fix:异步执行

.idea/dataSources.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name=".langchain" uuid="6ed2f640-6bd4-426b-bab0-933c1e5074e0">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:$PROJECT_DIR$/00-chartgpt/chainlit/.chainlit/.langchain.db</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+  </component>
+</project>
\ No newline at end of file

00-chartgpt/chainlit/app.py

+from langchain import PromptTemplate, LLMChain
+from langchain.chat_models import ChatOpenAI
+import chainlit as cl
+import os
+
+# os.environ["OPENAI_API_KEY"] = '70846324f01c4e5cb3cc11da28a1e091'
+# os.environ["OPENAI_API_KEY"] = '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'
+# os.environ["OPENAI_API_KEY"] = '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'
+# os.environ["OPENAI_API_KEY"] = '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'
+os.environ["OPENAI_API_KEY"] = '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'
+
+template = """Question: {question}
+Answer: Let's think step by step."""
+
+
+@cl.langchain_factory(use_async=False)
+def factory():
+    prompt = PromptTemplate(template=template, input_variables=["question"])
+    llm_chain = LLMChain(prompt=prompt, llm=ChatOpenAI(temperature=0, streaming=True))
+    return llm_chain

00-chartgpt/chainlit/azure_demo.py

+import os
+import chainlit as cl
+
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import (
+    HumanMessage,
+    SystemMessage
+)
+
+# 公司的key
+os.environ["OPENAI_API_KEY"] = '70846324f01c4e5cb3cc11da28a1e091'
+os.environ["OPENAI_API_BASE"] = 'https://opencatgpt.openai.azure.com/'
+os.environ["OPENAI_API_TYPE"] = 'azure'
+os.environ["OPENAI_API_VERSION"] = '2023-05-15'
+
+chat = ChatOpenAI(model_name="gpt-35-turbo", engine="gpt-35-turbo")
+history = [SystemMessage(content="你是一个聊天机器人，请回答下列问题。\n")]
+
+
+@cl.on_message  # this function will be called every time a user inputs a message in the UI
+async def main(message: str):
+    # history = [SystemMessage(content="你是一个聊天机器人，请回答下列问题。\n")]
+    history.append(HumanMessage(content=message))
+    res = await cl.make_async(sync_func)()
+    # res = chat(history)
+    print(res.content)
+    # this is an intermediate step
+    # await cl.Message(author="Tool 1", content=f"Response from tool1", indent=1).send()
+    # send back the final answer
+    history.append(res)
+    await cl.Message(content=f"{res.content}").send()
+
+
+def sync_func():
+    return chat(history)

00-chartgpt/chainlit/chainlit_test_01.py

+import chainlit as cl
+
+
+@cl.on_chat_start
+async def start():
+    file = None
+
+    # Wait for the user to upload a file
+    while files == None:
+        files = await cl.AskFileMessage(
+            content="Please upload a text file to begin!", accept=["text/plain"]
+        ).send()
+    # Decode the file
+    text_file = files[0]
+    text = text_file.content.decode("utf-8")
+
+    # Let the user know that the system is ready
+    await cl.Message(
+        content=f"`{text_file.name}` uploaded, it contains {len(text)} characters!"
+    ).send()

00-chartgpt/chainlit/chainlit_test_02.py

+import chainlit as cl
+
+
+async def some_function():
+    file = await cl.AskFileMessage(
+        content="Please upload a python file to begin!", accept={"text/plain": [".py"]}
+    ).send()
+
+

00-chartgpt/chainlit/demo.py

+import chainlit as cl
+
+
+@cl.on_message  # this function will be called every time a user inputs a message in the UI
+async def main(message: str):
+
+
+    # this is an intermediate step
+    await cl.Message(author="Tool 1", content=f"Response from tool1", indent=1).send()
+
+    # send back the final answer
+    await cl.Message(content=f"This is the final answer").send()

00-chartgpt/chainlit/test_01.py

+import os
+from langchain import PromptTemplate, OpenAI, LLMChain
+import chainlit as cl
+
+# os.environ["OPENAI_API_KEY"] = "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"
+os.environ["OPENAI_API_KEY"] = "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"
+
+template = """Question: {question}
+
+Answer: Let's think step by step."""
+
+
+@cl.langchain_factory(use_async=True)
+def factory():
+    prompt = PromptTemplate(template=template, input_variables=["question"])
+    llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)
+
+    return llm_chain

00-chartgpt/chainlit/test_app.py

+import openai
+import chainlit as cl
+
+openai.api_key = "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"
+# model_name = "text-davinci-003"
+model_name = "gpt-3.5-turbo"
+settings = {
+    "temperature": 0.7,
+    "max_tokens": 500,
+    "top_p": 1,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+}
+
+
+@cl.on_chat_start
+def start_chat():
+    cl.user_session.set(
+        "message_history",
+        [{"role": "system", "content": "You are a helpful assistant."}],
+    )
+
+
+@cl.on_message
+async def main(message: str):
+    message_history = cl.user_session.get("message_history")
+    message_history.append({"role": "user", "content": message})
+
+    msg = cl.Message(content="")
+
+    async for stream_resp in await openai.ChatCompletion.acreate(
+            model=model_name, messages=message_history, stream=True, **settings
+    ):
+        token = stream_resp.choices[0]["delta"].get("content", "")
+        await msg.stream_token(token)
+
+    message_history.append({"role": "assistant", "content": msg.content})
+    await msg.send()

00-chartgpt/chainlit/test_app_01.py

+import openai
+import chainlit as cl
+
+openai.api_key = "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"
+model_name = "text-davinci-003"
+settings = {
+    "temperature": 0.7,
+    "max_tokens": 500,
+    "top_p": 1,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+    "stop": ["```"],
+}
+prompt = """Answer the following question:
+{question}
+"""
+
+
+@cl.on_message
+async def main(message: str):
+    fromatted_prompt = prompt.format(question=message)
+    msg = cl.Message(
+        content="",
+        prompt=fromatted_prompt,
+        llm_settings=cl.LLMSettings(model_name=model_name, **settings),
+    )
+    async for stream_resp in await openai.Completion.acreate(
+            model=model_name, prompt=fromatted_prompt, stream=True, **settings
+    ):
+        token = stream_resp.get("choices")[0].get("text")
+        await msg.stream_token(token)
+    await msg.send()

00-chartgpt/http_01.py

+# 使用urllib发送GET请求的示例代码如下：
+import urllib.request
+
+response = urllib.request.urlopen('https://www.baidu.com/')
+html = response.read()
+print(html)

00-chartgpt/http_02.py

+# 使用requests发送GET请求的示例代码如下：
+import requests
+
+response = requests.get('https://www.baidu.com/')
+print(response.text)

00-chartgpt/http_03.py

+# 对于POST请求，您可以通过向请求函数传递data参数来发送数据，如下所示：
+import urllib.request
+import urllib.parse
+
+url = 'https://www.baidu.com/'
+values = {'name': 'John', 'age': 25}
+data = urllib.parse.urlencode(values)
+data = data.encode('ascii')  # data应该是bytes类型
+req = urllib.request.Request(url, data)
+response = urllib.request.urlopen(req)
+print(response.read())

00-chartgpt/http_04.py

+# 使用requests发送POST请求的示例代码如下：
+import requests
+
+url = 'https://www.baidu.com/'
+values = {'name': 'John', 'age': 25}
+response = requests.post(url, data=values)
+print(response.text)

14_刷题/problem_solving_01.py

+"""
+01.两数之和
+"""
+
 from typing import List
 
+
 class Solution:
 
     def twoSum(self, nums: List[int], target: int) -> List[int]:

14_刷题/problem_solving_02.py

+"""
+26. 删除有序数组中的重复项
+给你一个 升序排列 的数组 nums ，请你 原地 删除重复出现的元素，使每个元素 只出现一次 ，返回删除后数组的新长度。元素的 相对顺序 应该保持 一致 。然后返回 nums 中唯一元素的个数。
+
+考虑 nums 的唯一元素的数量为 k ，你需要做以下事情确保你的题解可以被通过：
+
+更改数组 nums ，使 nums 的前 k 个元素包含唯一元素，并按照它们最初在 nums 中出现的顺序排列。nums 的其余元素与 nums 的大小不重要。
+返回 k 。
+判题标准:
+
+系统会用下面的代码来测试你的题解:
+
+int[] nums = [...]; // 输入数组
+int[] expectedNums = [...]; // 长度正确的期望答案
+
+int k = removeDuplicates(nums); // 调用
+
+assert k == expectedNums.length;
+for (int i = 0; i < k; i++) {
+    assert nums[i] == expectedNums[i];
+}
+如果所有断言都通过，那么您的题解将被 通过。
+
+
+
+示例 1：
+
+输入：nums = [1,1,2]
+输出：2, nums = [1,2,_]
+解释：函数应该返回新的长度 2 ，并且原数组 nums 的前两个元素被修改为 1, 2 。不需要考虑数组中超出新长度后面的元素。
+示例 2：
+
+输入：nums = [0,0,1,1,1,2,2,3,3,4]
+输出：5, nums = [0,1,2,3,4]
+解释：函数应该返回新的长度 5 ， 并且原数组 nums 的前五个元素被修改为 0, 1, 2, 3, 4 。不需要考虑数组中超出新长度后面的元素。
+
+
+提示：
+
+1 <= nums.length <= 3 * 104
+-104 <= nums[i] <= 104
+nums 已按 升序 排列
+"""
+from typing import List
+
+
+class Solution:
+    def removeDuplicates(self, nums: List[int]) -> int:
+        """
+        列表去重后列表的长度
+        :param nums:
+        :return:
+        """
+        result = set(nums)
+        return len(result)
+
+
+if __name__ == '__main__':
+    s = Solution()
+    li = s.removeDuplicates([0, 0, 1, 1, 1, 2, 2, 3, 3, 4])
+    print(li)

main.py

-# This is a sample Python script.
-
-# Press ⌃R to execute it or replace it with your code.
-# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
-
-
-def print_hi(name):
-    # Use a breakpoint in the code line below to debug your script.
-    print(f'Hi, {name}')  # Press ⌘F8 to toggle the breakpoint.
-
-
-# Press the green button in the gutter to run the script.
-if __name__ == '__main__':
-    print_hi('PyCharm')
-
-# See PyCharm help at https://www.jetbrains.com/help/pycharm/