 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: metadata = row.get("metadata") if (metadata is not None) and isinstance(metadata, str): metadata = json.loads(metadata) - document = TextChunkingDocument(text=row["text"]) - row["chunks"] = chunker.chunk(document, metadata=metadata) + row["chunks"] = chunker.chunk(row["text"], metadata=metadata) tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 9819f1faf5..275c9c77d6 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -7,7 +7,6 @@ from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.chunking.chunker_factory import create_chunker from graphrag.chunking.chunking_config import ChunkingConfig -from graphrag.chunking.text_chunking_document import TextChunkingDocument from graphrag.chunking.token_chunker import ( split_text_on_tokens, ) @@ -32,7 +31,7 @@ def setup_method(self, method): def test_basic_functionality(self): """Test basic sentence splitting without metadata""" - input = TextChunkingDocument(text="This is a test. Another sentence.") + input = "This is a test. Another sentence." chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) @@ -43,7 +42,7 @@ def test_basic_functionality(self): def test_mixed_whitespace_handling(self): """Test input with irregular whitespace""" - input = TextChunkingDocument(text=" Sentence with spaces. Another one! ") + input = " Sentence with spaces. Another one! " chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) assert chunks[0] == " Sentence with spaces." @@ -51,7 +50,7 @@ def test_mixed_whitespace_handling(self): def test_prepend_metadata(self): """Test prepending metadata to chunks""" - input = TextChunkingDocument(text="This is a test. Another sentence.") + input = "This is a test. Another sentence." config = ChunkingConfig( strategy=ChunkStrategyType.Sentence, prepend_metadata=True ) @@ -70,9 +69,8 @@ def test_basic_functionality(self, mock_get_encoding): mock_encoder.decode.side_effect = lambda x: bytes(x).decode() mock_get_encoding.return_value = mock_encoder - input = TextChunkingDocument( - text="Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to." - ) + input = "Marley was dead: to begin with. There is no doubt whatever about that. The register of his burial was signed by the clergyman, the clerk, the undertaker, and the chief mourner. Scrooge signed it. And Scrooge's name was good upon 'Change, for anything he chose to put his hand to." + config = ChunkingConfig( size=5, overlap=1, @@ -88,7 +86,7 @@ def test_basic_functionality(self, mock_get_encoding): def test_prepend_metadata(self): """Test prepending metadata to chunks""" mocked_tokenizer = MockTokenizer() - input = TextChunkingDocument(text="This is a test.") + input = "This is a test." config = ChunkingConfig( strategy=ChunkStrategyType.Tokens, size=5, overlap=0, prepend_metadata=True ) From 247547f5bcfea5622189d2808a3b07272ca8e837 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 11:10:39 -0800 Subject: [PATCH 15/23] Move metadata prepending to a util --- .../graphrag/graphrag/chunking/chunker.py | 2 +- .../graphrag/chunking/chunking_config.py | 2 +- .../graphrag/chunking/prepend_metadata.py | 15 ++++++++ .../graphrag/chunking/sentence_chunker.py | 12 ++----- .../graphrag/chunking/token_chunker.py | 12 ++----- .../index/workflows/create_base_text_units.py | 13 ++++--- tests/unit/chunking/test_chunker.py | 26 -------------- tests/unit/chunking/test_prepend_metadata.py | 34 +++++++++++++++++++ 8 files changed, 65 insertions(+), 51 deletions(-) create mode 100644 packages/graphrag/graphrag/chunking/prepend_metadata.py create mode 100644 tests/unit/chunking/test_prepend_metadata.py diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag/graphrag/chunking/chunker.py index 06fdff300b..e760e5d32b 100644 --- a/packages/graphrag/graphrag/chunking/chunker.py +++ b/packages/graphrag/graphrag/chunking/chunker.py @@ -15,5 +15,5 @@ def __init__(self, **kwargs: Any) -> None: """Create a chunker instance.""" @abstractmethod - def chunk(self, text: str, metadata: dict | None = None) -> list[str]: + def chunk(self, text: str) -> list[str]: """Chunk method definition.""" diff --git a/packages/graphrag/graphrag/chunking/chunking_config.py b/packages/graphrag/graphrag/chunking/chunking_config.py index 437ed3fb5a..625b486040 100644 --- a/packages/graphrag/graphrag/chunking/chunking_config.py +++ b/packages/graphrag/graphrag/chunking/chunking_config.py @@ -32,5 +32,5 @@ class ChunkingConfig(BaseModel): ) prepend_metadata: bool | None = Field( description="Prepend metadata into each chunk.", - default=None, + default=False, ) diff --git a/packages/graphrag/graphrag/chunking/prepend_metadata.py b/packages/graphrag/graphrag/chunking/prepend_metadata.py new file mode 100644 index 0000000000..dea0471a44 --- /dev/null +++ b/packages/graphrag/graphrag/chunking/prepend_metadata.py @@ -0,0 +1,15 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'prepend_metadata' function.""" + + +def prepend_metadata( + text: str, metadata: dict, delimiter: str = ": ", line_delimiter: str = "\n" +) -> str: + """Prepend metadata to the given text. This utility writes the dict as rows of key/value pairs.""" + metadata_str = ( + line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items()) + + line_delimiter + ) + return metadata_str + text diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag/graphrag/chunking/sentence_chunker.py index 441693a441..f72296f55b 100644 --- a/packages/graphrag/graphrag/chunking/sentence_chunker.py +++ b/packages/graphrag/graphrag/chunking/sentence_chunker.py @@ -14,16 +14,10 @@ class SentenceChunker(Chunker): """A chunker that splits text into sentence-based chunks.""" - def __init__(self, prepend_metadata: bool = False, **kwargs: Any) -> None: + def __init__(self, **kwargs: Any) -> None: """Create a sentence chunker instance.""" - self._prepend_metadata = prepend_metadata bootstrap() - def chunk(self, text: str, metadata: dict | None = None) -> list[str]: + def chunk(self, text) -> list[str]: """Chunk the text into sentence-based chunks.""" - chunks = nltk.sent_tokenize(text) - - if self._prepend_metadata and metadata is not None: - metadata_str = ".\n".join(f"{k}: {v}" for k, v in metadata.items()) + ".\n" - chunks = [metadata_str + chunk for chunk in chunks] - return chunks + return nltk.sent_tokenize(text) diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index a07daa9fe5..f177a6cfd8 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -23,18 +23,16 @@ def __init__( size: int, overlap: int, tokenizer: Tokenizer, - prepend_metadata: bool = False, **kwargs: Any, ) -> None: """Create a token chunker instance.""" self._size = size self._overlap = overlap - self._prepend_metadata = prepend_metadata self._tokenizer = tokenizer - def chunk(self, text: str, metadata: dict | None = None) -> list[str]: + def chunk(self, text: str) -> list[str]: """Chunk the text into token-based chunks.""" - chunks = split_text_on_tokens( + return split_text_on_tokens( text, chunk_size=self._size, chunk_overlap=self._overlap, @@ -42,12 +40,6 @@ def chunk(self, text: str, metadata: dict | None = None) -> list[str]: decode=self._tokenizer.decode, ) - if self._prepend_metadata and metadata is not None: - metadata_str = ".\n".join(f"{k}: {v}" for k, v in metadata.items()) + ".\n" - chunks = [metadata_str + chunk for chunk in chunks] - - return chunks - def split_text_on_tokens( text: str, diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index c757ea0d7e..a6becfed27 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -13,6 +13,7 @@ from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.chunker import Chunker from graphrag.chunking.chunker_factory import create_chunker +from graphrag.chunking.prepend_metadata import prepend_metadata as prepend_metadata_fn from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput @@ -39,6 +40,7 @@ async def run_workflow( context.callbacks, tokenizer=tokenizer, chunker=chunker, + prepend_metadata=config.chunks.prepend_metadata, ) await write_table_to_storage(output, "text_units", context.output_storage) @@ -52,6 +54,7 @@ def create_base_text_units( callbacks: WorkflowCallbacks, tokenizer: Tokenizer, chunker: Chunker, + prepend_metadata: bool | None = False, ) -> pd.DataFrame: """All the steps to transform base text_units.""" documents.sort_values(by=["id"], ascending=[True], inplace=True) @@ -63,10 +66,12 @@ def create_base_text_units( logger.info("Starting chunking process for %d documents", total_rows) def chunker_with_logging(row: pd.Series, row_index: int) -> Any: - metadata = row.get("metadata") - if (metadata is not None) and isinstance(metadata, str): - metadata = json.loads(metadata) - row["chunks"] = chunker.chunk(row["text"], metadata=metadata) + row["chunks"] = chunker.chunk(row["text"]) + + metadata = row.get("metadata", None) + if prepend_metadata and metadata is not None: + metadata = json.loads(metadata) if isinstance(metadata, str) else metadata + row["chunks"] = [prepend_metadata_fn(chunk, metadata) for chunk in row["chunks"]] tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 275c9c77d6..9e717985c7 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -48,18 +48,6 @@ def test_mixed_whitespace_handling(self): assert chunks[0] == " Sentence with spaces." assert chunks[1] == "Another one!" - def test_prepend_metadata(self): - """Test prepending metadata to chunks""" - input = "This is a test. Another sentence." - config = ChunkingConfig( - strategy=ChunkStrategyType.Sentence, prepend_metadata=True - ) - chunker = create_chunker(config) - chunks = chunker.chunk(input, metadata={"message": "hello"}) - - assert chunks[0] == "message: hello.\nThis is a test." - assert chunks[1] == "message: hello.\nAnother sentence." - class TestRunTokens: @patch("tiktoken.get_encoding") @@ -83,20 +71,6 @@ def test_basic_functionality(self, mock_get_encoding): assert len(chunks) > 0 - def test_prepend_metadata(self): - """Test prepending metadata to chunks""" - mocked_tokenizer = MockTokenizer() - input = "This is a test." - config = ChunkingConfig( - strategy=ChunkStrategyType.Tokens, size=5, overlap=0, prepend_metadata=True - ) - chunker = create_chunker(config, tokenizer=mocked_tokenizer) - chunks = chunker.chunk(input, metadata={"message": "hello"}) - - assert chunks[0] == "message: hello.\nThis " - assert chunks[1] == "message: hello.\nis a " - assert chunks[2] == "message: hello.\ntest." - def test_split_text_str_empty(): result = split_text_on_tokens( diff --git a/tests/unit/chunking/test_prepend_metadata.py b/tests/unit/chunking/test_prepend_metadata.py new file mode 100644 index 0000000000..b7bc6e0404 --- /dev/null +++ b/tests/unit/chunking/test_prepend_metadata.py @@ -0,0 +1,34 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +from graphrag.chunking.prepend_metadata import prepend_metadata + + +def test_prepend_metadata_one_row(): + """Test prepending metadata to chunks""" + chunks = ["This is a test.", "Another sentence."] + metadata = {"message": "hello"} + results = [prepend_metadata(chunk, metadata) for chunk in chunks] + assert results[0] == "message: hello\nThis is a test." + assert results[1] == "message: hello\nAnother sentence." + + +def test_prepend_metadata_multiple_rows(): + """Test prepending metadata to chunks""" + chunks = ["This is a test.", "Another sentence."] + metadata = {"message": "hello", "tag": "first"} + results = [prepend_metadata(chunk, metadata) for chunk in chunks] + assert results[0] == "message: hello\ntag: first\nThis is a test." + assert results[1] == "message: hello\ntag: first\nAnother sentence." + + +def test_prepend_metadata_custom_delimiters(): + """Test prepending metadata to chunks""" + chunks = ["This is a test.", "Another sentence."] + metadata = {"message": "hello", "tag": "first"} + results = [ + prepend_metadata(chunk, metadata, delimiter="-", line_delimiter="_") + for chunk in chunks + ] + assert results[0] == "message-hello_tag-first_This is a test." + assert results[1] == "message-hello_tag-first_Another sentence." From 90479c0b1c8257b453354ba834e183b4b66474e7 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 11:18:42 -0800 Subject: [PATCH 16/23] Move Tokenizer back to GR core --- .../graphrag_common/types/__init__.py | 8 -------- .../graphrag/chunking/chunker_factory.py | 11 ++++++---- .../graphrag/chunking/token_chunker.py | 20 ++++++++----------- .../index/operations/embed_text/embed_text.py | 2 +- .../operations/embed_text/run_embed_text.py | 2 +- .../build_mixed_context.py | 2 +- .../graph_context/context_builder.py | 2 +- .../graph_context/sort_context.py | 2 +- .../summarize_communities.py | 2 +- .../text_unit_context/context_builder.py | 2 +- .../text_unit_context/sort_context.py | 2 +- .../index/text_splitting/text_splitting.py | 2 +- .../index/workflows/create_base_text_units.py | 8 +++++--- .../workflows/create_community_reports.py | 2 +- .../create_community_reports_text.py | 2 +- .../workflows/generate_text_embeddings.py | 2 +- .../generator/extract_graph_prompt.py | 3 +-- .../context_builder/community_context.py | 2 +- .../context_builder/conversation_history.py | 2 +- .../dynamic_community_selection.py | 3 +-- .../query/context_builder/local_context.py | 2 +- .../query/context_builder/rate_relevancy.py | 2 +- .../query/context_builder/source_context.py | 2 +- .../graphrag/graphrag/query/llm/text_utils.py | 2 +- .../graphrag/query/question_gen/base.py | 3 +-- .../graphrag/query/question_gen/local_gen.py | 3 +-- .../graphrag/query/structured_search/base.py | 2 +- .../basic_search/basic_context.py | 2 +- .../structured_search/basic_search/search.py | 3 +-- .../drift_search/drift_context.py | 2 +- .../structured_search/drift_search/primer.py | 2 +- .../structured_search/drift_search/search.py | 2 +- .../global_search/community_context.py | 3 +-- .../structured_search/global_search/search.py | 2 +- .../local_search/mixed_context.py | 2 +- .../structured_search/local_search/search.py | 3 +-- .../graphrag/tokenizer/get_tokenizer.py | 3 +-- .../graphrag/tokenizer/litellm_tokenizer.py | 3 ++- .../graphrag/tokenizer/tiktoken_tokenizer.py | 3 ++- .../graphrag/tokenizer}/tokenizer.py | 0 tests/unit/chunking/test_chunker.py | 2 +- 41 files changed, 58 insertions(+), 71 deletions(-) delete mode 100644 packages/graphrag-common/graphrag_common/types/__init__.py rename packages/{graphrag-common/graphrag_common/types => graphrag/graphrag/tokenizer}/tokenizer.py (100%) diff --git a/packages/graphrag-common/graphrag_common/types/__init__.py b/packages/graphrag-common/graphrag_common/types/__init__.py deleted file mode 100644 index f897ef3dd1..0000000000 --- a/packages/graphrag-common/graphrag_common/types/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""The GraphRAG types module: contains commonly-reused types across the monorepo packages.""" - -from graphrag_common.types.tokenizer import Tokenizer - -__all__ = ["Tokenizer"] diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag/graphrag/chunking/chunker_factory.py index 5986f73e5f..06adba2c3b 100644 --- a/packages/graphrag/graphrag/chunking/chunker_factory.py +++ b/packages/graphrag/graphrag/chunking/chunker_factory.py @@ -6,7 +6,6 @@ from collections.abc import Callable from graphrag_common.factory.factory import Factory, ServiceScope -from graphrag_common.types.tokenizer import Tokenizer from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.chunking.chunker import Chunker @@ -38,7 +37,9 @@ def register_chunker( def create_chunker( - config: ChunkingConfig, tokenizer: Tokenizer | None = None + config: ChunkingConfig, + encode: Callable[[str], list[int]] | None, + decode: Callable[[list[int]], str] | None, ) -> Chunker: """Create a chunker implementation based on the given configuration. @@ -53,8 +54,10 @@ def create_chunker( The created chunker implementation. """ config_model = config.model_dump() - if tokenizer is not None: - config_model["tokenizer"] = tokenizer + if encode is not None: + config_model["encode"] = encode + if decode is not None: + config_model["decode"] = decode chunker_strategy = config.strategy if chunker_strategy not in chunker_factory: diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag/graphrag/chunking/token_chunker.py index f177a6cfd8..97f846d5d8 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag/graphrag/chunking/token_chunker.py @@ -6,14 +6,8 @@ from collections.abc import Callable from typing import Any -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.chunking.chunker import Chunker -EncodedText = list[int] -DecodeFn = Callable[[EncodedText], str] -EncodeFn = Callable[[str], EncodedText] - class TokenChunker(Chunker): """A chunker that splits text into token-based chunks.""" @@ -22,13 +16,15 @@ def __init__( self, size: int, overlap: int, - tokenizer: Tokenizer, + encode: Callable[[str], list[int]], + decode: Callable[[list[int]], str], **kwargs: Any, ) -> None: """Create a token chunker instance.""" self._size = size self._overlap = overlap - self._tokenizer = tokenizer + self._encode = encode + self._decode = decode def chunk(self, text: str) -> list[str]: """Chunk the text into token-based chunks.""" @@ -36,8 +32,8 @@ def chunk(self, text: str) -> list[str]: text, chunk_size=self._size, chunk_overlap=self._overlap, - encode=self._tokenizer.encode, - decode=self._tokenizer.decode, + encode=self._encode, + decode=self._decode, ) @@ -45,8 +41,8 @@ def split_text_on_tokens( text: str, chunk_size: int, chunk_overlap: int, - encode: EncodeFn, - decode: DecodeFn, + encode: Callable[[str], list[int]], + decode: Callable[[list[int]], str], ) -> list[str]: """Split a single text and return chunks using the tokenizer.""" result = [] diff --git a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py index 58aae657ee..08f941f908 100644 --- a/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py +++ b/packages/graphrag/graphrag/index/operations/embed_text/embed_text.py @@ -7,11 +7,11 @@ import numpy as np import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.index.operations.embed_text.run_embed_text import run_embed_text from graphrag.language_model.protocol.base import EmbeddingModel +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore, VectorStoreDocument logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py index 3891ca6cb9..188d19ffcc 100644 --- a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py +++ b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py @@ -8,13 +8,13 @@ from dataclasses import dataclass import numpy as np -from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.token_chunker import split_text_on_tokens from graphrag.index.utils.is_null import is_null from graphrag.language_model.protocol.base import EmbeddingModel from graphrag.logger.progress import ProgressTicker, progress_ticker +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py index a519a21343..6ae59a40c8 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/build_mixed_context.py @@ -4,12 +4,12 @@ """A module containing build_mixed_context method definition.""" import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.index.operations.summarize_communities.graph_context.sort_context import ( sort_context, ) +from graphrag.tokenizer.tokenizer import Tokenizer def build_mixed_context( diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py index 4b1ba4c201..34d281b3f8 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/context_builder.py @@ -7,7 +7,6 @@ from typing import cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks @@ -31,6 +30,7 @@ where_column_equals, ) from graphrag.logger.progress import progress_iterable +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py index 1630b9f96b..3b7863f2f8 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/graph_context/sort_context.py @@ -3,9 +3,9 @@ """Sort context by degree in descending order.""" import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas +from graphrag.tokenizer.tokenizer import Tokenizer def sort_context( diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py b/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py index 51044b4260..709a4e3b5a 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/summarize_communities.py @@ -7,7 +7,6 @@ from collections.abc import Callable import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks @@ -27,6 +26,7 @@ from graphrag.index.utils.derive_from_rows import derive_from_rows from graphrag.language_model.protocol.base import ChatModel from graphrag.logger.progress import progress_ticker +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py index d2b11c6a36..8d6e0ae2e0 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/context_builder.py @@ -7,7 +7,6 @@ from typing import cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.index.operations.summarize_communities.build_mixed_context import ( @@ -19,6 +18,7 @@ from graphrag.index.operations.summarize_communities.text_unit_context.sort_context import ( sort_context, ) +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py index f06ecad34d..7bad931b59 100644 --- a/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py +++ b/packages/graphrag/graphrag/index/operations/summarize_communities/text_unit_context/sort_context.py @@ -6,9 +6,9 @@ import logging import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py index 52c428786a..7be6c538ae 100644 --- a/packages/graphrag/graphrag/index/text_splitting/text_splitting.py +++ b/packages/graphrag/graphrag/index/text_splitting/text_splitting.py @@ -9,9 +9,9 @@ from typing import cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer EncodedText = list[int] DecodeFn = Callable[[EncodedText], str] diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index a6becfed27..67a1f625f0 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -8,7 +8,6 @@ from typing import Any, cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.chunking.chunker import Chunker @@ -20,6 +19,7 @@ from graphrag.index.utils.hashing import gen_sha512_hash from graphrag.logger.progress import progress_ticker from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import load_table_from_storage, write_table_to_storage logger = logging.getLogger(__name__) @@ -34,7 +34,7 @@ async def run_workflow( documents = await load_table_from_storage("documents", context.output_storage) tokenizer = get_tokenizer(encoding_model=config.chunks.encoding_model) - chunker = create_chunker(config.chunks, tokenizer) + chunker = create_chunker(config.chunks, tokenizer.encode, tokenizer.decode) output = create_base_text_units( documents, context.callbacks, @@ -71,7 +71,9 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: metadata = row.get("metadata", None) if prepend_metadata and metadata is not None: metadata = json.loads(metadata) if isinstance(metadata, str) else metadata - row["chunks"] = [prepend_metadata_fn(chunk, metadata) for chunk in row["chunks"]] + row["chunks"] = [ + prepend_metadata_fn(chunk, metadata) for chunk in row["chunks"] + ] tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/packages/graphrag/graphrag/index/workflows/create_community_reports.py b/packages/graphrag/graphrag/index/workflows/create_community_reports.py index f0320d3d10..981e47227e 100644 --- a/packages/graphrag/graphrag/index/workflows/create_community_reports.py +++ b/packages/graphrag/graphrag/index/workflows/create_community_reports.py @@ -6,7 +6,6 @@ import logging import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer import graphrag.data_model.schemas as schemas from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks @@ -30,6 +29,7 @@ from graphrag.language_model.manager import ModelManager from graphrag.language_model.protocol.base import ChatModel from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import ( load_table_from_storage, storage_has_table, diff --git a/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py b/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py index c0100b8645..ac269df8bc 100644 --- a/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py +++ b/packages/graphrag/graphrag/index/workflows/create_community_reports_text.py @@ -6,7 +6,6 @@ import logging import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.config.enums import AsyncType @@ -29,6 +28,7 @@ from graphrag.language_model.manager import ModelManager from graphrag.language_model.protocol.base import ChatModel from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import load_table_from_storage, write_table_to_storage logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py index 879a445565..ef24d3e348 100644 --- a/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py +++ b/packages/graphrag/graphrag/index/workflows/generate_text_embeddings.py @@ -6,7 +6,6 @@ import logging import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks from graphrag.config.embeddings import ( @@ -24,6 +23,7 @@ from graphrag.language_model.manager import ModelManager from graphrag.language_model.protocol.base import EmbeddingModel from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.utils.storage import ( load_table_from_storage, write_table_to_storage, diff --git a/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py b/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py index 6c0720c1c9..db0f87cf97 100644 --- a/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py +++ b/packages/graphrag/graphrag/prompt_tune/generator/extract_graph_prompt.py @@ -5,8 +5,6 @@ from pathlib import Path -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.prompt_tune.template.extract_graph import ( EXAMPLE_EXTRACTION_TEMPLATE, GRAPH_EXTRACTION_JSON_PROMPT, @@ -15,6 +13,7 @@ UNTYPED_GRAPH_EXTRACTION_PROMPT, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer EXTRACT_GRAPH_FILENAME = "extract_graph.txt" diff --git a/packages/graphrag/graphrag/query/context_builder/community_context.py b/packages/graphrag/graphrag/query/context_builder/community_context.py index 6d92052c23..4917be0186 100644 --- a/packages/graphrag/graphrag/query/context_builder/community_context.py +++ b/packages/graphrag/graphrag/query/context_builder/community_context.py @@ -8,11 +8,11 @@ from typing import Any, cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.community_report import CommunityReport from graphrag.data_model.entity import Entity from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/context_builder/conversation_history.py b/packages/graphrag/graphrag/query/context_builder/conversation_history.py index bfeba28074..c20998121c 100644 --- a/packages/graphrag/graphrag/query/context_builder/conversation_history.py +++ b/packages/graphrag/graphrag/query/context_builder/conversation_history.py @@ -7,9 +7,9 @@ from enum import Enum import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer """ Enum for conversation roles diff --git a/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py b/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py index 3de6d99d6b..904478f738 100644 --- a/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py +++ b/packages/graphrag/graphrag/query/context_builder/dynamic_community_selection.py @@ -10,13 +10,12 @@ from time import time from typing import Any -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.data_model.community import Community from graphrag.data_model.community_report import CommunityReport from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.rate_prompt import RATE_QUERY from graphrag.query.context_builder.rate_relevancy import rate_relevancy +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/context_builder/local_context.py b/packages/graphrag/graphrag/query/context_builder/local_context.py index b803262931..a2d8a54533 100644 --- a/packages/graphrag/graphrag/query/context_builder/local_context.py +++ b/packages/graphrag/graphrag/query/context_builder/local_context.py @@ -7,7 +7,6 @@ from typing import Any, cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.covariate import Covariate from graphrag.data_model.entity import Entity @@ -25,6 +24,7 @@ to_relationship_dataframe, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer def build_entity_context( diff --git a/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py b/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py index 9084b8ef04..6fa128999f 100644 --- a/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py +++ b/packages/graphrag/graphrag/query/context_builder/rate_relevancy.py @@ -9,11 +9,11 @@ from typing import Any import numpy as np -from graphrag_common.types.tokenizer import Tokenizer from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.rate_prompt import RATE_QUERY from graphrag.query.llm.text_utils import try_parse_json_object +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/context_builder/source_context.py b/packages/graphrag/graphrag/query/context_builder/source_context.py index 68f5e5ecda..eaf308f629 100644 --- a/packages/graphrag/graphrag/query/context_builder/source_context.py +++ b/packages/graphrag/graphrag/query/context_builder/source_context.py @@ -7,11 +7,11 @@ from typing import Any, cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.relationship import Relationship from graphrag.data_model.text_unit import TextUnit from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer """ Contain util functions to build text unit context for the search's system prompt diff --git a/packages/graphrag/graphrag/query/llm/text_utils.py b/packages/graphrag/graphrag/query/llm/text_utils.py index ccae99b894..ddd6abe1ef 100644 --- a/packages/graphrag/graphrag/query/llm/text_utils.py +++ b/packages/graphrag/graphrag/query/llm/text_utils.py @@ -9,10 +9,10 @@ from collections.abc import Iterator from itertools import islice -from graphrag_common.types.tokenizer import Tokenizer from json_repair import repair_json from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/question_gen/base.py b/packages/graphrag/graphrag/query/question_gen/base.py index 3231cac281..2195aee52a 100644 --- a/packages/graphrag/graphrag/query/question_gen/base.py +++ b/packages/graphrag/graphrag/query/question_gen/base.py @@ -7,14 +7,13 @@ from dataclasses import dataclass from typing import Any -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.builders import ( GlobalContextBuilder, LocalContextBuilder, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer @dataclass diff --git a/packages/graphrag/graphrag/query/question_gen/local_gen.py b/packages/graphrag/graphrag/query/question_gen/local_gen.py index 1e21bf14e8..82795e9cf3 100644 --- a/packages/graphrag/graphrag/query/question_gen/local_gen.py +++ b/packages/graphrag/graphrag/query/question_gen/local_gen.py @@ -7,8 +7,6 @@ import time from typing import Any, cast -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.callbacks.llm_callbacks import BaseLLMCallback from graphrag.language_model.protocol.base import ChatModel from graphrag.prompts.query.question_gen_system_prompt import QUESTION_SYSTEM_PROMPT @@ -20,6 +18,7 @@ ConversationHistory, ) from graphrag.query.question_gen.base import BaseQuestionGen, QuestionResult +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/base.py b/packages/graphrag/graphrag/query/structured_search/base.py index 22f6a5d969..753b419f69 100644 --- a/packages/graphrag/graphrag/query/structured_search/base.py +++ b/packages/graphrag/graphrag/query/structured_search/base.py @@ -9,7 +9,6 @@ from typing import Any, Generic, TypeVar import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.language_model.protocol.base import ChatModel from graphrag.query.context_builder.builders import ( @@ -22,6 +21,7 @@ ConversationHistory, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer @dataclass diff --git a/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py b/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py index 1fb0591016..b7390017fc 100644 --- a/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py +++ b/packages/graphrag/graphrag/query/structured_search/basic_search/basic_context.py @@ -7,7 +7,6 @@ from typing import cast import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.text_unit import TextUnit from graphrag.language_model.protocol.base import EmbeddingModel @@ -17,6 +16,7 @@ ) from graphrag.query.context_builder.conversation_history import ConversationHistory from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/basic_search/search.py b/packages/graphrag/graphrag/query/structured_search/basic_search/search.py index 418b7ffe34..ce5f656845 100644 --- a/packages/graphrag/graphrag/query/structured_search/basic_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/basic_search/search.py @@ -8,8 +8,6 @@ from collections.abc import AsyncGenerator from typing import Any -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.callbacks.query_callbacks import QueryCallbacks from graphrag.language_model.protocol.base import ChatModel from graphrag.prompts.query.basic_search_system_prompt import ( @@ -18,6 +16,7 @@ from graphrag.query.context_builder.builders import BasicContextBuilder from graphrag.query.context_builder.conversation_history import ConversationHistory from graphrag.query.structured_search.base import BaseSearch, SearchResult +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) """ diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py index d217b714a4..4b4325ae2e 100644 --- a/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py +++ b/packages/graphrag/graphrag/query/structured_search/drift_search/drift_context.py @@ -9,7 +9,6 @@ import numpy as np import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.config.models.drift_search_config import DRIFTSearchConfig from graphrag.data_model.community_report import CommunityReport @@ -29,6 +28,7 @@ LocalSearchMixedContext, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py b/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py index 0fc17a69f5..2a7f145711 100644 --- a/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py +++ b/packages/graphrag/graphrag/query/structured_search/drift_search/primer.py @@ -10,7 +10,6 @@ import numpy as np import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from tqdm.asyncio import tqdm_asyncio from graphrag.config.models.drift_search_config import DRIFTSearchConfig @@ -21,6 +20,7 @@ ) from graphrag.query.structured_search.base import SearchResult from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/drift_search/search.py b/packages/graphrag/graphrag/query/structured_search/drift_search/search.py index 8e26ab1191..64a8e52b43 100644 --- a/packages/graphrag/graphrag/query/structured_search/drift_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/drift_search/search.py @@ -8,7 +8,6 @@ from collections.abc import AsyncGenerator from typing import Any -from graphrag_common.types.tokenizer import Tokenizer from tqdm.asyncio import tqdm_asyncio from graphrag.callbacks.query_callbacks import QueryCallbacks @@ -27,6 +26,7 @@ from graphrag.query.structured_search.drift_search.state import QueryState from graphrag.query.structured_search.local_search.search import LocalSearch from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py b/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py index fa05e17f5f..1709aab1a8 100644 --- a/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py +++ b/packages/graphrag/graphrag/query/structured_search/global_search/community_context.py @@ -5,8 +5,6 @@ from typing import Any -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.data_model.community import Community from graphrag.data_model.community_report import CommunityReport from graphrag.data_model.entity import Entity @@ -22,6 +20,7 @@ ) from graphrag.query.structured_search.base import GlobalContextBuilder from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer class GlobalCommunityContext(GlobalContextBuilder): diff --git a/packages/graphrag/graphrag/query/structured_search/global_search/search.py b/packages/graphrag/graphrag/query/structured_search/global_search/search.py index e201194403..86b95d0088 100644 --- a/packages/graphrag/graphrag/query/structured_search/global_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/global_search/search.py @@ -12,7 +12,6 @@ from typing import Any import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.callbacks.query_callbacks import QueryCallbacks from graphrag.language_model.protocol.base import ChatModel @@ -32,6 +31,7 @@ ) from graphrag.query.llm.text_utils import try_parse_json_object from graphrag.query.structured_search.base import BaseSearch, SearchResult +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py b/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py index 981fc333d9..b91272d164 100644 --- a/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py +++ b/packages/graphrag/graphrag/query/structured_search/local_search/mixed_context.py @@ -7,7 +7,6 @@ from typing import Any import pandas as pd -from graphrag_common.types.tokenizer import Tokenizer from graphrag.data_model.community_report import CommunityReport from graphrag.data_model.covariate import Covariate @@ -42,6 +41,7 @@ from graphrag.query.input.retrieval.text_units import get_candidate_text_units from graphrag.query.structured_search.base import LocalContextBuilder from graphrag.tokenizer.get_tokenizer import get_tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer from graphrag.vector_stores.base import BaseVectorStore logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/query/structured_search/local_search/search.py b/packages/graphrag/graphrag/query/structured_search/local_search/search.py index b0db4097fa..fdd72949da 100644 --- a/packages/graphrag/graphrag/query/structured_search/local_search/search.py +++ b/packages/graphrag/graphrag/query/structured_search/local_search/search.py @@ -8,8 +8,6 @@ from collections.abc import AsyncGenerator from typing import Any -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.callbacks.query_callbacks import QueryCallbacks from graphrag.language_model.protocol.base import ChatModel from graphrag.prompts.query.local_search_system_prompt import ( @@ -20,6 +18,7 @@ ConversationHistory, ) from graphrag.query.structured_search.base import BaseSearch, SearchResult +from graphrag.tokenizer.tokenizer import Tokenizer logger = logging.getLogger(__name__) diff --git a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py index fcfd29ae2a..d1bac40a1a 100644 --- a/packages/graphrag/graphrag/tokenizer/get_tokenizer.py +++ b/packages/graphrag/graphrag/tokenizer/get_tokenizer.py @@ -3,12 +3,11 @@ """Get Tokenizer.""" -from graphrag_common.types.tokenizer import Tokenizer - from graphrag.config.defaults import ENCODING_MODEL from graphrag.config.models.language_model_config import LanguageModelConfig from graphrag.tokenizer.litellm_tokenizer import LitellmTokenizer from graphrag.tokenizer.tiktoken_tokenizer import TiktokenTokenizer +from graphrag.tokenizer.tokenizer import Tokenizer def get_tokenizer( diff --git a/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py b/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py index a486c77d6a..1a85f56086 100644 --- a/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py +++ b/packages/graphrag/graphrag/tokenizer/litellm_tokenizer.py @@ -3,9 +3,10 @@ """LiteLLM Tokenizer.""" -from graphrag_common.types.tokenizer import Tokenizer from litellm import decode, encode # type: ignore +from graphrag.tokenizer.tokenizer import Tokenizer + class LitellmTokenizer(Tokenizer): """LiteLLM Tokenizer.""" diff --git a/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py b/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py index b069dc8e48..fa6c6e9b43 100644 --- a/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py +++ b/packages/graphrag/graphrag/tokenizer/tiktoken_tokenizer.py @@ -4,7 +4,8 @@ """Tiktoken Tokenizer.""" import tiktoken -from graphrag_common.types.tokenizer import Tokenizer + +from graphrag.tokenizer.tokenizer import Tokenizer class TiktokenTokenizer(Tokenizer): diff --git a/packages/graphrag-common/graphrag_common/types/tokenizer.py b/packages/graphrag/graphrag/tokenizer/tokenizer.py similarity index 100% rename from packages/graphrag-common/graphrag_common/types/tokenizer.py rename to packages/graphrag/graphrag/tokenizer/tokenizer.py diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 9e717985c7..2655317b92 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -11,7 +11,7 @@ split_text_on_tokens, ) from graphrag.tokenizer.get_tokenizer import get_tokenizer -from graphrag_common.types.tokenizer import Tokenizer +from graphrag.tokenizer.tokenizer import Tokenizer class MockTokenizer(Tokenizer): From b32f403e8f584c3ce8fc5165a01d7daaff054e5b Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 11:20:43 -0800 Subject: [PATCH 17/23] Fix tokenizer removal from chunker --- packages/graphrag/graphrag/chunking/chunker_factory.py | 4 ++-- packages/graphrag/graphrag/prompt_tune/loader/input.py | 2 +- tests/unit/chunking/test_chunker.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag/graphrag/chunking/chunker_factory.py index 06adba2c3b..fac623d06b 100644 --- a/packages/graphrag/graphrag/chunking/chunker_factory.py +++ b/packages/graphrag/graphrag/chunking/chunker_factory.py @@ -38,8 +38,8 @@ def register_chunker( def create_chunker( config: ChunkingConfig, - encode: Callable[[str], list[int]] | None, - decode: Callable[[list[int]], str] | None, + encode: Callable[[str], list[int]] | None = None, + decode: Callable[[list[int]], str] | None = None, ) -> Chunker: """Create a chunker implementation based on the given configuration. diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py index 5f06df8a53..871f0a91e0 100644 --- a/packages/graphrag/graphrag/prompt_tune/loader/input.py +++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py @@ -62,7 +62,7 @@ async def load_docs_in_chunks( cache=NoopCache(), ) tokenizer = get_tokenizer(embeddings_llm_settings) - chunker = create_chunker(config.chunks, tokenizer) + chunker = create_chunker(config.chunks, tokenizer.encode, tokenizer.decode) input_storage = create_storage(config.input.storage) input_reader = InputReaderFactory().create( config.input.file_type, diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 2655317b92..bbdb2235e0 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -66,7 +66,7 @@ def test_basic_functionality(self, mock_get_encoding): strategy=ChunkStrategyType.Tokens, ) - chunker = create_chunker(config, tokenizer=tokenizer) + chunker = create_chunker(config, mock_encoder.encode, mock_encoder.decode) chunks = chunker.chunk(input) assert len(chunks) > 0 From d9ba63f4d674f114f43c9502bec7711259b67c73 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 11:25:53 -0800 Subject: [PATCH 18/23] Set defaults for chunking config --- .../graphrag/chunking/add_metadata.py | 19 +++++++++++++++ .../graphrag/chunking/chunking_config.py | 18 +++++++-------- .../graphrag/chunking/prepend_metadata.py | 15 ------------ .../index/workflows/create_base_text_units.py | 6 ++--- tests/unit/chunking/test_prepend_metadata.py | 23 +++++++++++++------ 5 files changed, 46 insertions(+), 35 deletions(-) create mode 100644 packages/graphrag/graphrag/chunking/add_metadata.py delete mode 100644 packages/graphrag/graphrag/chunking/prepend_metadata.py diff --git a/packages/graphrag/graphrag/chunking/add_metadata.py b/packages/graphrag/graphrag/chunking/add_metadata.py new file mode 100644 index 0000000000..3b02994d4c --- /dev/null +++ b/packages/graphrag/graphrag/chunking/add_metadata.py @@ -0,0 +1,19 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'prepend_metadata' function.""" + + +def add_metadata( + text: str, + metadata: dict, + delimiter: str = ": ", + line_delimiter: str = "\n", + append: bool = False, +) -> str: + """Add metadata to the given text, prepending by default. This utility writes the dict as rows of key/value pairs.""" + metadata_str = ( + line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items()) + + line_delimiter + ) + return text + metadata_str if append else metadata_str + text diff --git a/packages/graphrag/graphrag/chunking/chunking_config.py b/packages/graphrag/graphrag/chunking/chunking_config.py index 625b486040..e3f80cc86e 100644 --- a/packages/graphrag/graphrag/chunking/chunking_config.py +++ b/packages/graphrag/graphrag/chunking/chunking_config.py @@ -18,19 +18,19 @@ class ChunkingConfig(BaseModel): description="The chunking strategy to use.", default=ChunkStrategyType.Tokens, ) - size: int | None = Field( - description="The chunk size to use.", - default=None, - ) - overlap: int | None = Field( - description="The chunk overlap to use.", - default=None, - ) encoding_model: str | None = Field( description="The encoding model to use.", default=None, ) - prepend_metadata: bool | None = Field( + size: int = Field( + description="The chunk size to use.", + default=1200, + ) + overlap: int = Field( + description="The chunk overlap to use.", + default=100, + ) + prepend_metadata: bool = Field( description="Prepend metadata into each chunk.", default=False, ) diff --git a/packages/graphrag/graphrag/chunking/prepend_metadata.py b/packages/graphrag/graphrag/chunking/prepend_metadata.py deleted file mode 100644 index dea0471a44..0000000000 --- a/packages/graphrag/graphrag/chunking/prepend_metadata.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) 2024 Microsoft Corporation. -# Licensed under the MIT License - -"""A module containing 'prepend_metadata' function.""" - - -def prepend_metadata( - text: str, metadata: dict, delimiter: str = ": ", line_delimiter: str = "\n" -) -> str: - """Prepend metadata to the given text. This utility writes the dict as rows of key/value pairs.""" - metadata_str = ( - line_delimiter.join(f"{k}{delimiter}{v}" for k, v in metadata.items()) - + line_delimiter - ) - return metadata_str + text diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 67a1f625f0..7759e90f4a 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -10,9 +10,9 @@ import pandas as pd from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks +from graphrag.chunking.add_metadata import add_metadata from graphrag.chunking.chunker import Chunker from graphrag.chunking.chunker_factory import create_chunker -from graphrag.chunking.prepend_metadata import prepend_metadata as prepend_metadata_fn from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput @@ -71,9 +71,7 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: metadata = row.get("metadata", None) if prepend_metadata and metadata is not None: metadata = json.loads(metadata) if isinstance(metadata, str) else metadata - row["chunks"] = [ - prepend_metadata_fn(chunk, metadata) for chunk in row["chunks"] - ] + row["chunks"] = [add_metadata(chunk, metadata) for chunk in row["chunks"]] tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/tests/unit/chunking/test_prepend_metadata.py b/tests/unit/chunking/test_prepend_metadata.py index b7bc6e0404..0d14c73457 100644 --- a/tests/unit/chunking/test_prepend_metadata.py +++ b/tests/unit/chunking/test_prepend_metadata.py @@ -1,33 +1,42 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -from graphrag.chunking.prepend_metadata import prepend_metadata +from graphrag.chunking.add_metadata import add_metadata -def test_prepend_metadata_one_row(): +def test_add_metadata_one_row(): """Test prepending metadata to chunks""" chunks = ["This is a test.", "Another sentence."] metadata = {"message": "hello"} - results = [prepend_metadata(chunk, metadata) for chunk in chunks] + results = [add_metadata(chunk, metadata) for chunk in chunks] assert results[0] == "message: hello\nThis is a test." assert results[1] == "message: hello\nAnother sentence." -def test_prepend_metadata_multiple_rows(): +def test_add_metadata_one_row_append(): + """Test prepending metadata to chunks""" + chunks = ["This is a test.", "Another sentence."] + metadata = {"message": "hello"} + results = [add_metadata(chunk, metadata, append=True) for chunk in chunks] + assert results[0] == "This is a test.message: hello\n" + assert results[1] == "Another sentence.message: hello\n" + + +def test_add_metadata_multiple_rows(): """Test prepending metadata to chunks""" chunks = ["This is a test.", "Another sentence."] metadata = {"message": "hello", "tag": "first"} - results = [prepend_metadata(chunk, metadata) for chunk in chunks] + results = [add_metadata(chunk, metadata) for chunk in chunks] assert results[0] == "message: hello\ntag: first\nThis is a test." assert results[1] == "message: hello\ntag: first\nAnother sentence." -def test_prepend_metadata_custom_delimiters(): +def test_add_metadata_custom_delimiters(): """Test prepending metadata to chunks""" chunks = ["This is a test.", "Another sentence."] metadata = {"message": "hello", "tag": "first"} results = [ - prepend_metadata(chunk, metadata, delimiter="-", line_delimiter="_") + add_metadata(chunk, metadata, delimiter="-", line_delimiter="_") for chunk in chunks ] assert results[0] == "message-hello_tag-first_This is a test." From bd968f2710813dbafca145e17226baceaec882c6 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 11:57:12 -0800 Subject: [PATCH 19/23] Move chunking to monorepo package --- packages/graphrag-chunking/README.md | 32 + .../graphrag_chunking}/__init__.py | 0 .../graphrag_chunking}/add_metadata.py | 0 .../graphrag_chunking}/bootstrap_nltk.py | 0 .../graphrag_chunking}/chunk_strategy_type.py | 0 .../graphrag_chunking}/chunker.py | 0 .../graphrag_chunking}/chunker_factory.py | 10 +- .../graphrag_chunking}/chunking_config.py | 2 +- .../graphrag_chunking}/sentence_chunker.py | 4 +- .../graphrag_chunking}/token_chunker.py | 2 +- packages/graphrag-chunking/pyproject.toml | 43 + packages/graphrag/graphrag/config/defaults.py | 2 +- .../config/models/graph_rag_config.py | 2 +- .../operations/embed_text/run_embed_text.py | 2 +- .../index/workflows/create_base_text_units.py | 8 +- .../graphrag/prompt_tune/loader/input.py | 2 +- pyproject.toml | 1 + tests/unit/chunking/test_chunker.py | 56 +- tests/unit/chunking/test_prepend_metadata.py | 2 +- tests/unit/config/utils.py | 2 +- uv.lock | 2522 +++++++++-------- 21 files changed, 1403 insertions(+), 1289 deletions(-) create mode 100644 packages/graphrag-chunking/README.md rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/__init__.py (100%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/add_metadata.py (100%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/bootstrap_nltk.py (100%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/chunk_strategy_type.py (100%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/chunker.py (100%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/chunker_factory.py (87%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/chunking_config.py (93%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/sentence_chunker.py (83%) rename packages/{graphrag/graphrag/chunking => graphrag-chunking/graphrag_chunking}/token_chunker.py (97%) create mode 100644 packages/graphrag-chunking/pyproject.toml diff --git a/packages/graphrag-chunking/README.md b/packages/graphrag-chunking/README.md new file mode 100644 index 0000000000..fa1a215202 --- /dev/null +++ b/packages/graphrag-chunking/README.md @@ -0,0 +1,32 @@ +# GraphRAG Chunking + +This package contains a collection of text chunkers, a core config model, and a factory for acquiring instances. + +## Examples + +Basic sentence chunking with nltk +```python +chunker = SentenceChunker() +chunks = chunker.chunk("This is a test. Another sentence.") +print(chunks) # ["This is a test.", "Another sentence."] +``` + +Token chunking +```python +tokenizer = tiktoken.get_encoding("o200k_base") +chunker = TokenChunker(size=3, overlap=0, encode=tokenizer.encode, decode=tokenizer.decode) +chunks = chunker.chunk("This is a random test fragment of some text") +print(chunks) # ["This is a", " random test fragment", " of some text"] +``` + +Using the factory via helper util +```python + tokenizer = tiktoken.get_encoding("o200k_base") + config = ChunkingConfig( + strategy="tokens", + size=3, + overlap=0 +) + chunker = create_chunker(config, tokenizer.encode, tokenizer.decode) + ... +` \ No newline at end of file diff --git a/packages/graphrag/graphrag/chunking/__init__.py b/packages/graphrag-chunking/graphrag_chunking/__init__.py similarity index 100% rename from packages/graphrag/graphrag/chunking/__init__.py rename to packages/graphrag-chunking/graphrag_chunking/__init__.py diff --git a/packages/graphrag/graphrag/chunking/add_metadata.py b/packages/graphrag-chunking/graphrag_chunking/add_metadata.py similarity index 100% rename from packages/graphrag/graphrag/chunking/add_metadata.py rename to packages/graphrag-chunking/graphrag_chunking/add_metadata.py diff --git a/packages/graphrag/graphrag/chunking/bootstrap_nltk.py b/packages/graphrag-chunking/graphrag_chunking/bootstrap_nltk.py similarity index 100% rename from packages/graphrag/graphrag/chunking/bootstrap_nltk.py rename to packages/graphrag-chunking/graphrag_chunking/bootstrap_nltk.py diff --git a/packages/graphrag/graphrag/chunking/chunk_strategy_type.py b/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py similarity index 100% rename from packages/graphrag/graphrag/chunking/chunk_strategy_type.py rename to packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py diff --git a/packages/graphrag/graphrag/chunking/chunker.py b/packages/graphrag-chunking/graphrag_chunking/chunker.py similarity index 100% rename from packages/graphrag/graphrag/chunking/chunker.py rename to packages/graphrag-chunking/graphrag_chunking/chunker.py diff --git a/packages/graphrag/graphrag/chunking/chunker_factory.py b/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py similarity index 87% rename from packages/graphrag/graphrag/chunking/chunker_factory.py rename to packages/graphrag-chunking/graphrag_chunking/chunker_factory.py index fac623d06b..c1d60e0b72 100644 --- a/packages/graphrag/graphrag/chunking/chunker_factory.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py @@ -7,9 +7,9 @@ from graphrag_common.factory.factory import Factory, ServiceScope -from graphrag.chunking.chunk_strategy_type import ChunkStrategyType -from graphrag.chunking.chunker import Chunker -from graphrag.chunking.chunking_config import ChunkingConfig +from graphrag_chunking.chunk_strategy_type import ChunkStrategyType +from graphrag_chunking.chunker import Chunker +from graphrag_chunking.chunking_config import ChunkingConfig class ChunkerFactory(Factory[Chunker]): @@ -63,11 +63,11 @@ def create_chunker( if chunker_strategy not in chunker_factory: match chunker_strategy: case ChunkStrategyType.Tokens: - from graphrag.chunking.token_chunker import TokenChunker + from graphrag_chunking.token_chunker import TokenChunker register_chunker(ChunkStrategyType.Tokens, TokenChunker) case ChunkStrategyType.Sentence: - from graphrag.chunking.sentence_chunker import SentenceChunker + from graphrag_chunking.sentence_chunker import SentenceChunker register_chunker(ChunkStrategyType.Sentence, SentenceChunker) case _: diff --git a/packages/graphrag/graphrag/chunking/chunking_config.py b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py similarity index 93% rename from packages/graphrag/graphrag/chunking/chunking_config.py rename to packages/graphrag-chunking/graphrag_chunking/chunking_config.py index e3f80cc86e..9ceec8336d 100644 --- a/packages/graphrag/graphrag/chunking/chunking_config.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py @@ -5,7 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field -from graphrag.chunking.chunk_strategy_type import ChunkStrategyType +from graphrag_chunking.chunk_strategy_type import ChunkStrategyType class ChunkingConfig(BaseModel): diff --git a/packages/graphrag/graphrag/chunking/sentence_chunker.py b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py similarity index 83% rename from packages/graphrag/graphrag/chunking/sentence_chunker.py rename to packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py index f72296f55b..0efee67d69 100644 --- a/packages/graphrag/graphrag/chunking/sentence_chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py @@ -7,8 +7,8 @@ import nltk -from graphrag.chunking.bootstrap_nltk import bootstrap -from graphrag.chunking.chunker import Chunker +from graphrag_chunking.bootstrap_nltk import bootstrap +from graphrag_chunking.chunker import Chunker class SentenceChunker(Chunker): diff --git a/packages/graphrag/graphrag/chunking/token_chunker.py b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py similarity index 97% rename from packages/graphrag/graphrag/chunking/token_chunker.py rename to packages/graphrag-chunking/graphrag_chunking/token_chunker.py index 97f846d5d8..b1da7bc5b3 100644 --- a/packages/graphrag/graphrag/chunking/token_chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py @@ -6,7 +6,7 @@ from collections.abc import Callable from typing import Any -from graphrag.chunking.chunker import Chunker +from graphrag_chunking.chunker import Chunker class TokenChunker(Chunker): diff --git a/packages/graphrag-chunking/pyproject.toml b/packages/graphrag-chunking/pyproject.toml new file mode 100644 index 0000000000..4393245bbc --- /dev/null +++ b/packages/graphrag-chunking/pyproject.toml @@ -0,0 +1,43 @@ +[project] +name = "graphrag-chunking" +version = "2.7.0" +description = "Chunking utilities for GraphRAG" +authors = [ + {name = "Alonso Guevara Fernández", email = "alonsog@microsoft.com"}, + {name = "Andrés Morales Esquivel", email = "andresmor@microsoft.com"}, + {name = "Chris Trevino", email = "chtrevin@microsoft.com"}, + {name = "David Tittsworth", email = "datittsw@microsoft.com"}, + {name = "Dayenne de Souza", email = "ddesouza@microsoft.com"}, + {name = "Derek Worthen", email = "deworthe@microsoft.com"}, + {name = "Gaudy Blanco Meneses", email = "gaudyb@microsoft.com"}, + {name = "Ha Trinh", email = "trinhha@microsoft.com"}, + {name = "Jonathan Larson", email = "jolarso@microsoft.com"}, + {name = "Josh Bradley", email = "joshbradley@microsoft.com"}, + {name = "Kate Lytvynets", email = "kalytv@microsoft.com"}, + {name = "Kenny Zhang", email = "zhangken@microsoft.com"}, + {name = "Mónica Carvajal"}, + {name = "Nathan Evans", email = "naevans@microsoft.com"}, + {name = "Rodrigo Racanicci", email = "rracanicci@microsoft.com"}, + {name = "Sarah Smith", email = "smithsarah@microsoft.com"}, +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.11,<3.14" +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "graphrag-common==2.7.0", + "pydantic~=2.10", +] + +[project.urls] +Source = "https://github.com/microsoft/graphrag" + +[build-system] +requires = ["hatchling>=1.27.0,<2.0.0"] +build-backend = "hatchling.build" + diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py index 85ca07330d..0cb6e190c2 100644 --- a/packages/graphrag/graphrag/config/defaults.py +++ b/packages/graphrag/graphrag/config/defaults.py @@ -8,9 +8,9 @@ from typing import ClassVar from graphrag_cache import CacheType +from graphrag_chunking.chunk_strategy_type import ChunkStrategyType from graphrag_storage import StorageType -from graphrag.chunking.chunk_strategy_type import ChunkStrategyType from graphrag.config.embeddings import default_embeddings from graphrag.config.enums import ( AsyncType, diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py index b303672192..ab2704b40a 100644 --- a/packages/graphrag/graphrag/config/models/graph_rag_config.py +++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py @@ -8,11 +8,11 @@ from devtools import pformat from graphrag_cache import CacheConfig +from graphrag_chunking.chunking_config import ChunkingConfig from graphrag_storage import StorageConfig, StorageType from pydantic import BaseModel, Field, model_validator import graphrag.config.defaults as defs -from graphrag.chunking.chunking_config import ChunkingConfig from graphrag.config.defaults import graphrag_config_defaults from graphrag.config.enums import VectorStoreType from graphrag.config.models.basic_search_config import BasicSearchConfig diff --git a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py index 188d19ffcc..43ed669567 100644 --- a/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py +++ b/packages/graphrag/graphrag/index/operations/embed_text/run_embed_text.py @@ -8,9 +8,9 @@ from dataclasses import dataclass import numpy as np +from graphrag_chunking.token_chunker import split_text_on_tokens from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks -from graphrag.chunking.token_chunker import split_text_on_tokens from graphrag.index.utils.is_null import is_null from graphrag.language_model.protocol.base import EmbeddingModel from graphrag.logger.progress import ProgressTicker, progress_ticker diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 7759e90f4a..0635fd1cd3 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -8,11 +8,11 @@ from typing import Any, cast import pandas as pd +from graphrag_chunking.add_metadata import add_metadata +from graphrag_chunking.chunker import Chunker +from graphrag_chunking.chunker_factory import create_chunker from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks -from graphrag.chunking.add_metadata import add_metadata -from graphrag.chunking.chunker import Chunker -from graphrag.chunking.chunker_factory import create_chunker from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.typing.context import PipelineRunContext from graphrag.index.typing.workflow import WorkflowFunctionOutput @@ -71,7 +71,7 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: metadata = row.get("metadata", None) if prepend_metadata and metadata is not None: metadata = json.loads(metadata) if isinstance(metadata, str) else metadata - row["chunks"] = [add_metadata(chunk, metadata) for chunk in row["chunks"]] + row["chunks"] = [add_metadata(chunk, metadata, line_delimiter=".\n") for chunk in row["chunks"]] tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py index 871f0a91e0..78a12c86c3 100644 --- a/packages/graphrag/graphrag/prompt_tune/loader/input.py +++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py @@ -9,10 +9,10 @@ import numpy as np import pandas as pd from graphrag_cache.noop_cache import NoopCache +from graphrag_chunking.chunker_factory import create_chunker from graphrag_storage import create_storage from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks -from graphrag.chunking.chunker_factory import create_chunker from graphrag.config.models.graph_rag_config import GraphRagConfig from graphrag.index.input.factory import InputReaderFactory from graphrag.index.operations.embed_text.run_embed_text import ( diff --git a/pyproject.toml b/pyproject.toml index 433e45ebee..e545fc17b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,6 +53,7 @@ package = false members = ["packages/*"] [tool.uv.sources] +graphrag-chunking = { workspace = true } graphrag-common = { workspace = true } graphrag-storage = { workspace = true } graphrag-cache = { workspace = true } diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index bbdb2235e0..0b2206cb99 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -3,15 +3,15 @@ from unittest.mock import Mock, patch -from graphrag.chunking.bootstrap_nltk import bootstrap -from graphrag.chunking.chunk_strategy_type import ChunkStrategyType -from graphrag.chunking.chunker_factory import create_chunker -from graphrag.chunking.chunking_config import ChunkingConfig -from graphrag.chunking.token_chunker import ( - split_text_on_tokens, -) from graphrag.tokenizer.get_tokenizer import get_tokenizer from graphrag.tokenizer.tokenizer import Tokenizer +from graphrag_chunking.bootstrap_nltk import bootstrap +from graphrag_chunking.chunk_strategy_type import ChunkStrategyType +from graphrag_chunking.chunker_factory import create_chunker +from graphrag_chunking.chunking_config import ChunkingConfig +from graphrag_chunking.token_chunker import ( + split_text_on_tokens, +) class MockTokenizer(Tokenizer): @@ -22,9 +22,6 @@ def decode(self, tokens) -> str: return "".join(chr(id) for id in tokens) -tokenizer = get_tokenizer() - - class TestRunSentences: def setup_method(self, method): bootstrap() @@ -73,6 +70,7 @@ def test_basic_functionality(self, mock_get_encoding): def test_split_text_str_empty(): + tokenizer = get_tokenizer() result = split_text_on_tokens( "", chunk_size=5, @@ -112,9 +110,9 @@ def test_split_text_on_tokens(): assert result == expected_splits -def test_split_text_on_tokens_no_overlap(): +def test_split_text_on_tokens_one_overlap(): text = "This is a test text, meaning to be taken seriously by this test only." - tok = get_tokenizer(encoding_model="cl100k_base") + tokenizer = get_tokenizer(encoding_model="o200k_base") expected_splits = [ "This is", @@ -125,10 +123,10 @@ def test_split_text_on_tokens_no_overlap(): ", meaning", " meaning to", " to be", - " be taken", # cspell:disable-line - " taken seriously", # cspell:disable-line + " be taken", + " taken seriously", " seriously by", - " by this", # cspell:disable-line + " by this", " this test", " test only", " only.", @@ -138,7 +136,31 @@ def test_split_text_on_tokens_no_overlap(): text=text, chunk_size=2, chunk_overlap=1, - decode=tok.decode, - encode=tok.encode, + decode=tokenizer.decode, + encode=tokenizer.encode, + ) + assert result == expected_splits + + +def test_split_text_on_tokens_no_overlap(): + text = "This is a test text, meaning to be taken seriously by this test only." + tokenizer = get_tokenizer(encoding_model="o200k_base") + + expected_splits = [ + "This is a", + " test text,", + " meaning to be", + " taken seriously by", + " this test only", + ".", + ] + + result = split_text_on_tokens( + text=text, + chunk_size=3, + chunk_overlap=0, + decode=tokenizer.decode, + encode=tokenizer.encode, ) + assert result == expected_splits diff --git a/tests/unit/chunking/test_prepend_metadata.py b/tests/unit/chunking/test_prepend_metadata.py index 0d14c73457..99f99c3093 100644 --- a/tests/unit/chunking/test_prepend_metadata.py +++ b/tests/unit/chunking/test_prepend_metadata.py @@ -1,7 +1,7 @@ # Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License -from graphrag.chunking.add_metadata import add_metadata +from graphrag_chunking.add_metadata import add_metadata def test_add_metadata_one_row(): diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py index 8e3613714e..4c9a6376a1 100644 --- a/tests/unit/config/utils.py +++ b/tests/unit/config/utils.py @@ -4,7 +4,6 @@ from dataclasses import asdict import graphrag.config.defaults as defs -from graphrag.chunking.chunking_config import ChunkingConfig from graphrag.config.models.basic_search_config import BasicSearchConfig from graphrag.config.models.cluster_graph_config import ClusterGraphConfig from graphrag.config.models.community_reports_config import CommunityReportsConfig @@ -29,6 +28,7 @@ ) from graphrag.config.models.vector_store_config import VectorStoreConfig from graphrag_cache import CacheConfig +from graphrag_chunking.chunking_config import ChunkingConfig from graphrag_storage import StorageConfig from pydantic import BaseModel diff --git a/uv.lock b/uv.lock index 8072353c3a..94c8626cc5 100644 --- a/uv.lock +++ 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a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -71,7 +71,10 @@ def chunker_with_logging(row: pd.Series, row_index: int) -> Any: metadata = row.get("metadata", None) if prepend_metadata and metadata is not None: metadata = json.loads(metadata) if isinstance(metadata, str) else metadata - row["chunks"] = [add_metadata(chunk, metadata, line_delimiter=".\n") for chunk in row["chunks"]] + row["chunks"] = [ + add_metadata(chunk, metadata, line_delimiter=".\n") + for chunk in row["chunks"] + ] tick() logger.info("chunker progress: %d/%d", row_index + 1, total_rows) return row From ee20153d8cd8bd6f33c05649e12eb5a67ff137de Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 12:48:25 -0800 Subject: [PATCH 21/23] Typo --- packages/graphrag-chunking/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/packages/graphrag-chunking/README.md b/packages/graphrag-chunking/README.md index fa1a215202..278b8961d5 100644 --- a/packages/graphrag-chunking/README.md +++ b/packages/graphrag-chunking/README.md @@ -21,12 +21,12 @@ print(chunks) # ["This is a", " random test fragment", " of some text"] Using the factory via helper util ```python - tokenizer = tiktoken.get_encoding("o200k_base") - config = ChunkingConfig( - strategy="tokens", - size=3, - overlap=0 +tokenizer = tiktoken.get_encoding("o200k_base") +config = ChunkingConfig( + strategy="tokens", + size=3, + overlap=0 ) - chunker = create_chunker(config, tokenizer.encode, tokenizer.decode) - ... -` \ No newline at end of file +chunker = create_chunker(config, tokenizer.encode, tokenizer.decode) +... +``` \ No newline at end of file From a741bfb8d73595fe6c37da244453eccfe3db3dd0 Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Mon, 22 Dec 2025 14:18:21 -0800 Subject: [PATCH 22/23] Add ChunkResult model --- .../graphrag_chunking/chunk_result.py | 17 ++++++++++ .../graphrag_chunking/chunker.py | 4 ++- .../graphrag_chunking/create_chunk_results.py | 30 ++++++++++++++++ .../graphrag_chunking/sentence_chunker.py | 27 +++++++++++++-- .../graphrag_chunking/token_chunker.py | 7 ++-- .../index/workflows/create_base_text_units.py | 2 +- tests/unit/chunking/test_chunker.py | 34 +++++++++++++++---- 7 files changed, 107 insertions(+), 14 deletions(-) create mode 100644 packages/graphrag-chunking/graphrag_chunking/chunk_result.py create mode 100644 packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py diff --git a/packages/graphrag-chunking/graphrag_chunking/chunk_result.py b/packages/graphrag-chunking/graphrag_chunking/chunk_result.py new file mode 100644 index 0000000000..d9b3177511 --- /dev/null +++ b/packages/graphrag-chunking/graphrag_chunking/chunk_result.py @@ -0,0 +1,17 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""The ChunkResult dataclass.""" + +from dataclasses import dataclass + + +@dataclass +class ChunkResult: + """Result of chunking a document.""" + + text: str + index: int + start_char: int + end_char: int + token_count: int | None = None diff --git a/packages/graphrag-chunking/graphrag_chunking/chunker.py b/packages/graphrag-chunking/graphrag_chunking/chunker.py index e760e5d32b..552b28a8a8 100644 --- a/packages/graphrag-chunking/graphrag_chunking/chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunker.py @@ -6,6 +6,8 @@ from abc import ABC, abstractmethod from typing import Any +from graphrag_chunking.chunk_result import ChunkResult + class Chunker(ABC): """Abstract base class for document chunkers.""" @@ -15,5 +17,5 @@ def __init__(self, **kwargs: Any) -> None: """Create a chunker instance.""" @abstractmethod - def chunk(self, text: str) -> list[str]: + def chunk(self, text: str) -> list[ChunkResult]: """Chunk method definition.""" diff --git a/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py new file mode 100644 index 0000000000..3eb15befff --- /dev/null +++ b/packages/graphrag-chunking/graphrag_chunking/create_chunk_results.py @@ -0,0 +1,30 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License + +"""A module containing 'create_chunk_results' function.""" + +from collections.abc import Callable + +from graphrag_chunking.chunk_result import ChunkResult + + +def create_chunk_results( + chunks: list[str], + encode: Callable[[str], list[int]] | None = None, +) -> list[ChunkResult]: + """Create chunk results from a list of text chunks. The index assignments are 0-based and assume chunks we not stripped relative to the source text.""" + results = [] + start_char = 0 + for index, chunk in enumerate(chunks): + end_char = start_char + len(chunk) - 1 # 0-based indices + chunk = ChunkResult( + text=chunk, + index=index, + start_char=start_char, + end_char=end_char, + ) + if encode: + chunk.token_count = len(encode(chunk.text)) + results.append(chunk) + start_char = end_char + 1 + return results diff --git a/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py index 0efee67d69..91e68ccaf2 100644 --- a/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/sentence_chunker.py @@ -3,21 +3,42 @@ """A module containing 'SentenceChunker' class.""" +from collections.abc import Callable from typing import Any import nltk from graphrag_chunking.bootstrap_nltk import bootstrap +from graphrag_chunking.chunk_result import ChunkResult from graphrag_chunking.chunker import Chunker +from graphrag_chunking.create_chunk_results import create_chunk_results class SentenceChunker(Chunker): """A chunker that splits text into sentence-based chunks.""" - def __init__(self, **kwargs: Any) -> None: + def __init__( + self, encode: Callable[[str], list[int]] | None = None, **kwargs: Any + ) -> None: """Create a sentence chunker instance.""" + self._encode = encode bootstrap() - def chunk(self, text) -> list[str]: + def chunk(self, text) -> list[ChunkResult]: """Chunk the text into sentence-based chunks.""" - return nltk.sent_tokenize(text) + sentences = nltk.sent_tokenize(text.strip()) + results = create_chunk_results(sentences, encode=self._encode) + # nltk sentence tokenizer may trim whitespace, so we need to adjust start/end chars + for index, result in enumerate(results): + txt = result.text + start = result.start_char + actual_start = text.find(txt, start) + delta = actual_start - start + if delta > 0: + result.start_char += delta + result.end_char += delta + # bump the next to keep the start check from falling too far behind + if index < len(results) - 1: + results[index + 1].start_char += delta + results[index + 1].end_char += delta + return results diff --git a/packages/graphrag-chunking/graphrag_chunking/token_chunker.py b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py index b1da7bc5b3..9a5e8ecfb0 100644 --- a/packages/graphrag-chunking/graphrag_chunking/token_chunker.py +++ b/packages/graphrag-chunking/graphrag_chunking/token_chunker.py @@ -6,7 +6,9 @@ from collections.abc import Callable from typing import Any +from graphrag_chunking.chunk_result import ChunkResult from graphrag_chunking.chunker import Chunker +from graphrag_chunking.create_chunk_results import create_chunk_results class TokenChunker(Chunker): @@ -26,15 +28,16 @@ def __init__( self._encode = encode self._decode = decode - def chunk(self, text: str) -> list[str]: + def chunk(self, text: str) -> list[ChunkResult]: """Chunk the text into token-based chunks.""" - return split_text_on_tokens( + chunks = split_text_on_tokens( text, chunk_size=self._size, chunk_overlap=self._overlap, encode=self._encode, decode=self._decode, ) + return create_chunk_results(chunks, encode=self._encode) def split_text_on_tokens( diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 8e1b55b237..26c5e9af5f 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -66,7 +66,7 @@ def create_base_text_units( logger.info("Starting chunking process for %d documents", total_rows) def chunker_with_logging(row: pd.Series, row_index: int) -> Any: - row["chunks"] = chunker.chunk(row["text"]) + row["chunks"] = [chunk.text for chunk in chunker.chunk(row["text"])] metadata = row.get("metadata", None) if prepend_metadata and metadata is not None: diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index 0b2206cb99..c1d77212b1 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -28,22 +28,42 @@ def setup_method(self, method): def test_basic_functionality(self): """Test basic sentence splitting without metadata""" - input = "This is a test. Another sentence." + input = "This is a test. Another sentence. And a third one!" chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) - assert len(chunks) == 2 - assert chunks[0] == "This is a test." - assert chunks[1] == "Another sentence." + assert len(chunks) == 3 + assert chunks[0].text == "This is a test." + assert chunks[0].index == 0 + assert chunks[0].start_char == 0 + assert chunks[0].end_char == 14 + + assert chunks[1].text == "Another sentence." + assert chunks[1].index == 1 + assert chunks[1].start_char == 16 + assert chunks[1].end_char == 32 + + assert chunks[2].text == "And a third one!" + assert chunks[2].index == 2 + assert chunks[2].start_char == 34 + assert chunks[2].end_char == 49 def test_mixed_whitespace_handling(self): """Test input with irregular whitespace""" - input = " Sentence with spaces. Another one! " chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) chunks = chunker.chunk(input) - assert chunks[0] == " Sentence with spaces." - assert chunks[1] == "Another one!" + + assert len(chunks) == 2 + assert chunks[0].text == "Sentence with spaces." + assert chunks[0].index == 0 + assert chunks[0].start_char == 3 + assert chunks[0].end_char == 23 + + assert chunks[1].text == "Another one!" + assert chunks[1].index == 1 + assert chunks[1].start_char == 25 + assert chunks[1].end_char == 36 class TestRunTokens: From 7748493fdf6b4b28d3fdcaa5f1adf30f45c950bb Mon Sep 17 00:00:00 2001 From: Nathan Evans Date: Tue, 23 Dec 2025 10:28:14 -0800 Subject: [PATCH 23/23] Streamline chunking config --- .../graphrag_chunking/chunk_strategy_type.py | 4 ++-- .../graphrag_chunking/chunker_factory.py | 12 ++++++------ .../graphrag_chunking/chunking_config.py | 8 ++++---- packages/graphrag/graphrag/cli/main.py | 8 ++++---- packages/graphrag/graphrag/cli/prompt_tune.py | 8 ++++---- packages/graphrag/graphrag/config/defaults.py | 14 +++++--------- packages/graphrag/graphrag/config/init_content.py | 10 +++++----- .../graphrag/config/models/graph_rag_config.py | 12 ++++++------ .../index/workflows/create_base_text_units.py | 6 +++--- .../graphrag/graphrag/prompt_tune/loader/input.py | 2 +- tests/unit/chunking/test_chunker.py | 8 ++++---- tests/unit/config/utils.py | 4 ++-- tests/verbs/test_create_base_text_units.py | 2 +- 13 files changed, 47 insertions(+), 51 deletions(-) diff --git a/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py b/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py index df3c9801e2..5de6321519 100644 --- a/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunk_strategy_type.py @@ -6,8 +6,8 @@ from enum import StrEnum -class ChunkStrategyType(StrEnum): - """ChunkStrategy class definition.""" +class ChunkerType(StrEnum): + """ChunkerType class definition.""" Tokens = "tokens" Sentence = "sentence" diff --git a/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py b/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py index c1d60e0b72..636d0909f2 100644 --- a/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunker_factory.py @@ -7,7 +7,7 @@ from graphrag_common.factory.factory import Factory, ServiceScope -from graphrag_chunking.chunk_strategy_type import ChunkStrategyType +from graphrag_chunking.chunk_strategy_type import ChunkerType from graphrag_chunking.chunker import Chunker from graphrag_chunking.chunking_config import ChunkingConfig @@ -58,18 +58,18 @@ def create_chunker( config_model["encode"] = encode if decode is not None: config_model["decode"] = decode - chunker_strategy = config.strategy + chunker_strategy = config.type if chunker_strategy not in chunker_factory: match chunker_strategy: - case ChunkStrategyType.Tokens: + case ChunkerType.Tokens: from graphrag_chunking.token_chunker import TokenChunker - register_chunker(ChunkStrategyType.Tokens, TokenChunker) - case ChunkStrategyType.Sentence: + register_chunker(ChunkerType.Tokens, TokenChunker) + case ChunkerType.Sentence: from graphrag_chunking.sentence_chunker import SentenceChunker - register_chunker(ChunkStrategyType.Sentence, SentenceChunker) + register_chunker(ChunkerType.Sentence, SentenceChunker) case _: msg = f"ChunkingConfig.strategy '{chunker_strategy}' is not registered in the ChunkerFactory. Registered types: {', '.join(chunker_factory.keys())}." raise ValueError(msg) diff --git a/packages/graphrag-chunking/graphrag_chunking/chunking_config.py b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py index 9ceec8336d..1bb5b84e44 100644 --- a/packages/graphrag-chunking/graphrag_chunking/chunking_config.py +++ b/packages/graphrag-chunking/graphrag_chunking/chunking_config.py @@ -5,7 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field -from graphrag_chunking.chunk_strategy_type import ChunkStrategyType +from graphrag_chunking.chunk_strategy_type import ChunkerType class ChunkingConfig(BaseModel): @@ -14,9 +14,9 @@ class ChunkingConfig(BaseModel): model_config = ConfigDict(extra="allow") """Allow extra fields to support custom cache implementations.""" - strategy: str = Field( - description="The chunking strategy to use.", - default=ChunkStrategyType.Tokens, + type: str = Field( + description="The chunking type to use.", + default=ChunkerType.Tokens, ) encoding_model: str | None = Field( description="The encoding model to use.", diff --git a/packages/graphrag/graphrag/cli/main.py b/packages/graphrag/graphrag/cli/main.py index 5ede2faa6a..f79517f575 100644 --- a/packages/graphrag/graphrag/cli/main.py +++ b/packages/graphrag/graphrag/cli/main.py @@ -306,14 +306,14 @@ def _prompt_tune_cli( help="The minimum number of examples to generate/include in the entity extraction prompt.", ), chunk_size: int = typer.Option( - graphrag_config_defaults.chunks.size, + graphrag_config_defaults.chunking.size, "--chunk-size", - help="The size of each example text chunk. Overrides chunks.size in the configuration file.", + help="The size of each example text chunk. Overrides chunking.size in the configuration file.", ), overlap: int = typer.Option( - graphrag_config_defaults.chunks.overlap, + graphrag_config_defaults.chunking.overlap, "--overlap", - help="The overlap size for chunking documents. Overrides chunks.overlap in the configuration file.", + help="The overlap size for chunking documents. Overrides chunking.overlap in the configuration file.", ), language: str | None = typer.Option( None, diff --git a/packages/graphrag/graphrag/cli/prompt_tune.py b/packages/graphrag/graphrag/cli/prompt_tune.py index 0c80092770..249dd7cd07 100644 --- a/packages/graphrag/graphrag/cli/prompt_tune.py +++ b/packages/graphrag/graphrag/cli/prompt_tune.py @@ -61,11 +61,11 @@ async def prompt_tune( ) # override chunking config in the configuration - if chunk_size != graph_config.chunks.size: - graph_config.chunks.size = chunk_size + if chunk_size != graph_config.chunking.size: + graph_config.chunking.size = chunk_size - if overlap != graph_config.chunks.overlap: - graph_config.chunks.overlap = overlap + if overlap != graph_config.chunking.overlap: + graph_config.chunking.overlap = overlap # configure the root logger with the specified log level from graphrag.logger.standard_logging import init_loggers diff --git a/packages/graphrag/graphrag/config/defaults.py b/packages/graphrag/graphrag/config/defaults.py index 0cb6e190c2..cab8732af2 100644 --- a/packages/graphrag/graphrag/config/defaults.py +++ b/packages/graphrag/graphrag/config/defaults.py @@ -8,7 +8,7 @@ from typing import ClassVar from graphrag_cache import CacheType -from graphrag_chunking.chunk_strategy_type import ChunkStrategyType +from graphrag_chunking.chunk_strategy_type import ChunkerType from graphrag_storage import StorageType from graphrag.config.embeddings import default_embeddings @@ -57,10 +57,10 @@ class BasicSearchDefaults: @dataclass -class ChunksDefaults: - """Default values for chunks.""" +class ChunkingDefaults: + """Default values for chunking.""" - strategy: str = ChunkStrategyType.Tokens + type: str = ChunkerType.Tokens size: int = 1200 overlap: int = 100 encoding_model: str = ENCODING_MODEL @@ -126,7 +126,6 @@ class EmbedTextDefaults: batch_size: int = 16 batch_max_tokens: int = 8191 names: list[str] = field(default_factory=lambda: default_embeddings) - strategy: None = None @dataclass @@ -139,7 +138,6 @@ class ExtractClaimsDefaults: "Any claims or facts that could be relevant to information discovery." ) max_gleanings: int = 1 - strategy: None = None model_id: str = DEFAULT_CHAT_MODEL_ID model_instance_name: str = "extract_claims" @@ -153,7 +151,6 @@ class ExtractGraphDefaults: default_factory=lambda: ["organization", "person", "geo", "event"] ) max_gleanings: int = 1 - strategy: None = None model_id: str = DEFAULT_CHAT_MODEL_ID model_instance_name: str = "extract_graph" @@ -360,7 +357,6 @@ class SummarizeDescriptionsDefaults: prompt: None = None max_length: int = 500 max_input_tokens: int = 4_000 - strategy: None = None model_id: str = DEFAULT_CHAT_MODEL_ID model_instance_name: str = "summarize_descriptions" @@ -401,7 +397,7 @@ class GraphRagConfigDefaults: cache: CacheDefaults = field(default_factory=CacheDefaults) input: InputDefaults = field(default_factory=InputDefaults) embed_text: EmbedTextDefaults = field(default_factory=EmbedTextDefaults) - chunks: ChunksDefaults = field(default_factory=ChunksDefaults) + chunking: ChunkingDefaults = field(default_factory=ChunkingDefaults) snapshots: SnapshotsDefaults = field(default_factory=SnapshotsDefaults) extract_graph: ExtractGraphDefaults = field(default_factory=ExtractGraphDefaults) extract_graph_nlp: ExtractGraphNLPDefaults = field( diff --git a/packages/graphrag/graphrag/config/init_content.py b/packages/graphrag/graphrag/config/init_content.py index 8d2c85af0a..086228bf55 100644 --- a/packages/graphrag/graphrag/config/init_content.py +++ b/packages/graphrag/graphrag/config/init_content.py @@ -54,11 +54,11 @@ base_dir: "{graphrag_config_defaults.input.storage.base_dir}" file_type: {graphrag_config_defaults.input.file_type.value} # [csv, text, json] -chunks: - strategy: {graphrag_config_defaults.chunks.strategy} - size: {graphrag_config_defaults.chunks.size} - overlap: {graphrag_config_defaults.chunks.overlap} - encoding_model: {graphrag_config_defaults.chunks.encoding_model} +chunking: + type: {graphrag_config_defaults.chunking.type} + size: {graphrag_config_defaults.chunking.size} + overlap: {graphrag_config_defaults.chunking.overlap} + encoding_model: {graphrag_config_defaults.chunking.encoding_model} ### Output/storage settings ### ## If blob storage is specified in the following four sections, diff --git a/packages/graphrag/graphrag/config/models/graph_rag_config.py b/packages/graphrag/graphrag/config/models/graph_rag_config.py index ab2704b40a..f79537cd04 100644 --- a/packages/graphrag/graphrag/config/models/graph_rag_config.py +++ b/packages/graphrag/graphrag/config/models/graph_rag_config.py @@ -125,14 +125,14 @@ def _validate_input_base_dir(self) -> None: Path(self.input.storage.base_dir).resolve() ) - chunks: ChunkingConfig = Field( + chunking: ChunkingConfig = Field( description="The chunking configuration to use.", default=ChunkingConfig( - strategy=graphrag_config_defaults.chunks.strategy, - size=graphrag_config_defaults.chunks.size, - overlap=graphrag_config_defaults.chunks.overlap, - encoding_model=graphrag_config_defaults.chunks.encoding_model, - prepend_metadata=graphrag_config_defaults.chunks.prepend_metadata, + type=graphrag_config_defaults.chunking.type, + size=graphrag_config_defaults.chunking.size, + overlap=graphrag_config_defaults.chunking.overlap, + encoding_model=graphrag_config_defaults.chunking.encoding_model, + prepend_metadata=graphrag_config_defaults.chunking.prepend_metadata, ), ) """The chunking configuration to use.""" diff --git a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py index 26c5e9af5f..fce186e0c6 100644 --- a/packages/graphrag/graphrag/index/workflows/create_base_text_units.py +++ b/packages/graphrag/graphrag/index/workflows/create_base_text_units.py @@ -33,14 +33,14 @@ async def run_workflow( logger.info("Workflow started: create_base_text_units") documents = await load_table_from_storage("documents", context.output_storage) - tokenizer = get_tokenizer(encoding_model=config.chunks.encoding_model) - chunker = create_chunker(config.chunks, tokenizer.encode, tokenizer.decode) + tokenizer = get_tokenizer(encoding_model=config.chunking.encoding_model) + chunker = create_chunker(config.chunking, tokenizer.encode, tokenizer.decode) output = create_base_text_units( documents, context.callbacks, tokenizer=tokenizer, chunker=chunker, - prepend_metadata=config.chunks.prepend_metadata, + prepend_metadata=config.chunking.prepend_metadata, ) await write_table_to_storage(output, "text_units", context.output_storage) diff --git a/packages/graphrag/graphrag/prompt_tune/loader/input.py b/packages/graphrag/graphrag/prompt_tune/loader/input.py index 78a12c86c3..a9ef358a30 100644 --- a/packages/graphrag/graphrag/prompt_tune/loader/input.py +++ b/packages/graphrag/graphrag/prompt_tune/loader/input.py @@ -62,7 +62,7 @@ async def load_docs_in_chunks( cache=NoopCache(), ) tokenizer = get_tokenizer(embeddings_llm_settings) - chunker = create_chunker(config.chunks, tokenizer.encode, tokenizer.decode) + chunker = create_chunker(config.chunking, tokenizer.encode, tokenizer.decode) input_storage = create_storage(config.input.storage) input_reader = InputReaderFactory().create( config.input.file_type, diff --git a/tests/unit/chunking/test_chunker.py b/tests/unit/chunking/test_chunker.py index c1d77212b1..4dbefa4cf3 100644 --- a/tests/unit/chunking/test_chunker.py +++ b/tests/unit/chunking/test_chunker.py @@ -6,7 +6,7 @@ from graphrag.tokenizer.get_tokenizer import get_tokenizer from graphrag.tokenizer.tokenizer import Tokenizer from graphrag_chunking.bootstrap_nltk import bootstrap -from graphrag_chunking.chunk_strategy_type import ChunkStrategyType +from graphrag_chunking.chunk_strategy_type import ChunkerType from graphrag_chunking.chunker_factory import create_chunker from graphrag_chunking.chunking_config import ChunkingConfig from graphrag_chunking.token_chunker import ( @@ -29,7 +29,7 @@ def setup_method(self, method): def test_basic_functionality(self): """Test basic sentence splitting without metadata""" input = "This is a test. Another sentence. And a third one!" - chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) + chunker = create_chunker(ChunkingConfig(type=ChunkerType.Sentence)) chunks = chunker.chunk(input) assert len(chunks) == 3 @@ -51,7 +51,7 @@ def test_basic_functionality(self): def test_mixed_whitespace_handling(self): """Test input with irregular whitespace""" input = " Sentence with spaces. Another one! " - chunker = create_chunker(ChunkingConfig(strategy=ChunkStrategyType.Sentence)) + chunker = create_chunker(ChunkingConfig(type=ChunkerType.Sentence)) chunks = chunker.chunk(input) assert len(chunks) == 2 @@ -80,7 +80,7 @@ def test_basic_functionality(self, mock_get_encoding): size=5, overlap=1, encoding_model="fake-encoding", - strategy=ChunkStrategyType.Tokens, + type=ChunkerType.Tokens, ) chunker = create_chunker(config, mock_encoder.encode, mock_encoder.decode) diff --git a/tests/unit/config/utils.py b/tests/unit/config/utils.py index 4c9a6376a1..586cd1e18e 100644 --- a/tests/unit/config/utils.py +++ b/tests/unit/config/utils.py @@ -164,7 +164,7 @@ def assert_text_embedding_configs( def assert_chunking_configs(actual: ChunkingConfig, expected: ChunkingConfig) -> None: assert actual.size == expected.size assert actual.overlap == expected.overlap - assert actual.strategy == expected.strategy + assert actual.type == expected.type assert actual.encoding_model == expected.encoding_model assert actual.prepend_metadata == expected.prepend_metadata @@ -344,7 +344,7 @@ def assert_graphrag_configs(actual: GraphRagConfig, expected: GraphRagConfig) -> assert_cache_configs(actual.cache, expected.cache) assert_input_configs(actual.input, expected.input) assert_text_embedding_configs(actual.embed_text, expected.embed_text) - assert_chunking_configs(actual.chunks, expected.chunks) + assert_chunking_configs(actual.chunking, expected.chunking) assert_snapshots_configs(actual.snapshots, expected.snapshots) assert_extract_graph_configs(actual.extract_graph, expected.extract_graph) assert_extract_graph_nlp_configs( diff --git a/tests/verbs/test_create_base_text_units.py b/tests/verbs/test_create_base_text_units.py index e13bb569d1..89018e1609 100644 --- a/tests/verbs/test_create_base_text_units.py +++ b/tests/verbs/test_create_base_text_units.py @@ -35,7 +35,7 @@ async def test_create_base_text_units_metadata(): config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG) # type: ignore config.input.metadata = ["title"] - config.chunks.prepend_metadata = True + config.chunking.prepend_metadata = True await update_document_metadata(config.input.metadata, context)