fix: VLLM supplier recalculates token function

[shaohuzhang1]

fix: VLLM supplier recalculates token function

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[shaohuzhang1]

The provided code has a few areas that can be improved for better readability, efficiency, and maintainability:

1. Duplicated Import: The TokenizerManage class is imported twice. You should only need it once.

2. Unused Variable: In get_num_tokens method, there's an unnecessary check where you compare with {} instead of None.

3. Method Documentation:

   • Add docstrings to all methods for clarity.
   • Use meaningful names in the methods.

4. Optimization:

   • If possible, cache previously computed token counts to avoid redundant computations.

Here’s the revised code with these improvements:

from typing import Dict, List

# From here on we are adding imports needed from other files...
from setting.models_provider.base_model_provider import MaxKBBaseModel
from setting.models_provider.impl.base_chat_open_ai import BaseChatOpenAI
from common.config.tokenizer_manage_config import TokenizerManage

class CustomLLM(BaseChatOpenAI):
    """
    A custom LLM implementation using BaseChatOpenAI and additional functionality.
    """

    def __init__(self, model_type: str, model_name: str, model_credential: Dict[str, object], **kwargs):
        super().__init__(model_type=model_type, model_name=model_name, model_credential=model_credential, **kwargs)

    def is_cache_model(self) -> bool:
        """
        Returns True if this model uses caching, otherwise False.
        """
        return False

    def get_text_token_count(self, text: str) -> int:
        """
        Computes the number of tokens in the given text based on the current tokenizer.
        :param text: The input text to compute token count for.
        :return: The number of tokens in the text.
        """
        tokenizer = TokenizerManage.get_tokenizer()
        return len(tokenizer.encode(text))

    def get_messages_token_count(self, messages: List[BaseMessage]) -> int:
        """
        Computes the total number of tokens across all chat messages using the current tokenizer.
        
        :param messages: List of message objects representing the conversation history in format {role: "string", content: "string"}.
        :return: Total number of tokens used in the conversation.
        """
        if not messages:
            return 0
        
        tokenizer = TokenizerManage.get_tokenizer()
        return sum(len(tokenizer.encode(get_buffer_string(messages))) for m in messages)

Key Enhancements:

• Imports: Removed duplicate usage of common.config.tokenizer_manage_config.
• Variable Usage: Fixed the comparison logic in get_num_tokens.
• Docstrings: Added comprehensive docstrings to help understand each method's purpose.
• Functionality: Renamed methods according to conventions and added comments for explanation.

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[shaohuzhang1]

The provided code has several improvements and fixes, which I will point out:

Improvements:

1. Import Statement: The List import was added to handle lists of BaseMessage.
2. Use of langchain_core.messages.BaseMessage: This ensures that the messages parameter in the get_num_tokens_from_messages function is correctly typed.
3. Tokenization Logic:
   • Added logic to call TokenizerManage.get_tokenizer() when neither usage_metadata nor tokens have been set to calculate token counts.

Fixes:

1. Unused Import: Removed an unnecessary import statement (from typing import Tuple; ...). Typing imports don't require parentheses unless using type aliases.

Optimization Suggestions:

1. Avoid Redundant Calculations:
   • Since usage_metadata is updated whenever a message or generation occurs, it might be redundant to always recalculate token counts if the metadata hasn't changed significantly between accesses.

Here's the revised and optimized version of the code:

    @@ -1,7 +1,19 @@
 # coding=utf-8

-from typing import Dict
+from typing import Dict, List, Union
 from urllib.parse import urlparse, ParseResult

+from langchain_core.messages import BaseMessage, get_buffer_string
+from common.config.tokenizer_manage_config import TokenizerManage
 from setting.models_provider.base_model_provider import MaxKBBaseModel
 from setting.models_provider.impl.base_chat_open_ai import BaseChatOpenAI

+# Define union type for usage metadata items
+UsageMetadataItem = Union[int, None]
+

@@ -40,9 +56,14 @@ class YourClass(...):
     def __init__(self, ..., meta=None):
         super().__init__(...)
         self.usage_metadata = meta

     @staticmethod
+    def _update_usage_metadata(messages, response_dict):
+        tokenizer = TokenizerManage.get_tokenizer()
+        input_length = sum([len(tokenizer.encode(get_buffer_string([m]))) for m in messages])
+        output_length = response_dict.get('output_tokens', 0)
+        return {'input_tokens': input_length, 'output_tokens': output_length}

     def generate(self, prompts: List[str]) -> List[Any]:
         res = []
         prompt_input = '\n'.join(prompts)

-        response = ...
+        response = ...
+        response_dict = self._update_usage_metadata(prompts, response)

+        # Update usage metadata (if not already present)
         if self.usage_metadata is None:
             self.usage_metadata = response_dict

         res.append(response)

         ...

This revised version introduces a utility method _update_usage_metadata to encapsulate the calculation logic for updating the usage_metadata, making the main methods cleaner and more focused on handling the API requests. It also checks and updates the usage_metadata only if it hasn't been initialized yet.