refactor: update ZhipuChatModel to use BaseChatOpenAI and improve token counting

[shaohuzhang1]

refactor: update ZhipuChatModel to use BaseChatOpenAI and improve token counting --bug=1061305 --user=刘瑞斌 【应用】ai对话启用工具后部分模型（智谱）不统计tokens https://www.tapd.cn/62980211/s/1791683

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[shaohuzhang1]

No significant changes have been identified in the provided code snippet, but here are some minor improvements or clarifications:

• Remove self.optional_params assignment as it's not used anywhere in the class.

• Adjust the usage of token counting methods to use consistent approach between classes.

Here's a revised version of the code with these corrections applied:

# Import necessary libraries and classes
import json

from typing import Dict, List

from langchain_core.messages import BaseMessage
from common.config.tokenizer_manage_config import TokenizerManage


class ZhipuChatModel(MaxKBBaseModel, BaseChatOpenAI):

    @staticmethod
    def is_cache_model():
        pass
    
    def __init__(self, *args, max_length=None, **kwargs):
        super().__init__(*args, **kwargs)
        self.max_length = max_length

    def _token_count(self, text: str) -> int:
            # Use the default method provided by BaseChatOpenAI if available
            try:
                return super().get_num_tokens(text)
            except Exception as e:
                tokenizer = TokenizerManage.get_tokenizer()
                tokens = tokenizer.encode(text)
                return min(len(tokens), self.max_length) if max_length else len(tokens)

    def generate_responses(self, messages: List[BaseMessage]) -> List[str]:
        total_tokens = 0
        responses = []
        for message in messages:
            token_count = self._token_count(message.content)
            total_tokens += token_count
            
            # Perform your logic here based on whether you want to split or concatenate input data
        
        # Return all generated responses or handle further processing

Key Changes:

1. Initialization: Added max_length parameter during initialization and set it to an instance variable.

2. Token Count Calculation: Modified _token_count method to first call the superclass’s method (super().get_num_tokens(text)), which should be more reliable than manually encoding texts via TokenizerManage (since it might have additional optimizations).

3. Replaced Custom Method: Removed the custom custom_get_token_ids method since we can now directly get a list of IDs using the same technique.

This change will ensure that the token count reflects the actual input length rather than potentially over-counting due to the use of multiple encoders. This may make future modifications easier without changing the core logic too much.