fix reasoning pipeline doc (add dataflow init guidance) (#130)

Author: scuuy

docs/en/notes/guide/pipelines/ReasoningPipeline.md

@@ -24,7 +24,54 @@ The main processes of the pipeline include:
 2. **Answer Generation and Processing**: Processing based on standard answers or model-generated answers for problems, including format filtering, length filtering, and correctness verification.
 3. **Data Deduplication**: Deduplicating generated question-answer data to ensure dataset quality.
 
-## 2. Data Flow and Pipeline Logic
+## 2. Quick Start
+
+### Step 1: Install DataFlow Environment
+```shell
+pip install open-dataflow
+```
+
+### Step 2: Create New DataFlow Working Directory
+```shell
+mkdir run_dataflow
+cd run_dataflow
+```
+
+### Step 3: Initialize DataFlow
+```shell
+dataflow init
+```
+You will see:
+```shell
+run_dataflow/pipelines/api_pipelines/reasoning_math_pipeline.py  
+```
+
+### Step 4: Configure API Key and API URL
+For Linux and Mac OS:
+```shell
+export DF_API_KEY="sk-xxxxx"
+```
+
+For Windows:
+```powershell
+$env:DF_API_KEY = "sk-xxxxx"
+```
+Configure the api_url in `reasoning_general_pipeline.py` as follows:
+```python
+self.llm_serving = APILLMServing_request(
+        api_url="https://api.openai.com/v1/chat/completions",
+        model_name="gpt-4o",
+        max_workers=100
+)
+```
+
+### Step 5: One-Click Execution
+```bash
+python pipelines/api_pipelines/reasoning_math_pipeline.py
+```
+Additionally, you can choose to run any other Pipeline code according to your needs, and the execution method is similar. Next, we will introduce the operators used in the Pipeline and how to configure parameters.
+
+## 3. Data Flow and Pipeline Logic
 
 ### 1. **Input Data**
 
@@ -240,21 +287,6 @@ Finally, the output data generated by the pipeline will contain the following co
 * **primary\_category**: Primary category of the problem
 * **secondary\_category**: Secondary category of the problem
 
-## 3. Execution Methods
-
-The pipeline executes different configurations through simple Python commands to meet different data needs:
-
-* **Strong reasoning instruction fine-tuning data synthesis**:
-
-  ```bash
-  python test/test_reasoning.py
-  ```
-
-* **Large-scale pretraining data synthesis**:
-
-  ```bash
-  python test/test_reasoning_pretrain.py
-  ```
 
 ## 4. Pipeline Example
 
@@ -371,7 +403,7 @@ class ReasoningPipeline():
         )
 
 if __name__ == "__main__":
-    model = ReasoningPipeline()
-    model.forward()
+    pl = ReasoningPipeline()
+    pl.forward()
 ```
 

docs/zh/notes/guide/pipelines/ReasoningPipeline.md

@@ -24,7 +24,54 @@ permalink: /zh/guide/reasoningpipeline/
 2. **答案生成与处理**：根据问题的标准答案或模型生成的答案进行处理，包括格式过滤、长度过滤和正确性验证等。
 3. **数据去重**：对生成的问答数据进行去重，确保数据集的质量。
 
-## 2. 数据流与流水线逻辑
+## 2. 快速开始
+
+### 第一步：安装dataflow环境
+```shell
+pip install open-dataflow
+```
+
+### 第二步：创建新的dataflow工作文件夹
+```shell
+mkdir run_dataflow
+cd run_dataflow
+```
+
+### 第三步：初始化Dataflow
+```shell
+dataflow init
+```
+这时你会看见
+```shell
+run_dataflow/pipelines/api_pipelines/reasoning_math_pipeline.py  
+```
+
+### 第四步：填入你的api key以及api_url
+对于Linux和Mac OS
+```shell
+export DF_API_KEY="sk-xxxxx"
+```
+
+对于Windows
+```powershell
+$env:DF_API_KEY = "sk-xxxxx"
+```
+`reasoning_general_pipeline.py`中的api_url填写方式如下：
+```python
+self.llm_serving = APILLMServing_request(
+        api_url="https://api.openai.com/v1/chat/completions",
+        model_name="gpt-4o",
+        max_workers=100
+)
+```
+
+### 第五步：一键运行
+```bash
+python pipelines/api_pipelines/reasoning_math_pipeline.py
+```
+此外，你可以根据自己的需求选择任意其他的Pipeline代码运行，其运行方式都是类似的。接下来，我们会介绍在Pipeline中使用到的算子以及如何进行参数配置。
+
+## 3. 数据流与流水线逻辑
 
 ### 1. **输入数据**
 
@@ -240,21 +287,6 @@ ngram_filter = ReasoningAnswerNgramFilter(
 * **primary\_category**：问题的主要类别
 * **secondary\_category**：问题的次要类别
 
-## 3. 运行方式
-
-该流水线通过简单的Python命令执行不同的配置，满足不同的数据需求：
-
-* **强推理指令微调数据合成**：
-
-  ```bash
-  python test/test_reasoning.py
-  ```
-
-* **大规模预训练数据合成**：
-
-  ```bash
-  python test/test_reasoning_pretrain.py
-  ```
 
 ## 4. 流水线示例
 以下给出示例流水线，演示如何使用多个算子进行推理数据处理。该示例展示了如何初始化一个推理数据处理流水线，并且顺序执行各个过滤和清理步骤。
@@ -370,7 +402,7 @@ class ReasoningPipeline():
         )
 
 if __name__ == "__main__":
-    model = ReasoningPipeline()
-    model.forward()
+    pl = ReasoningPipeline()
+    pl.forward()
 ```