韩振峰作业：Sales consultant

langchain/sales_chatbot/homework/help.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sun Sep 17 21:31:02 CST 2023\n"
+     ]
+    }
+   ],
+   "source": [
+    "!date"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

langchain/sales_chatbot/homework/sales_chatbot.py

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+import gradio as gr
+import random
+import time
+import os
+import openai
+
+from typing import List
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+
+api_key = os.getenv("AZURE_OPENAI_API_KEY")
+api_base = "https://hzf-gpt.openai.azure.com/"
+api_version = "2023-07-01-preview"
+open_ai_type = "azure"
+
+
+def getOpenAIEmbeddingObject() -> OpenAIEmbeddings:
+    """
+    获取Azure OpenAI Embeddings对象
+    """
+    embeddings = OpenAIEmbeddings(
+        deployment="embedding",  # 部署名称
+        openai_api_base=api_base,
+        openai_api_type=open_ai_type,
+        openai_api_key=api_key,
+        chunk_size=1,  # 限定并发数为1
+    )
+    return embeddings
+
+
+def getChatOpenAIObject() -> ChatOpenAI:
+    """
+    获取ChatOpenAI对象
+    """
+    openai.api_type = "azure"
+    openai.api_version = api_version
+    chat = ChatOpenAI(
+        deployment_id="gpt-4",  # 部署名称
+        openai_api_base=api_base,
+        openai_api_key=api_key,
+        temperature=0,
+    )
+    return chat
+
+
+def get_qa_chain_prompt() -> PromptTemplate:
+    """
+    获取QA问答Prompt模板
+    """
+    template = """你是中国顶级的房地产销售；你的公司是'大模型集团'，销售各种舒适的房型。
+    你的任务是：使用下面的背景信息回答客户咨询的问题，你需要专业而且礼貌的回答客户的问题，以便能够销售出去房产，回答要有吸引力。
+    如果你不清楚客户问的问题，你可以向客户提问。如果你不知道答案，请回答：'对不起，我需要问一下我的领导！'，请不要尝试回答。 
+    {context}
+    Question: {question}
+    Helpful Answer:"""
+    return PromptTemplate.from_template(template)
+
+
+def initialize_sales_bot(vector_store_dir: str = "real_estates_sale"):
+    db = FAISS.load_local(vector_store_dir, getOpenAIEmbeddingObject())
+
+    llm = getChatOpenAIObject()
+
+    global SALES_BOT
+    SALES_BOT = RetrievalQA.from_chain_type(
+        llm,
+        retriever=db.as_retriever(
+            search_type="similarity_score_threshold",
+            search_kwargs={"score_threshold": 0.8},
+        ),
+        chain_type_kwargs={"prompt": get_qa_chain_prompt()},
+    )
+    # 返回向量数据库的检索结果
+    SALES_BOT.return_source_documents = True
+
+    return SALES_BOT
+
+
+def sales_chat(message, history):
+    print(f"[message]{message}")
+    print(f"[history]{history}")
+    # TODO: 从命令行参数中获取
+    enable_chat = True
+
+    ans = SALES_BOT({"query": message})
+    # 如果检索出结果，或者开了大模型聊天模式
+    # 返回 RetrievalQA combine_documents_chain 整合的结果
+    if ans["source_documents"] or enable_chat:
+        print(f"[result]{ans['result']}")
+        print(f"[source_documents]{ans['source_documents']}")
+        return ans["result"]
+    # 否则输出套路话术
+    else:
+        return "这个问题我要问问领导"
+
+
+demo = gr.ChatInterface(
+    fn=sales_chat,
+    title="房产销售",
+    # retry_btn=None,
+    # undo_btn=None,
+    chatbot=gr.Chatbot(height=600),
+)
+
+
+def launch_gradio():
+    demo.launch(share=False, server_name="0.0.0.0")
+
+
+# 初始化房产销售机器人
+initialize_sales_bot()
+# 启动 Gradio 服务
+launch_gradio()