Switch to using HuggingFace tokenizers
This commit is contained in:
Yiorgis Gozadinos
parent
fe7d0c5c24
commit
541552215e
7 changed files with 31 additions and 103 deletions
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@ -5,6 +5,11 @@
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### Changed
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- **BREAKING: Chunking Tokenizer**: Switched from tiktoken to HuggingFace tokenizers for consistency with docling-serve
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- Default tokenizer changed from tiktoken "gpt-4o" to "Qwen/Qwen3-Embedding-0.6B"
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- New `chunking_tokenizer` config option in `ProcessingConfig` for customization
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- Removed `tiktoken` dependency
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- `download-models` CLI command now also downloads the configured HuggingFace tokenizer
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- **Evaluations**: Refactored QA benchmark to run entire dataset as single evaluation for better Logfire experiment tracking
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- **Evaluations**: Added `.env` file loading support via `python-dotenv` dependency
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@ -1,6 +1,4 @@
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from typing import TYPE_CHECKING, ClassVar
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import tiktoken
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from typing import TYPE_CHECKING
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from haiku.rag.config import Config
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@ -16,21 +14,25 @@ class Chunker:
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Args:
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chunk_size: The maximum size of a chunk in tokens.
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tokenizer_name: HuggingFace model name for tokenization.
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"""
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encoder: ClassVar[tiktoken.Encoding] = tiktoken.encoding_for_model("gpt-4o")
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def __init__(
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self,
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chunk_size: int = Config.processing.chunk_size,
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tokenizer_name: str = Config.processing.chunking_tokenizer,
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):
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from docling_core.transforms.chunker.hybrid_chunker import HybridChunker
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from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer
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from docling_core.transforms.chunker.tokenizer.huggingface import (
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HuggingFaceTokenizer,
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)
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from transformers import AutoTokenizer
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self.chunk_size = chunk_size
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tokenizer = OpenAITokenizer(
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tokenizer=tiktoken.encoding_for_model("gpt-4o"), max_tokens=chunk_size
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)
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self.tokenizer_name = tokenizer_name
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hf_tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
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tokenizer = HuggingFaceTokenizer(tokenizer=hf_tokenizer, max_tokens=chunk_size)
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self.chunker = HybridChunker(tokenizer=tokenizer)
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@ -51,4 +53,4 @@ class Chunker:
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return [self.chunker.contextualize(chunk) for chunk in chunks]
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chunker = Chunker()
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chunker = Chunker()
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@ -55,6 +55,7 @@ class ProcessingConfig(BaseModel):
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context_chunk_radius: int = 0
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markdown_preprocessor: str = ""
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converter: str = "docling-local"
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chunking_tokenizer: str = "Qwen/Qwen3-Embedding-0.6B"
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class OllamaConfig(BaseModel):
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@ -1,9 +1,6 @@
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import asyncio
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import importlib
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import importlib.util
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import sys
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from collections.abc import Callable
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from functools import wraps
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from importlib import metadata
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from pathlib import Path
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from types import ModuleType
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@ -11,42 +8,6 @@ from types import ModuleType
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from packaging.version import Version, parse
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def debounce(wait: float) -> Callable:
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"""
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A decorator to debounce a function, ensuring it is called only after a specified delay
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and always executes after the last call.
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Args:
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wait (float): The debounce delay in seconds.
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Returns:
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Callable: The decorated function.
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"""
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def decorator(func: Callable) -> Callable:
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last_call = None
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task = None
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@wraps(func)
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async def debounced(*args, **kwargs):
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nonlocal last_call, task
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last_call = asyncio.get_event_loop().time()
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if task:
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task.cancel()
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async def call_func():
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await asyncio.sleep(wait)
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if asyncio.get_event_loop().time() - last_call >= wait: # type: ignore
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await func(*args, **kwargs)
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task = asyncio.create_task(call_func())
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return debounced
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return decorator
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def get_default_data_dir() -> Path:
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"""Get the user data directory for the current system platform.
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@ -144,7 +105,7 @@ def load_callable(path: str):
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def prefetch_models():
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"""Prefetch runtime models (Docling + Ollama as configured)."""
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"""Prefetch runtime models (Docling + Ollama + HuggingFace tokenizer as configured)."""
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import httpx
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from haiku.rag.config import Config
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@ -157,6 +118,11 @@ def prefetch_models():
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# Docling not installed, skip downloading docling models
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pass
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# Download HuggingFace tokenizer
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from transformers import AutoTokenizer
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AutoTokenizer.from_pretrained(Config.processing.chunking_tokenizer)
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# Collect Ollama models from config
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required_models: set[str] = set()
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if Config.embeddings.provider == "ollama":
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@ -179,4 +145,4 @@ def prefetch_models():
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"POST", f"{base_url}/api/pull", json={"model": model}
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) as r:
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for _ in r.iter_lines():
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pass
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pass
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@ -31,7 +31,6 @@ dependencies = [
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"python-dotenv>=1.2.1",
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"pyyaml>=6.0.3",
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"rich>=14.2.0",
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"tiktoken>=0.12.0",
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"typer>=0.19.2,<0.20.0",
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"watchfiles>=1.1.1",
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]
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@ -1,5 +1,6 @@
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import pytest
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from datasets import Dataset
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from transformers import AutoTokenizer
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from haiku.rag.chunker import Chunker
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from haiku.rag.config import Config
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@ -20,10 +21,13 @@ async def test_chunker(qa_corpus: Dataset):
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# Ensure that the text is split into multiple chunks
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assert len(chunks) > 1
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# Load tokenizer for verification
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tokenizer = AutoTokenizer.from_pretrained(chunker.tokenizer_name)
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# Ensure that chunks are reasonably sized (allowing more flexibility for structure-aware chunking)
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total_tokens = 0
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for chunk in chunks:
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encoded_tokens = Chunker.encoder.encode(chunk, disallowed_special=())
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encoded_tokens = tokenizer.encode(chunk, add_special_tokens=False)
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token_count = len(encoded_tokens)
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total_tokens += token_count
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@ -34,7 +38,7 @@ async def test_chunker(qa_corpus: Dataset):
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assert token_count > 5 # Ensure chunks aren't too small
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# Ensure that all chunks together contain roughly the same content as original
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original_tokens = len(Chunker.encoder.encode(doc_text, disallowed_special=()))
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original_tokens = len(tokenizer.encode(doc_text, add_special_tokens=False))
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# Due to structure-aware chunking, we might have some variation in token count
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# but it should be reasonable
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49
uv.lock
49
uv.lock
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@ -1206,7 +1206,6 @@ dependencies = [
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{ name = "python-dotenv" },
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{ name = "pyyaml" },
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{ name = "rich" },
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{ name = "tiktoken" },
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{ name = "typer" },
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{ name = "watchfiles" },
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]
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@ -1266,7 +1265,6 @@ requires-dist = [
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{ name = "python-dotenv", specifier = ">=1.2.1" },
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{ name = "pyyaml", specifier = ">=6.0.3" },
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{ name = "rich", specifier = ">=14.2.0" },
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{ name = "tiktoken", specifier = ">=0.12.0" },
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{ name = "typer", specifier = ">=0.19.2,<0.20.0" },
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{ name = "voyageai", marker = "extra == 'voyageai'", specifier = ">=0.3.5" },
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{ name = "watchfiles", specifier = ">=1.1.1" },
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@ -4077,53 +4075,6 @@ wheels = [
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]
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[[package]]
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name = "tiktoken"
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version = "0.12.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "regex" },
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{ name = "requests" },
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]
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|
||||
{ url = "https://files.pythonhosted.org/packages/af/df/c7891ef9d2712ad774777271d39fdef63941ffba0a9d59b7ad1fd2765e57/tiktoken-0.12.0-cp314-cp314t-win_amd64.whl", hash = "sha256:f61c0aea5565ac82e2ec50a05e02a6c44734e91b51c10510b084ea1b8e633a71", size = 920667, upload-time = "2025-10-06T20:22:34.444Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tokenizers"
|
||||
version = "0.22.0"
|
||||
|
|
|
|||
Loading…
Reference in a new issue