llama-index-tools-neo4j 0.1.3

llama-index tools neo4j integration

Neo4j Schema Query Builder

The Neo4jQueryToolSpec class provides a way to query a Neo4j graph database based on a provided schema definition. The class uses a language model to generate Cypher queries from user questions and has the capability to recover from Cypher syntax errors through a self-healing mechanism.

Table of Contents

• Usage
  • Initialization
  • Running a Query
• Features

Usage

Initialization

Initialize the Neo4jQueryToolSpec class with:

from llama_index.tools.neo4j_db import Neo4jQueryToolSpec
from llama_index.llms import OpenAI
from llama_index.agent import OpenAIAgent

llm = OpenAI(model="gpt-4", openai_api_key="XXXX-XXXX", temperature=0)

gds_db = Neo4jQueryToolSpec(
    url="neo4j-url",
    user="neo4j-user",
    password="neo4j=password",
    llm=llm,
    database="neo4j",
)

tools = gds_db.to_tool_list()
agent = OpenAIAgent.from_tools(tools, verbose=True)

Where:

• url: Connection string for the Neo4j database.
• user: Username for the Neo4j database.
• password: Password for the Neo4j database.
• llm: A language model for generating Cypher queries (any type of LLM).
• database: The database name.

Running a Query

To use the agent:

# use agent
agent.chat("Where is JFK airport is located?")

Generated Cypher:

MATCH (p:Port {port_code: 'JFK'})
RETURN p.location_name_wo_diacritics AS Location

Final answer:
'The port code JFK is located in New York, United States.'

Features

• Schema-Based Querying: The class extracts the Neo4j database schema to guide the Cypher query generation.
• Self-Healing: On a Cypher syntax error, the class corrects itself to produce a valid query.
• Language Model Integration: Uses a language model for natural and accurate Cypher query generation.