91 lines
3.2 KiB
JavaScript
91 lines
3.2 KiB
JavaScript
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"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.createOpenAIToolsAgent = exports.OpenAIToolsAgentOutputParser = void 0;
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const runnables_1 = require("@langchain/core/runnables");
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const function_calling_1 = require("@langchain/core/utils/function_calling");
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const openai_tools_js_1 = require("../format_scratchpad/openai_tools.cjs");
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const output_parser_js_1 = require("./output_parser.cjs");
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Object.defineProperty(exports, "OpenAIToolsAgentOutputParser", { enumerable: true, get: function () { return output_parser_js_1.OpenAIToolsAgentOutputParser; } });
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const agent_js_1 = require("../agent.cjs");
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/**
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* Create an agent that uses OpenAI-style tool calling.
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* @param params Params required to create the agent. Includes an LLM, tools, and prompt.
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* @returns A runnable sequence representing an agent. It takes as input all the same input
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* variables as the prompt passed in does. It returns as output either an
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* AgentAction or AgentFinish.
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*
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* @example
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* ```typescript
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* import { AgentExecutor, createOpenAIToolsAgent } from "langchain/agents";
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* import { pull } from "langchain/hub";
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* import type { ChatPromptTemplate } from "@langchain/core/prompts";
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* import { AIMessage, HumanMessage } from "@langchain/core/messages";
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*
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* import { ChatOpenAI } from "@langchain/openai";
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*
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* // Define the tools the agent will have access to.
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* const tools = [...];
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*
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* // Get the prompt to use - you can modify this!
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* // If you want to see the prompt in full, you can at:
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* // https://smith.langchain.com/hub/hwchase17/openai-tools-agent
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* const prompt = await pull<ChatPromptTemplate>(
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* "hwchase17/openai-tools-agent"
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* );
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*
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* const llm = new ChatOpenAI({
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* temperature: 0,
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* modelName: "gpt-3.5-turbo-1106",
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* });
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*
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* const agent = await createOpenAIToolsAgent({
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* llm,
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* tools,
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* prompt,
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* });
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*
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* const agentExecutor = new AgentExecutor({
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* agent,
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* tools,
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* });
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*
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* const result = await agentExecutor.invoke({
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* input: "what is LangChain?",
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* });
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*
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* // With chat history
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* const result2 = await agentExecutor.invoke({
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* input: "what's my name?",
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* chat_history: [
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* new HumanMessage("hi! my name is cob"),
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* new AIMessage("Hello Cob! How can I assist you today?"),
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* ],
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* });
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* ```
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*/
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async function createOpenAIToolsAgent({ llm, tools, prompt, streamRunnable, }) {
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if (!prompt.inputVariables.includes("agent_scratchpad")) {
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throw new Error([
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`Prompt must have an input variable named "agent_scratchpad".`,
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`Found ${JSON.stringify(prompt.inputVariables)} instead.`,
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].join("\n"));
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}
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const modelWithTools = llm.bind({
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tools: tools.map((tool) => (0, function_calling_1.convertToOpenAITool)(tool)),
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});
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const agent = agent_js_1.AgentRunnableSequence.fromRunnables([
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runnables_1.RunnablePassthrough.assign({
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agent_scratchpad: (input) => (0, openai_tools_js_1.formatToOpenAIToolMessages)(input.steps),
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}),
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prompt,
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modelWithTools,
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new output_parser_js_1.OpenAIToolsAgentOutputParser(),
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], {
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name: "OpenAIToolsAgent",
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streamRunnable,
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singleAction: false,
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});
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return agent;
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}
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exports.createOpenAIToolsAgent = createOpenAIToolsAgent;
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