n8n-nodes-databricks/dist/nodes/vector_store/VectorStoreDatabricks/VectorStoreDatabricks.node.js

Databricks node for n8n

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.VectorStoreDatabricks = void 0;
const createVectorStoreNode_1 = require("../shared/createVectorStoreNode/createVectorStoreNode");
const DatabricksVectorStoreLangChain_1 = require("../../../utils/DatabricksVectorStoreLangChain");
class VectorStoreDatabricks extends (0, createVectorStoreNode_1.createVectorStoreNode)({
    meta: {
        displayName: 'Databricks Vector Store',
        name: 'databricksVectorStore',
        description: 'Operations for Databricks Vector Store',
        docsUrl: 'https://docs.databricks.com/aws/en/generative-ai/vector-search',
        icon: 'file:databricks.svg',
        operationModes: ['load', 'insert', 'retrieve', 'retrieve-as-tool'],
        credentials: [
            {
                name: 'databricks',
                required: true,
            },
        ],
    },
    sharedFields: [
        {
            displayName: 'Index Name',
            name: 'indexName',
            type: 'string',
            default: '',
            required: true,
            description: 'Name of the vector search index in Databricks',
        },
        {
            displayName: 'Text Column',
            name: 'textColumn',
            type: 'string',
            default: 'text',
            required: true,
            description: 'Name of the column containing the document text',
        },
        {
            displayName: 'Metadata Columns',
            name: 'metadataColumns',
            type: 'string',
            default: '',
            description: 'Comma-separated list of columns to include as metadata',
        },
        {
            displayName: 'Score Threshold',
            name: 'scoreThreshold',
            type: 'number',
            default: 0.8,
            description: 'Minimum similarity score threshold',
        },
    ],
    retrieveFields: [],
    async getVectorStoreClient(context, filter, embeddings, itemIndex) {
        const credentials = await context.getCredentials('databricks');
        const indexName = context.getNodeParameter('indexName', itemIndex);
        const textColumn = context.getNodeParameter('textColumn', itemIndex);
        const metadataColumns = context.getNodeParameter('metadataColumns', itemIndex);
        const scoreThreshold = context.getNodeParameter('scoreThreshold', itemIndex, 0.8);
        if (!indexName || !textColumn) {
            throw new Error('Missing required input properties: indexName or textColumn');
        }
        const metadataColumnsList = metadataColumns
            ? metadataColumns.split(',').map((s) => s.trim())
            : [];
        return await DatabricksVectorStoreLangChain_1.DatabricksVectorStoreLangChain.fromExistingIndex(embeddings, {
            workspaceUrl: credentials.host,
            token: credentials.token,
            indexName,
            textColumn,
            metadataColumns: metadataColumnsList,
            scoreThreshold,
        });
    },
    async populateVectorStore(context, embeddings, documents, itemIndex) {
        const credentials = await context.getCredentials('databricks');
        const inputData = context.getInputData()[itemIndex].json;
        if (!(inputData === null || inputData === void 0 ? void 0 : inputData.indexName) || !(inputData === null || inputData === void 0 ? void 0 : inputData.textColumn)) {
            throw new Error('Missing required input properties: indexName or textColumn');
        }
        const metadataColumnsList = inputData.metadataColumns ?
            inputData.metadataColumns.split(',').map((s) => s.trim()) :
            [];
        await DatabricksVectorStoreLangChain_1.DatabricksVectorStoreLangChain.fromDocuments(documents, embeddings, {
            workspaceUrl: credentials.host,
            token: credentials.token,
            indexName: inputData.indexName,
            textColumn: inputData.textColumn,
            metadataColumns: metadataColumnsList,
        });
    },
    async releaseVectorStoreClient() {
    },
}) {
}
exports.VectorStoreDatabricks = VectorStoreDatabricks;
exports.default = VectorStoreDatabricks;
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