1

api/chatText2ImageCommit.py

@@ -0,0 +1,35 @@
+import requests
+import json
+from tool.getSign import  sign
+
+def getImageCommit(caption):
+  # url = "https://llm-bot-cn.heytapmobi.com/chat/v1/completions"
+  # intput_content = "黑色的 短脚 胖猫"
+
+  payload = json.dumps({
+    "model": "midjourney",
+    "caption": caption,
+    "action": "IMAGINE",
+  })
+
+  app_id = 'auto_58d4be458b124966b29c61fad4186a2c'
+  secret_key = 'ce2aca400cf64be0b582a66fa2362817'
+  Authorization= sign([],payload,app_id,secret_key)
+
+
+  url = "http://llm.oppo.local/image/v1/text2image/commit"
+
+  headers = {
+    'recordId': 'auto_58d4be458b124966b29c61fad4186a2c',
+    'Authorization': Authorization,
+    'Content-Type': 'application/json'
+  }
+
+  response = requests.request("POST", url, headers=headers, data=payload)
+  if response.status_code == 200:
+    res = json.loads(response.text)
+    taskId = res['data']["taskId"]
+  else:
+    print("请求错误")
+
+  return taskId

api/chatText2ImageQuery.py

@@ -0,0 +1,64 @@
+import json
+from tool.getSign import  sign
+from api.chatText2ImageCommit import getImageCommit
+import requests
+import time
+from PIL import Image
+
+app_id = 'auto_58d4be458b124966b29c61fad4186a2c'
+secret_key = 'ce2aca400cf64be0b582a66fa2362817'
+#画图助手
+
+
+class DrawAssistant:
+  def getImageByCaption(self,taskId):
+    time.sleep(10)
+    payload = json.dumps({
+      "model": "midjourney",
+      "taskId": taskId
+    })
+
+    Authorization= sign([],payload,app_id,secret_key)
+    url = "http://llm.oppo.local/image/v1/text2image/query"
+    headers = {
+      'recordId': 'auto_58d4be458b124966b29c61fad4186a2c',
+      'Authorization': Authorization,
+      'Content-Type': 'application/json'
+    }
+
+    response = requests.request("POST", url, headers=headers, data=payload)
+    if response.status_code == 200:
+      res = json.loads(response.text)
+      # progress = res['data']['status']
+      result = res['data']['result']
+    else:
+      print("请求错误")
+      return 0
+
+    # print(progress)
+    if result != {}:
+      image_url = result['contentUrl']
+
+      response = requests.get(image_url)
+      image_data = response.content
+
+      # 保存图片
+      with open('image.png', 'wb') as f:
+        f.write(image_data)
+
+      # 展示图片
+      image = Image.open('image.png')
+      # image.show()
+      return image
+
+    return 0
+
+  def drawOnePic(self,caption):
+    taskId = getImageCommit(caption)
+    time.sleep(20)
+    for i in range(10):
+      res = self.getImageByCaption(taskId)
+      if(res):
+        break
+    return res
+

api/chatV1.py

@@ -0,0 +1,43 @@
+import requests
+import json
+from tool.getSign import  sign
+import tool.keyFile as key
+
+
+# url = "https://llm-bot-cn.heytapmobi.com/chat/v1/completions"
+intput_content = [
+    { "role": "system", "content": "You are a chatbot" },
+    { "role": "user", "content": "我能用中文向你提问题吗？" },
+    { "role": "assistant", "content": "当然可以，我可以用中文回答你的问题。请问你有什么需要帮助的吗？" },
+    { "role": "user", "content": "我想更好的与你交流，必须把每次交流信息告诉你是吗？" },
+    {
+                "role": "assistant",
+                "content": "是的，您需要输入您想要与我交流的信息，我才能理解并回答您的问题。如果您有任何疑问或需要帮助，请随时告诉我。"
+    },
+    { "role": "user", "content": "我上句话说了什么？" }
+  ]
+
+payload = json.dumps({
+  "model": "gpt-4-8k",
+  "messages": intput_content,
+  "n": 1,
+  "topP": 0.1,
+  "stop": None,
+  "maxTokens": 500
+})
+
+
+Authorization= sign([],payload,key.app_id,key.secret_key)
+
+
+url = "http://llm.oppo.local/chat/v1/completions"
+
+headers = {
+  'recordId': 'auto_58d4be458b124966b29c61fad4186a2c',
+  'Authorization': Authorization,
+  'Content-Type': 'application/json'
+}
+
+response = requests.request("POST", url, headers=headers, data=payload)
+
+print(response.text)

app.py

@@ -0,0 +1,115 @@
+import gradio as gr
+
+from sdk.Chat_with_memory import Conversation
+from sdk.culculate import Cuculator
+from sdk.chat_with_net import ChatWithNet
+from api.chatText2ImageQuery import DrawAssistant
+from sdk.textSummary import SummaryAssistant
+from sdk.chatWithKnowledgeBase import KnowledgeBaseAssistant
+import tempfile
+
+chat = Conversation()
+culculator = Cuculator()
+chatWithNet = ChatWithNet()
+drawAssistant = DrawAssistant()
+summaryAssistant = SummaryAssistant()
+KnowledgeBaseAssistant = KnowledgeBaseAssistant()
+
+
+with gr.Blocks() as myAIBox:
+    with gr.Tab("AI聊天助手"):
+        #Blocks特有组件，设置所有子组件按垂直排列
+        #垂直排列是默认情况，不加也没关系
+        with gr.Column():
+            text_input = gr.Textbox(label="请输入你的问题")
+            chatbot = gr.Chatbot()
+            ask = gr.Button("一键答疑")
+            ask.click(chat.ask,text_input,chatbot)
+            clearHistory = gr.Button("清空当前搜索结果")
+            clearHistory.click(chat.clearHistory,None,chatbot, queue=False)
+
+
+    with gr.Tab("智能计算器"):
+        # Blocks特有组件，设置所有子组件按水平排列
+        with gr.Column():
+            math_input = gr.Textbox(label="请输入你的问题")
+            CulChatbot = gr.Chatbot()
+            ask = gr.Button("生成答案")
+            ask.click(culculator.culculate,math_input,CulChatbot)
+            clearHistory = gr.Button("清空当前搜索结果")
+            clearHistory.click(culculator.clearHistory,None,CulChatbot, queue=False)
+
+    with gr.Tab("联网问答"):
+        # Blocks特有组件，设置所有子组件按水平排列
+        with gr.Column():
+            text_input = gr.Textbox(label="请输入你的问题")
+            NetChatbot = gr.Chatbot()
+            ask = gr.Button("谷歌搜索问答")
+            ask.click(chatWithNet.searhAndChat,text_input,NetChatbot)
+            clearHistory = gr.Button("清空当前搜索结果")
+            clearHistory.click(chatWithNet.searhAndChat,None,NetChatbot)
+
+    with gr.Tab("绘图助手"):
+        # Blocks特有组件，设置所有子组件按水平排列
+        with gr.Row():
+            text_input = gr.Textbox(label="请输入你希望画的图片及其细节(生成图片较慢，请耐心等待)")
+            image_output = gr.Image()
+        ask = gr.Button("生成图片")
+        ask.click(drawAssistant.drawOnePic,text_input,image_output )
+        clearPic = gr.Button("清空当前搜索结果")
+        clearPic.click(None,None,image_output, queue=False)
+
+    with gr.Tab("文本总结器"):
+        # Blocks特有组件，设置所有子组件按水平排列
+        global tmpdir
+        with tempfile.TemporaryDirectory() as tmpdir:
+            with gr.Column():
+                with gr.Row():
+                    summary_text_input = gr.Textbox(label="请输入你希望总结的文本")
+                    summary_output = gr.Textbox(label="总结文本")
+                send_btn = gr.Button("对文本生成总结")
+                send_btn.click(summaryAssistant.summary_text,summary_text_input,summary_output)
+
+                with gr.Row():
+                    summary_url_input = gr.Textbox(label="请输入你希望总结的网页")
+                    summary_output2 = gr.Textbox(label="网页地址")
+                send_btn2 = gr.Button("对网页生成总结")
+                send_btn2.click(summaryAssistant.summary_url,summary_url_input,summary_output2)
+
+                with gr.Row():
+                    summary_file_input = gr.components.File(label="请输入你希望总结的文本文件（现在仅支持txt,pdf,word格式）")
+                    summary_output3 = gr.Textbox(label="总结文本")
+                send_btn3 = gr.Button("对文件生成总结")
+                send_btn3.click(summaryAssistant.summary_file,summary_file_input,summary_output3)
+
+    with gr.Tab("个人知识库"):
+        # Blocks特有组件，设置所有子组件按水平排列
+        with gr.Column(scale=4):
+            with gr.Row():
+                knowledge_text_input = gr.Textbox(label="请输入你的知识库的文本")
+                q_input = gr.Textbox(label="请输入你的问题")
+                res_output = gr.Textbox(label="结果输出")
+            s_btn = gr.Button("一键问答")
+            s_btn.click(KnowledgeBaseAssistant.knowledge_base_text,[knowledge_text_input,q_input],res_output)
+
+            with gr.Row():
+                knowledge_URL_input = gr.Textbox(label="请输入你的网页链接")
+                q2_input = gr.Textbox(label="请输入你的问题")
+                res2_output = gr.Textbox(label="结果输出")
+            s2_btn = gr.Button("一键问答")
+            s2_btn.click(KnowledgeBaseAssistant.knowledge_base_url,[knowledge_URL_input,q2_input],res2_output)
+
+            with gr.Row():
+                knowledge_file_input = gr.components.File(label="请输入你的知识库文本（现在仅支持txt,pdf,word格式）")
+                q3_input = gr.Textbox(label="请输入你的问题")
+                res3_output = gr.Textbox(label="结果输出")
+            s3_btn = gr.Button("一键问答")
+            s3_btn.click(KnowledgeBaseAssistant.knowledge_base_file,[knowledge_file_input,q3_input],res3_output)
+
+
+
+if __name__ == "__main__":
+    myAIBox.launch(share=True)
+
+
+

requirements.txt

@@ -0,0 +1,6 @@
+beautifulsoup4==4.12.2
+gradio==3.44.4
+langchain==0.0.301
+langkit==0.1.6
+Pillow==10.0.1
+requests==2.31.0

sdk/CChat.py

@@ -0,0 +1,35 @@
+import langkit.api
+from langkit.api.chat import chat_completion
+import tool.keyFile as key
+
+langkit.api.app_id= key.app_id
+langkit.api.secret_key=key.secret_key
+#连续对话
+class Conversation:
+    def __init__(self, prompt, num_of_round):
+        self.prompt = prompt
+        self.num_of_round = num_of_round
+        self.messages = []
+        self.messages.append({"role": "system", "content": self.prompt})
+
+    def ask(self, question):
+        try:
+            self.messages.append({"role": "user", "content": question})
+
+            ret = chat_completion(model='gpt-4-8k', messages=self.messages)
+        except Exception as e:
+            print(e)
+            return e
+
+        message = ret.data["choices"][0]["message"]
+        self.messages.append(message)
+
+        if len(self.messages) > self.num_of_round*2 + 1:
+            del self.messages[1:3] #Remove the first round conversation left.
+        print(message["content"])
+        return message["content"]
+
+# chat = Conversation("""你是一个中国厨师，用中文回答做菜的问题。你的回答需要满足以下要求: 1. 你的回答必须是中文 2. 回答限制在100个字以内""", 5)
+# chat.ask("宫保鸡丁怎么做")
+# chat.ask("糖醋里脊怎么做")
+# chat.ask("我的第一个问题是什么")

sdk/Chat_with_memory.py

@@ -0,0 +1,59 @@
+from langchain import PromptTemplate
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationSummaryBufferMemory
+from langkit.langchain_helpers import LLMAdaptor
+import tool.keyFile as key
+import langkit.api
+
+langkit.api.app_id= key.app_id
+langkit.api.secret_key=key.secret_key
+
+#连续对话
+class Conversation:
+    def __init__(self):
+        self.SUMMARIZER_TEMPLATE = """请将以下内容逐步概括所提供的对话内容，并将新的概括添加到之前的概括中，形成新的概括。
+                    Current summary:
+                    {summary}
+                    New lines of conversation:
+                    {new_lines}
+                    New summary:
+                    """
+        self.CHAT_TEMPLATE = """
+                            {history}
+                            Human: {input}
+                            AI:"""
+
+        self.SUMMARY_PROMPT = PromptTemplate(
+            input_variables=["summary", "new_lines"], template=self.SUMMARIZER_TEMPLATE
+        )
+
+        self.CHAT_PROMPT = PromptTemplate( input_variables=["history", "input"], template=self.CHAT_TEMPLATE)
+
+        self.memory = ConversationSummaryBufferMemory(llm=LLMAdaptor(model='gpt-4-8k'), prompt=self.SUMMARY_PROMPT, max_token_limit=4000,k=5)
+        self.conversation_histroy = []
+
+
+        self.conversation_with_summary = ConversationChain(
+            llm=LLMAdaptor(model='gpt-4-8k'),
+            prompt=self.CHAT_PROMPT,
+            memory=self.memory,
+            verbose=False
+        )
+
+    def ask(self, question):
+        ans = self.conversation_with_summary.predict(input=question)
+        self.conversation_histroy.append((question,ans))
+        return self.conversation_histroy
+
+    def clearHistory(self):
+        self.memory = ConversationSummaryBufferMemory(llm=LLMAdaptor(model='gpt-4-8k'), prompt=self.SUMMARY_PROMPT, max_token_limit=4000,k=5)
+        self.conversation_histroy = []
+        return []
+
+
+
+# chat = Conversation()
+#
+# ans , history = chat.ask("宫保鸡丁怎么做")
+
+# print(chat.ask("我的上一个问题是什么"))

sdk/LLMAdaptor.py

@@ -0,0 +1,48 @@
+import logging
+from typing import Optional, List, Mapping, Any
+
+from langchain.callbacks.manager import CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langkit.api import chat_completion, a_chat_completion
+
+logger = logging.getLogger(__name__)
+
+
+class LLMAdaptor(LLM):
+    model: str
+    temperature: float = 1.0
+    topP: int = 1
+    maxTokens: int = 4096
+    presencePenalty: float = 0
+    frequencyPenalty: float = 0
+    timeout_secs: int = 180
+
+    def _call(
+            self,
+            prompt: str,
+            stop: Optional[List[str]] = None,
+            run_manager: Optional[CallbackManagerForLLMRun] = None,
+            **kwargs: Any
+    ) -> str:
+        params = dict(self._identifying_params)
+        if stop:
+            params['stop'] = stop
+        resp = chat_completion(self.model, [{'role': 'user', 'content': prompt}], **params)
+        if resp.code != 0:
+            raise ValueError(f"LLM Adaptor resp error, resp: {resp}")
+        return resp.data['choices'][0]['message']['content']
+
+    @property
+    def _llm_type(self) -> str:
+        return "llm-adaptor"
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {
+            "temperature": self.temperature,
+            "topP": self.topP,
+            "maxTokens": self.maxTokens,
+            "presencePenalty": self.presencePenalty,
+            "frequencyPenalty": self.frequencyPenalty,
+            'timeout_secs': self.timeout_secs
+        }

sdk/chat.py

@@ -0,0 +1,16 @@
+import langkit.api
+from langkit.api.chat import chat_completion
+import tool.keyFile as key
+
+langkit.api.app_id= key.app_id
+langkit.api.secret_key=key.secret_key
+
+def chat(content):
+    ret = chat_completion(model='gpt-4-8k', messages=[
+        {'role': 'user', 'content': content}
+    ])
+    if ret.msg == 'ok':
+        return ret.data['choices'][0]['message']['content']
+    return "try again"
+
+print(chat("你好"))

sdk/chatLLM.py

@@ -0,0 +1,23 @@
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+import langkit.api
+import tool.keyFile as key
+from langkit.langchain_helpers import LLMAdaptor
+langkit.api.app_id= key.app_id
+langkit.api.secret_key=key.secret_key
+from langchain.memory import ConversationBufferMemory
+
+
+def test_llm_adaptor():
+    llm = LLMAdaptor(model='gpt-4-8k')
+    prompt = PromptTemplate(
+        input_variables=["product"],
+        template="What is a good name for a company that makes {product}?",
+    )
+    chain = LLMChain(llm=llm, prompt=prompt,memory=ConversationBufferMemory())
+    print(chain.run("colorful socks"))
+
+memory = ConversationBufferMemory()
+memory.chat_memory.add_user_message("hi!")
+memory.chat_memory.add_ai_message("whats up?")
+test_llm_adaptor()

sdk/chatTest.py

@@ -0,0 +1,41 @@
+from langchain import PromptTemplate
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationSummaryBufferMemory
+from langkit.langchain_helpers import LLMAdaptor
+import tool.keyFile as key
+import langkit.api
+
+langkit.api.app_id= key.app_id
+langkit.api.secret_key=key.secret_key
+
+
+SUMMARIZER_TEMPLATE = """请将以下内容逐步概括所提供的对话内容，并将新的概括添加到之前的概括中，形成新的概括。
+Current summary:
+{summary}
+New lines of conversation:
+{new_lines}
+New summary:
+"""
+
+SUMMARY_PROMPT = PromptTemplate(
+    input_variables=["summary", "new_lines"], template=SUMMARIZER_TEMPLATE
+)
+
+memory = ConversationSummaryBufferMemory(llm=LLMAdaptor(model='gpt-4-8k'), prompt=SUMMARY_PROMPT, max_token_limit=4000,k=5)
+
+CHAT_TEMPLATE = """
+{history}
+Human: {input}
+AI:"""
+CHAT_PROMPT = PromptTemplate(
+    input_variables=["history", "input"], template=CHAT_TEMPLATE
+)
+
+conversation_with_summary = ConversationChain(
+    llm=LLMAdaptor(model='gpt-4-8k'),
+    prompt=CHAT_PROMPT,
+    memory=memory,
+    verbose=True
+)
+answer = conversation_with_summary.predict(input="如何做一道好吃的宫保鸡丁？")
+print(answer)

sdk/chatWithKnowledgeBase.py

@@ -0,0 +1,71 @@
+import langkit.api
+from langchain.chains import RetrievalQA
+from langchain.vectorstores import Chroma
+from langkit.langchain_helpers import LLMAdaptor, EmbeddingsAdaptor
+from langchain.document_loaders import UnstructuredFileLoader, PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import WebBaseLoader
+
+import tool.keyFile as key
+langkit.api.app_id = key.app_id
+langkit.api.secret_key = key.secret_key
+
+#知识库问答
+class KnowledgeBaseAssistant:
+    def __init__(self):
+        self.llm = LLMAdaptor(model='gpt-4-8k')
+        self.embeddings = EmbeddingsAdaptor()
+
+    def knowledge_base_text(self,text,question):
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=50)
+        docs = text_splitter.create_documents([text])
+
+        db = Chroma.from_documents(docs, self.embeddings)
+        retriever = db.as_retriever(search_kwargs={"k": 2})
+        qa = RetrievalQA.from_chain_type(llm=self.llm, chain_type="stuff", retriever=retriever)
+
+        try:
+            res = qa.run(question)
+        except:
+            res = "请重新尝试"
+        return res
+
+    def knowledge_base_url(self,url,question):
+        loader = WebBaseLoader(url)
+        documents = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=50)
+        docs = text_splitter.split_documents(documents)
+
+        db = Chroma.from_documents(docs, self.embeddings)
+        retriever = db.as_retriever(search_kwargs={"k": 2})
+        qa = RetrievalQA.from_chain_type(llm=self.llm, chain_type="stuff", retriever=retriever)
+
+        try:
+            res = qa.run(question)
+        except:
+            res = "请重新尝试"
+        return res
+
+    def knowledge_base_file(self,file_obj,question):
+        if file_obj.name.endswith(".pdf"):
+            loader = PyPDFLoader(file_obj.name)
+        else:
+            loader = UnstructuredFileLoader(file_obj.name)
+        document = loader.load()
+        # 初始化文本分割器
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=500,
+            chunk_overlap=100
+        )
+        docs = text_splitter.split_documents(document)
+        db = Chroma.from_documents(docs, self.embeddings)
+        retriever = db.as_retriever(search_kwargs={"k": 2})
+        qa = RetrievalQA.from_chain_type(llm=self.llm, chain_type="stuff", retriever=retriever)
+
+        try:
+            res = qa.run(question)
+        except:
+            res = "请重新尝试"
+        return res
+
+

sdk/chat_with_net.py

@@ -0,0 +1,51 @@
+import langkit.api
+import tool.keyFile as key
+from langchain import LLMMathChain
+from langkit.langchain_helpers import LLMAdaptor
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent
+import os
+
+
+
+class ChatWithNet:
+    def __init__(self):
+        langkit.api.app_id = key.app_id
+        langkit.api.secret_key = key.secret_key
+        os.environ['SERPAPI_API_KEY'] = key.serApi_key
+        self.model = 'gpt-4-8k'
+        self.res = []
+
+    #superCuculator
+    def searhAndChat(self,expression):
+        llm = LLMAdaptor(model=self.model)
+        tools = load_tools(["serpapi"], llm=llm)
+        agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
+        try:
+            result = agent.run(expression)
+        except Exception as e:
+            print(e)
+            result = "抱歉，我还不知道怎么回答这个问题"
+
+        self.res.append((expression,result))
+
+        return self.res
+
+    def clearHistory(self):
+        self.res = []
+        return []
+
+
+# coculator = Cuculator()
+# res = coculator.culculate("1+1")
+# print(res)
+
+    # tools = load_tools(["serpapi", "llm-math"], llm=llm)
+    # agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
+    # # 输出结果
+    # agent.run("Who isTaylor's boyfriend? What is his current age raised to the 3 power?")
+
+# llm_math = LLMMathChain(llm=llm, verbose=True)
+# result = llm_math.run("add(12 20 3  231 4)")
+# print(result)
+

sdk/culculate.py

@@ -0,0 +1,59 @@
+import langkit.api
+import tool.keyFile as key
+from langchain import LLMMathChain
+from langkit.langchain_helpers import LLMAdaptor
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent
+import os
+
+
+
+class Cuculator:
+    def __init__(self):
+        langkit.api.app_id = key.app_id
+        langkit.api.secret_key = key.secret_key
+        os.environ['SERPAPI_API_KEY'] = key.serApi_key
+        self.model = 'gpt-4-8k'
+        self.res = []
+
+
+
+    #superCuculator
+    def culculate(self,expression):
+        llm = LLMAdaptor(model=self.model)
+        llm_math = LLMMathChain(llm=llm, verbose=True)
+        ask_flag = True
+        try:
+            result = llm_math.run(expression)
+        except Exception as e:
+            print(e)
+            ask_flag = False
+
+
+        if  not ask_flag:
+            tools = load_tools(["serpapi", "llm-math"], llm=llm)
+            agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
+            result = agent.run(expression)
+
+        self.res.append((expression,result))
+
+        return self.res
+
+    def clearHistory(self):
+        self.res = []
+        return []
+
+
+# coculator = Cuculator()
+# res = coculator.culculate("1+1")
+# print(res)
+
+    # tools = load_tools(["serpapi", "llm-math"], llm=llm)
+    # agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
+    # # 输出结果
+    # agent.run("Who isTaylor's boyfriend? What is his current age raised to the 3 power?")
+
+# llm_math = LLMMathChain(llm=llm, verbose=True)
+# result = llm_math.run("add(12 20 3  231 4)")
+# print(result)
+

sdk/textSummary.py

@@ -0,0 +1,75 @@
+import langkit.api
+from langkit.langchain_helpers import LLMAdaptor
+from langchain.document_loaders import UnstructuredFileLoader, PyPDFLoader
+from langchain.chains.summarize import load_summarize_chain
+from langchain.prompts import PromptTemplate
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import WebBaseLoader
+
+import tool.keyFile as key
+langkit.api.app_id = key.app_id
+langkit.api.secret_key = key.secret_key
+
+#长文总结
+class SummaryAssistant:
+    def __init__(self):
+        self.llm = LLMAdaptor(model='gpt-4-8k')
+
+    def summary_text(self,text):
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=50)
+        docs = text_splitter.create_documents([text])
+        prompt_template = """写出以下内容的简洁摘要:
+        {text}
+        简洁摘要:"""
+        PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+        chain = load_summarize_chain(self.llm, chain_type="refine",verbose=True,question_prompt=PROMPT)
+        try:
+            res = chain.run(docs)
+        except:
+            res = "生成摘要失败,请重新尝试"
+        return res
+
+    def summary_url(self,url):
+        loader = WebBaseLoader(url)
+        documents = loader.load()
+        prompt_template = """写出以下内容的简洁摘要:
+        {text}
+        简洁摘要:"""
+        PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+        chain = load_summarize_chain(self.llm, chain_type="refine",verbose=True,question_prompt=PROMPT)
+        try:
+            res = chain.run(documents)
+        except:
+            res = "生成摘要失败,请重新尝试"
+        return res
+
+    def summary_file(self,file_obj):
+        if file_obj.name.endswith(".pdf"):
+            loader = PyPDFLoader(file_obj.name)
+        else:
+            loader = UnstructuredFileLoader(file_obj.name)
+        document = loader.load()
+        # 初始化文本分割器
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=500,
+            chunk_overlap=100
+        )
+
+        # 切分文本
+        split_documents = text_splitter.split_documents(document)
+        prompt_template = """写出以下内容的简洁摘要:
+        {text}
+        简洁摘要:"""
+        PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
+        chain = load_summarize_chain(self.llm, chain_type="refine",verbose=False,question_prompt=PROMPT)
+        try:
+            res = chain.run(split_documents)
+        except:
+            res = "生成摘要失败,请重新尝试"
+        return res
+
+# if __name__ == "__main__":
+# summaryAssistant = SummaryAssistant()
+# print(summaryAssistant.summary_text("你好"))
+# print(summaryAssistant.summary_url("https://mp.weixin.qq.com/s/RhzHa1oMd0WHk0JamdfVRA"))
+# print(summaryAssistant.summary_file("test.txt"))

tool/getSign.py

@@ -0,0 +1,24 @@
+import hmac
+import hashlib
+from collections import OrderedDict
+import time
+
+def sign(params, body, app_id, secret_key):
+
+    # 1. 构建认证字符串前缀，格式为 bot-auth-v1/{appId}/{timestamp}, timestamp为时间戳，精确到毫秒，用以验证请求是否失效
+    auth_string_prefix = f"bot-auth-v1/{app_id}/{int(time.time() * 1000)}/"
+    sb = [auth_string_prefix]
+
+    # 2. 构建url参数字符串，按照参数名字典序升序排列
+    if params:
+        ordered_params = OrderedDict(sorted(params.items()))
+        sb.extend(["{}={}&".format(k, v) for k, v in ordered_params.items()])
+
+    # 3. 拼接签名原文字符串
+    sign_str = "".join(sb) + body
+
+    # 4. hmac_sha_256算法签名
+    signature = hmac.new(secret_key.encode('utf-8'), sign_str.encode('utf-8'), hashlib.sha256).hexdigest()
+
+    # 5. 拼接认证字符串
+    return auth_string_prefix + signature

tool/keyFile.py

@@ -0,0 +1,3 @@
+app_id = 'auto_58d4be458b124966b29c61fad4186a2c'
+secret_key = 'ce2aca400cf64be0b582a66fa2362817'
+serApi_key = 'fc4668c995941ca731a8a03a178d9fdefbc261d48c76f785db07454bd2c20254'