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