PlanetScale Chat Memory
Because PlanetScale works via a REST API, you can use this with Vercel Edge, Cloudflare Workers and other Serverless environments.
For longer-term persistence across chat sessions, you can swap out the default in-memory chatHistory
that backs chat memory classes like BufferMemory
for an PlanetScale Database instance.
Setup
You will need to install @planetscale/database in your project:
- npm
- Yarn
- pnpm
npm install @langchain/openai @planetscale/database @langchain/community
yarn add @langchain/openai @planetscale/database @langchain/community
pnpm add @langchain/openai @planetscale/database @langchain/community
You will also need an PlanetScale Account and a database to connect to. See instructions on PlanetScale Docs on how to create a HTTP client.
Usage
Each chat history session stored in PlanetScale database must have a unique id.
The config
parameter is passed directly into the new Client()
constructor of @planetscale/database, and takes all the same arguments.
import { BufferMemory } from "langchain/memory";
import { PlanetScaleChatMessageHistory } from "@langchain/community/stores/message/planetscale";
import { ChatOpenAI } from "@langchain/openai";
import { ConversationChain } from "langchain/chains";
const memory = new BufferMemory({
chatHistory: new PlanetScaleChatMessageHistory({
tableName: "stored_message",
sessionId: "lc-example",
config: {
url: "ADD_YOURS_HERE", // Override with your own database instance's URL
},
}),
});
const model = new ChatOpenAI();
const chain = new ConversationChain({ llm: model, memory });
const res1 = await chain.invoke({ input: "Hi! I'm Jim." });
console.log({ res1 });
/*
{
res1: {
text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?"
}
}
*/
const res2 = await chain.invoke({ input: "What did I just say my name was?" });
console.log({ res2 });
/*
{
res1: {
text: "You said your name was Jim."
}
}
*/
API Reference:
- BufferMemory from
langchain/memory
- PlanetScaleChatMessageHistory from
@langchain/community/stores/message/planetscale
- ChatOpenAI from
@langchain/openai
- ConversationChain from
langchain/chains
Advanced Usage
You can also directly pass in a previously created @planetscale/database client instance:
import { BufferMemory } from "langchain/memory";
import { PlanetScaleChatMessageHistory } from "@langchain/community/stores/message/planetscale";
import { ChatOpenAI } from "@langchain/openai";
import { ConversationChain } from "langchain/chains";
import { Client } from "@planetscale/database";
// Create your own Planetscale database client
const client = new Client({
url: "ADD_YOURS_HERE", // Override with your own database instance's URL
});
const memory = new BufferMemory({
chatHistory: new PlanetScaleChatMessageHistory({
tableName: "stored_message",
sessionId: "lc-example",
client, // You can reuse your existing database client
}),
});
const model = new ChatOpenAI();
const chain = new ConversationChain({ llm: model, memory });
const res1 = await chain.invoke({ input: "Hi! I'm Jim." });
console.log({ res1 });
/*
{
res1: {
text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?"
}
}
*/
const res2 = await chain.invoke({ input: "What did I just say my name was?" });
console.log({ res2 });
/*
{
res1: {
text: "You said your name was Jim."
}
}
*/
API Reference:
- BufferMemory from
langchain/memory
- PlanetScaleChatMessageHistory from
@langchain/community/stores/message/planetscale
- ChatOpenAI from
@langchain/openai
- ConversationChain from
langchain/chains