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LangChain.js is a JavaScript/TypeScript implementation of the LangChain library. It uses similar concepts, with Prompts, Chains, Transformers, Document Loaders, Agents, and more.

Here is an overview of the Graph Integrations.

The Neo4j Integration makes the Neo4j Vector index as well as Cypher generation and execution available in the LangChain.js library.

Learn how to build a chatbot in TypeScript using LangChain.js with our new GraphAcademy course.

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npm install @langchain/openai neo4j-driver @langchain/community

Functionality Includes


The Neo4j Vector integration supports a number of operations

  • create vector from langchain documents

  • query vector

  • query vector with additional graph retrieval Cypher query

  • construct vector instance from existing graph data

  • hybrid search

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(
  new OpenAIEmbeddings(),

const results = await neo4jVectorIndex.similaritySearch("water", 1);


  [ 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.

Neo4j Graph

The Neo4j Graph integration is a wrapper for the Neo4j Python driver. It allows querying and updating the Neo4j databsae in a simplified manner from LangChain. Many integrations allow to use the Neo4j Graph as a source of data for LangChain.

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")


The CypherQAChain is a LangChain component that allows you to interact with a Neo4j graph database in natural language. Using an LLM and the graph schema it translates the user question into a Cypher query, executes it against the graph and uses the returned context information and the original question with a second LLM to generate a natural language response.

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"Who acted in Pulp Fiction?");
// Bruce Willis acted in Pulp Fiction.

Knowledge Graph Construction

Creating a Knowledge Graph from unstructured data like PDF documents used to be a complex and time-consuming task that required training and using dedicated, large NLP models.

The Graph Transformers are tools that allows you to extract structured data from unstructured documents and transform it into a Knowledge Graph.

Videos & Tutorials