LangChain JS/TS Integration with FalkorDB

The @falkordb/langchain-ts package enables developers to integrate FalkorDB with LangChain applications. The integration allows applications to accept natural language questions, generate Cypher queries automatically, retrieve relevant context from the graph database, and return responses in natural language.

Installation

Step 1

npm install @falkordb/langchain-ts falkordb

Step 2

Ensure LangChain and a language model are installed

npm install langchain @langchain/openai

Getting Started

Movie data example

In this example, we’ll initialize the connection to FalkorDB, define a language model (E.g, OpenAI), and both create and populate the graph with movie-related data. We’ll then query the graph in natural language to see the integration at work.

Note: You can change the LLM’s temperature

import { FalkorDBGraph } from "@falkordb/langchain-ts";
import { ChatOpenAI } from "@langchain/openai";
import { GraphCypherQAChain } from "@langchain/community/chains/graph_qa/cypher";

// Initialize FalkorDB connection
const graph = await FalkorDBGraph.initialize({
  host: "localhost",
  port: 6379,
  graph: "movies"
});

// Set up the language model
const model = new ChatOpenAI({ temperature: 0 });

// Create and populate the graph with some data
await graph.query(
  "CREATE (a:Actor {name:'Bruce Willis'})" +
  "-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})"
);

// Refresh the graph schema
await graph.refreshSchema();

// Create a graph QA chain
const chain = GraphCypherQAChain.fromLLM({
  llm: model,
  graph: graph as any,
});

// Ask questions about your graph
const response = await chain.run("Who played in Pulp Fiction?");
console.log(response);
// Output: Bruce Willis played in Pulp Fiction.

// Clean up
await graph.close();

API Reference

FalkorDBGraph

The following command creates and initializes a new FalkorDB connection.

initialize(config: FalkorDBGraphConfig): Promise<FalkorDBGraph>

Config Options:

• host (string, optional): Database host. Default: "localhost"
• port (number, optional): Database port. Default: 6379
• graph (string, optional): Graph name to use
• url (string, optional): Alternative connection URL format
• enhancedSchema (boolean, optional): Enable enhanced schema details. Default: false

Example:

const graph = await FalkorDBGraph.initialize({
  host: "localhost",
  port: 6379,
  graph: "myGraph",
  enhancedSchema: true
});

query(query: string): Promise<any>

Executes a Cypher query on the graph.

const result = await graph.query(
  "MATCH (n:Person) RETURN n.name LIMIT 10"
);

refreshSchema(): Promise<void>

Updates the graph schema information.

await graph.refreshSchema();
console.log(graph.getSchema());

getSchema(): string

Returns the current graph schema as a formatted string.

getStructuredSchema(): StructuredSchema

Returns the structured schema object containing node properties, relationship properties, and relationships.

close(): Promise<void>

Closes the database connection.

await graph.close();

Advanced Usage

Custom Cypher Queries

const graph = await FalkorDBGraph.initialize({
  host: "localhost",
  port: 6379,
  graph: "movies"
});

// Complex query
const result = await graph.query(`
  MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)
  WHERE m.year > 2000
  RETURN a.name, m.title, m.year
  ORDER BY m.year DESC
  LIMIT 10
`);

console.log(result.data);

Multiple Queries

await graph.executeQueries([
  "CREATE (p:Person {name: 'Alice'})",
  "CREATE (p:Person {name: 'Bob'})",
  "MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'}) CREATE (a)-[:KNOWS]->(b)"
]);

Working with Schema

await graph.refreshSchema();

// Get formatted schema
const schema = graph.getSchema();
console.log(schema);

// Get structured schema
const structuredSchema = graph.getStructuredSchema();
console.log(structuredSchema.nodeProps);
console.log(structuredSchema.relationships);

Prerequisites

• Node.js >= 18
• FalkorDB server running
• LangChain >= 0.1.0

Additional Examples

For more examples and source code, see the @falkordb/langchain-ts repository on GitHub.