@n8n/n8n-nodes-langchain
Version:
139 lines • 5.84 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.ToolVectorStore = void 0;
const chains_1 = require("@langchain/classic/chains");
const tools_1 = require("@langchain/classic/tools");
const n8n_workflow_1 = require("n8n-workflow");
const ai_utilities_1 = require("@n8n/ai-utilities");
async function getTool(ctx, itemIndex) {
const node = ctx.getNode();
const { typeVersion } = node;
const name = typeVersion <= 1
? ctx.getNodeParameter('name', itemIndex)
: (0, n8n_workflow_1.nodeNameToToolName)(node);
const toolDescription = ctx.getNodeParameter('description', itemIndex);
const topK = ctx.getNodeParameter('topK', itemIndex, 4);
const description = tools_1.VectorStoreQATool.getDescription(name, toolDescription);
const vectorStore = (await ctx.getInputConnectionData(n8n_workflow_1.NodeConnectionTypes.AiVectorStore, itemIndex));
const llm = (await ctx.getInputConnectionData(n8n_workflow_1.NodeConnectionTypes.AiLanguageModel, itemIndex));
const vectorStoreTool = new tools_1.VectorStoreQATool(name, description, {
llm,
vectorStore,
});
vectorStoreTool.chain = chains_1.VectorDBQAChain.fromLLM(llm, vectorStore, {
k: topK,
});
return vectorStoreTool;
}
class ToolVectorStore {
constructor() {
this.description = {
displayName: 'Vector Store Question Answer Tool',
name: 'toolVectorStore',
icon: 'node:vector-store-question-answer-tool',
iconColor: 'black',
group: ['transform'],
version: [1, 1.1],
description: 'Answer questions with a vector store',
defaults: {
name: 'Answer questions with a vector store',
},
codex: {
categories: ['AI'],
subcategories: {
AI: ['Tools'],
Tools: ['Other Tools'],
},
resources: {
primaryDocumentation: [
{
url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.toolvectorstore/',
},
],
},
},
inputs: [
{
displayName: 'Vector Store',
maxConnections: 1,
type: n8n_workflow_1.NodeConnectionTypes.AiVectorStore,
required: true,
},
{
displayName: 'Model',
maxConnections: 1,
type: n8n_workflow_1.NodeConnectionTypes.AiLanguageModel,
required: true,
},
],
outputs: [n8n_workflow_1.NodeConnectionTypes.AiTool],
outputNames: ['Tool'],
builderHint: {
inputs: {
ai_vectorStore: { required: true },
ai_languageModel: { required: true },
},
},
properties: [
(0, ai_utilities_1.getConnectionHintNoticeField)([n8n_workflow_1.NodeConnectionTypes.AiAgent]),
{
displayName: 'Data Name',
name: 'name',
type: 'string',
default: '',
placeholder: 'e.g. users_info',
validateType: 'string-alphanumeric',
description: 'Name of the data in vector store. This will be used to fill this tool description: Useful for when you need to answer questions about [name]. Whenever you need information about [data description], you should ALWAYS use this. Input should be a fully formed question.',
displayOptions: {
show: {
'@version': [1],
},
},
},
{
displayName: 'Description of Data',
name: 'description',
type: 'string',
default: '',
placeholder: "[Describe your data here, e.g. a user's name, email, etc.]",
description: 'Describe the data in vector store. This will be used to fill this tool description: Useful for when you need to answer questions about [name]. Whenever you need information about [data description], you should ALWAYS use this. Input should be a fully formed question.',
typeOptions: {
rows: 3,
},
},
{
displayName: 'Limit',
name: 'topK',
type: 'number',
default: 4,
description: 'The maximum number of results to return',
},
],
};
}
async supplyData(itemIndex) {
const vectorStoreTool = await getTool(this, itemIndex);
return {
response: (0, ai_utilities_1.logWrapper)(vectorStoreTool, this),
};
}
async execute() {
const inputData = this.getInputData();
const result = [];
for (let itemIndex = 0; itemIndex < inputData.length; itemIndex++) {
const tool = await getTool(this, itemIndex);
const outputData = await tool.invoke(inputData[itemIndex].json);
result.push({
json: {
response: outputData,
},
pairedItem: {
item: itemIndex,
},
});
}
return [result];
}
}
exports.ToolVectorStore = ToolVectorStore;
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