1260fc7db0
<!-- .github/pull_request_template.md --> ## Description Add re-raising of errors in general exception handling ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes & Stability Improvements** - Enhanced error handling throughout the system, ensuring issues during operations like server startup, data processing, and graph management are properly logged and reported. - **Refactor** - Standardized logging practices replace basic output statements, improving traceability and providing better insights for troubleshooting. - **New Features** - Updated search functionality now returns only unique results, enhancing data consistency and the overall user experience. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: holchan <61059652+holchan@users.noreply.github.com> Co-authored-by: Boris <boris@topoteretes.com>
104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
import logging
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from uuid import NAMESPACE_OID, uuid5
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from cognee.tasks.chunks import chunk_by_paragraph
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from .models.DocumentChunk import DocumentChunk
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logger = logging.getLogger(__name__)
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class TextChunker:
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document = None
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max_chunk_size: int
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chunk_index = 0
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chunk_size = 0
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token_count = 0
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def __init__(self, document, get_text: callable, max_chunk_tokens: int, chunk_size: int = 1024):
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self.document = document
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self.max_chunk_size = chunk_size
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self.get_text = get_text
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self.max_chunk_tokens = max_chunk_tokens
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def check_word_count_and_token_count(self, word_count_before, token_count_before, chunk_data):
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word_count_fits = word_count_before + chunk_data["word_count"] <= self.max_chunk_size
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token_count_fits = token_count_before + chunk_data["token_count"] <= self.max_chunk_tokens
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return word_count_fits and token_count_fits
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def read(self):
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paragraph_chunks = []
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for content_text in self.get_text():
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for chunk_data in chunk_by_paragraph(
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content_text,
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self.max_chunk_tokens,
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self.max_chunk_size,
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batch_paragraphs=True,
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):
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if self.check_word_count_and_token_count(
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self.chunk_size, self.token_count, chunk_data
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):
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paragraph_chunks.append(chunk_data)
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self.chunk_size += chunk_data["word_count"]
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self.token_count += chunk_data["token_count"]
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else:
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if len(paragraph_chunks) == 0:
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yield DocumentChunk(
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id=chunk_data["chunk_id"],
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text=chunk_data["text"],
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word_count=chunk_data["word_count"],
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token_count=chunk_data["token_count"],
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is_part_of=self.document,
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chunk_index=self.chunk_index,
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cut_type=chunk_data["cut_type"],
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contains=[],
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_metadata={
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"index_fields": ["text"],
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},
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)
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paragraph_chunks = []
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self.chunk_size = 0
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else:
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chunk_text = " ".join(chunk["text"] for chunk in paragraph_chunks)
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try:
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yield DocumentChunk(
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id=uuid5(
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NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"
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),
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text=chunk_text,
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word_count=self.chunk_size,
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token_count=self.token_count,
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is_part_of=self.document,
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chunk_index=self.chunk_index,
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cut_type=paragraph_chunks[len(paragraph_chunks) - 1]["cut_type"],
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contains=[],
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_metadata={
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"index_fields": ["text"],
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},
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)
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except Exception as e:
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logger.error(e)
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raise e
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paragraph_chunks = [chunk_data]
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self.chunk_size = chunk_data["word_count"]
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self.token_count = chunk_data["token_count"]
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self.chunk_index += 1
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if len(paragraph_chunks) > 0:
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try:
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yield DocumentChunk(
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id=uuid5(NAMESPACE_OID, f"{str(self.document.id)}-{self.chunk_index}"),
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text=" ".join(chunk["text"] for chunk in paragraph_chunks),
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word_count=self.chunk_size,
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token_count=self.token_count,
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is_part_of=self.document,
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chunk_index=self.chunk_index,
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cut_type=paragraph_chunks[len(paragraph_chunks) - 1]["cut_type"],
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contains=[],
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_metadata={"index_fields": ["text"]},
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)
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except Exception as e:
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logger.error(e)
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raise e
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