diff --git a/selected_homework/role-play/README.md b/selected_homework/role-play/README.md
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+++ b/selected_homework/role-play/README.md
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+### 实现 role-play 对话数据生成工具，要求包含下列功能：
+
++ 给定两个角色的人设，调用 CharacterGLM 交替生成他们的回复。
+
+streamlit run --server.address 127.0.0.1 characterglm_api_demo_streamlit.py
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diff --git a/selected_homework/role-play/api.py b/selected_homework/role-play/api.py
new file mode 100644
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--- /dev/null
+++ b/selected_homework/role-play/api.py
@@ -0,0 +1,183 @@
+import requests
+import time
+import os
+from typing import Generator
+
+import jwt
+
+from data_types import TextMsg, ImageMsg, TextMsgList, MsgList, CharacterMeta
+
+
+# 智谱开放平台API key，参考 https://open.bigmodel.cn/usercenter/apikeys
+os.environ["API_KEY"] = "04559ca20c345d282e76d07238d03ad9.PfCA5xBWPWBGHnzY"
+API_KEY: str = os.getenv("API_KEY", "c175ae7f0ba6a73eb22e4ec0cddcdcc9.55fbjTiX2iacE9wG")
+
+
+class ApiKeyNotSet(ValueError):
+    pass
+
+
+def verify_api_key_not_empty():
+    if not API_KEY:
+        raise ApiKeyNotSet
+
+
+def generate_token(apikey: str, exp_seconds: int) -> str:
+    # reference: https://open.bigmodel.cn/dev/api#nosdk
+    try:
+        id, secret = apikey.split(".")
+    except Exception as e:
+        raise Exception("invalid apikey", e)
+ 
+    payload = {
+        "api_key": id,
+        "exp": int(round(time.time() * 1000)) + exp_seconds * 1000,
+        "timestamp": int(round(time.time() * 1000)),
+    }
+ 
+    return jwt.encode(
+        payload,
+        secret,
+        algorithm="HS256",
+        headers={"alg": "HS256", "sign_type": "SIGN"},
+    )
+
+
+def get_characterglm_response(messages: TextMsgList, meta: CharacterMeta) -> Generator[str, None, None]:
+    """ 通过http调用characterglm """
+    # Reference: https://open.bigmodel.cn/dev/api#characterglm
+    verify_api_key_not_empty()
+    url = "https://open.bigmodel.cn/api/paas/v3/model-api/charglm-3/sse-invoke"
+    resp = requests.post(
+        url,
+        headers={"Authorization": generate_token(API_KEY, 1800)},
+        json=dict(
+            model="charglm-3",
+            meta=meta,
+            prompt=messages,
+            incremental=True)
+    )
+    resp.raise_for_status()
+    
+    # 解析响应（非官方实现）
+    sep = b':'
+    last_event = None
+    for line in resp.iter_lines():
+        if not line or line.startswith(sep):
+            continue
+        field, value = line.split(sep, maxsplit=1)
+        if field == b'event':
+            last_event = value
+        elif field == b'data' and last_event == b'add':
+            yield value.decode()
+
+
+def get_characterglm_response_via_sdk(messages: TextMsgList, meta: CharacterMeta) -> Generator[str, None, None]:
+    """ 通过旧版sdk调用characterglm """
+    # 与get_characterglm_response等价
+    # Reference: https://open.bigmodel.cn/dev/api#characterglm
+    # 需要安装旧版sdk，zhipuai==1.0.7
+    import zhipuai
+    verify_api_key_not_empty()
+    zhipuai.api_key = API_KEY
+    response = zhipuai.model_api.sse_invoke(
+        model="charglm-3",
+        meta= meta,
+        prompt= messages,
+        incremental=True
+    )
+    for event in response.events():
+        if event.event == 'add':
+            yield event.data
+
+
+def get_chatglm_response_via_sdk(messages: TextMsgList) -> Generator[str, None, None]:
+    """ 通过sdk调用chatglm """
+    # reference: https://open.bigmodel.cn/dev/api#glm-3-turbo  `GLM-3-Turbo`相关内容
+    # 需要安装新版zhipuai
+    from zhipuai import ZhipuAI
+    verify_api_key_not_empty()
+    client = ZhipuAI(api_key=API_KEY) # 请填写您自己的APIKey
+    response = client.chat.completions.create(
+        model="glm-3-turbo",  # 填写需要调用的模型名称
+        messages=messages,
+        stream=True,
+    )
+    for chunk in response:
+        yield chunk.choices[0].delta.content
+
+
+def generate_role_appearance(role_profile: str) -> Generator[str, None, None]:
+    """ 用chatglm生成角色的外貌描写 """
+    
+    instruction = f"""
+请从下列文本中，抽取人物的外貌描写。若文本中不包含外貌描写，请你推测人物的性别、年龄，并生成一段外貌描写。要求：
+1. 只生成外貌描写，不要生成任何多余的内容。
+2. 外貌描写不能包含敏感词，人物形象需得体。
+3. 尽量用短语描写，而不是完整的句子。
+4. 不要超过50字
+
+文本：
+{role_profile}
+"""
+    return get_chatglm_response_via_sdk(
+        messages=[
+            {
+                "role": "user",
+                "content": instruction.strip()
+            }
+        ]
+    )
+
+
+def generate_chat_scene_prompt(messages: TextMsgList, meta: CharacterMeta) -> Generator[str, None, None]:
+    """ 调用chatglm生成cogview的prompt，描写对话场景 """
+    instruction = f"""
+阅读下面的角色人设与对话，生成一段文字描写场景。
+
+{meta['bot_name_1']}的人设：
+{meta['bot_info_1']}
+    """.strip()
+    
+    if meta["bot_info_2"]:
+        instruction += f"""
+
+{meta["bot_name_2"]}的人设：
+{meta["bot_info_2"]}
+""".rstrip()
+
+    if messages:
+        instruction += "\n\n对话：" + '\n'.join((meta['bot_name_1'] if msg['role'] == "user_1" else meta['bot_name_2']) + '：' + msg['content'].strip() for msg in messages)
+    
+    instruction += """
+    
+要求如下：
+1. 只生成场景描写，不要生成任何多余的内容
+2. 描写不能包含敏感词，人物形象需得体
+3. 尽量用短语描写，而不是完整的句子
+4. 不要超过50字
+""".rstrip()
+    print(instruction)
+    
+    return get_chatglm_response_via_sdk(
+        messages=[
+            {
+                "role": "user",
+                "content": instruction.strip()
+            }
+        ]
+    )
+
+
+def generate_cogview_image(prompt: str) -> str:
+    """ 调用cogview生成图片，返回url """
+    # reference: https://open.bigmodel.cn/dev/api#cogview
+    from zhipuai import ZhipuAI
+    client = ZhipuAI(api_key=API_KEY) # 请填写您自己的APIKey
+    
+    response = client.images.generations(
+        model="cogview-3", #填写需要调用的模型名称
+        prompt=prompt
+    )
+    return response.data[0].url
+
diff --git a/selected_homework/role-play/characterglm_api_demo_streamlit.py b/selected_homework/role-play/characterglm_api_demo_streamlit.py
new file mode 100644
index 00000000..31b062b1
--- /dev/null
+++ b/selected_homework/role-play/characterglm_api_demo_streamlit.py
@@ -0,0 +1,215 @@
+"""
+一个简单的demo，调用CharacterGLM实现角色扮演，调用CogView生成图片，调用ChatGLM生成CogView所需的prompt。
+
+依赖：
+pyjwt
+requests
+streamlit
+zhipuai
+python-dotenv
+
+运行方式：
+```bash
+streamlit run characterglm_api_demo_streamlit.py
+```
+"""
+import os
+import itertools
+from typing import Iterator, Optional
+
+import streamlit as st
+from dotenv import load_dotenv
+
+# 通过.env文件设置环境变量
+# reference: https://github.com/theskumar/python-dotenv
+load_dotenv()
+
+import api
+from api import get_characterglm_response
+from data_types import TextMsg, filter_text_msg
+
+st.set_page_config(page_title="CharacterGLM API Demo", page_icon="🤖", layout="wide")
+debug = os.getenv("DEBUG", "").lower() in ("1", "yes", "y", "true", "t", "on")
+
+
+def update_api_key(key: Optional[str] = None):
+    if debug:
+        print(f'update_api_key. st.session_state["API_KEY"] = {st.session_state["API_KEY"]}, key = {key}')
+    key = key or st.session_state["API_KEY"]
+    if key:
+        api.API_KEY = key
+
+
+# 设置API KEY
+api_key = st.sidebar.text_input("API_KEY", value=os.getenv("API_KEY", ""), key="API_KEY", type="password",
+                                on_change=update_api_key)
+update_api_key(api_key)
+
+# 初始化
+if "history" not in st.session_state:
+    st.session_state["history"] = []
+if "meta" not in st.session_state:
+    st.session_state["meta"] = {
+        "user_info": "",
+        "bot_info": "",
+        "bot_name": "",
+        "user_name": ""
+    }
+
+
+def init_session():
+    st.session_state["history"] = []
+
+
+# 4个输入框，设置meta的4个字段
+meta_labels = {
+    "bot_name": "角色名",
+    "user_name": "用户名",
+    "bot_info": "角色人设",
+    "user_info": "用户人设"
+}
+
+
+# 2x2 layout
+with st.container():
+    col1, col2 = st.columns(2)
+    with col1:
+        st.text_input(label="角色名", value="小白", key="bot_name",
+                      on_change=lambda: st.session_state["meta"].update(bot_name=st.session_state["bot_name"]),
+                      help="模型所扮演的角色的名字，不可以为空")
+        st.text_area(label="角色人设", value="善良", key="bot_info",
+                     on_change=lambda: st.session_state["meta"].update(bot_info=st.session_state["bot_info"]),
+                     help="角色的详细人设信息，不可以为空")
+
+    with col2:
+        st.text_input(label="用户名", value="小黑", key="user_name",
+                      on_change=lambda: st.session_state["meta"].update(user_name=st.session_state["user_name"]),
+                      help="用户的名字，默认为小黑")
+        st.text_area(label="用户人设", value="内向", key="user_info",
+                     on_change=lambda: st.session_state["meta"].update(user_info=st.session_state["user_info"]),
+                     help="用户的详细人设信息，可以为空")
+
+
+def verify_meta() -> bool:
+    # 检查`角色名`和`角色人设`是否空，若为空，则弹出提醒
+    if st.session_state["meta"]["bot_name"] == "" or st.session_state["meta"]["bot_info"] == "":
+        st.session_state["meta"]["bot_name"] = "小白"
+        st.session_state["meta"]["bot_info"] = "善良"
+        return True
+    else:
+        return True
+
+
+button_labels = {
+    "clear_meta": "清空人设",
+    "clear_history": "清空对话历史",
+    "gen_picture": "生成图片"
+}
+if debug:
+    button_labels.update({
+        "show_api_key": "查看API_KEY",
+        "show_meta": "查看meta",
+        "show_history": "查看历史"
+    })
+
+# 在同一行排列按钮
+with st.container():
+    n_button = len(button_labels)
+    cols = st.columns(n_button)
+    button_key_to_col = dict(zip(button_labels.keys(), cols))
+
+    with button_key_to_col["clear_meta"]:
+        clear_meta = st.button(button_labels["clear_meta"], key="clear_meta")
+        if clear_meta:
+            st.session_state["meta"] = {
+                "user_info": "",
+                "bot_info": "",
+                "bot_name": "",
+                "user_name": ""
+            }
+            st.rerun()
+
+    with button_key_to_col["clear_history"]:
+        clear_history = st.button(button_labels["clear_history"], key="clear_history")
+        if clear_history:
+            init_session()
+            st.rerun()
+
+    if debug:
+        with button_key_to_col["show_api_key"]:
+            show_api_key = st.button(button_labels["show_api_key"], key="show_api_key")
+            if show_api_key:
+                print(f"API_KEY = {api.API_KEY}")
+
+        with button_key_to_col["show_meta"]:
+            show_meta = st.button(button_labels["show_meta"], key="show_meta")
+            if show_meta:
+                print(f"meta = {st.session_state['meta']}")
+
+        with button_key_to_col["show_history"]:
+            show_history = st.button(button_labels["show_history"], key="show_history")
+            if show_history:
+                print(f"history = {st.session_state['history']}")
+
+# 展示对话历史
+for msg in st.session_state["history"]:
+    if msg["role"] == "user":
+        with st.chat_message(name="user", avatar="user"):
+            st.markdown(msg["content"])
+    elif msg["role"] == "assistant":
+        with st.chat_message(name="assistant", avatar="assistant"):
+            st.markdown(msg["content"])
+    else:
+        raise Exception("Invalid role")
+
+
+with st.chat_message(name="user", avatar="user"):
+    message_placeholder_1 = st.empty()
+with st.chat_message(name="assistant", avatar="assistant"):
+    message_placeholder_2 = st.empty()
+
+
+def output_stream_response(response_stream: Iterator[str], placeholder):
+    content = ""
+    for content in itertools.accumulate(response_stream):
+        placeholder.markdown(content)
+    return content
+
+
+def start_chat():
+    # query = st.chat_input("开始对话吧")
+    query = '你好, 我是小白'
+    role_play = "user"
+    if not query:
+        return
+    else:
+        for i in range(10):
+            if not verify_meta():
+                return
+            if not api.API_KEY:
+                st.error("未设置API_KEY")
+
+            if role_play == "user":
+                message_placeholder_1.markdown(query)
+                place_holder = message_placeholder_2
+            else:
+                message_placeholder_2.markdown(query)
+                place_holder = message_placeholder_1
+            st.session_state["history"].append(TextMsg({"role": role_play, "content": query}))
+
+            response_stream = get_characterglm_response(filter_text_msg(st.session_state["history"]),
+                                                        meta=st.session_state["meta"])
+            bot_response = output_stream_response(response_stream, place_holder)
+            print(bot_response)
+            if not bot_response:
+                message_placeholder_2.markdown("生成出错")
+                st.session_state["history"].pop()
+            # else:
+            #     st.session_state["history"].append(TextMsg({"role": "assistant", "content": bot_response}))
+            if role_play == "user":
+                role_play = "assistant"
+            else:
+                role_play = "user"
+            query = bot_response
+
+start_chat()
diff --git a/selected_homework/role-play/data_types.py b/selected_homework/role-play/data_types.py
new file mode 100644
index 00000000..fe9c72e2
--- /dev/null
+++ b/selected_homework/role-play/data_types.py
@@ -0,0 +1,51 @@
+"""
+相关数据类型的定义
+"""
+from typing import Literal, TypedDict, List, Union, Optional, TYPE_CHECKING
+if TYPE_CHECKING:
+    import streamlit.elements.image
+
+class BaseMsg(TypedDict):
+    pass
+
+
+class TextMsg(BaseMsg):
+    """文本消息"""
+    
+    # 在类属性标注的下一行用三引号注释，vscode中
+    role: Literal["user", "assistant"]
+    """消息来源"""
+    content: str
+    """消息内容"""
+
+
+class ImageMsg(BaseMsg):
+    """图片消息"""
+    role: Literal["image"]
+    image: "streamlit.elements.image.ImageOrImageList"
+    """图片内容"""
+    caption: Optional[Union[str, List[str]]]
+    """说明文字"""
+
+
+Msg = Union[TextMsg, ImageMsg]
+TextMsgList = List[TextMsg]
+MsgList = List[Msg]
+
+
+class CharacterMeta(TypedDict):
+    """角色扮演设定，它是CharacterGLM API所需的参数"""
+    user_info: str
+    user_name: str
+
+
+def filter_text_msg(messages: MsgList) -> TextMsgList:
+    return [m for m in messages if m["role"] != "image"]
+
+
+if __name__ == "__main__":
+    # 尝试在VSCode等IDE中自己敲一遍下面的代码，观察IDE能提供哪些代码提示
+    text_msg = TextMsg(role="user")
+    text_msg["content"] = "42"
+    print(type(text_msg))
+    print(text_msg)
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diff --git "a/selected_homework/\346\257\225\344\270\232\346\200\273\347\273\223.md" "b/selected_homework/\346\257\225\344\270\232\346\200\273\347\273\223.md"
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+++ "b/selected_homework/\346\257\225\344\270\232\346\200\273\347\273\223.md"
@@ -0,0 +1,8 @@
+目前还是一个普通的后端开发工程师，公司对于我提出的各种技术变化，给出的回应只有会议上的沉默和点头。
+所以自身还是求变的我，选择了参加AI训练营，希望借此了解更多更好玩的，带来更多便捷的技术。更希望借此了解的技术，能够突破自己的瓶颈。
+
+通过彭老师的教授，学习了关于openAI、LangChain等相关的知识结构和应用。
+这部分虽然还没有在工作中找到实际使用的地方，但相信通过此次学习，能够对我将来的工作带来很多很多帮助。
+
+感谢彭老师、感谢群里的助教豆浆（私下麻烦了他很多次），也很感谢群里热心的小伙伴～
+希望将来真的有机会从事到AI应用相关的工作～
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