95 lines
3.2 KiB
JavaScript
95 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.createToolCallingAgent = void 0;
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const runnables_1 = require("@langchain/core/runnables");
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const agent_js_1 = require("../agent.cjs");
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const output_parser_js_1 = require("./output_parser.cjs");
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const tool_calling_js_1 = require("../format_scratchpad/tool_calling.cjs");
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function _isBaseChatModel(x) {
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const model = x;
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return (typeof model._modelType === "function" &&
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model._modelType() === "base_chat_model");
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}
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/**
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* Create an agent that uses tools.
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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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* @example
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* ```typescript
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* import { ChatAnthropic } from "@langchain/anthropic";
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* import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
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* import { AgentExecutor, createToolCallingAgent } from "langchain/agents";
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*
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* const prompt = ChatPromptTemplate.fromMessages(
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* [
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* ["system", "You are a helpful assistant"],
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* ["placeholder", "{chat_history}"],
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* ["human", "{input}"],
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* ["placeholder", "{agent_scratchpad}"],
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* ]
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* );
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*
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*
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* const llm = new ChatAnthropic({
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* modelName: "claude-3-opus-20240229",
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* temperature: 0,
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* });
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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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* const agent = createToolCallingAgent({ llm, tools, prompt });
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*
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* const agentExecutor = new AgentExecutor({ agent, tools });
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*
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* const result = await agentExecutor.invoke({input: "what is LangChain?"});
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*
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* // Using with chat history
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* import { AIMessage, HumanMessage } from "@langchain/core/messages";
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*
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* const result2 = await agentExecutor.invoke(
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* {
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* input: "what's my name?",
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* chat_history: [
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* new HumanMessage({content: "hi! my name is bob"}),
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* new AIMessage({content: "Hello Bob! 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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*/
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function createToolCallingAgent({ 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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let modelWithTools;
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if (_isBaseChatModel(llm)) {
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if (llm.bindTools === undefined) {
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throw new Error(`This agent requires that the "bind_tools()" method be implemented on the input model.`);
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}
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modelWithTools = llm.bindTools(tools);
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}
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else {
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modelWithTools = llm;
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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, tool_calling_js_1.formatToToolMessages)(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.ToolCallingAgentOutputParser(),
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], {
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name: "ToolCallingAgent",
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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.createToolCallingAgent = createToolCallingAgent;
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