From 2d351f25a569c7491189ce64ba0474eb5de6c05d Mon Sep 17 00:00:00 2001
From: garyzhang99 <garyzhang99@163.com>
Date: Fri, 17 May 2024 14:16:42 +0800
Subject: [PATCH] litellm format doc

---
 .../en/source/tutorial/206-prompt.md          | 49 +++++++++++++++++++
 .../zh_CN/source/tutorial/206-prompt.md       | 40 +++++++++++++++
 2 files changed, 89 insertions(+)

diff --git a/docs/sphinx_doc/en/source/tutorial/206-prompt.md b/docs/sphinx_doc/en/source/tutorial/206-prompt.md
index e30e8abd8..552d70406 100644
--- a/docs/sphinx_doc/en/source/tutorial/206-prompt.md
+++ b/docs/sphinx_doc/en/source/tutorial/206-prompt.md
@@ -300,6 +300,55 @@ print(prompt)
 ]
 ```
 
+
+### LiteLLMChatWrapper
+
+`LiteLLMChatWrapper` encapsulates the litellm chat API, which takes a list of
+messages as input. The litellm support different types of models, and each model
+might need to obey different formats. To simplify the usage, we provide a format
+that could be compatible with most models. If more specifical formats are needed, 
+you can refer to the specifical model you use as weel as the 
+[litellm](https://github.com/BerriAI/litellm) documentation to customize your 
+own format function for your model.
+
+- format all the messages in the chat history, into a single message with `"user"` as `role` 
+
+#### Prompt Strategy
+
+- Messages will consist dialogue history in the `user` message prefixed by the system message and "## Dialogue History".
+
+```python
+from agentscope.models import LiteLLMChatWrapper
+
+model = LiteLLMChatWrapper(
+    config_name="", # empty since we directly initialize the model wrapper
+    model_name="gpt-3.5-turbo",
+)
+
+prompt = model.format(
+  Msg("system", "You are a helpful assistant", role="system"),
+  [
+      Msg("user", "What is the weather today?", role="user"),
+      Msg("assistant", "It is sunny today", role="assistant"),
+  ],
+)
+
+print(prompt)
+```
+
+```bash
+[
+    {
+        "role": "user",
+        "content": (
+            "You are a helpful assistant\n\n"
+            "## Dialogue History\nuser: What is the weather today?\n"
+            "assistant: It is sunny today"
+        ),
+    },
+]
+```
+
 ### OllamaChatWrapper
 
 `OllamaChatWrapper` encapsulates the Ollama chat API, which takes a list of
diff --git a/docs/sphinx_doc/zh_CN/source/tutorial/206-prompt.md b/docs/sphinx_doc/zh_CN/source/tutorial/206-prompt.md
index 7ed143cfe..c2767d902 100644
--- a/docs/sphinx_doc/zh_CN/source/tutorial/206-prompt.md
+++ b/docs/sphinx_doc/zh_CN/source/tutorial/206-prompt.md
@@ -271,6 +271,46 @@ print(prompt)
 ]
 ```
 
+### LiteLLMChatWrapper
+
+`LiteLLMChatWrapper`封装了litellm聊天API，它接受消息列表作为输入。Litellm支持不同类型的模型，每个模型可能需要遵守不同的格式。为了简化使用，我们提供了一种与大多数模型兼容的格式。如果需要更特定的格式，您可以参考您所使用的特定模型以及[litellm](https://github.com/BerriAI/litellm)文档，来定制适合您模型的格式函数。
+- 格式化聊天历史中的所有消息，将其整合成一个以`"user"`作为`role`的单一消息
+#### 提示策略
+- 消息将包括对话历史，`user`消息由系统消息(system message)和"## Dialog History"前缀。
+
+
+```python
+from agentscope.models import LiteLLMChatWrapper
+
+model = LiteLLMChatWrapper(
+    config_name="", # empty since we directly initialize the model wrapper
+    model_name="gpt-3.5-turbo",
+)
+
+prompt = model.format(
+  Msg("system", "You are a helpful assistant", role="system"),
+  [
+      Msg("user", "What is the weather today?", role="user"),
+      Msg("assistant", "It is sunny today", role="assistant"),
+  ],
+)
+
+print(prompt)
+```
+
+```bash
+[
+    {
+        "role": "user",
+        "content": (
+            "You are a helpful assistant\n\n"
+            "## Dialogue History\nuser: What is the weather today?\n"
+            "assistant: It is sunny today"
+        ),
+    },
+]
+```
+
 ### `OllamaChatWrapper`
 
 `OllamaChatWrapper`封装了Ollama聊天API，它接受消息列表作为输入。消息必须遵守以下规则(更新于2024/03/22)：