remove tiktoken and lcc support reasoning

Author: JqRrt

CHANGLOG.md

@@ -1,3 +1,8 @@
+## [0.1.27] - 2026-03-18
+### Fixed
+- remove tiktoken
+- lcc support reasoning
+
 ## [0.1.26] - 2026-03-18
 ### Fixed
 - update tiktoken to a weak dependency

cozeloop/integration/langchain/trace_callback.py

@@ -23,7 +23,7 @@
 from cozeloop.integration.langchain.trace_model.llm_model import ModelTraceInput, ModelMeta, ModelTraceOutput, Message
 from cozeloop.integration.langchain.trace_model.prompt_template import PromptTraceOutput, Argument, PromptTraceInput
 from cozeloop.integration.langchain.trace_model.runtime import RuntimeInfo
-from cozeloop.integration.langchain.util import calc_token_usage, get_prompt_tag
+from cozeloop.integration.langchain.util import get_prompt_tag
 
 
 class LoopTracer:
@@ -291,21 +291,7 @@ def _get_model_token_tags(self, response: LLMResult, **kwargs: Any) -> Dict[str,
         if is_get_from_langchain:
             return result
         else:
-            try:
-                run_info = self.run_map[str(kwargs['run_id'])]
-                if run_info is not None and run_info.model_meta is not None:
-                    model_name = run_info.model_meta.model_name
-                    input_messages = run_info.model_meta.message
-                    token_usage = {
-                        'input_tokens': calc_token_usage(input_messages, model_name),
-                        'output_tokens': calc_token_usage(response, model_name),
-                        'tokens': 0
-                    }
-                    token_usage['tokens'] = token_usage['input_tokens'] + token_usage['output_tokens']
-                    return token_usage
-            except Exception as e:
-                span_tags = {'error_info': repr(e), 'error_trace': traceback.format_exc()}
-                return span_tags
+            return {}
 
     def _on_prompt_start(self, flow_span, serialized: Dict[str, Any], inputs: (Dict[str, Any], str),
                          **kwargs: Any) -> None:

cozeloop/integration/langchain/util.py

@@ -1,54 +1,7 @@
 # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
 # SPDX-License-Identifier: MIT
 
-from typing import List, Dict, Union, Any, Optional
-from langchain_core.outputs import LLMResult, Generation, ChatGeneration
-
-try:
-    import tiktoken
-    _cl100k_base_encoding = tiktoken.get_encoding('cl100k_base')
-except Exception:
-    tiktoken = None  # type: ignore[assignment]
-    _cl100k_base_encoding = None
-
-
-def calc_token_usage(inputs: Union[List[Dict], LLMResult], model: str = 'gpt-3.5-turbo-0613'):
-    """Return the number of tokens used by a list of messages."""
-    if tiktoken is None:
-        return 0
-    try:
-        encoding = tiktoken.encoding_for_model(model)
-    except KeyError:
-        print('Warning: model not found. Using cl100k_base encoding.')
-        encoding = _cl100k_base_encoding
-    if model == 'gpt-3.5-turbo-0301':
-        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
-        tokens_per_name = -1  # if there's a name, the role is omitted
-    elif model.startswith(('gpt-3.5', 'gpt-35', 'gpt-4')):
-        tokens_per_message = 3
-        tokens_per_name = 1
-    else:
-        tokens_per_message = 3
-        tokens_per_name = 1
-    num_tokens = 0
-    if isinstance(inputs, List):
-        for message in inputs:
-            num_tokens += tokens_per_message
-            for key, value in message.items():
-                num_tokens += len(encoding.encode(value))
-                if key == 'name':
-                    num_tokens += tokens_per_name
-    elif isinstance(inputs, LLMResult):
-        for inner_generations in inputs.generations:
-            for generation in inner_generations:
-                if isinstance(generation, ChatGeneration):
-                    num_tokens += len(encoding.encode(generation.message.type)) + len(encoding.encode(generation.message.content))
-                elif isinstance(generation, Generation):
-                    num_tokens += len(encoding.encode('ai')) + len(encoding.encode(generation.text))
-    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
-    return num_tokens
-
-
+from typing import List
 
 _startswith = 'fornax_prompt_tag'
 

cozeloop/internal/version.py

@@ -1,4 +1,4 @@
 # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
 # SPDX-License-Identifier: MIT
 
-VERSION = 'v0.1.26'
+VERSION = 'v0.1.27'

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "cozeloop"
-version = "0.1.26"
+version = "0.1.27"
 description = "coze loop sdk"
 authors = ["JiangQi715 <jiangqi.rrt@bytedance.com>"]
 license = "MIT"