dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
90 lines • 3.58 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const agents_1 = require("langchain/agents");
const hub_1 = require("langchain/hub");
const handler_1 = require("../../../src/handler");
const utils_1 = require("../../../src/utils");
const agents_2 = require("../../../src/agents");
const Moderation_1 = require("../../moderation/Moderation");
const OutputParserHelpers_1 = require("../../outputparsers/OutputParserHelpers");
class ReActAgentLLM_Agents {
constructor() {
this.label = 'ReAct Agent for LLMs';
this.name = 'reactAgentLLM';
this.version = 2.0;
this.type = 'AgentExecutor';
this.category = 'Agents';
this.icon = 'agent.svg';
this.description = 'Agent that uses the ReAct logic to decide what action to take, optimized to be used with LLMs';
this.baseClasses = [this.type, ...(0, utils_1.getBaseClasses)(agents_1.AgentExecutor)];
this.inputs = [
{
label: 'Allowed Tools',
name: 'tools',
type: 'Tool',
list: true
},
{
label: 'Language Model',
name: 'model',
type: 'BaseLanguageModel'
},
{
label: 'Input Moderation',
description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
name: 'inputModeration',
type: 'Moderation',
optional: true,
list: true
},
{
label: 'Max Iterations',
name: 'maxIterations',
type: 'number',
optional: true,
additionalParams: true
}
];
}
async init() {
return null;
}
async run(nodeData, input, options) {
const model = nodeData.inputs?.model;
const maxIterations = nodeData.inputs?.maxIterations;
let tools = nodeData.inputs?.tools;
const moderations = nodeData.inputs?.inputModeration;
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the ReAct Agent for LLMs
input = await (0, Moderation_1.checkInputs)(moderations, input);
}
catch (e) {
await new Promise((resolve) => setTimeout(resolve, 500));
// if (options.shouldStreamResponse) {
// streamResponse(options.sseStreamer, options.chatId, e.message)
// }
return (0, OutputParserHelpers_1.formatResponse)(e.message);
}
}
tools = (0, lodash_1.flatten)(tools);
const prompt = await (0, hub_1.pull)('hwchase17/react');
const agent = await (0, agents_2.createReactAgent)({
llm: model,
tools,
prompt
});
const executor = new agents_1.AgentExecutor({
agent,
tools,
verbose: process.env.DEBUG === 'true' ? true : false,
maxIterations: maxIterations ? parseFloat(maxIterations) : undefined
});
const callbacks = await (0, handler_1.additionalCallbacks)(nodeData, options);
const result = await executor.invoke({ input }, { callbacks });
return result?.output;
}
}
module.exports = { nodeClass: ReActAgentLLM_Agents };
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