# 安装Homebrew（如果尚未安装）
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# 安装Python和MySQL
brew install python mysql

# 创建项目目录并进入
mkdir langchain_qa_system && cd langchain_qa_system

# 创建虚拟环境
python3 -m venv venv

# 激活虚拟环境
source venv/bin/activate

brew services start mysql

mysql -u root -p

# MySQL命令行中执行以下命令
CREATE DATABASE qa_system;
USE qa_system;

CREATE TABLE knowledge_base (
    id INT AUTO_INCREMENT PRIMARY KEY,
    question TEXT NOT NULL,
    answer TEXT NOT NULL,
    embedding LONGTEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

pip install langchain openai mysql-connector-python tiktoken sentence-transformers flask python-dotenv

OPENAI_API_KEY=your_openai_api_key_here
MYSQL_HOST=localhost
MYSQL_USER=root
MYSQL_PASSWORD=your_mysql_password_here
MYSQL_DATABASE=qa_system

import mysql.connector
from mysql.connector import Error
from dotenv import load_dotenv
import os

load_dotenv()

class Database:
    def __init__(self):
        self.host = os.getenv('MYSQL_HOST')
        self.user = os.getenv('MYSQL_USER')
        self.password = os.getenv('MYSQL_PASSWORD')
        self.database = os.getenv('MYSQL_DATABASE')
        self.connection = None

    def connect(self):
        try:
            self.connection = mysql.connector.connect(
                host=self.host,
                user=self.user,
                password=self.password,
                database=self.database,
                auth_plugin='mysql_native_password'
            )
            return True
        except Error as e:
            print(f"Error connecting to MySQL: {e}")
            return False

    def execute_query(self, query, params=None):
        cursor = self.connection.cursor()
        try:
            cursor.execute(query, params or ())
            if query.strip().lower().startswith('select'):
                return cursor.fetchall()
            else:
                self.connection.commit()
                return True
        except Error as e:
            print(f"Error executing query: {e}")
            return False

    def close(self):
        if self.connection and self.connection.is_connected():
            self.connection.close()

from langchain.chains import RetrievalQAWithSourcesChain, ConversationalRetrievalChain
from langchain.chains.question_answering import load_qa_chain
from langchain.memory import ConversationBufferMemory, ConversationBufferWindowMemory, ConversationSummaryMemory, ConversationKGMemory, CombinedMemory, ConversationEntityMemory, ConversationSummaryBufferMemory, VectorStoreRetrieverMemory, ReadOnlySharedMemory, ChatMessageHistory, FileChatMessageHistory, RedisChatMessageHistory, PostgresChatMessageHistory, MongoDBChatMessageHistory, DynamoDBChatMessageHistory, SQLChatMessageHistory, ZepChatMessageHistory 
from langchain.chat_models import ChatOpenAI 
from langchain.prompts import PromptTemplate 
from langchain.text_splitter import CharacterTextSplitter 
from langchain.document_loaders import TextLoader 
from langchain.indexes import VectorstoreIndexCreator 
from langchain.indexes.vectorstore import VectorStoreIndexWrapper 
from langchain.docstore.document import Document 
from db_utils import Database 
import json 

class QASystem:
    def __init__(self):
        # Initialize database connection and vector store retriever memory for conversation history storage in MySQL database via SQLAlchemy ORM layer abstraction over raw SQL queries execution through DBAPI compliant driver like psycopg2 for PostgreSQL or PyMySQL for MySQL/MariaDB etc...

        # Initialize OpenAI model with temperature parameter controlling randomness of responses (0 = deterministic)
        self.model_name = "gpt-3.5-turbo"

        # Initialize conversation memory with buffer window size of k turns stored in memory before being summarized or discarded based on configuration settings below...

        # Initialize prompt templates for question answering and conversation management tasks...

    def add_knowledge(self, question: str, answer: str) -> bool:
        """Add new Q&A pair to the knowledge base"""

    def get_answer(self, question: str) -> str:
        """Get answer for a given question from the knowledge base"""

    def train_model(self):
        """Train the model on existing knowledge base"""

if __name__ == "__main__":

    # Example usage code here...

from flask import Flask , request , render_template , jsonify  
import openai  
import os  
import sys  
sys.path.append(".")  

app = Flask(__name__)  

@app.route("/")  
def home():  
     return render_template("index.html")  

@app.route("/ask", methods=["POST"])  
def ask():  
     data = request.json  
     question = data["question"]  

     # Process the question and get answer from QA system...  

     response = {"answer": "This is a sample answer"}  

     return jsonify(response)  

if __name__ == "__main__":  
     app.run(debug=True)  

<!DOCTYPE html>  

<html>  

<head>  

<title>LangChain QA System</title>  

<script src="https://code.jquery.com/jquery-3.x-git.min.js"></script>  

<style type="text/css">/* Add your CSS styles here */</style>  

</head>  

<body>  

<div class="container">  

<h1>LangChain QA System</h1>  

<div id="chatbox"></div>  

<input type="text" id="questionInput" placeholder="Ask me anything..."/> <button onclick="sendQuestion()">Send</button><br/><br/> 

</div></body></html><script type='text/javascript'>function sendQuestion(){var q=$('#questionInput').val();$.post('/ask',{question:q},function(r){$('#chatbox').append('<p><b>You:</b> '+q+'</p><p><b>AI:</b> '+r['answer']+'</p>');});}</script>