LangchainJS

import { OpenAIEmbeddings } from "@langchain/openai";
import { Neo4jVectorStore } from "@langchain/community/vectorstores/neo4j_vector";
// Configuration object for Neo4j connection and other related settings
const config = {
 url: "bolt://localhost:7687", // URL for the Neo4j instance
 username: "neo4j", // Username for Neo4j authentication
 password: "pleaseletmein", // Password for Neo4j authentication
 indexName: "vector", // Name of the vector index
 keywordIndexName: "keyword", // Name of the keyword index if using hybrid search
 searchType: "vector" as const, // Type of search (e.g., vector, hybrid)
 nodeLabel: "Chunk", // Label for the nodes in the graph
 textNodeProperty: "text", // Property of the node containing text
 embeddingNodeProperty: "embedding", // Property of the node containing embedding
};
const documents = [
 { pageContent: "what's this", metadata: { a: 2 } },
 { pageContent: "Cat drinks milk", metadata: { a: 1 } },
];
const neo4jVectorIndex = await Neo4jVectorStore.fromDocuments(
 documents,
 new OpenAIEmbeddings(),
 config
);
const results = await neo4jVectorIndex.similaritySearch("water", 1);
console.log(results);
/*
 [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]
*/
await neo4jVectorIndex.close();

Hybrid search combines vector search with fulltext search with re-ranking and de-duplication of the results.

import { Neo4jGraph } from "@langchain/community/graphs/neo4j_graph";
const graph = await Neo4jGraph.initialize({ NEO4J_URL, NEO4J_USERNAME, NEO4J_PASSWORD });
QUERY = """
"MATCH (m:Movie)-[:IN_GENRE]->(:Genre {name:$genre})
RETURN m.title, m.plot
ORDER BY m.imdbRating DESC LIMIT 5"
"""
await graph.query(QUERY, genre="action")

import { Neo4jGraph } from "@langchain/community/graphs/neo4j_graph";
import { OpenAI } from "@langchain/openai";
import { GraphCypherQAChain } from "langchain/chains/graph_qa/cypher";
const graph = await Neo4jGraph.initialize({ NEO4J_URL, NEO4J_USERNAME, NEO4J_PASSWORD });
const model = new OpenAI({ temperature: 0 });
// Populate the database with two nodes and a relationship
await graph.query(`
 CREATE (a:Actor {name:'Bruce Willis'})
 -[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})
`);
await graph.refreshSchema();
const chain = GraphCypherQAChain.fromLLM({ llm: model, graph });
const res = await chain.run("Who acted in Pulp Fiction?");
// Bruce Willis acted in Pulp Fiction.