dtamind-components
Version:
DTAmindai Components
85 lines • 3.49 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const chains_1 = require("langchain/chains");
const handler_1 = require("../../../src/handler");
const utils_1 = require("../../../src/utils");
const Moderation_1 = require("../../moderation/Moderation");
const OutputParserHelpers_1 = require("../../outputparsers/OutputParserHelpers");
class VectorDBQAChain_Chains {
constructor() {
this.label = 'VectorDB QA Chain';
this.name = 'vectorDBQAChain';
this.version = 2.0;
this.type = 'VectorDBQAChain';
this.icon = 'vectordb.svg';
this.category = 'Chains';
this.badge = 'DEPRECATING';
this.description = 'QA chain for vector databases';
this.baseClasses = [this.type, ...(0, utils_1.getBaseClasses)(chains_1.VectorDBQAChain)];
this.inputs = [
{
label: 'Language Model',
name: 'model',
type: 'BaseLanguageModel'
},
{
label: 'Vector Store',
name: 'vectorStore',
type: 'VectorStore'
},
{
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
}
];
}
async init(nodeData) {
const model = nodeData.inputs?.model;
const vectorStore = nodeData.inputs?.vectorStore;
const chain = chains_1.VectorDBQAChain.fromLLM(model, vectorStore, {
k: vectorStore?.k ?? 4,
verbose: process.env.DEBUG === 'true' ? true : false
});
return chain;
}
async run(nodeData, input, options) {
const chain = nodeData.instance;
const moderations = nodeData.inputs?.inputModeration;
const shouldStreamResponse = options.shouldStreamResponse;
const sseStreamer = options.sseStreamer;
const chatId = options.chatId;
if (moderations && moderations.length > 0) {
try {
// Use the output of the moderation chain as input for the VectorDB QA Chain
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);
}
}
const obj = {
query: input
};
const loggerHandler = new handler_1.ConsoleCallbackHandler(options.logger, options?.orgId);
const callbacks = await (0, handler_1.additionalCallbacks)(nodeData, options);
if (shouldStreamResponse) {
const handler = new handler_1.CustomChainHandler(sseStreamer, chatId);
const res = await chain.call(obj, [loggerHandler, handler, ...callbacks]);
return res?.text;
}
else {
const res = await chain.call(obj, [loggerHandler, ...callbacks]);
return res?.text;
}
}
}
module.exports = { nodeClass: VectorDBQAChain_Chains };
//# sourceMappingURL=VectorDBQAChain.js.map