Azure Cosmos DB Mongo vCore 聊天消息历史记录
AzureCosmosDBMongoChatMessageHistory 使用 Azure Cosmos DB Mongo vCore 来存储聊天消息历史记录。对于跨聊天会话的长期持久化,您可以替换默认的内存中 chatHistory,该存储机制支持如 BufferMemory 这类聊天记忆类。
如果您还没有 Azure 账户,可以 创建一个免费账户 来开始使用。
安装配置
首先,您需要安装 @langchain/azure-cosmosdb 包:
- npm
- Yarn
- pnpm
npm install @langchain/azure-cosmosdb @langchain/core
yarn add @langchain/azure-cosmosdb @langchain/core
pnpm add @langchain/azure-cosmosdb @langchain/core
:::提示 请参阅安装集成包的一般说明部分。 :::
- npm
- Yarn
- pnpm
npm install @langchain/openai @langchain/community @langchain/core
yarn add @langchain/openai @langchain/community @langchain/core
pnpm add @langchain/openai @langchain/community @langchain/core
此外,您还需要运行一个 Azure Cosmos DB Mongo vCore 实例。您可以按照 本指南 在 Azure 门户中免费部署,无需任何费用。
实例运行后,请确保您已获得连接字符串。
使用方法
import { ChatOpenAI } from "@langchain/openai";
import {
AzureCosmosDBMongoChatMessageHistory,
AzureCosmosDBMongoChatHistoryDBConfig,
} from "@langchain/azure-cosmosdb";
import { RunnableWithMessageHistory } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
const model = new ChatOpenAI({
model: "gpt-3.5-turbo",
temperature: 0,
});
const prompt = ChatPromptTemplate.fromMessages([
[
"system",
"You are a helpful assistant. Answer all questions to the best of your ability.",
],
new MessagesPlaceholder("chat_history"),
["human", "{input}"],
]);
const chain = prompt.pipe(model).pipe(new StringOutputParser());
const dbcfg: AzureCosmosDBMongoChatHistoryDBConfig = {
connectionString: process.env.AZURE_COSMOSDB_MONGODB_CONNECTION_STRING,
databaseName: "langchain",
collectionName: "chathistory",
};
const chainWithHistory = new RunnableWithMessageHistory({
runnable: chain,
inputMessagesKey: "input",
historyMessagesKey: "chat_history",
getMessageHistory: async (sessionId) => {
const chatHistory = new AzureCosmosDBMongoChatMessageHistory(
dbcfg,
sessionId,
"user-id"
);
return chatHistory;
},
});
const res1 = await chainWithHistory.invoke(
{ input: "Hi! I'm Jim." },
{ configurable: { sessionId: "langchain-test-session" } }
);
console.log({ res1 });
/*
{ res1: 'Hi Jim! How can I assist you today?' }
*/
const res2 = await chainWithHistory.invoke(
{ input: "What did I just say my name was?" },
{ configurable: { sessionId: "langchain-test-session" } }
);
console.log({ res2 });
/*
{ res2: { response: 'You said your name was Jim.' }
*/
// Give this session a title
const chatHistory = (await chainWithHistory.getMessageHistory(
"langchain-test-session"
)) as AzureCosmosDBMongoChatMessageHistory;
await chatHistory.setContext({ title: "Introducing Jim" });
// List all session for the user
const sessions = await chatHistory.getAllSessions();
console.log(sessions);
/*
[
{
id: 'langchain-test-session',
user_id: 'user-id',
context: { title: 'Introducing Jim' }
}
]
*/
API Reference:
- ChatOpenAI from
@langchain/openai - AzureCosmosDBMongoChatMessageHistory from
@langchain/azure-cosmosdb - AzureCosmosDBMongoChatHistoryDBConfig from
@langchain/azure-cosmosdb - RunnableWithMessageHistory from
@langchain/core/runnables - StringOutputParser from
@langchain/core/output_parsers - ChatPromptTemplate from
@langchain/core/prompts - MessagesPlaceholder from
@langchain/core/prompts