86 lines
3.1 KiB
TypeScript
86 lines
3.1 KiB
TypeScript
import { ChatAnthropic } from "@langchain/anthropic";
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import { Annotation, END, START, StateGraph } from "@langchain/langgraph";
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import { GenerativeUIAnnotation } from "../types";
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import { z } from "zod";
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import { AIMessage, ToolMessage } from "@langchain/langgraph-sdk";
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import { v4 as uuidv4 } from "uuid";
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const PizzaOrdererAnnotation = Annotation.Root({
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messages: GenerativeUIAnnotation.spec.messages,
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})
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async function sleep(ms = 5000) {
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return new Promise((resolve) => setTimeout(resolve, ms));
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}
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const workflow = new StateGraph(PizzaOrdererAnnotation)
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.addNode("findStore", async (state) => {
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const findShopSchema = z.object({
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location: z.string().describe("The location the user is in. E.g. 'San Francisco' or 'New York'"),
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pizza_company: z.string().optional().describe("The name of the pizza company. E.g. 'Dominos' or 'Papa John's'. Optional, if not defined it will search for all pizza shops"),
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}).describe("The schema for finding a pizza shop for the user")
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const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest", temperature: 0 }).withStructuredOutput(findShopSchema, {
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name: "find_pizza_shop",
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includeRaw: true,
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})
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const response = await model.invoke([
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{
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role: "system",
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content: "You are a helpful AI assistant, tasked with extracting information from the conversation between you, and the user, in order to find a pizza shop for them."
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},
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...state.messages,
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])
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await sleep();
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const toolResponse: ToolMessage = {
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type: "tool",
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id: uuidv4(),
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content: "I've found a pizza shop at 1119 19th St, San Francisco, CA 94107. The phone number for the shop is 415-555-1234.",
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tool_call_id: (response.raw as unknown as AIMessage).tool_calls?.[0].id ?? "",
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}
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return {
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messages: [response.raw, toolResponse]
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}
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})
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.addNode("orderPizza", async (state) => {
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await sleep(1500);
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const placeOrderSchema = z.object({
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address: z.string().describe("The address of the store to order the pizza from"),
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phone_number: z.string().describe("The phone number of the store to order the pizza from"),
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order: z.string().describe("The full pizza order for the user"),
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}).describe("The schema for ordering a pizza for the user")
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const model = new ChatAnthropic({ model: "claude-3-5-sonnet-latest", temperature: 0 }).withStructuredOutput(placeOrderSchema, {
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name: "place_pizza_order",
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includeRaw: true,
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})
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const response = await model.invoke([
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{
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role: "system",
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content: "You are a helpful AI assistant, tasked with placing an order for a pizza for the user."
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},
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...state.messages,
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])
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const toolResponse: ToolMessage = {
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type: "tool",
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id: uuidv4(),
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content: "Pizza order successfully placed.",
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tool_call_id: (response.raw as unknown as AIMessage).tool_calls?.[0].id ?? "",
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}
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return {
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messages: [response.raw, toolResponse]
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}
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})
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.addEdge(START, "findStore")
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.addEdge("findStore", "orderPizza")
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.addEdge("orderPizza", END)
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export const graph = workflow.compile()
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graph.name = "Order Pizza Graph";
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