我创建了一个使用 llamaindex 打字稿的 LINE 聊天机器人，它可以很好地回答问题。并希望使用一个能够记住之前的问题并能够做出响应的代理，该代理可以在循环时使用它，如下面的代码所示。
import { OpenAI } from "llamaindex";
import fs from "node:fs/promises";
import { Document, VectorStoreIndex, QueryEngineTool, OpenAIAgent } from "llamaindex";
import readlineSync from 'readline-sync'; // Changed import to readline-sync
const path = "data/car.json";
async function main() {
const essay = await fs.readFile(path, "utf-8");
const document = new Document({ text: essay, id_: path });
const index = await VectorStoreIndex.fromDocuments([document]);
const individual_query_engine_tools = [
new QueryEngineTool({
queryEngine: index.asQueryEngine(),
metadata: {
name: "vector_index",
description: "Useful when you want to answer questions about information about cars assembled in Thailand.",
},
}),
];
const agent = new OpenAIAgent({
tools: [...individual_query_engine_tools],
verbose: true
});
while (true) {
const userInput = readlineSync.question("Enter your question (type 'exit' to quit): ");
if (userInput.toLowerCase() === 'exit') {
console.log("Exiting...");
break;
}
else if(userInput.toLowerCase() !== '') {
const response = await agent.chat({
message: userInput,
});
console.log(response.message.content);
}
}
}
main().catch(console.error);
输出
Enter your question (type 'exit' to quit): toyota yaris price?
The price of the Toyota Yaris ranges from 559,000 to 709,000 baht.
Enter your question (type 'exit' to quit): color?
The Toyota Yaris is available in 6 colors: white, black, red, blue, gray, and yellow.
Enter your question (type 'exit' to quit): sound system?
The Toyota Yaris is equipped with a JBL sound system featuring 8 speakers, providing a high-quality audio experience.
但是当我在 LINE 聊天机器人项目中使用它时，结果并没有达到预期。代码如下
const line = require('@line/bot-sdk');
const express = require('express');
const axios = require('axios').default;
const dotenv = require('dotenv');
const { OpenAI } = require("llamaindex");
const fs = require("fs").promises;
const { Document, VectorStoreIndex, QueryEngineTool, OpenAIAgent } = require("llamaindex");
const readlineSync = require('readline-sync');
dotenv.config();
const lineConfig = {
channelAccessToken: process.env.ACESS_TOKEN,
channelSecret: process.env.SECRET_TOKEN
};
const client = new line.Client(lineConfig);
const app = express();
app.post('/webhook', line.middleware(lineConfig), async(req, res) => {
try {
const events = req.body.events; // Corrected variable name to events
console.log('events =>>>>', events); // Corrected console log message
if (events && events.length > 0) {
await Promise.all(events.map(async(event) => {
await handleEvent(event);
}));
}
res.status(200).send('OK');
} catch (error) {
console.error('Error processing events:', error);
res.status(500).end();
}
});
const handleEvent = async(event) => {
if (event.type !== 'message' || event.message.type !== 'text') {
return null;
} else if (event.type === 'message') {
const userQuery = event.message.text;
// setTimeout(function() {
// client.pushMessage(event.source.userId, {
// "type": "sticker",
// "packageId": "11538",
// "stickerId": "51626518"
// });
// }, 2000);
// Load the chatbot model and process the user query
const path = "data/car.json";
const essay = await fs.readFile(path, "utf-8");
const document = new Document({ text: essay, id_: path });
const index = await VectorStoreIndex.fromDocuments([document]);
const individual_query_engine_tools = [
new QueryEngineTool({
queryEngine: index.asQueryEngine(),
metadata: {
name: "vector_index",
description: "Useful when you want to answer questions about information about cars assembled in Thailand.",
},
}),
];
const agent = new OpenAIAgent({
tools: [...individual_query_engine_tools],
verbose: true
});
try {
const { response } = await agent.chat({ message: userQuery });
// Second message with the actual response
await client.pushMessage(event.source.userId, {
type: 'text',
text: response
});
} catch (error) {
console.error("Error handling query:", error);
return client.pushMessage(event.source.userId, {
type: 'text',
text: 'Sorry, there was an error processing your request.'
});
}
}
};
app.listen(4000, () => {
console.log('listening on 4000');
});
LINE 聊天机器人我可以在第一句话中通过 LINE 回答我的问题，但是当我提出一个需要记住上一个问题的上下文的问题时，它不记得并且总是认为这是第一个问题。
Line 聊天机器人中的答案是这样的。
我个人的看法是，这是因为每次事件发生时代码都会重新启动。 来自代码第 38 行（我不知道我是否正确。）
const handleEvent = async(event) => {我是泰国大学四年级的学生。我想尝试研究如何从 llamaindex 制作聊天机器人。因此，如果我在代码的任何部分犯了错误，我希望阅读这篇文章的每个人都能教我。感谢您前来阅读。我希望我能尽快解决这个问题。谢谢！
我强烈建议您使用 langchain，这样您每次都可以将历史记录传递到提示中。
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.callbacks import get_openai_callback
contextualize_q_system_prompt = """Given a chat history and the latest user question \
which might reference context in the chat history, formulate a standalone question \
which can be understood without the chat history. Do NOT answer the question, \
just reformulate it if needed and otherwise return it as is."""
contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", contextualize_q_system_prompt),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
# This using Streamlit for Session but you can sub whatevewr you want
with get_openai_callback() as cb:
ai_msg = contextualize_q_prompt.invoke({"question": question,
"chat_history": st.session_state['history']})
if 'costs' not in st.session_state:
st.session_state['costs'] = []
with st.chat_message('Question:'):
st.markdown(question)
with st.chat_message('Answer'):
st.markdown(ai_msg.content)
for message in st.session_state['history']:
if isinstance(message, AIMessage):
with st.chat_message('Answer'):
st.markdown(message.content)
elif isinstance(message, HumanMessage):
with st.chat_message('Question'):
st.markdown(message.content)
st.session_state['history'].extend([HumanMessage(content=question), ai_msg])
costs = st.session_state.get('costs', [])
st.sidebar.markdown("## Costs")
st.sidebar.markdown(f"**Total cost: ${sum(costs):.5f}**")
for cost in costs:
st.sidebar.markdown(f"- ${cost:.5f}")