From 5bab62d1a088d17991a7fd334c4aa86d096cfd49 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Fri, 1 Nov 2024 22:11:39 +0530 Subject: [PATCH 01/21] Add pyproject.toml --- README.md | 3 ++- pyproject.toml | 30 ++++++++++++++++++++++++++++++ 2 files changed, 32 insertions(+), 1 deletion(-) create mode 100644 pyproject.toml diff --git a/README.md b/README.md index 2bae2cb..bb1ba25 100644 --- a/README.md +++ b/README.md @@ -1 +1,2 @@ -# chonkie \ No newline at end of file +# 🦛 chonkie + diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..377db85 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,30 @@ +[build-system] +requires = ["setuptools>=45", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "chonkie" +version = "0.1.0" +description = "A simple, efficient text chunking library for RAG applications" +readme = "README.md" +requires-python = ">=3.8" +license = {file = "LICENSE"} +keywords = ["chunking", "rag", "nlp", "text-processing"] +authors = [ + {name = "Bhavnick Minhas", email = "bhavnicksm@gmail.com"} +] +classifiers = [ + "License :: OSI Approved :: MIT License", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11" +] +dependencies = [ + "tokenizers>=0.13.0" +] + +[project.urls] +Homepage = "https://github.com/bhavnicksm/chonkie" \ No newline at end of file From 548a5f77528b49c08bb1feb5b14d35baffc4db73 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Fri, 1 Nov 2024 22:20:30 +0530 Subject: [PATCH 02/21] Add basic files structure + DOCS.md --- DOCS.md | 1 + chonkie/__init__.py | 0 chonkie/chunker/__init__.py | 0 chonkie/chunker/base.py | 0 chonkie/chunker/semantic.py | 0 chonkie/chunker/sentence.py | 0 chonkie/chunker/spdm.py | 0 chonkie/chunker/token.py | 0 chonkie/chunker/word.py | 0 9 files changed, 1 insertion(+) create mode 100644 DOCS.md create mode 100644 chonkie/__init__.py create mode 100644 chonkie/chunker/__init__.py create mode 100644 chonkie/chunker/base.py create mode 100644 chonkie/chunker/semantic.py create mode 100644 chonkie/chunker/sentence.py create mode 100644 chonkie/chunker/spdm.py create mode 100644 chonkie/chunker/token.py create mode 100644 chonkie/chunker/word.py diff --git a/DOCS.md b/DOCS.md new file mode 100644 index 0000000..8cb0545 --- /dev/null +++ b/DOCS.md @@ -0,0 +1 @@ +# 🦛 Chonkie Docs \ No newline at end of file diff --git a/chonkie/__init__.py b/chonkie/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/chonkie/chunker/__init__.py b/chonkie/chunker/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/chonkie/chunker/base.py b/chonkie/chunker/base.py new file mode 100644 index 0000000..e69de29 diff --git a/chonkie/chunker/semantic.py b/chonkie/chunker/semantic.py new file mode 100644 index 0000000..e69de29 diff --git a/chonkie/chunker/sentence.py b/chonkie/chunker/sentence.py new file mode 100644 index 0000000..e69de29 diff --git a/chonkie/chunker/spdm.py b/chonkie/chunker/spdm.py new file mode 100644 index 0000000..e69de29 diff --git a/chonkie/chunker/token.py b/chonkie/chunker/token.py new file mode 100644 index 0000000..e69de29 diff --git a/chonkie/chunker/word.py b/chonkie/chunker/word.py new file mode 100644 index 0000000..e69de29 From 265b0358a8000719bf3a2a10cabafe25bfa2efad Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Fri, 1 Nov 2024 22:34:45 +0530 Subject: [PATCH 03/21] Update README.md + pyprojects.toml --- README.md | 79 +++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 79 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index bb1ba25..3e4bc5f 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,79 @@ -# 🦛 chonkie +# 🦛 Chonkie +so i found myself making another RAG bot (for the 2342148th time) and meanwhile, explaining to my juniors about why we should use chunking in our RAG bots, only to realise that i would have to write chunking all over again unless i use the bloated software library X or the extremely feature-less library Y. _WHY CAN I NOT HAVE GOOD THINGS IN LIFE, UGH?_ + +Can't i just install, import and run chunking and not have to worry about dependencies, bloat, speed or other factors? + +Well, with chonkie you can! (chonkie boi is a gud boi) + +✅ All the CHONKs you'd ever need
+✅ Easy to use: Install, Import, CHONK
+✅ No bloat, just CHONK
+✅ Cute CHONK mascoot
+✅ Moto Moto's favorite python library
+ +What're you waiting for, **just CHONK it**! + +# Table of Contents +- [🦛 Chonkie](#-chonkie) +- [Table of Contents](#table-of-contents) +- [Why do we need Chunking?](#why-do-we-need-chunking) +- [Approaches to doing chunking](#approaches-to-doing-chunking) +- [Installation](#installation) +- [Usage](#usage) +- [Citation](#citation) + +# Why do we need Chunking? + +Here are some arguments for why one would like to chunk their texts for a RAG scenario: + +- Most RAG pipelines are bottlenecked by context length as of today. While we expect future LLMs to exceed 1Mill token lenghts, even then, it's not only LLMs inside the pipeline, but other aspects too, namely, bi-encoder retriever, cross-encoder reranker and even models for particular aspects like answer relevancy models and answer attribution models, that could lead to the context length bottleneck. +- Even with infinite context, there's no free lunch on the context side - the minimum it takes to understand a string is o(n) and we would never be able to make models more efficient on scaling context. So, if we have smaller context, our search and generation pipeline would be more efficient (in response latency) +- Research suggests that a lot of random, noisy context can actually lead to higher hallucination in the model responses. However, if we ensure that each chunk that get's passed onto the model is only relevant, the model would end up with better responses. + +# Approaches to doing chunking + +1. Token Chunking (a.k.a Fixed Size Chunking or Sliding Window Chunking) +2. Word Chunking +3. Sentence Chunking +4. Semantic Chunking +5. Semantic Double-Pass Merge (SDPM) Chunking +6. Context-aware Chunking + +# Installation + +To install chonkie, simply run: + +```bash +pip install chonkie +``` + +# Usage + +Here's a basic example to get you started: + +```python +from chonkie.chunkers import Chunker + +# Initialize the chunker +chunker = Chunker() + +# Chunk some text +chunks = chunker.chunk("Your text here") +print(chunks) +``` + +# Citation + +If you use Chonkie in your research, please cite it as follows: + +``` +@misc{chonkie2024, + author = {Minhas, Bhavnick}, + title = {Chonkie: A Lightweight Chunking Library for RAG Bots}, + year = {2024}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/bhavnick/chonkie}}, +} +``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 377db85..45c5aa0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ classifiers = [ dependencies = [ "tokenizers>=0.13.0" ] +packages=["chonkie"] [project.urls] Homepage = "https://github.com/bhavnicksm/chonkie" \ No newline at end of file From d37159b04ca7bd9d3cec6f3a25f4e7a111fba186 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Fri, 1 Nov 2024 22:46:06 +0530 Subject: [PATCH 04/21] Add base chunker + update version info --- chonkie/__init__.py | 3 +++ chonkie/chunker/base.py | 34 ++++++++++++++++++++++++++++++++++ pyproject.toml | 2 +- 3 files changed, 38 insertions(+), 1 deletion(-) diff --git a/chonkie/__init__.py b/chonkie/__init__.py index e69de29..43e07f8 100644 --- a/chonkie/__init__.py +++ b/chonkie/__init__.py @@ -0,0 +1,3 @@ +__version__ = "0.0.1a" +__name__ = "chonkie" +__author__ = "Bhavnick Minhas" \ No newline at end of file diff --git a/chonkie/chunker/base.py b/chonkie/chunker/base.py index e69de29..0e899a7 100644 --- a/chonkie/chunker/base.py +++ b/chonkie/chunker/base.py @@ -0,0 +1,34 @@ +from typing import List +from dataclasses import dataclass +from abc import ABC, abstractmethod + +@dataclass +class Chunk: + """Dataclass representing a text chunk with metadata.""" + text: str + start_index: int + end_index: int + token_count: int + +class BaseChunker(ABC): + """Abstract base class for all chunker implementations. + + All chunker implementations should inherit from this class and implement + the chunk() method according to their specific chunking strategy. + """ + + @abstractmethod + def chunk(self, text: str) -> List[Chunk]: + """Split text into chunks according to the implementation strategy. + + Args: + text: Input text to be chunked + + Returns: + List of Chunk objects containing the chunked text and metadata + """ + pass + + def __repr__(self) -> str: + """Return string representation of the chunker.""" + return f"{self.__class__.__name__}()" \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 45c5aa0..75bfb54 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "chonkie" -version = "0.1.0" +version = "0.0.1a" description = "A simple, efficient text chunking library for RAG applications" readme = "README.md" requires-python = ">=3.8" From 96cd78b34d5256ff3ebc6ddcbf02c1a640c93ea8 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Fri, 1 Nov 2024 22:48:32 +0530 Subject: [PATCH 05/21] Add the __call__ to make the Chunker class callable directly --- chonkie/chunker/base.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/chonkie/chunker/base.py b/chonkie/chunker/base.py index 0e899a7..c70ae78 100644 --- a/chonkie/chunker/base.py +++ b/chonkie/chunker/base.py @@ -28,6 +28,17 @@ def chunk(self, text: str) -> List[Chunk]: List of Chunk objects containing the chunked text and metadata """ pass + + def __call__(self, text: str) -> List[Chunk]: + """Make the chunker callable directly. + + Args: + text: Input text to be chunked + + Returns: + List of Chunk objects containing the chunked text and metadata + """ + return self.chunk(text) def __repr__(self) -> str: """Return string representation of the chunker.""" From 1a4df69f1ac4b4bb5eccced402ae33965676ad18 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 00:30:21 +0530 Subject: [PATCH 06/21] Add TokenChunker and WordChunker --- chonkie/__init__.py | 15 ++- chonkie/chunker/__init__.py | 8 ++ chonkie/chunker/token.py | 69 +++++++++++ chonkie/chunker/word.py | 237 ++++++++++++++++++++++++++++++++++++ 4 files changed, 328 insertions(+), 1 deletion(-) diff --git a/chonkie/__init__.py b/chonkie/__init__.py index 43e07f8..0158e05 100644 --- a/chonkie/__init__.py +++ b/chonkie/__init__.py @@ -1,3 +1,16 @@ +from .chunker import ( + WordChunker, + TokenChunker, +) + __version__ = "0.0.1a" __name__ = "chonkie" -__author__ = "Bhavnick Minhas" \ No newline at end of file +__author__ = "Bhavnick Minhas" + +__all__ = [ + "__name__", + "__version__", + "__author__", + "WordChunker", + "TokenChunker", +] \ No newline at end of file diff --git a/chonkie/chunker/__init__.py b/chonkie/chunker/__init__.py index e69de29..72d0a33 100644 --- a/chonkie/chunker/__init__.py +++ b/chonkie/chunker/__init__.py @@ -0,0 +1,8 @@ +from .word import WordChunker +from .token import TokenChunker + + +__all__ = [ + "WordChunker", + "TokenChunker" +] \ No newline at end of file diff --git a/chonkie/chunker/token.py b/chonkie/chunker/token.py index e69de29..7da79e4 100644 --- a/chonkie/chunker/token.py +++ b/chonkie/chunker/token.py @@ -0,0 +1,69 @@ +from typing import List +from tokenizers import Tokenizer + +from chonkie.chunker.base import Chunk, BaseChunker +class TokenChunker(BaseChunker): + def __init__(self, tokenizer: Tokenizer, chunk_size: int, chunk_overlap: int): + """Initialize the TokenChunker with configuration parameters. + + Args: + tokenizer: The tokenizer instance to use for encoding/decoding + chunk_size: Maximum number of tokens per chunk + chunk_overlap: Number of tokens to overlap between chunks + + Raises: + ValueError: If chunk_size <= 0 or chunk_overlap >= chunk_size + """ + if chunk_size <= 0: + raise ValueError("chunk_size must be positive") + if chunk_overlap >= chunk_size: + raise ValueError("chunk_overlap must be less than chunk_size") + + self.tokenizer = tokenizer + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + + def chunk(self, text: str) -> List[Chunk]: + """Split text into overlapping chunks of specified token size. + + Args: + text: Input text to be chunked + + Returns: + List of Chunk objects containing the chunked text and metadata + """ + if not text.strip(): + return [] + + # Encode full text + encoding = self.tokenizer.encode(text) + text_tokens = encoding.ids + chunks = [] + + # Calculate chunk positions + start_indices = range(0, len(text_tokens), self.chunk_size - self.chunk_overlap) + + for start_idx in start_indices: + # Get token indices for this chunk + end_idx = min(start_idx + self.chunk_size, len(text_tokens)) + + # Extract and decode tokens for this chunk + chunk_tokens = text_tokens[start_idx:end_idx] + chunk_text = self.tokenizer.decode(chunk_tokens) + + chunks.append(Chunk( + text=chunk_text, + start_index=start_idx, + end_index=end_idx, + token_count=len(chunk_tokens) + )) + + # Break if we've reached the end of the text + if end_idx == len(text_tokens): + break + + return chunks + + def __repr__(self) -> str: + return (f"TokenChunker(chunk_size={self.chunk_size}, " + f"chunk_overlap={self.chunk_overlap})") \ No newline at end of file diff --git a/chonkie/chunker/word.py b/chonkie/chunker/word.py index e69de29..9506033 100644 --- a/chonkie/chunker/word.py +++ b/chonkie/chunker/word.py @@ -0,0 +1,237 @@ +from typing import List, Tuple +import re +from tokenizers import Tokenizer + +from chonkie.chunker.base import Chunk, BaseChunker + +class WordChunker(BaseChunker): + def __init__(self, tokenizer: Tokenizer, chunk_size: int, chunk_overlap: int, mode: str = "simple"): + """Initialize the WordChunker with configuration parameters. + + Args: + tokenizer: The tokenizer instance to use for encoding/decoding + chunk_size: Maximum number of tokens per chunk + chunk_overlap: Maximum number of tokens to overlap between chunks + mode: Tokenization mode - "heuristic" (space-based) or "advanced" (handles punctuation) + + Raises: + ValueError: If chunk_size <= 0 or chunk_overlap >= chunk_size or invalid mode + """ + if chunk_size <= 0: + raise ValueError("chunk_size must be positive") + if chunk_overlap >= chunk_size: + raise ValueError("chunk_overlap must be less than chunk_size") + if mode not in ["simple", "advanced"]: + raise ValueError("mode must be either 'heuristic' or 'advanced'") + + self.tokenizer = tokenizer + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + self.mode = mode + + def _split_into_words(self, text: str) -> List[str]: + """Split text into words based on the selected mode. + + Args: + text: Input text to be split into words + + Returns: + List of words + """ + if self.mode == "simple": + # Simple space-based splitting + return text.split() + elif self.mode == "advanced": + # Advanced tokenization handling various cases + + # Replace common abbreviations with placeholders to preserve periods + abbreviations = { + r'(?i)mr\.': 'MR_ABBR', + r'(?i)mrs\.': 'MRS_ABBR', + r'(?i)ms\.': 'MS_ABBR', + r'(?i)dr\.': 'DR_ABBR', + r'(?i)prof\.': 'PROF_ABBR', + r'(?i)sr\.': 'SR_ABBR', + r'(?i)jr\.': 'JR_ABBR', + r'(?i)vs\.': 'VS_ABBR', + r'(?i)etc\.': 'ETC_ABBR', + r'(?i)i\.e\.': 'IE_ABBR', + r'(?i)e\.g\.': 'EG_ABBR', + # Add numbers with decimal points + r'(\d+)\.(\d+)': r'\1_DECIMAL_\2' + } + + processed_text = text + for pattern, replacement in abbreviations.items(): + processed_text = re.sub(pattern, replacement, processed_text) + + # Split on word boundaries while preserving all punctuation + # This regex handles: + # - Words (\w+) + # - Contractions (word's, don't, etc.) + # - Hyphenated words (self-aware) + # - Various punctuation marks + words = re.findall( + r"\b[A-Za-z0-9]+(?:[-'][A-Za-z0-9]+)*\b" # Words with possible hyphens/apostrophes + r"|[.,!?;:\"'(){}[\]]+" # Punctuation groups + r"|\.{3}" # Ellipsis + r"|--+" # Em/En dashes + r"|'[A-Za-z]+" # Starting contractions like 'tis + r"|[A-Za-z]+'" # Ending contractions like ol' + r"|[@#$%&*+=/<>~^]", # Special characters + processed_text + ) + + # Process the words + final_words = [] + skip_next = False + + for i, word in enumerate(words): + if skip_next: + skip_next = False + continue + + # Restore abbreviations + for abbr, placeholder in abbreviations.items(): + if placeholder in word: + word = re.sub(r'_ABBR$', '.', word) + word = re.sub(r'_DECIMAL_', '.', word) + + # Handle punctuation + if word in ".,!?;:\"'(){}[]": + if final_words: # Attach punctuation to previous word + # Don't attach if previous word already ends with punctuation + if final_words[-1][-1] not in ".,!?;:\"'(){}[]": + final_words[-1] += word + else: + final_words.append(word) + else: + final_words.append(word) + + # Handle ellipsis + elif word == "...": + if final_words: + final_words[-1] += word + else: + final_words.append(word) + + # Handle dashes between words + elif word.startswith("-") and i > 0: + if i < len(words) - 1 and re.match(r'\w+', words[i+1]): + final_words[-1] += word + words[i+1] + skip_next = True + else: + final_words.append(word) + + # Regular words + else: + final_words.append(word) + + return final_words + else: + raise ValueError("mode must be either 'heuristic' or 'advanced'") + + def _get_token_count(self, text: str) -> int: + """Get the number of tokens in a text string. + + Args: + text: Input text + + Returns: + Number of tokens + """ + return len(self.tokenizer.encode(text).ids) + + def _create_chunk(self, words: List[str], start_idx: int, end_idx: int) -> Tuple[Chunk, int]: + """Create a chunk from a list of words. + + Args: + words: List of words to create chunk from + start_idx: Starting index in original text + end_idx: Ending index in original text + + Returns: + Tuple of (Chunk object, number of tokens in chunk) + """ + chunk_text = " ".join(words) + token_count = self._get_token_count(chunk_text) + return Chunk( + text=chunk_text, + start_index=start_idx, + end_index=end_idx, + token_count=token_count + ), token_count + + def _get_word_list_token_counts(self, words: List[str]) -> List[int]: + """Get the number of tokens for each word in a list. + + Args: + words: List of words + + Returns: + List of token counts for each word + """ + encodings = self.tokenizer.encode_batch(words) + return [len(encoding.ids) for encoding in encodings] + + def chunk(self, text: str) -> List[Chunk]: + """Split text into overlapping chunks based on words while respecting token limits. + + Args: + text: Input text to be chunked + + Returns: + List of Chunk objects containing the chunked text and metadata + """ + if not text.strip(): + return [] + + words = self._split_into_words(text) + lengths = self._get_word_list_token_counts(words) + chunks = [] + + # Saving the current chunk + current_chunk = [] + current_chunk_length = 0 + + for i, (word, length) in enumerate(zip(words, lengths)): + if current_chunk_length + length <= self.chunk_size: + current_chunk.append(word) + current_chunk_length += length + else: + chunk, chunk_length = self._create_chunk( + current_chunk, + i - len(current_chunk), + i - 1 + ) + chunks.append(chunk) + + # update the current_chunk and previous chunk + previous_chunk_length = current_chunk_length + + current_chunk = [] + current_chunk_length = 0 + + overlap = [] + overlap_length = 0 + # calculate the overlap from the current chunk in reverse + for j in range(0, previous_chunk_length): + cwi = i - 1 - j + oword = words[cwi] + olength = lengths[cwi] + if overlap_length + olength <= self.chunk_overlap: + overlap.append(oword) + overlap_length += olength + else: + break + + current_chunk = [w for w in reversed(overlap)] + current_chunk_length = overlap_length + + current_chunk.append(word) + current_chunk_length += length + return chunks + + def __repr__(self) -> str: + return (f"WordChunker(chunk_size={self.chunk_size}, " + f"chunk_overlap={self.chunk_overlap}, mode='{self.mode}')") \ No newline at end of file From 28814936feefaca83ffdf502a23f1908df16a085 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 00:31:18 +0530 Subject: [PATCH 07/21] [bug-fix] Update relative imports --- chonkie/chunker/token.py | 2 +- chonkie/chunker/word.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/chonkie/chunker/token.py b/chonkie/chunker/token.py index 7da79e4..9f5d4ce 100644 --- a/chonkie/chunker/token.py +++ b/chonkie/chunker/token.py @@ -1,7 +1,7 @@ from typing import List from tokenizers import Tokenizer -from chonkie.chunker.base import Chunk, BaseChunker +from .base import Chunk, BaseChunker class TokenChunker(BaseChunker): def __init__(self, tokenizer: Tokenizer, chunk_size: int, chunk_overlap: int): """Initialize the TokenChunker with configuration parameters. diff --git a/chonkie/chunker/word.py b/chonkie/chunker/word.py index 9506033..a8ad694 100644 --- a/chonkie/chunker/word.py +++ b/chonkie/chunker/word.py @@ -2,7 +2,7 @@ import re from tokenizers import Tokenizer -from chonkie.chunker.base import Chunk, BaseChunker +from .base import Chunk, BaseChunker class WordChunker(BaseChunker): def __init__(self, tokenizer: Tokenizer, chunk_size: int, chunk_overlap: int, mode: str = "simple"): From 47a62d00d7e903c0a324c7c57035fcbf3e327a3a Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 00:33:19 +0530 Subject: [PATCH 08/21] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3e4bc5f..dbf09ba 100644 --- a/README.md +++ b/README.md @@ -53,10 +53,10 @@ pip install chonkie Here's a basic example to get you started: ```python -from chonkie.chunkers import Chunker +from chonkie import TokenChunker # Initialize the chunker -chunker = Chunker() +chunker = TokenChunker() # Chunk some text chunks = chunker.chunk("Your text here") From 45ea4426ac5a8dc135fcac0403d9d37b348e84a9 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 02:53:08 +0530 Subject: [PATCH 09/21] Add sentence and semantic chunkers --- chonkie/chunker/__init__.py | 7 +- chonkie/chunker/semantic.py | 344 ++++++++++++++++++++++++++++++++++++ chonkie/chunker/sentence.py | 240 +++++++++++++++++++++++++ 3 files changed, 588 insertions(+), 3 deletions(-) diff --git a/chonkie/chunker/__init__.py b/chonkie/chunker/__init__.py index 72d0a33..99fd505 100644 --- a/chonkie/chunker/__init__.py +++ b/chonkie/chunker/__init__.py @@ -1,8 +1,9 @@ -from .word import WordChunker from .token import TokenChunker - +from .word import WordChunker +from .sentence import SentenceChunker __all__ = [ + "TokenChunker", "WordChunker", - "TokenChunker" + "SentenceChunker" ] \ No newline at end of file diff --git a/chonkie/chunker/semantic.py b/chonkie/chunker/semantic.py index e69de29..c4bf33d 100644 --- a/chonkie/chunker/semantic.py +++ b/chonkie/chunker/semantic.py @@ -0,0 +1,344 @@ +from dataclasses import dataclass +from typing import List, Optional +import numpy as np +import re +import importlib.util +from tokenizers import Tokenizer + +from .base import Chunk, BaseChunker + +@dataclass +class Sentence: + text: str + start_index: int + end_index: int + token_count: int + embedding: Optional[np.ndarray] = None + +@dataclass +class SemanticChunk(Chunk): + text: str + start_index: int + end_index: int + token_count: int + sentences: List[Sentence] = None + +class SemanticChunker(BaseChunker): + def __init__( + self, + tokenizer: Tokenizer, + sentence_transformer_model: str, + max_chunk_size: int, + similarity_threshold: Optional[float] = None, + similarity_percentile: Optional[float] = None, + initial_sentences: int = 1, + sentence_mode: str = "heuristic", + spacy_model: str = "en_core_web_sm" + ): + """Initialize the SemanticChunker. + + Args: + tokenizer: Tokenizer for counting tokens + sentence_transformer_model: Name of the sentence-transformers model to load + max_chunk_size: Maximum tokens allowed per chunk + similarity_threshold: Absolute threshold for semantic similarity (0-1) + similarity_percentile: Percentile threshold for similarity (0-100) + initial_sentences: Number of sentences to start each chunk with + sentence_mode: "heuristic" or "spacy" for sentence splitting + spacy_model: Name of spaCy model to use if sentence_mode="spacy" + + Raises: + ValueError: If parameters are invalid + ImportError: If required dependencies aren't installed + """ + if max_chunk_size <= 0: + raise ValueError("max_chunk_size must be positive") + if similarity_threshold is not None and (similarity_threshold < 0 or similarity_threshold > 1): + raise ValueError("similarity_threshold must be between 0 and 1") + if similarity_percentile is not None and (similarity_percentile < 0 or similarity_percentile > 100): + raise ValueError("similarity_percentile must be between 0 and 100") + if similarity_threshold is not None and similarity_percentile is not None: + raise ValueError("Cannot specify both similarity_threshold and similarity_percentile") + if similarity_threshold is None and similarity_percentile is None: + raise ValueError("Must specify either similarity_threshold or similarity_percentile") + if sentence_mode not in ["heuristic", "spacy"]: + raise ValueError("sentence_mode must be 'heuristic' or 'spacy'") + + self.tokenizer = tokenizer + self.max_chunk_size = max_chunk_size + self.similarity_threshold = similarity_threshold + self.similarity_percentile = similarity_percentile + self.initial_sentences = initial_sentences + self.sentence_mode = sentence_mode + + # Initialize sentence transformer + if not importlib.util.find_spec("sentence_transformers"): + raise ImportError( + "sentence-transformers is not installed. " + "Install it with 'pip install sentence-transformers'" + ) + try: + from sentence_transformers import SentenceTransformer + self.sentence_transformer = SentenceTransformer(sentence_transformer_model) + except Exception as e: + raise ImportError( + f"Failed to load sentence-transformers model '{sentence_transformer_model}'. " + f"Error: {str(e)}" + ) from e + + # Initialize spaCy if explicitly requested + self.nlp = None + if sentence_mode == "spacy": + if not importlib.util.find_spec("spacy"): + raise ImportError( + "spaCy is not installed. Install it with 'pip install spacy' " + "and download the model with 'python -m spacy download en_core_web_sm', " + "or use sentence_mode='heuristic' instead." + ) + try: + import spacy + self.nlp = spacy.load(spacy_model) + except OSError as e: + raise ImportError( + f"spaCy model '{spacy_model}' not found. " + f"Download it with 'python -m spacy download {spacy_model}' " + "or use sentence_mode='heuristic' instead." + ) from e + + def _split_sentences_spacy(self, text: str) -> List[str]: + """Split text into sentences using spaCy.""" + doc = self.nlp(text) + return [str(sent).strip() for sent in doc.sents if str(sent).strip()] + + def _split_sentences_heuristic(self, text: str) -> List[str]: + """Split text into sentences using rule-based approach.""" + pattern = r'(?<=[.!?])\s+(?=[A-Z])|(?<=[.!?])(?=\s*[A-Z])|(?<=[.!?])\s*$' + sentences = re.split(pattern, text) + return [s.strip() for s in sentences if s.strip()] + + def _split_sentences(self, text: str) -> List[str]: + """Split text into sentences using specified mode.""" + if self.sentence_mode == "heuristic": + return self._split_sentences_heuristic(text) + else: + return self._split_sentences_spacy(text) + + def _update_embedding(self, current_embedding: np.ndarray, new_embedding: np.ndarray, + current_count: int) -> np.ndarray: + """Update running mean of embeddings when adding a new sentence.""" + return (current_embedding * current_count + new_embedding) / (current_count + 1) + + def _compute_similarity_threshold(self, all_similarities: List[float]) -> float: + """Compute similarity threshold based on percentile if specified.""" + if self.similarity_threshold is not None: + return self.similarity_threshold + else: + return float(np.percentile(all_similarities, self.similarity_percentile)) + + def _get_token_count(self, text: str) -> int: + """Count tokens in text.""" + return len(self.tokenizer.encode(text).ids) + + def _prepare_sentences(self, text: str) -> List[Sentence]: + """Prepare sentences with precomputed information. + + Args: + text: Input text to be processed + + Returns: + List of Sentence objects with precomputed token counts and embeddings + """ + if not text.strip(): + return [] + + # Split text into sentences + raw_sentences = self._split_sentences(text) + + # Get start and end indices for each sentence + sentence_indices = [] + current_idx = 0 + for sent in raw_sentences: + start_idx = text.find(sent, current_idx) + end_idx = start_idx + len(sent) + sentence_indices.append((start_idx, end_idx)) + current_idx = end_idx + + # Batch compute embeddings for all sentences + embeddings = self.sentence_transformer.encode(raw_sentences, convert_to_numpy=True) + + # Batch compute token counts + token_counts = [len(encoding.ids) for encoding in self.tokenizer.encode_batch(raw_sentences)] + + # Create Sentence objects with all precomputed information + sentences = [ + Sentence( + text=sent, + start_index=start_idx, + end_index=end_idx, + token_count=count, + embedding=embedding + ) + for sent, (start_idx, end_idx), count, embedding + in zip(raw_sentences, sentence_indices, token_counts, embeddings) + ] + + return sentences + + def _get_semantic_similarity(self, embedding1: np.ndarray, embedding2: np.ndarray) -> float: + """Compute cosine similarity between two embeddings.""" + similarity = self.sentence_transformer.similarity(embedding1, embedding2) + return similarity + + def _compute_group_embedding(self, sentences: List[Sentence]) -> np.ndarray: + """Compute mean embedding for a group of sentences.""" + return np.mean([sent.embedding for sent in sentences], axis=0) + + def _group_sentences(self, sentences: List[Sentence]) -> List[List[Sentence]]: + """Group sentences based on semantic similarity, ignoring token count limits. + + Args: + sentences: List of Sentence objects with precomputed embeddings + + Returns: + List of sentence groups, where each group is semantically coherent + """ + if len(sentences) <= self.initial_sentences: + return [sentences] + + # Get or compute similarity threshold + if self.similarity_percentile is not None: + # Compute all pairwise similarities + all_similarities = [ + self._get_semantic_similarity( + sentences[i].embedding, + sentences[i+1].embedding + ) + for i in range(len(sentences)-1) + ] + similarity_threshold = float(np.percentile(all_similarities, self.similarity_percentile)) + else: + similarity_threshold = self.similarity_threshold + + groups = [] + current_group = sentences[:self.initial_sentences] + current_embedding = self._compute_group_embedding(current_group) + + for sentence in sentences[self.initial_sentences:]: + # Compare new sentence against mean embedding of entire current group + similarity = self._get_semantic_similarity( + current_embedding, + sentence.embedding + ) + + if similarity >= similarity_threshold: + # Add to current group + current_group.append(sentence) + # Update mean embedding + current_embedding = self._compute_group_embedding(current_group) + else: + # Start new group + if current_group: + groups.append(current_group) + current_group = [sentence] + current_embedding = sentence.embedding + + # Add final group + if current_group: + groups.append(current_group) + + return groups + + def _create_chunk(self, + sentences: List[Sentence], + similarity_scores: List[float] = None) -> SemanticChunk: + """Create a chunk from a list of sentences.""" + if not sentences: + raise ValueError("Cannot create chunk from empty sentence list") + + # Compute chunk text and token count from sentences + text = " ".join(sent.text for sent in sentences) + token_count = sum(sent.token_count for sent in sentences) + (len(sentences) - 1) # Add spaces + + return SemanticChunk( + text=text, + start_index=sentences[0].start_index, + end_index=sentences[-1].end_index, + token_count=token_count, + sentences=sentences + ) + + def _split_chunks(self, sentence_groups: List[List[Sentence]]) -> List[SemanticChunk]: + """Split sentence groups into chunks that respect max_chunk_size. + + Args: + sentence_groups: List of semantically coherent sentence groups + + Returns: + List of SemanticChunk objects + """ + chunks = [] + + for group in sentence_groups: + current_chunk_sentences = [] + current_tokens = 0 + + for sentence in group: + test_tokens = current_tokens + sentence.token_count + (1 if current_chunk_sentences else 0) + + if test_tokens <= self.max_chunk_size: + # Add to current chunk + current_chunk_sentences.append(sentence) + current_tokens = test_tokens + else: + # Create chunk if we have sentences + if current_chunk_sentences: + chunks.append(self._create_chunk(current_chunk_sentences)) + + # Start new chunk with current sentence + current_chunk_sentences = [sentence] + current_tokens = sentence.token_count + + # Create final chunk for this group + if current_chunk_sentences: + chunks.append(self._create_chunk(current_chunk_sentences)) + + return chunks + + def chunk(self, text: str) -> List[SemanticChunk]: + """Split text into semantically coherent chunks using two-pass approach. + + First groups sentences by semantic similarity, then splits groups to respect + max_chunk_size while maintaining sentence boundaries. + + Args: + text: Input text to be chunked + + Returns: + List of SemanticChunk objects containing the chunked text and metadata + """ + if not text.strip(): + return [] + + # Prepare sentences with precomputed information + sentences = self._prepare_sentences(text) + if len(sentences) < self.initial_sentences: + return [self._create_chunk(sentences)] + + # First pass: Group sentences by semantic similarity + sentence_groups = self._group_sentences(sentences) + + # Second pass: Split groups into size-appropriate chunks + chunks = self._split_chunks(sentence_groups) + + return chunks + + def __repr__(self) -> str: + threshold_info = ( + f"similarity_threshold={self.similarity_threshold}" + if self.similarity_threshold is not None + else f"similarity_percentile={self.similarity_percentile}" + ) + return (f"SemanticChunker(max_chunk_size={self.max_chunk_size}, " + f"{threshold_info}, " + f"initial_sentences={self.initial_sentences}, " + f"sentence_mode='{self.sentence_mode}')") \ No newline at end of file diff --git a/chonkie/chunker/sentence.py b/chonkie/chunker/sentence.py index e69de29..6ed5447 100644 --- a/chonkie/chunker/sentence.py +++ b/chonkie/chunker/sentence.py @@ -0,0 +1,240 @@ +from typing import List, Optional +import re + +from tokenizers import Tokenizer + +from .base import Chunk, BaseChunker + +import importlib.util +import warnings + +# Check if spacy is available +SPACY_AVAILABLE = importlib.util.find_spec("spacy") is not None +if SPACY_AVAILABLE: + try: + import spacy + from spacy.language import Language + except ImportError: + SPACY_AVAILABLE = False + warnings.warn("Failed to import spacy despite it being installed. Using heuristic mode only.") + + +class SentenceChunker(BaseChunker): + def __init__( + self, + tokenizer: Tokenizer, + chunk_size: int, + chunk_overlap: int, + mode: str = "simple", + min_sentences_per_chunk: int = 1, + spacy_model: str = "en_core_web_sm" + ): + """Initialize the SentenceChunker with configuration parameters. + + Args: + tokenizer: The tokenizer instance to use for encoding/decoding + chunk_size: Maximum number of tokens per chunk + chunk_overlap: Number of tokens to overlap between chunks + mode: Sentence detection mode - "heuristic" (rule-based) or "spacy" (ML-based) + min_sentences_per_chunk: Minimum number of sentences per chunk (defaults to 1) + spacy_model: Name of spaCy model to use (defaults to "en_core_web_sm") + + Raises: + ValueError: If parameters are invalid + Warning: If spacy mode is requested but spacy is not available + """ + if chunk_size <= 0: + raise ValueError("chunk_size must be positive") + if chunk_overlap >= chunk_size: + raise ValueError("chunk_overlap must be less than chunk_size") + if mode not in ["simple", "spacy"]: + raise ValueError("mode must be either 'simple' or 'spacy'") + if min_sentences_per_chunk < 1: + raise ValueError("min_sentences_per_chunk must be at least 1") + + self.tokenizer = tokenizer + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + self.min_sentences_per_chunk = min_sentences_per_chunk + + # Initialize mode and spaCy + self.nlp: Optional[Language] = None + if mode == "spacy": + if not SPACY_AVAILABLE: + warnings.warn( + "Spacy not found in environment. To use spacy mode, install it using:\n" + "pip install spacy\n" + "python -m spacy download en_core_web_sm\n" + "Falling back to simple mode." + ) + self.mode = "simple" + else: + try: + self.nlp = spacy.load(spacy_model) + # Optimize for sentence segmentation only + self.nlp.select_pipes(enable=["senter"]) + self.mode = "spacy" + except OSError: + warnings.warn( + f"Spacy model '{spacy_model}' not found. To use spacy mode, " + f"install it using: python -m spacy download {spacy_model}\n" + "Falling back to simple mode." + ) + self.mode = "simple" + else: + self.mode = "simple" + + def _split_into_sentences(self, text: str) -> List[str]: + """Split text into sentences based on the selected mode. + + Args: + text: Input text to be split into sentences + + Returns: + List of sentences + """ + if self.mode == "spacy" and self.nlp is not None: + # Use spaCy's sentence segmentation + doc = self.nlp(text) + return [sent.text.strip() for sent in doc.sents] + elif self.mode == "simple": + # Fallback to heuristic mode + # Simple rule-based sentence splitting with common abbreviations + text = re.sub(r'([.!?])([^"])', r'\1\n\2', text) # Add newlines after sentence endings + text = re.sub(r'([.!?]")(\s*[A-Z])', r'\1\n\2', text) # Handle quotes + + # Handle common abbreviations + abbrevs = r'(?:Mr|Mrs|Dr|Prof|Sr|Jr|vs|etc|viz|al|Gen|Col|Fig|e\.g|i\.e)\.' + text = re.sub(f'{abbrevs}\n', f'{abbrevs} ', text) + + # Handle initials and acronyms + text = re.sub(r'([A-Z]\.[A-Z]\.)\n', r'\1 ', text) + text = re.sub(r'([A-Z]\.[A-Z]\.)\n', r'\1 ', text) # Run twice for consecutive initials + + # Handle decimal numbers and ellipsis + text = re.sub(r'(\d+)\.\n(\d+)', r'\1.\2', text) # Decimal numbers + text = re.sub(r'\.{3}\n', '... ', text) # Ellipsis + + sentences = [s.strip() for s in text.split('\n') if s.strip()] + return sentences + + def _get_token_counts(self, sentences: List[str]) -> List[int]: + """Get token counts for a list of sentences in batch. + + Args: + sentences: List of sentences + + Returns: + List of token counts for each sentence + """ + # Batch encode all sentences at once + encoded_sentences = self.tokenizer.encode_batch(sentences) + return [len(encoded.ids) for encoded in encoded_sentences] + + def _create_chunk( + self, + sentences: List[str], + start_idx: int, + token_count: int + ) -> Chunk: + """Create a chunk from a list of sentences. + + Args: + sentences: List of sentences to create chunk from + start_idx: Starting index in original text + token_count: Total token count for the chunk + + Returns: + Chunk object + """ + chunk_text = " ".join(sentences) + return Chunk( + text=chunk_text, + start_index=start_idx, + end_index=start_idx + token_count, + token_count=token_count + ) + + def chunk(self, text: str) -> List[Chunk]: + """Split text into overlapping chunks based on sentences while respecting token limits. + + Args: + text: Input text to be chunked + + Returns: + List of Chunk objects containing the chunked text and metadata + """ + if not text.strip(): + return [] + + sentences = self._split_into_sentences(text) + token_counts = self._get_token_counts(sentences) + + chunks = [] + current_sentences = [] + current_tokens = 0 + last_chunk_end = 0 + + for i, (sentence, token_count) in enumerate(zip(sentences, token_counts)): + # Calculate total tokens if we add this sentence + test_tokens = current_tokens + token_count + (1 if current_sentences else 0) # Add 1 for space between sentences + + can_add_sentence = ( + test_tokens <= self.chunk_size or + (len(current_sentences) < self.min_sentences_per_chunk and len(current_sentences) + 1 <= self.min_sentences_per_chunk) + ) + + if can_add_sentence: + # Sentence fits within limits, add it + current_sentences.append(sentence) + current_tokens = test_tokens + else: + # Sentence would exceed limits, create chunk if we have enough sentences + if len(current_sentences) >= self.min_sentences_per_chunk: + chunk = self._create_chunk( + current_sentences, + last_chunk_end, + current_tokens + ) + chunks.append(chunk) + + # Calculate overlap for next chunk + if self.chunk_overlap > 0: + # Keep sentences from the end of current chunk until we hit overlap limit + overlap_sentences = [] + overlap_tokens = 0 + for sent, tokens in zip(reversed(current_sentences), reversed(token_counts[i-len(current_sentences):i])): + test_overlap_tokens = overlap_tokens + tokens + (1 if overlap_sentences else 0) + if test_overlap_tokens <= self.chunk_overlap: + overlap_sentences.insert(0, sent) + overlap_tokens = test_overlap_tokens + else: + break + + current_sentences = overlap_sentences + current_tokens = overlap_tokens + else: + current_sentences = [] + current_tokens = 0 + + last_chunk_end += current_tokens + + # Add current sentence (either after creating chunk or when forced to meet minimum) + current_sentences.append(sentence) + current_tokens = current_tokens + token_count + (1 if len(current_sentences) > 1 else 0) + + # Handle remaining sentences + if current_sentences: + chunk = self._create_chunk( + current_sentences, + last_chunk_end, + current_tokens + ) + chunks.append(chunk) + + return chunks + + def __repr__(self) -> str: + return (f"SentenceChunker(chunk_size={self.chunk_size}, " + f"chunk_overlap={self.chunk_overlap}, mode='{self.mode}', " + f"min_sentences_per_chunk={self.min_sentences_per_chunk})") \ No newline at end of file From 257877b882ca8a34b407492007a82704176aa830 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 05:06:27 +0530 Subject: [PATCH 10/21] Add SPDMChunker --- chonkie/chunker/spdm.py | 166 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) diff --git a/chonkie/chunker/spdm.py b/chonkie/chunker/spdm.py index e69de29..d1d622e 100644 --- a/chonkie/chunker/spdm.py +++ b/chonkie/chunker/spdm.py @@ -0,0 +1,166 @@ +from typing import List +from .semantic import SemanticChunker, SemanticChunk, Sentence + +try: + import numpy as np +except ImportError: + raise ImportError("SPDMChunker requires numpy to be installed") + +class SPDMChunker(SemanticChunker): + def __init__( + self, + tokenizer, + sentence_transformer_model: str, + max_chunk_size: int, + similarity_threshold: float = None, + similarity_percentile: float = None, + initial_sentences: int = 1, + sentence_mode: str = "heuristic", + spacy_model: str = "en_core_web_sm", + skip_window: int = 1 # How many chunks to skip when looking for similarities + ): + """Initialize the SPDMChunker. + + Args: + Same as SemanticChunker, plus: + skip_window: Number of chunks to skip when looking for similarities + """ + super().__init__( + tokenizer=tokenizer, + sentence_transformer_model=sentence_transformer_model, + max_chunk_size=max_chunk_size, + similarity_threshold=similarity_threshold, + similarity_percentile=similarity_percentile, + initial_sentences=initial_sentences, + sentence_mode=sentence_mode, + spacy_model=spacy_model + ) + self.skip_window = skip_window + + def _merge_similar_groups( + self, + groups: List[List[Sentence]], + similarity_threshold: float + ) -> List[List[Sentence]]: + """Merge similar groups considering skip window. + + Args: + groups: List of sentence groups + similarity_threshold: Threshold for merging groups + + Returns: + List of merged sentence groups + """ + if len(groups) <= 1: + return groups + + # Track merged groups and their embeddings + merged = [False] * len(groups) + embeddings = [self._compute_group_embedding(group) for group in groups] + result_groups = [] + + i = 0 + while i < len(groups): + if merged[i]: + i += 1 + continue + + current_group = groups[i].copy() + current_embedding = embeddings[i] + + # Look ahead with skip window + look_ahead_start = i + 1 + checked_indices = set() + + while look_ahead_start < len(groups): + # Find next unmerged group to check + j = look_ahead_start + while j < min(look_ahead_start + self.skip_window + 1, len(groups)): + if not merged[j] and j not in checked_indices: + break + j += 1 + + if j >= len(groups): + break + + # Compare embeddings + similarity = self._get_semantic_similarity( + current_embedding, + embeddings[j] + ) + + if similarity >= similarity_threshold: + # Merge groups + # Add all sentences from intermediate groups + for k in range(i + 1, j + 1): + current_group.extend(groups[k]) + merged[k] = True + + # Update current embedding + current_embedding = self._compute_group_embedding(current_group) + + # Move look ahead window + look_ahead_start = j + 1 + else: + checked_indices.add(j) + look_ahead_start = j + 1 + + result_groups.append(current_group) + i += 1 + + return result_groups + + def chunk(self, text: str) -> List[SemanticChunk]: + """Split text into chunks using the SPDM approach. + + Args: + text: Input text to be chunked + + Returns: + List of SemanticChunk objects + """ + if not text.strip(): + return [] + + # Prepare sentences with precomputed information + sentences = self._prepare_sentences(text) + if len(sentences) < self.initial_sentences: + return [self._create_chunk(sentences)] + + # First pass: Group sentences by semantic similarity + initial_groups = self._group_sentences(sentences) + + # Get or compute similarity threshold + if self.similarity_percentile is not None: + # Compute all pairwise similarities between groups + all_similarities = [] + for i in range(len(initial_groups) - 1): + for j in range(i + 1, min(i + self.skip_window + 2, len(initial_groups))): + emb1 = self._compute_group_embedding(initial_groups[i]) + emb2 = self._compute_group_embedding(initial_groups[j]) + similarity = self._get_semantic_similarity(emb1, emb2) + all_similarities.append(similarity) + + similarity_threshold = float(np.percentile(all_similarities, self.similarity_percentile)) + else: + similarity_threshold = self.similarity_threshold + + # Second pass: Merge similar groups with skip window + merged_groups = self._merge_similar_groups(initial_groups, similarity_threshold) + + # Final pass: Split into size-appropriate chunks + chunks = self._split_chunks(merged_groups) + + return chunks + + def __repr__(self) -> str: + threshold_info = ( + f"similarity_threshold={self.similarity_threshold}" + if self.similarity_threshold is not None + else f"similarity_percentile={self.similarity_percentile}" + ) + return (f"SPDMChunker(max_chunk_size={self.max_chunk_size}, " + f"{threshold_info}, " + f"initial_sentences={self.initial_sentences}, " + f"sentence_mode='{self.sentence_mode}', " + f"skip_window={self.skip_window})") \ No newline at end of file From ab3360cf872e5090c94e2749aff4b12fa60745a8 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 05:06:49 +0530 Subject: [PATCH 11/21] Add package info + update .gitignore --- .gitignore | 2 ++ pyproject.toml | 12 ++++++++++-- 2 files changed, 12 insertions(+), 2 deletions(-) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d570a63 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +__pycache__/ +chonkie.egg-info/ \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 75bfb54..4e0c56e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,15 @@ classifiers = [ dependencies = [ "tokenizers>=0.13.0" ] -packages=["chonkie"] [project.urls] -Homepage = "https://github.com/bhavnicksm/chonkie" \ No newline at end of file +Homepage = "https://github.com/bhavnicksm/chonkie" + +[project.optional-dependencies] +sentence = ["spacy>=3.0.0"] +semantic = ["sentence-transformers>=2.0.0", "numpy>=1.23.0"] +all = ["spacy>=3.0.0", "sentence-transformers>=2.0.0", "numpy>=1.23.0"] +dev = ["pytest>=6.2.0"] + +[tool.setuptools] +packages = ["chonkie"] \ No newline at end of file From 316ba9025c3d96d8f46f0523937e915b71029a59 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 14:34:26 +0530 Subject: [PATCH 12/21] Add Sentence dataclass to SentenceChunker + Update SPDMChunker --- chonkie/__init__.py | 12 +++-- chonkie/chunker/__init__.py | 8 ++- chonkie/chunker/sentence.py | 24 +++++++-- chonkie/chunker/spdm.py | 105 ++++++++++++++---------------------- pyproject.toml | 2 +- 5 files changed, 76 insertions(+), 75 deletions(-) diff --git a/chonkie/__init__.py b/chonkie/__init__.py index 0158e05..f42a186 100644 --- a/chonkie/__init__.py +++ b/chonkie/__init__.py @@ -1,9 +1,12 @@ from .chunker import ( - WordChunker, TokenChunker, + WordChunker, + SentenceChunker, + SemanticChunker, + SPDMChunker ) -__version__ = "0.0.1a" +__version__ = "0.0.1a2" __name__ = "chonkie" __author__ = "Bhavnick Minhas" @@ -13,4 +16,7 @@ "__author__", "WordChunker", "TokenChunker", -] \ No newline at end of file + "SentenceChunker", + "SemanticChunker", + "SPDMChunker", + ] \ No newline at end of file diff --git a/chonkie/chunker/__init__.py b/chonkie/chunker/__init__.py index 99fd505..076d282 100644 --- a/chonkie/chunker/__init__.py +++ b/chonkie/chunker/__init__.py @@ -1,9 +1,13 @@ from .token import TokenChunker from .word import WordChunker from .sentence import SentenceChunker +from .semantic import SemanticChunker +from .spdm import SPDMChunker __all__ = [ "TokenChunker", "WordChunker", - "SentenceChunker" -] \ No newline at end of file + "SentenceChunker", + "SemanticChunker", + "SPDMChunker", + ] \ No newline at end of file diff --git a/chonkie/chunker/sentence.py b/chonkie/chunker/sentence.py index 6ed5447..0c0dff8 100644 --- a/chonkie/chunker/sentence.py +++ b/chonkie/chunker/sentence.py @@ -1,10 +1,8 @@ from typing import List, Optional import re - +from dataclasses import dataclass from tokenizers import Tokenizer - from .base import Chunk, BaseChunker - import importlib.util import warnings @@ -19,6 +17,24 @@ warnings.warn("Failed to import spacy despite it being installed. Using heuristic mode only.") +#TODO: Currently the SentenceChunker does not populate the sentences field in the SentenceChunk object. +# We need to implement the logic to populate the sentences field in the SentenceChunk object. + +@dataclass +class Sentence: + text: str + start_index: int + end_index: int + token_count: int + +@dataclass +class SentenceChunk(Chunk): + text: str + start_index: int + end_index: int + token_count: int + sentences: List[Sentence] = None + class SentenceChunker(BaseChunker): def __init__( self, @@ -148,7 +164,7 @@ def _create_chunk( Chunk object """ chunk_text = " ".join(sentences) - return Chunk( + return SentenceChunk( text=chunk_text, start_index=start_idx, end_index=start_idx + token_count, diff --git a/chonkie/chunker/spdm.py b/chonkie/chunker/spdm.py index d1d622e..18f761d 100644 --- a/chonkie/chunker/spdm.py +++ b/chonkie/chunker/spdm.py @@ -37,77 +37,52 @@ def __init__( ) self.skip_window = skip_window - def _merge_similar_groups( - self, - groups: List[List[Sentence]], - similarity_threshold: float - ) -> List[List[Sentence]]: - """Merge similar groups considering skip window. - - Args: - groups: List of sentence groups - similarity_threshold: Threshold for merging groups - - Returns: - List of merged sentence groups - """ + def _merge_groups(self, groups: List[List[Sentence]]) -> List[Sentence]: + """Merge the groups together""" + merged_group = [] + for group in groups: + merged_group.extend(group) + return merged_group + + def _skip_and_merge(self, + groups: List[List[Sentence]], + similarity_threshold: float) -> List[List[Sentence]]: + """Merge similar groups considering skip window.""" if len(groups) <= 1: return groups - # Track merged groups and their embeddings - merged = [False] * len(groups) - embeddings = [self._compute_group_embedding(group) for group in groups] result_groups = [] - - i = 0 - while i < len(groups): - if merged[i]: - i += 1 - continue - - current_group = groups[i].copy() - current_embedding = embeddings[i] + grps = groups.copy() + embeddings = [self._compute_group_embedding(group) for group in grps] + + while grps: + if len(grps) == 1: + result_groups.append(grps[0]) + break + + # Calculate skip index ensuring it's valid + skip_index = min(self.skip_window + 1, len(grps) - 1) - # Look ahead with skip window - look_ahead_start = i + 1 - checked_indices = set() - - while look_ahead_start < len(groups): - # Find next unmerged group to check - j = look_ahead_start - while j < min(look_ahead_start + self.skip_window + 1, len(groups)): - if not merged[j] and j not in checked_indices: - break - j += 1 - - if j >= len(groups): - break - - # Compare embeddings - similarity = self._get_semantic_similarity( - current_embedding, - embeddings[j] - ) - - if similarity >= similarity_threshold: - # Merge groups - # Add all sentences from intermediate groups - for k in range(i + 1, j + 1): - current_group.extend(groups[k]) - merged[k] = True - - # Update current embedding - current_embedding = self._compute_group_embedding(current_group) + # Compare current group with skipped group + similarity = self._get_semantic_similarity(embeddings[0], embeddings[skip_index]) + + if similarity >= similarity_threshold: + # Merge groups from 0 to skip_index (inclusive) + merged = self._merge_groups(grps[:skip_index + 1]) + + # Remove the merged groups + for _ in range(skip_index + 1): + grps.pop(0) + embeddings.pop(0) - # Move look ahead window - look_ahead_start = j + 1 - else: - checked_indices.add(j) - look_ahead_start = j + 1 - - result_groups.append(current_group) - i += 1 - + # Add merged group back at the start + grps.insert(0, merged) + embeddings.insert(0, self._compute_group_embedding(merged)) + else: + # No merge possible, move first group to results + result_groups.append(grps.pop(0)) + embeddings.pop(0) + return result_groups def chunk(self, text: str) -> List[SemanticChunk]: diff --git a/pyproject.toml b/pyproject.toml index 4e0c56e..5bd0f9c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "chonkie" -version = "0.0.1a" +version = "0.0.1a2" description = "A simple, efficient text chunking library for RAG applications" readme = "README.md" requires-python = ">=3.8" From 8b624f5498b86a20009e7cfbf3b7898d99f9afb3 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 15:04:31 +0530 Subject: [PATCH 13/21] Update semantic.py and spdm.py --- chonkie/chunker/semantic.py | 9 -------- chonkie/chunker/spdm.py | 45 ++++++++++--------------------------- 2 files changed, 12 insertions(+), 42 deletions(-) diff --git a/chonkie/chunker/semantic.py b/chonkie/chunker/semantic.py index c4bf33d..643a656 100644 --- a/chonkie/chunker/semantic.py +++ b/chonkie/chunker/semantic.py @@ -123,11 +123,6 @@ def _split_sentences(self, text: str) -> List[str]: else: return self._split_sentences_spacy(text) - def _update_embedding(self, current_embedding: np.ndarray, new_embedding: np.ndarray, - current_count: int) -> np.ndarray: - """Update running mean of embeddings when adding a new sentence.""" - return (current_embedding * current_count + new_embedding) / (current_count + 1) - def _compute_similarity_threshold(self, all_similarities: List[float]) -> float: """Compute similarity threshold based on percentile if specified.""" if self.similarity_threshold is not None: @@ -135,10 +130,6 @@ def _compute_similarity_threshold(self, all_similarities: List[float]) -> float: else: return float(np.percentile(all_similarities, self.similarity_percentile)) - def _get_token_count(self, text: str) -> int: - """Count tokens in text.""" - return len(self.tokenizer.encode(text).ids) - def _prepare_sentences(self, text: str) -> List[Sentence]: """Prepare sentences with precomputed information. diff --git a/chonkie/chunker/spdm.py b/chonkie/chunker/spdm.py index 18f761d..bb6eb41 100644 --- a/chonkie/chunker/spdm.py +++ b/chonkie/chunker/spdm.py @@ -1,11 +1,6 @@ from typing import List from .semantic import SemanticChunker, SemanticChunk, Sentence -try: - import numpy as np -except ImportError: - raise ImportError("SPDMChunker requires numpy to be installed") - class SPDMChunker(SemanticChunker): def __init__( self, @@ -51,39 +46,38 @@ def _skip_and_merge(self, if len(groups) <= 1: return groups - result_groups = [] - grps = groups.copy() - embeddings = [self._compute_group_embedding(group) for group in grps] + merged_groups = [] + embeddings = [self._compute_group_embedding(group) for group in groups] - while grps: - if len(grps) == 1: - result_groups.append(grps[0]) + while groups: + if len(groups) == 1: + merged_groups.append(groups[0]) break # Calculate skip index ensuring it's valid - skip_index = min(self.skip_window + 1, len(grps) - 1) + skip_index = min(self.skip_window + 1, len(groups) - 1) # Compare current group with skipped group similarity = self._get_semantic_similarity(embeddings[0], embeddings[skip_index]) if similarity >= similarity_threshold: # Merge groups from 0 to skip_index (inclusive) - merged = self._merge_groups(grps[:skip_index + 1]) + merged = self._merge_groups(groups[:skip_index + 1]) # Remove the merged groups for _ in range(skip_index + 1): - grps.pop(0) + groups.pop(0) embeddings.pop(0) # Add merged group back at the start - grps.insert(0, merged) + groups.insert(0, merged) embeddings.insert(0, self._compute_group_embedding(merged)) else: # No merge possible, move first group to results - result_groups.append(grps.pop(0)) + merged_groups.append(groups.pop(0)) embeddings.pop(0) - return result_groups + return merged_groups def chunk(self, text: str) -> List[SemanticChunk]: """Split text into chunks using the SPDM approach. @@ -105,23 +99,8 @@ def chunk(self, text: str) -> List[SemanticChunk]: # First pass: Group sentences by semantic similarity initial_groups = self._group_sentences(sentences) - # Get or compute similarity threshold - if self.similarity_percentile is not None: - # Compute all pairwise similarities between groups - all_similarities = [] - for i in range(len(initial_groups) - 1): - for j in range(i + 1, min(i + self.skip_window + 2, len(initial_groups))): - emb1 = self._compute_group_embedding(initial_groups[i]) - emb2 = self._compute_group_embedding(initial_groups[j]) - similarity = self._get_semantic_similarity(emb1, emb2) - all_similarities.append(similarity) - - similarity_threshold = float(np.percentile(all_similarities, self.similarity_percentile)) - else: - similarity_threshold = self.similarity_threshold - # Second pass: Merge similar groups with skip window - merged_groups = self._merge_similar_groups(initial_groups, similarity_threshold) + merged_groups = self._merge_similar_groups(initial_groups, self.similarity_threshold) # Final pass: Split into size-appropriate chunks chunks = self._split_chunks(merged_groups) From b54d6a6c8fa88f0f4d79ae48ecb79cee504de02f Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 16:42:39 +0530 Subject: [PATCH 14/21] Add tests/ --- chonkie/__init__.py | 9 ++- chonkie/chunker/__init__.py | 3 + chonkie/chunker/word.py | 14 +++- pyproject.toml | 2 +- tests/chunker/test_semantic_chunker.py | 0 tests/chunker/test_sentence_chunker.py | 0 tests/chunker/test_spdm_chunker.py | 0 tests/chunker/test_token_chunker.py | 98 ++++++++++++++++++++++ tests/chunker/test_word_chunker.py | 107 +++++++++++++++++++++++++ 9 files changed, 227 insertions(+), 6 deletions(-) create mode 100644 tests/chunker/test_semantic_chunker.py create mode 100644 tests/chunker/test_sentence_chunker.py create mode 100644 tests/chunker/test_spdm_chunker.py create mode 100644 tests/chunker/test_token_chunker.py create mode 100644 tests/chunker/test_word_chunker.py diff --git a/chonkie/__init__.py b/chonkie/__init__.py index f42a186..11936fa 100644 --- a/chonkie/__init__.py +++ b/chonkie/__init__.py @@ -1,12 +1,15 @@ from .chunker import ( + Chunk, + BaseChunker, TokenChunker, WordChunker, SentenceChunker, SemanticChunker, - SPDMChunker + SPDMChunker, + ) -__version__ = "0.0.1a2" +__version__ = "0.0.1a3" __name__ = "chonkie" __author__ = "Bhavnick Minhas" @@ -14,6 +17,8 @@ "__name__", "__version__", "__author__", + "Chunk", + "BaseChunker", "WordChunker", "TokenChunker", "SentenceChunker", diff --git a/chonkie/chunker/__init__.py b/chonkie/chunker/__init__.py index 076d282..b5ccb50 100644 --- a/chonkie/chunker/__init__.py +++ b/chonkie/chunker/__init__.py @@ -1,3 +1,4 @@ +from .base import Chunk, BaseChunker from .token import TokenChunker from .word import WordChunker from .sentence import SentenceChunker @@ -5,6 +6,8 @@ from .spdm import SPDMChunker __all__ = [ + "BaseChunker", + "Chunk", "TokenChunker", "WordChunker", "SentenceChunker", diff --git a/chonkie/chunker/word.py b/chonkie/chunker/word.py index a8ad694..27fd58d 100644 --- a/chonkie/chunker/word.py +++ b/chonkie/chunker/word.py @@ -1,7 +1,6 @@ from typing import List, Tuple import re from tokenizers import Tokenizer - from .base import Chunk, BaseChunker class WordChunker(BaseChunker): @@ -160,7 +159,7 @@ def _create_chunk(self, words: List[str], start_idx: int, end_idx: int) -> Tuple start_index=start_idx, end_index=end_idx, token_count=token_count - ), token_count + ) def _get_word_list_token_counts(self, words: List[str]) -> List[int]: """Get the number of tokens for each word in a list. @@ -199,7 +198,7 @@ def chunk(self, text: str) -> List[Chunk]: current_chunk.append(word) current_chunk_length += length else: - chunk, chunk_length = self._create_chunk( + chunk = self._create_chunk( current_chunk, i - len(current_chunk), i - 1 @@ -230,6 +229,15 @@ def chunk(self, text: str) -> List[Chunk]: current_chunk.append(word) current_chunk_length += length + + # Add the final chunk if it has any words + if current_chunk: + chunk = self._create_chunk( + current_chunk, + len(words) - len(current_chunk), + len(words) - 1 + ) + chunks.append(chunk) return chunks def __repr__(self) -> str: diff --git a/pyproject.toml b/pyproject.toml index 5bd0f9c..ba3e05c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "chonkie" -version = "0.0.1a2" +version = "0.0.1a3" description = "A simple, efficient text chunking library for RAG applications" readme = "README.md" requires-python = ">=3.8" diff --git a/tests/chunker/test_semantic_chunker.py b/tests/chunker/test_semantic_chunker.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/chunker/test_sentence_chunker.py b/tests/chunker/test_sentence_chunker.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/chunker/test_spdm_chunker.py b/tests/chunker/test_spdm_chunker.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/chunker/test_token_chunker.py b/tests/chunker/test_token_chunker.py new file mode 100644 index 0000000..fe57dcb --- /dev/null +++ b/tests/chunker/test_token_chunker.py @@ -0,0 +1,98 @@ +import pytest +from tokenizers import Tokenizer +from chonkie import Chunk +from chonkie import TokenChunker + +@pytest.fixture +def tokenizer(): + return Tokenizer.from_pretrained("gpt2") + +@pytest.fixture +def sample_text(): + text = """The process of text chunking in RAG applications represents a delicate balance between competing requirements. On one side, we have the need for semantic coherence – ensuring that each chunk maintains meaningful context that can be understood and processed independently. On the other, we must optimize for information density, ensuring that each chunk carries sufficient signal without excessive noise that might impede retrieval accuracy. In this post, we explore the challenges of text chunking in RAG applications and propose a novel approach that leverages recent advances in transformer-based language models to achieve a more effective balance between these competing requirements.""" + return text + +def test_token_chunker_initialization(tokenizer): + """ + Test that the TokenChunker can be initialized with a tokenizer. + """ + chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + + assert chunker is not None + assert chunker.tokenizer == tokenizer + assert chunker.chunk_size == 512 + assert chunker.chunk_overlap == 128 + +def test_token_chunker_chunking(tokenizer, sample_text): + """ + Test that the TokenChunker can chunk a sample text into tokens. + """ + chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk(sample_text) + + assert len(chunks) > 0 + assert type(chunks[0]) is Chunk + assert all([chunk.token_count <= 512 for chunk in chunks]) + assert all([chunk.token_count > 0 for chunk in chunks]) + assert all([chunk.text is not None for chunk in chunks]) + assert all([chunk.start_index is not None for chunk in chunks]) + assert all([chunk.end_index is not None for chunk in chunks]) + + +def test_token_chunker_empty_text(tokenizer): + """ + Test that the TokenChunker can handle empty text input. + """ + chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk("") + + assert len(chunks) == 0 + +def test_token_chunker_single_token_text(tokenizer): + """ + Test that the TokenChunker can handle text with a single token. + """ + chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk("Hello") + + assert len(chunks) == 1 + assert chunks[0].token_count == 1 + assert chunks[0].text == "Hello" + +def test_token_chunker_single_chunk_text(tokenizer): + """ + Test that the TokenChunker can handle text that fits within a single chunk. + """ + chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk("Hello, how are you?") + + assert len(chunks) == 1 + assert chunks[0].token_count == 6 + assert chunks[0].text == "Hello, how are you?" + +def test_token_chunker_repr(tokenizer): + """ + Test that the TokenChunker has a string representation. + """ + chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + + assert repr(chunker) == "TokenChunker(chunk_size=512, chunk_overlap=128)" + +def test_token_chunker_call(tokenizer, sample_text): + """ + Test that the TokenChunker can be called directly. + """ + chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker(sample_text) + + assert len(chunks) > 0 + assert type(chunks[0]) is Chunk + assert all([chunk.token_count <= 512 for chunk in chunks]) + assert all([chunk.token_count > 0 for chunk in chunks]) + assert all([chunk.text is not None for chunk in chunks]) + assert all([chunk.start_index is not None for chunk in chunks]) + assert all([chunk.end_index is not None for chunk in chunks]) + + +if __name__ == "__main__": + pytest.main() \ No newline at end of file diff --git a/tests/chunker/test_word_chunker.py b/tests/chunker/test_word_chunker.py new file mode 100644 index 0000000..03efdf8 --- /dev/null +++ b/tests/chunker/test_word_chunker.py @@ -0,0 +1,107 @@ +import pytest +from tokenizers import Tokenizer +from chonkie.chunker.base import Chunk +from chonkie.chunker.word import WordChunker + +@pytest.fixture +def tokenizer(): + return Tokenizer.from_pretrained("gpt2") + +@pytest.fixture +def sample_text(): + text = """The process of text chunking in RAG applications represents a delicate balance between competing requirements. On one side, we have the need for semantic coherence – ensuring that each chunk maintains meaningful context that can be understood and processed independently. On the other, we must optimize for information density, ensuring that each chunk carries sufficient signal without excessive noise that might impede retrieval accuracy. In this post, we explore the challenges of text chunking in RAG applications and propose a novel approach that leverages recent advances in transformer-based language models to achieve a more effective balance between these competing requirements.""" + return text + +def test_word_chunker_initialization(tokenizer): + """ + Test that the WordChunker can be initialized with a tokenizer. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + + assert chunker is not None + assert chunker.tokenizer == tokenizer + assert chunker.chunk_size == 512 + assert chunker.chunk_overlap == 128 + assert chunker.mode == "simple" + +def test_word_chunker_chunking(tokenizer, sample_text): + """ + Test that the WordChunker can chunk a sample text into words. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=64, chunk_overlap=16, mode="simple") + chunks = chunker.chunk(sample_text) + + assert len(chunks) > 0, print(f"Chunks: {chunks}") + assert type(chunks[0]) is Chunk + assert all([chunk.token_count <= 512 for chunk in chunks]) + assert all([chunk.token_count > 0 for chunk in chunks]) + assert all([chunk.text is not None for chunk in chunks]) + assert all([chunk.start_index is not None for chunk in chunks]) + assert all([chunk.end_index is not None for chunk in chunks]) + +def test_word_chunker_empty_text(tokenizer): + """ + Test that the WordChunker can handle empty text input. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + chunks = chunker.chunk("") + + assert len(chunks) == 0 + +def test_word_chunker_single_word_text(tokenizer): + """ + Test that the WordChunker can handle text with a single word. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + chunks = chunker.chunk("Hello") + + assert len(chunks) == 1 + assert chunks[0].token_count == 1 + assert chunks[0].text == "Hello" + +def test_word_chunker_single_chunk_text(tokenizer): + """ + Test that the WordChunker can handle text that fits within a single chunk. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + chunks = chunker.chunk("Hello, how are you?") + + assert len(chunks) == 1, print(f"Chunks: {chunks}") + assert chunks[0].token_count == 6 + assert chunks[0].text == "Hello, how are you?" + +def test_word_chunker_repr(tokenizer): + """ + Test that the WordChunker has a string representation. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + + assert repr(chunker) == "WordChunker(chunk_size=512, chunk_overlap=128, mode='simple')" + +def test_word_chunker_call(tokenizer, sample_text): + """ + Test that the WordChunker can be called directly. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + chunks = chunker(sample_text) + + assert len(chunks) > 0 + assert type(chunks[0]) is Chunk + assert all([chunk.token_count <= 512 for chunk in chunks]) + assert all([chunk.token_count > 0 for chunk in chunks]) + assert all([chunk.text is not None for chunk in chunks]) + assert all([chunk.start_index is not None for chunk in chunks]) + assert all([chunk.end_index is not None for chunk in chunks]) + +def test_word_chunker_overlap(tokenizer, sample_text): + """ + Test that the WordChunker creates overlapping chunks correctly. + """ + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + chunks = chunker.chunk(sample_text) + + for i in range(1, len(chunks)): + assert chunks[i].start_index < chunks[i-1].end_index + +if __name__ == "__main__": + pytest.main() \ No newline at end of file From 2da89976362be80b8e8231c43b7950ae8a65038f Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 18:09:25 +0530 Subject: [PATCH 15/21] Add tests for chunkers --- chonkie/chunker/sentence.py | 94 ++++++++++++---- chonkie/chunker/spdm.py | 2 +- tests/chunker/test_semantic_chunker.py | 146 +++++++++++++++++++++++++ tests/chunker/test_sentence_chunker.py | 99 +++++++++++++++++ tests/chunker/test_spdm_chunker.py | 93 ++++++++++++++++ tests/chunker/test_word_chunker.py | 2 +- 6 files changed, 411 insertions(+), 25 deletions(-) diff --git a/chonkie/chunker/sentence.py b/chonkie/chunker/sentence.py index 0c0dff8..760ac1a 100644 --- a/chonkie/chunker/sentence.py +++ b/chonkie/chunker/sentence.py @@ -100,6 +100,74 @@ def __init__( else: self.mode = "simple" + def _split_into_sentences_via_spacy(self, text: str) -> List[str]: + """Split text into sentences via spaCy. + + Args: + text: Input text to be split into sentences + + Returns: + List of sentences + """ + # Use spaCy's sentence segmentation + doc = self.nlp(text) + sentences = [] + current_pos = 0 + for sent in doc.sents: + sent_text = sent.text.strip() + if not sent_text: + continue + start_idx = text.find(sent_text, current_pos) + end_idx = start_idx + len(sent_text) + current_pos = end_idx + + # Get the token count for the sentence + token_count = len(self.tokenizer.encode(sent_text).ids) + sentences.append( + Sentence(text=sent_text, + start_index=start_idx, + end_index=end_idx, + token_count=token_count) + ) + return sentences + + def _split_into_sentences_simple(self, text: str) -> List[str]: + # Fallback to heuristic mode + # Simple rule-based sentence splitting with common abbreviations + text = re.sub(r'([.!?])([^"])', r'\1\n\2', text) # Add newlines after sentence endings + text = re.sub(r'([.!?]")(\s*[A-Z])', r'\1\n\2', text) # Handle quotes + + # Handle common abbreviations + abbrevs = r'(?:Mr|Mrs|Dr|Prof|Sr|Jr|vs|etc|viz|al|Gen|Col|Fig|e\.g|i\.e)\.' + text = re.sub(f'{abbrevs}\n', f'{abbrevs} ', text) + + # Handle initials and acronyms + text = re.sub(r'([A-Z]\.[A-Z]\.)\n', r'\1 ', text) + text = re.sub(r'([A-Z]\.[A-Z]\.)\n', r'\1 ', text) # Run twice for consecutive initials + + # Handle decimal numbers and ellipsis + text = re.sub(r'(\d+)\.\n(\d+)', r'\1.\2', text) # Decimal numbers + text = re.sub(r'\.{3}\n', '... ', text) # Ellipsis + + sents = [s.strip() for s in text.split('\n') if s.strip()] + + sentences = [] + current_pos = 0 + for sent in sents: + start_idx = text.find(sent, current_pos) + end_idx = start_idx + len(sent) + current_pos = end_idx + + # Get the token count for the sentence + token_count = len(self.tokenizer.encode(sent).ids) + sentences.append( + Sentence(text=sent, + start_index=start_idx, + end_index=end_idx, + token_count=token_count) + ) + return sentences + def _split_into_sentences(self, text: str) -> List[str]: """Split text into sentences based on the selected mode. @@ -110,30 +178,10 @@ def _split_into_sentences(self, text: str) -> List[str]: List of sentences """ if self.mode == "spacy" and self.nlp is not None: - # Use spaCy's sentence segmentation - doc = self.nlp(text) - return [sent.text.strip() for sent in doc.sents] + return self._split_into_sentences_via_spacy(text) elif self.mode == "simple": - # Fallback to heuristic mode - # Simple rule-based sentence splitting with common abbreviations - text = re.sub(r'([.!?])([^"])', r'\1\n\2', text) # Add newlines after sentence endings - text = re.sub(r'([.!?]")(\s*[A-Z])', r'\1\n\2', text) # Handle quotes - - # Handle common abbreviations - abbrevs = r'(?:Mr|Mrs|Dr|Prof|Sr|Jr|vs|etc|viz|al|Gen|Col|Fig|e\.g|i\.e)\.' - text = re.sub(f'{abbrevs}\n', f'{abbrevs} ', text) - - # Handle initials and acronyms - text = re.sub(r'([A-Z]\.[A-Z]\.)\n', r'\1 ', text) - text = re.sub(r'([A-Z]\.[A-Z]\.)\n', r'\1 ', text) # Run twice for consecutive initials - - # Handle decimal numbers and ellipsis - text = re.sub(r'(\d+)\.\n(\d+)', r'\1.\2', text) # Decimal numbers - text = re.sub(r'\.{3}\n', '... ', text) # Ellipsis - - sentences = [s.strip() for s in text.split('\n') if s.strip()] - return sentences - + return self._split_into_sentences_simple(text) + def _get_token_counts(self, sentences: List[str]) -> List[int]: """Get token counts for a list of sentences in batch. diff --git a/chonkie/chunker/spdm.py b/chonkie/chunker/spdm.py index bb6eb41..0fee5da 100644 --- a/chonkie/chunker/spdm.py +++ b/chonkie/chunker/spdm.py @@ -100,7 +100,7 @@ def chunk(self, text: str) -> List[SemanticChunk]: initial_groups = self._group_sentences(sentences) # Second pass: Merge similar groups with skip window - merged_groups = self._merge_similar_groups(initial_groups, self.similarity_threshold) + merged_groups = self._skip_and_merge(initial_groups, self.similarity_threshold) # Final pass: Split into size-appropriate chunks chunks = self._split_chunks(merged_groups) diff --git a/tests/chunker/test_semantic_chunker.py b/tests/chunker/test_semantic_chunker.py index e69de29..7c7dfc3 100644 --- a/tests/chunker/test_semantic_chunker.py +++ b/tests/chunker/test_semantic_chunker.py @@ -0,0 +1,146 @@ +import pytest +from tokenizers import Tokenizer +from chonkie.chunker.semantic import SemanticChunker, SemanticChunk + +@pytest.fixture +def tokenizer(): + return Tokenizer.from_pretrained("gpt2") + +@pytest.fixture +def sample_text(): + text = """The process of text chunking in RAG applications represents a delicate balance between competing requirements. On one side, we have the need for semantic coherence – ensuring that each chunk maintains meaningful context that can be understood and processed independently. On the other, we must optimize for information density, ensuring that each chunk carries sufficient signal without excessive noise that might impede retrieval accuracy. In this post, we explore the challenges of text chunking in RAG applications and propose a novel approach that leverages recent advances in transformer-based language models to achieve a more effective balance between these competing requirements.""" + return text + +def test_semantic_chunker_initialization(tokenizer): + """Test that the SemanticChunker can be initialized with required parameters.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + + assert chunker is not None + assert chunker.tokenizer == tokenizer + assert chunker.max_chunk_size == 512 + assert chunker.similarity_threshold == 0.5 + assert chunker.sentence_mode == "heuristic" + assert chunker.initial_sentences == 1 + +def test_semantic_chunker_chunking(tokenizer, sample_text): + """Test that the SemanticChunker can chunk a sample text.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + chunks = chunker.chunk(sample_text) + + assert len(chunks) > 0 + assert isinstance(chunks[0], SemanticChunk) + assert all([chunk.token_count <= 512 for chunk in chunks]) + assert all([chunk.token_count > 0 for chunk in chunks]) + assert all([chunk.text is not None for chunk in chunks]) + assert all([chunk.start_index is not None for chunk in chunks]) + assert all([chunk.end_index is not None for chunk in chunks]) + assert all([chunk.sentences is not None for chunk in chunks]) + +def test_semantic_chunker_empty_text(tokenizer): + """Test that the SemanticChunker can handle empty text input.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + chunks = chunker.chunk("") + + assert len(chunks) == 0 + +def test_semantic_chunker_single_sentence(tokenizer): + """Test that the SemanticChunker can handle text with a single sentence.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + chunks = chunker.chunk("This is a single sentence.") + + assert len(chunks) == 1 + assert chunks[0].text == "This is a single sentence." + assert len(chunks[0].sentences) == 1 + +def test_semantic_chunker_single_chunk_text(tokenizer): + """Test that the SemanticChunker can handle text that fits in a single chunk.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + text = "Hello, how are you? I am doing well." + chunks = chunker.chunk(text) + + assert len(chunks) == 1 + assert chunks[0].text == text + assert len(chunks[0].sentences) == 2 + +def test_semantic_chunker_repr(tokenizer): + """Test that the SemanticChunker has a string representation.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + + expected = ("SemanticChunker(max_chunk_size=512, similarity_threshold=0.5, " + "initial_sentences=1, sentence_mode='heuristic')") + assert repr(chunker) == expected + +def test_semantic_chunker_similarity_threshold(tokenizer): + """Test that the SemanticChunker respects similarity threshold.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.9 # High threshold should create more chunks + ) + text = ("This is about cars. This is about planes. " + "This is about trains. This is about boats.") + chunks = chunker.chunk(text) + + # With high similarity threshold, we expect more chunks due to low similarity + assert len(chunks) > 1 + +def test_semantic_chunker_percentile_mode(tokenizer, sample_text): + """Test that the SemanticChunker works with percentile-based similarity.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_percentile=50 # Use median similarity as threshold + ) + chunks = chunker.chunk(sample_text) + + assert len(chunks) > 0 + assert all([isinstance(chunk, SemanticChunk) for chunk in chunks]) + +def test_semantic_chunker_spacy_mode(tokenizer, sample_text): + """Test that the SemanticChunker works with spaCy mode.""" + chunker = SemanticChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5, + sentence_mode="spacy" + ) + chunks = chunker.chunk(sample_text) + + assert len(chunks) > 0 + assert chunker.sentence_mode == "spacy" + +if __name__ == "__main__": + pytest.main() \ No newline at end of file diff --git a/tests/chunker/test_sentence_chunker.py b/tests/chunker/test_sentence_chunker.py index e69de29..1a682b1 100644 --- a/tests/chunker/test_sentence_chunker.py +++ b/tests/chunker/test_sentence_chunker.py @@ -0,0 +1,99 @@ +import pytest +from tokenizers import Tokenizer +from chonkie.chunker.base import Chunk +from chonkie.chunker.sentence import SentenceChunker + +@pytest.fixture +def tokenizer(): + return Tokenizer.from_pretrained("gpt2") + +@pytest.fixture +def sample_text(): + text = """The process of text chunking in RAG applications represents a delicate balance between competing requirements. On one side, we have the need for semantic coherence – ensuring that each chunk maintains meaningful context that can be understood and processed independently. On the other, we must optimize for information density, ensuring that each chunk carries sufficient signal without excessive noise that might impede retrieval accuracy. In this post, we explore the challenges of text chunking in RAG applications and propose a novel approach that leverages recent advances in transformer-based language models to achieve a more effective balance between these competing requirements.""" + return text + +def test_sentence_chunker_initialization(tokenizer): + """Test that the SentenceChunker can be initialized with a tokenizer.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + + assert chunker is not None + assert chunker.tokenizer == tokenizer + assert chunker.chunk_size == 512 + assert chunker.chunk_overlap == 128 + assert chunker.mode == "simple" + assert chunker.min_sentences_per_chunk == 1 + +def test_sentence_chunker_chunking(tokenizer, sample_text): + """Test that the SentenceChunker can chunk a sample text into sentences.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk(sample_text) + + assert len(chunks) > 0 + assert isinstance(chunks[0], Chunk) + assert all([chunk.token_count <= 512 for chunk in chunks]) + assert all([chunk.token_count > 0 for chunk in chunks]) + assert all([chunk.text is not None for chunk in chunks]) + assert all([chunk.start_index is not None for chunk in chunks]) + assert all([chunk.end_index is not None for chunk in chunks]) + +def test_sentence_chunker_empty_text(tokenizer): + """Test that the SentenceChunker can handle empty text input.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk("") + + assert len(chunks) == 0 + +def test_sentence_chunker_single_sentence(tokenizer): + """Test that the SentenceChunker can handle text with a single sentence.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk("This is a single sentence.") + + assert len(chunks) == 1 + assert chunks[0].text == "This is a single sentence." + +def test_sentence_chunker_single_chunk_text(tokenizer): + """Test that the SentenceChunker can handle text that fits within a single chunk.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk("Hello, how are you? I am doing well.") + + assert len(chunks) == 1 + assert chunks[0].text == "Hello, how are you? I am doing well." + +def test_sentence_chunker_repr(tokenizer): + """Test that the SentenceChunker has a string representation.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + + assert repr(chunker) == "SentenceChunker(chunk_size=512, chunk_overlap=128, mode='simple', min_sentences_per_chunk=1)" + +def test_sentence_chunker_overlap(tokenizer, sample_text): + """Test that the SentenceChunker creates overlapping chunks correctly.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) + chunks = chunker.chunk(sample_text) + + for i in range(1, len(chunks)): + assert chunks[i].start_index < chunks[i-1].end_index + +def test_sentence_chunker_modes(tokenizer): + """Test that the SentenceChunker can handle different modes.""" + # Test simple mode + chunker_simple = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") + assert chunker_simple.mode == "simple" + + # Test spacy mode + chunker_spacy = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="spacy") + # Note: This might fall back to simple mode if spacy is not installed + assert chunker_spacy.mode in ["simple", "spacy"] + +def test_sentence_chunker_min_sentences(tokenizer): + """Test that the SentenceChunker respects minimum sentences per chunk.""" + chunker = SentenceChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, min_sentences_per_chunk=2) + chunks = chunker.chunk("First sentence. Second sentence. Third sentence.") + + assert len(chunks) > 0 + for chunk in chunks: + # Count sentences by splitting on periods + sentence_count = len([s for s in chunk.text.split('.') if s.strip()]) + assert sentence_count >= 2 or sentence_count == 1 # Last chunk might have fewer sentences + +if __name__ == "__main__": + pytest.main() \ No newline at end of file diff --git a/tests/chunker/test_spdm_chunker.py b/tests/chunker/test_spdm_chunker.py index e69de29..4351baf 100644 --- a/tests/chunker/test_spdm_chunker.py +++ b/tests/chunker/test_spdm_chunker.py @@ -0,0 +1,93 @@ +import pytest +from tokenizers import Tokenizer +from chonkie.chunker.spdm import SPDMChunker +from chonkie.chunker.semantic import SemanticChunk + +@pytest.fixture +def tokenizer(): + return Tokenizer.from_pretrained("gpt2") + +@pytest.fixture +def sample_text(): + text = """The process of text chunking in RAG applications represents a delicate balance between competing requirements. On one side, we have the need for semantic coherence – ensuring that each chunk maintains meaningful context that can be understood and processed independently. On the other, we must optimize for information density, ensuring that each chunk carries sufficient signal without excessive noise that might impede retrieval accuracy. In this post, we explore the challenges of text chunking in RAG applications and propose a novel approach that leverages recent advances in transformer-based language models to achieve a more effective balance between these competing requirements.""" + return text + +def test_spdm_chunker_initialization(tokenizer): + """Test that the SPDMChunker can be initialized with required parameters.""" + chunker = SPDMChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5, + skip_window=2 + ) + + assert chunker is not None + assert chunker.tokenizer == tokenizer + assert chunker.max_chunk_size == 512 + assert chunker.similarity_threshold == 0.5 + assert chunker.sentence_mode == "heuristic" + assert chunker.initial_sentences == 1 + assert chunker.skip_window == 2 + +def test_spdm_chunker_chunking(tokenizer, sample_text): + """Test that the SPDMChunker can chunk a sample text.""" + chunker = SPDMChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + chunks = chunker.chunk(sample_text) + + assert len(chunks) > 0 + assert isinstance(chunks[0], SemanticChunk) + assert all([chunk.token_count <= 512 for chunk in chunks]) + assert all([chunk.token_count > 0 for chunk in chunks]) + assert all([chunk.text is not None for chunk in chunks]) + assert all([chunk.start_index is not None for chunk in chunks]) + assert all([chunk.end_index is not None for chunk in chunks]) + assert all([chunk.sentences is not None for chunk in chunks]) + +def test_spdm_chunker_empty_text(tokenizer): + """Test that the SPDMChunker can handle empty text input.""" + chunker = SPDMChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + chunks = chunker.chunk("") + + assert len(chunks) == 0 + +def test_spdm_chunker_single_sentence(tokenizer): + """Test that the SPDMChunker can handle text with a single sentence.""" + chunker = SPDMChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5 + ) + chunks = chunker.chunk("This is a single sentence.") + + assert len(chunks) == 1 + assert chunks[0].text == "This is a single sentence." + assert len(chunks[0].sentences) == 1 + +def test_spdm_chunker_repr(tokenizer): + """Test that the SPDMChunker has a string representation.""" + chunker = SPDMChunker( + tokenizer=tokenizer, + sentence_transformer_model="all-MiniLM-L6-v2", + max_chunk_size=512, + similarity_threshold=0.5, + skip_window=2 + ) + + expected = ("SPDMChunker(max_chunk_size=512, similarity_threshold=0.5, " + "initial_sentences=1, sentence_mode='heuristic', skip_window=2)") + assert repr(chunker) == expected + +if __name__ == "__main__": + pytest.main() \ No newline at end of file diff --git a/tests/chunker/test_word_chunker.py b/tests/chunker/test_word_chunker.py index 03efdf8..9cd2edf 100644 --- a/tests/chunker/test_word_chunker.py +++ b/tests/chunker/test_word_chunker.py @@ -28,7 +28,7 @@ def test_word_chunker_chunking(tokenizer, sample_text): """ Test that the WordChunker can chunk a sample text into words. """ - chunker = WordChunker(tokenizer=tokenizer, chunk_size=64, chunk_overlap=16, mode="simple") + chunker = WordChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128, mode="simple") chunks = chunker.chunk(sample_text) assert len(chunks) > 0, print(f"Chunks: {chunks}") From a57a6a2dce3c49dc1e575e5e981a18e13173fc5e Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 18:11:15 +0530 Subject: [PATCH 16/21] Bump version to 0.0.1a4 in __init__.py and pyproject.toml --- chonkie/__init__.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/chonkie/__init__.py b/chonkie/__init__.py index 11936fa..8eb9a95 100644 --- a/chonkie/__init__.py +++ b/chonkie/__init__.py @@ -9,7 +9,7 @@ ) -__version__ = "0.0.1a3" +__version__ = "0.0.1a4" __name__ = "chonkie" __author__ = "Bhavnick Minhas" diff --git a/pyproject.toml b/pyproject.toml index ba3e05c..ed4ca0a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "chonkie" -version = "0.0.1a3" +version = "0.0.1a4" description = "A simple, efficient text chunking library for RAG applications" readme = "README.md" requires-python = ">=3.8" From 009848e831ef16423b7911269e4ed4cc407980e3 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 18:11:22 +0530 Subject: [PATCH 17/21] Update documentation for TokenChunker with usage examples and methods --- DOCS.md | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 56 insertions(+), 1 deletion(-) diff --git a/DOCS.md b/DOCS.md index 8cb0545..fb696d2 100644 --- a/DOCS.md +++ b/DOCS.md @@ -1 +1,56 @@ -# 🦛 Chonkie Docs \ No newline at end of file +# 🦛 Chonkie Docs + +> UGH, do I _need_ to explain how to use Chonkie? Man, that's a bummer... To be honest, Chonkie is very easy, with little documentation necessary, but just in case, I'll include some here. + +# Table Of Contents + +- [🦛 Chonkie Docs](#-chonkie-docs) +- [Table Of Contents](#table-of-contents) +- [Chunkers](#chunkers) + - [TokenChunker](#tokenchunker) + - [Initialization](#initialization) + - [Methods](#methods) + - [Example](#example) + +# Chunkers +## TokenChunker + +The `TokenChunker` class is designed to split text into overlapping chunks based on a specified token size. This is particularly useful for applications that need to process text in manageable pieces while maintaining some context between chunks. + +### Initialization + +To initialize a `TokenChunker`, you need to provide a tokenizer, the maximum number of tokens per chunk, and the number of tokens to overlap between chunks. + +```python +from tokenizers import Tokenizer +from chonkie.chunker import TokenChunker + +# Initialize the tokenizer (example using GPT-2 tokenizer) +tokenizer = Tokenizer.from_pretrained("gpt2") + +# Initialize the TokenChunker +chunker = TokenChunker(tokenizer=tokenizer, chunk_size=512, chunk_overlap=128) +``` + +### Methods + +`chunk(text: str) -> List[Chunk]` +This method splits the input text into overlapping chunks of the specified token size. + +`Args:` +* `text` (str): The input text to be chunked. +`Returns:` +* `List[Chunk]`: A list of Chunk objects containing the chunked text and metadata. + +### Example + +```python +text = "Your input text here." +chunks = chunker.chunk(text) + +for chunk in chunks: + print(f"Chunk text: {chunk.text}") + print(f"Start index: {chunk.start_index}") + print(f"End index: {chunk.end_index}") + print(f"Token count: {chunk.token_count}") +``` \ No newline at end of file From 94288132f3b2935b3a58a4915f18fd62dec14f2f Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 19:04:20 +0530 Subject: [PATCH 18/21] Refactor chunker module to introduce Sentence and Semantic classes, enhancing sentence and semantic chunking capabilities --- assets/chonkie_logo_transparent_bg.png | Bin 0 -> 128994 bytes assets/chonkie_logo_white_bg.png | Bin 0 -> 115386 bytes chonkie/__init__.py | 10 +++++++++- chonkie/chunker/__init__.py | 8 ++++++-- chonkie/chunker/semantic.py | 11 ++++++----- chonkie/chunker/sentence.py | 22 +++++++++++----------- 6 files changed, 32 insertions(+), 19 deletions(-) create mode 100644 assets/chonkie_logo_transparent_bg.png create mode 100644 assets/chonkie_logo_white_bg.png diff --git 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zxye&Oh$!x#ri&_8<~`p=P4sq!O%`0~Pwm0xWyeq1{(Tq05qA6-_^m zeeSS-4RR?$;tGM><1WI3a7Tm_LBgT List[Sentence]: # Create Sentence objects with all precomputed information sentences = [ - Sentence( + SemanticSentence( text=sent, start_index=start_idx, end_index=end_idx, diff --git a/chonkie/chunker/sentence.py b/chonkie/chunker/sentence.py index 760ac1a..5fe9dac 100644 --- a/chonkie/chunker/sentence.py +++ b/chonkie/chunker/sentence.py @@ -111,10 +111,10 @@ def _split_into_sentences_via_spacy(self, text: str) -> List[str]: """ # Use spaCy's sentence segmentation doc = self.nlp(text) - sentences = [] + sentences = [sent.text.strip() for sent in doc.sents if sent.text.strip()] + token_counts = self._get_token_counts(sentences) current_pos = 0 - for sent in doc.sents: - sent_text = sent.text.strip() + for sent_text, token_count in zip(sentences, token_counts): if not sent_text: continue start_idx = text.find(sent_text, current_pos) @@ -122,7 +122,6 @@ def _split_into_sentences_via_spacy(self, text: str) -> List[str]: current_pos = end_idx # Get the token count for the sentence - token_count = len(self.tokenizer.encode(sent_text).ids) sentences.append( Sentence(text=sent_text, start_index=start_idx, @@ -150,16 +149,16 @@ def _split_into_sentences_simple(self, text: str) -> List[str]: text = re.sub(r'\.{3}\n', '... ', text) # Ellipsis sents = [s.strip() for s in text.split('\n') if s.strip()] - + token_counts = self._get_token_counts(sents) + sentences = [] current_pos = 0 - for sent in sents: + for sent, token_count in zip(sents, token_counts): start_idx = text.find(sent, current_pos) end_idx = start_idx + len(sent) current_pos = end_idx # Get the token count for the sentence - token_count = len(self.tokenizer.encode(sent).ids) sentences.append( Sentence(text=sent, start_index=start_idx, @@ -197,7 +196,7 @@ def _get_token_counts(self, sentences: List[str]) -> List[int]: def _create_chunk( self, - sentences: List[str], + sentences: List[Sentence], start_idx: int, token_count: int ) -> Chunk: @@ -211,12 +210,13 @@ def _create_chunk( Returns: Chunk object """ - chunk_text = " ".join(sentences) + chunk_text = " ".join([sentence.text for sentence in sentences]) return SentenceChunk( text=chunk_text, start_index=start_idx, end_index=start_idx + token_count, - token_count=token_count + token_count=token_count, + sentences=sentences ) def chunk(self, text: str) -> List[Chunk]: @@ -232,7 +232,7 @@ def chunk(self, text: str) -> List[Chunk]: return [] sentences = self._split_into_sentences(text) - token_counts = self._get_token_counts(sentences) + token_counts = [sentence.token_count for sentence in sentences] chunks = [] current_sentences = [] From 6da88f5309a7865d749cbcaa95e6560ade596c10 Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 19:06:24 +0530 Subject: [PATCH 19/21] Add logo to README for enhanced visual appeal --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index dbf09ba..1942729 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +![Chonkie Logo](assets/chonkie_logo_transparent_bg.png) # 🦛 Chonkie so i found myself making another RAG bot (for the 2342148th time) and meanwhile, explaining to my juniors about why we should use chunking in our RAG bots, only to realise that i would have to write chunking all over again unless i use the bloated software library X or the extremely feature-less library Y. _WHY CAN I NOT HAVE GOOD THINGS IN LIFE, UGH?_ From 546b5125668f147a4f7537c4b483115bdb4fc34e Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 19:20:24 +0530 Subject: [PATCH 20/21] Update .gitignore to exclude chonkie.egg-info and .DS_Store files --- .gitignore | 3 ++- assets/chonkie_logo_transparent_bg.png | Bin 128994 -> 122776 bytes 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index d570a63..6170997 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ __pycache__/ -chonkie.egg-info/ \ No newline at end of file +chonkie.egg-info/ +.DS_Store diff --git a/assets/chonkie_logo_transparent_bg.png b/assets/chonkie_logo_transparent_bg.png index 9e697fe6f34cc488468af750ed8451c76b6a0ced..a8dc6e29a87ff955d5e07ef77db9b88401b5f9e6 100644 GIT binary patch literal 122776 zcmd3O^+Qz8_xA?f-JQ}YNVhbobazR2!vX>#-5ru59m3K}r=)Z@N_WH8cYmJ$;Mx1b 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From f17142287ecba1a6516f2d2da6128a7604903f6d Mon Sep 17 00:00:00 2001 From: bhavnicksm Date: Sat, 2 Nov 2024 19:25:17 +0530 Subject: [PATCH 21/21] Update the logo to have brown text --- README.md | 2 +- assets/chonkie_logo_br_transparent_bg.png | Bin 0 -> 124213 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 assets/chonkie_logo_br_transparent_bg.png diff --git a/README.md b/README.md index 1942729..8a54c3b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -![Chonkie Logo](assets/chonkie_logo_transparent_bg.png) +![Chonkie Logo](assets/chonkie_logo_br_transparent_bg.png) # 🦛 Chonkie so i found myself making another RAG bot (for the 2342148th time) and meanwhile, explaining to my juniors about why we should use chunking in our RAG bots, only to realise that i would have to write chunking all over again unless i use the bloated software library X or the extremely feature-less library Y. _WHY CAN I NOT HAVE GOOD THINGS IN LIFE, UGH?_ diff --git 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