fix file url for func call ops (#155)

Author: MOLYHECI

docs/en/notes/guide/domain_specific_operators/funccall_operators.md

@@ -10,7 +10,7 @@ permalink: /en/guide/qdq6vy95/
 
 Function call data synthesis operators are designed to synthesize structured function call data from dialogues or real-world task descriptions. These operators cover scenario extraction and expansion, task generation and validation, function generation, and multi-agent multi-turn conversation generation.
 
-All related operators are located in [dataflow/operators/conversations/func_call_operators.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/conversations/func_call_operators.py). The table below summarizes their applicable scenarios:
+All related operators are located in [dataflow/operators/conversations/](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/conversations/). The table below summarizes their applicable scenarios:
 
 <table class="tg">
   <thead>
@@ -249,4 +249,4 @@ Simulates multi-turn conversations involving User, Assistant, and Tool agents to
 
 ---
 
-For code examples, refer to the [Function Call Data Synthesis Pipeline](https://opendcai.github.io/DataFlow-Doc/en/guide/e6kz1s79/) or the [GitHub source file](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/conversations/func_call_operators.py).
+For code examples, refer to the [Function Call Data Synthesis Pipeline](https://opendcai.github.io/DataFlow-Doc/en/guide/e6kz1s79/) or the [GitHub source file](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/conversations/).

docs/en/notes/guide/quickstart/quick_general_text_evaluation.md

@@ -84,7 +84,7 @@ After running, the pipeline will invoke `MetaScorer`, which multi-dimensional da
   * `description`: Detailed description of the dimension
   * `example_list`: List of examples, where each follows a format like `{"text": "some text", "score": 1}`
 
-The dimensions used in the sample pipeline are listed in [dataflow/operators/eval/GeneralText/APIcaller/meta\_scorer.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/eval/GeneralText/APIcaller/meta_scorer.py):
+The dimensions used in the sample pipeline are listed in [dataflow/operators/text_pt/eval/meta_sample_evaluator.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/text_pt/eval/meta_sample_evaluator.py):
 
 ```python
 example_dimensions = [
@@ -179,7 +179,7 @@ example_dimensions = [
 
 The full implementation of the operator is located in the following files:
 
-* [dataflow/operators/eval/GeneralText/APIcaller/meta\_scorer.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/eval/GeneralText/APIcaller/meta_scorer.py)
+* [dataflow/operators/text_pt/eval/meta_sample_evaluator.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/text_pt/eval/meta_sample_evaluator.py)
 
 
 ### Output

docs/zh/notes/guide/domain_specific_operators/funccall_operators.md

@@ -8,7 +8,7 @@ permalink: /zh/guide/kgdzd34m/
 
 ## 概述
 
-函数调用数据合成算子是专门用于从对话或现实场景合成函数调用数据的算子集合，这些算子主要包括了真实任务场景的提取和生成，任务的生成与验证，函数生成，多智能体多轮对话生成等功能。这些算子位于[dataflow/operators/conversations/func_call_operators.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/conversations/func_call_operators.py)文件中，其适用场景可以参考下面的表格：
+函数调用数据合成算子是专门用于从对话或现实场景合成函数调用数据的算子集合，这些算子主要包括了真实任务场景的提取和生成，任务的生成与验证，函数生成，多智能体多轮对话生成等功能。这些算子位于[dataflow/operators/conversations](https://github.com/OpenDCAI/DataFlow/tree/main/dataflow/operators/conversations)文件夹中，其适用场景可以参考下面的表格：
 
 <table class="tg">
   <thead>
@@ -244,4 +244,4 @@ permalink: /zh/guide/kgdzd34m/
 - 多智能体对话模拟  
 - 支持函数嵌入与调用结构  
 
-算子的示例代码请参考文档[函数调用数据合成流水线](https://opendcai.github.io/DataFlow-Doc/zh/guide/hy4417i0/)或[GitHub仓库](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/conversations/func_call_operators.py)
+算子的示例代码请参考文档[函数调用数据合成流水线](https://opendcai.github.io/DataFlow-Doc/zh/guide/hy4417i0/)或[GitHub仓库](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/conversations/)

docs/zh/notes/guide/quickstart/quick_general_text_evaluation.md

@@ -66,7 +66,7 @@ python quick_evaluate.py
     * `description`: 维度的具体描述
     * `example_list`: 示例列表，其中每个示例都遵循类似于`{"text": "some text", "score": 1}`的字典格式。
 
-下面是示例流水线中用到的维度列表，位于[dataflow/operators/eval/GeneralText/APIcaller/meta_scorer.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/eval/GeneralText/APIcaller/meta_scorer.py)中：
+下面是示例流水线中用到的维度列表，位于[dataflow/operators/text_pt/eval/meta_sample_evaluator.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/text_pt/eval/meta_sample_evaluator.py)中：
 ```python
 example_dimensions = [
     {
@@ -157,7 +157,7 @@ example_dimensions = [
 ```
 
 ### 算子逻辑
-算子的完整实现位于[dataflow/operators/eval/GeneralText/APIcaller/meta_scorer.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/eval/GeneralText/APIcaller/meta_scorer.py)文件中。
+算子的完整实现位于[dataflow/operators/text_pt/eval/meta_sample_evaluator.py](https://github.com/OpenDCAI/DataFlow/blob/main/dataflow/operators/text_pt/eval/meta_sample_evaluator.py)文件中。
 
 ### 输出
 运行脚本执行完毕后，可在工作区的`playground/cache/`文件夹下观察到脚本的运行结果，下面是示例数据中的一个例子