perf: Optimize word segmentation retrieval

[shaohuzhang1]

perf: Optimize word segmentation retrieval

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[shaohuzhang1]

Code Review and Suggestions

1. Comments:

   • The comments in the code are mostly clear but could benefit from more specific detail on what each function does.

2. Imports:

   • Import analyse directly instead of using jieba.analyse. This makes it easier to understand where functions like extract_tags come from.

3. Functionality:

   • In to_ts_vector, replace steps related to keyword extraction and filtering with a direct call to jieba.lcut.
   • In to_query, similar changes can be made. Directly call jieba.lcut without unnecessary processing.

4. Optimization:

   • Replace the entire replace_word function call inside both to_ts_vector and to_query. If this function is necessary for specific purposes, consider refactoring its implementation or removing it entirely if no longer needed.

5. Character Handling:

   • Ensure that remove_chars is properly defined and referenced if used elsewhere in the code.

6. Consistency:

   • Make sure consistency between how different components handle text preprocessing (e.g., punctuation stripping).

Here's an updated version of the cleaned-up code:

import jieba

# Constants and caching
jieba_word_list_cache = [chr(item) for item in range(38, 84)]

def get_key_by_word_dict(key, word_dict):
    raise NotImplementedError("Not implemented yet")

def to_ts_vector(text: str):
    # Clean up text and split into words
    words = jieba.lcut(text)
    return " ".join(words)

def to_query(text: str):
    # Split text into words
    words = jieba.lcut(text)
    return " ".join(words)

This version removes unneeded logic and focuses on leveraging Jieba’s built-in functionality efficiently.

[shaohuzhang1]

There are no obvious irregularities in the provided code snippet. However, there are a few suggestions for improvement:

1. Jieba Import: The import of jieba at line 16 seems unnecessary since it's not used anywhere within the function _batch_save. You can remove this line to simplify the code.

2. Use of SearchVector Correctly: In the SQL query generation logic starting from line 68, you're trying to create a SearchVector using an EmbeddedFunctionCall, which might not be syntactically correct depending on your database setup. Consider whether you need to use Django's built-in support for searching (e.g., PostgreSQL's full-text search capabilities).

3. Empty Dictionary Check: Ensure that each dictionary entry has the necessary keys 'source_id', 'source_type', and 'text'. Using conditional statement like index < len(text_list) instead of assuming len(text_lists) == len(texts) is more robust, especially if other parameters might change in future versions.

4. Error Handling: There isn't any error handling in the function, which could make debugging more difficult. Adding try-except blocks around operations that interact with your database can help catch errors early.

Here is the revised version based on these points:

from abc import ABC, abstractmethod
from typing import Dict, List

from django.db.models import QuerySet

from langchain_core.embeddings import Embeddings

import jieba
from django.contrib.postgres.search import SearchVector
from django.db.models import Q, Value

def _batch_save(self, texts: List[Dict], embeddings: List[Embeddings], is_the_task_interrupted):
    embedding_list = [(Q(source_id=text.get('source_id'), source_type=text.get('source_type')),
                       text['text']) for text in texts]
    
    # Filter out empty entries
    filtered_embeddings = [emb for emb in embedding_list if all(val is not None for val in emb)]
    
    if not is_the_task_interrupted():
        embedding_objects = list(Embedding.from_query(Q(**{' OR '.join(f"{key}={value}" for key, value in q.as_q.items())) 
                                                     for q, _ in filtered_embeddings))
        
        batch_create_data = []
        for q, text in filtered_embeddings:
            obj = embedding_objects.pop(0)
            batch_create_data.append((Value(text), obj.id,))
            
        if batch_create_data:
            EmbeddedObject.objects.bulk_update(batch_create_data, fields=['embedding', 'search_vector'])

# Example usage of filter out invalid entries
texts_with_invalid_entries = [{'id': 1, 'source_id': '', 'text': 'A good example'}]
embeddings_with_valid_entries = ['this is a test', 'another good one']
_text_and_embedding_pairs = zip(texts_with_invalid_entries, embeddings_with_valid_entries)

filtered_pairs = [(t, e) for t, e in _text_and_embedding_pairs if all([str(v) if v != '' else None for v in t.values()])]

print(filtered_pairs)

This should help ensure reliability and maintainability of your code.