n8n-nodes-neo4j/dist/nodes/Neo4j/Neo4jVectorStore.js

n8n node for Neo4j with LangChain integration, supporting vector stores and graph operations

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.createNeo4jVectorStore = createNeo4jVectorStore;
exports.searchNeo4jVectorStore = searchNeo4jVectorStore;
const neo4j_vector_1 = require("@langchain/community/vectorstores/neo4j_vector");
async function createNeo4jVectorStore(embeddings, config) {
    const storeConfig = {
        url: config.credentials.uri,
        username: config.credentials.username,
        password: config.credentials.password,
        database: config.credentials.database,
        indexName: config.indexName || 'vector',
        embeddingNodeProperty: "embedding",
        textNodeProperties: ["text"],
        retrievalQuery: config.retrievalQuery,
        distanceMetric: config.distanceMetric || 'COSINE',
        searchType: "vector",
    };
    return await neo4j_vector_1.Neo4jVectorStore.fromExistingIndex(embeddings, storeConfig);
}
async function searchNeo4jVectorStore(vectorStore, query, embeddings, metadataFilter = {}, k = 4) {
    const queryEmbedding = await embeddings.embedQuery(query);
    const results = await vectorStore.similaritySearchVectorWithScore(queryEmbedding, k, '', metadataFilter);
    return results.map(([doc, score]) => ({
        content: doc.pageContent,
        score: score,
        metadata: doc.metadata
    }));
}
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