[Docs] chat format (#595)

* [Docs] chat format * Update chat_format.md
2024-01-17 12:22:09 +08:00 · 2024-01-17 12:22:09 +08:00 · d163169143
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+# Chat Format
+
+English | [简体中文](chat_format_zh-CN.md)
+
+InternLM2-Chat adopts a new chat format to flexibly support a wider range of applications, such as tool invocation, while avoiding user input attacks. This new format is similar to the [ChatML](https://github.com/openai/openai-python/blob/release-v0.28.0/chatml.md) format, but with an added `environment` role to support general-purpose AI applications, in addition to `system`, `user`, and `assistant`.
+
+## Basic Structure
+
+The regular chat structure usually contains three roles: `system`, `user`, and `assistant`, formatted as follows for multi-turn dialogues:
+
+```
+[UNUSED_TOKEN_146]system
+You are InternLM2-Chat, a harmless AI assistant[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]user
+Hello[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]assistant
+Hello, I am InternLM2-Chat, how can I assist you?[UNUSED_TOKEN_145]
+```
+
+Here, `[UNUSED_TOKEN_146]` acts as the start token for each turn of dialogue, and `[UNUSED_TOKEN_145]` as the end token. Each turn of dialogue typically starts with `[UNUSED_TOKEN_146]role` and ends with the model's output `[UNUSED_TOKEN_145]`, where role represents `system`, `user`, `assistant`, and `environment`. Currently, the InternLM2-Chat model's vocabulary also maintains the following mappings:
+
+- `[UNUSED_TOKEN_146]`: Start token for each role's dialogue
+- `[UNUSED_TOKEN_145]`: End token for each role's dialogue
+- `[UNUSED_TOKEN_144]`: Start token for invoking external plugins
+- `[UNUSED_TOKEN_143]`: End token for invoking external plugins
+- `[UNUSED_TOKEN_142]`: Code interpreter
+- `[UNUSED_TOKEN_141]`: External plugins, regular tools
+
+## Complete Structure
+
+The complete dialogue format of InternLM2-Chat, based on the basic structure, also includes designs for general-purpose AI agents. Its core purpose is to use a streaming format that allows the same format to support various types of plugin extensions and AI environments while being compatible with general dialogue. The state of a general AI agent dialogue is shown below:
+
+```
+[UNUSED_TOKEN_146]system
+You are InternLM2-Chat, a harmless AI assistant[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_142]
+You now have access to a Jupyter notebook environment supporting Python code execution. Just send code to python to run in this stateful environment. This feature is suitable for:
+- Data analysis or processing (such as data manipulation and graphic creation)
+- Complex calculations (such as math and physics problems)
+- Programming examples (for understanding programming concepts or language features)
+- Text processing and analysis (including text analysis and natural language processing)
+- Machine learning and data science (model training and data visualization)
+- File operations and data import (handling CSV, JSON, etc. formats)
+[UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_141]
+[
+    {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "unit": {"type": "string"},
+            },
+            "required": ["location"],
+        },
+    }
+]
+[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]user
+Please help me process and visualize this dataset.
+[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]user name=file
+[{"path": "data.csv", size='10K'}][UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]assistant
+I have processed the data and visualized it for you.
+
+[UNUSED_TOKEN_144][UNUSED_TOKEN_142]
+```python
+import plotly.express as px
+import pandas as pd
+
+# Load the data into a pandas dataframe
+df = pd.read_csv('data.csv')
+
+# Create a scatter plot of rainfall vs wind direction
+fig = px.scatter(df, x='WindDir9am', y='Rainfall', color='WindDir3pm',
+                 labels={'WindDir9am': 'Wind Direction 9am', 'Rainfall': '
+
+Rainfall', 'WindDir3pm': 'Wind Direction 3pm'},
+                 title='Rainfall vs Wind Direction',
+                 template='plotly_dark',
+                 width=600, height=500)
+
+# Add a hover effect to show the date
+fig.update_traces(hovertemplate='<b>Date: %{text}</b><br>Wind Direction 9am: %{x}<br>Rainfall: %{y}<br>Wind Direction 3pm: %{marker.color}')
+
+# Show the plot
+fig.show()
+```[UNUSED_TOKEN_143]
+[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]environment name=[UNUSED_TOKEN_142]
+![image](xxx.png)
+[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]assistant
+Since the code output is not included here, I cannot provide specific chart content. However, if the code executed correctly, it should display a polar plot with two filled areas representing the relationship between wind direction at 9 am and rainfall, and between wind direction at 3 pm and rainfall, respectively. The values for each direction are based on the average rainfall calculated from the provided dataset. The chart should have a clear title, a legend, and be intuitive for comparing rainfall with different wind directions. Given the use of a dark theme, the overall appearance of the chart should be bright lines and filled areas on a dark background.[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]user
+Can you explain the code?[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]assistant
+The code uses Python's Plotly library to create a polar plot that shows the relationship between wind direction and rainfall. It starts by creating a Plotly Figure object. Then, I added two `Scatterpolar` traces. The first trace shows the relationship between wind direction at 9 am and average rainfall, and the second trace shows the same for 3 pm. In these traces, the `r` parameter represents the average rainfall, and `theta` represents the wind direction. `fill='toself'` means the shape is filled to its own boundary. Next, I updated the chart layout, hid the radial axis, added a legend, set the title and its font, chose a dark theme, and set the chart's background color, width, and height. Finally, the chart was displayed using `fig.show()`.
+[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]user
+I want to know today's weather in Shanghai[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_144][UNUSED_TOKEN_141]
+{"name": "get_current_weather", "parameters": {"location": "Shanghai"}}[UNUSED_TOKEN_143]
+```
--- a/chat/chat_format_zh-CN.md
+++ b/chat/chat_format_zh-CN.md
@ -33,6 +33,14 @@ InternLM2-Chat 的完整对话格式在上述基本结构的基础上还包含
 ```
 [UNUSED_TOKEN_146]system
 你是书生浦语2，一个无害的人工智能助手[UNUSED_TOKEN_145]
+[UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_142]
+你现在可以使用一个支持 Python 代码执行的 Jupyter 笔记本环境。只需向 python 发送代码，即可在这个有状态环境中进行运行。这个功能适用于:
+- 数据分析或处理（如数据操作和图形制作）
+- 复杂计算（如数学和物理问题）
+- 编程示例（用于理解编程概念或语言特性）
+- 文本处理和分析（包括文本分析和自然语言处理）
+- 机器学习和数据科学（模型训练和数据可视化展示）
+- 文件操作和数据导入（处理CSV、JSON等格式文件）
 [UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_141]
 [
    {
@ -56,7 +64,7 @@ InternLM2-Chat 的完整对话格式在上述基本结构的基础上还包含
 请帮我对该数据集进行数据处理并可视化。
 [UNUSED_TOKEN_145]
 [UNUSED_TOKEN_146]user name=file
-{"path": "data.csv"}[UNUSED_TOKEN_145]
+[{"path": "data.csv", size='10K'}][UNUSED_TOKEN_145]
 [UNUSED_TOKEN_146]assistant
 我已经帮您处理了数据并进行了可视化。

@ -83,6 +91,7 @@ fig.show()
 ```[UNUSED_TOKEN_143]
 [UNUSED_TOKEN_145]
 [UNUSED_TOKEN_146]environment name=[UNUSED_TOKEN_142]
+![image](xxx.png)
 [UNUSED_TOKEN_145]
 [UNUSED_TOKEN_146]assistant
 由于代码输出没有包含在这里，我无法提供具体的图表内容。但是，如果代码执行无误，它会显示一个极坐标图，其中包含两个填充区域，一个代表上午9点的风向与降雨量的关系，另一个代表下午3点的风向与降雨量的关系。每个方向上的值是根据提供的数据集计算得到的平均降雨量。图表应该有一个清晰的标题、图例，并且能够直观地比较不同风向下的降雨量。由于使用了暗色主题，图表的整体外观应该是深色背景上的亮色线条和填充区域。[UNUSED_TOKEN_145]