79 lines
2.6 KiB
JavaScript
79 lines
2.6 KiB
JavaScript
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import { RunnablePassthrough } from "@langchain/core/runnables";
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import { renderTextDescription } from "../../tools/render.js";
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import { formatLogToString } from "../format_scratchpad/log.js";
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import { ReActSingleInputOutputParser } from "./output_parser.js";
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import { AgentRunnableSequence } from "../agent.js";
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/**
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* Create an agent that uses ReAct prompting.
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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, createReactAgent } from "langchain/agents";
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* import { pull } from "langchain/hub";
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* import type { PromptTemplate } from "@langchain/core/prompts";
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*
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* import { OpenAI } 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/react
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* const prompt = await pull<PromptTemplate>("hwchase17/react");
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*
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* const llm = new OpenAI({
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* temperature: 0,
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* });
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*
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* const agent = await createReactAgent({
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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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*/
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export async function createReactAgent({ llm, tools, prompt, streamRunnable, }) {
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const missingVariables = ["tools", "tool_names", "agent_scratchpad"].filter((v) => !prompt.inputVariables.includes(v));
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if (missingVariables.length > 0) {
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throw new Error(`Provided prompt is missing required input variables: ${JSON.stringify(missingVariables)}`);
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}
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const toolNames = tools.map((tool) => tool.name);
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const partialedPrompt = await prompt.partial({
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tools: renderTextDescription(tools),
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tool_names: toolNames.join(", "),
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});
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// TODO: Add .bind to core runnable interface.
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const llmWithStop = llm.bind({
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stop: ["\nObservation:"],
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});
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const agent = AgentRunnableSequence.fromRunnables([
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RunnablePassthrough.assign({
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agent_scratchpad: (input) => formatLogToString(input.steps),
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}),
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partialedPrompt,
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llmWithStop,
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new ReActSingleInputOutputParser({
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toolNames,
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}),
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], {
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name: "ReactAgent",
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streamRunnable,
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singleAction: true,
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});
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return agent;
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}
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