LangChain Agent

Overview

You will be updating a LangChain agent, adding a set of tools, to interact with Neo4j.

In this lesson you will:

Review the agent code
Investigate how the agent works
Experiment with different queries

Continue with the lesson to create the text to Cypher retriever.

Agent

Open workshop-genai/agent.py.

python

agent.py

import os
from dotenv import load_dotenv
load_dotenv()

from neo4j import GraphDatabase
from langchain.chat_models import init_chat_model
from langchain.agents import create_agent
from langchain_core.tools import tool

# Initialize the chat model
model = init_chat_model("gpt-4o", model_provider="openai")

# Connect to Neo4j database
driver = GraphDatabase.driver(
    os.getenv("NEO4J_URI"), 
    auth=(
        os.getenv("NEO4J_USERNAME"), 
        os.getenv("NEO4J_PASSWORD")
    )
)

# Define functions for each tool in the agent

@tool("Get-graph-database-schema")
def get_schema():
    """Get the schema of the graph database."""
    results, summary, keys = driver.execute_query(
        "CALL db.schema.visualization()",
        database_=os.getenv("NEO4J_DATABASE")
    )
    return results

# Define a list of tools for the agent
tools = [get_schema]

# Create the agent with the model and tools
agent = create_agent(
    model, 
    tools
)

# Run the application
query = "Summarise the schema of the graph database."

for step in agent.stream(
    {
        "messages": [{"role": "user", "content": query}]
    },
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

Review the code and try to answer the following questions:

What is the agent’s function?
What do you think the response to the query will be?
How could you extend the agent?

Run the agent to see what it does.

Review

This program is a LangChain agent that uses a Neo4j database. The agent has access to a single tool which retrieves the database schema. The agent uses this tool to answer questions about the database structure.

The code:

Creates an OpenAI LLM model.

python

# Initialize the chat model
model = init_chat_model("gpt-4o", model_provider="openai")

Connects to your Neo4j database.

python

# Connect to Neo4j database
driver = GraphDatabase.driver(
    os.getenv("NEO4J_URI"), 
    auth=(
        os.getenv("NEO4J_USERNAME"), 
        os.getenv("NEO4J_PASSWORD")
    )
)

Defines a Get-graph-database-schema tool.

python

# Define functions for each tool in the agent

@tool("Get-graph-database-schema")
def get_schema():
    """Get the schema of the graph database."""
    results, summary, keys = driver.execute_query(
        "CALL db.schema.visualization()",
        database_=os.getenv("NEO4J_DATABASE")
    )
    return results

# Define a list of tools for the agent
tools = [get_schema]

The tool uses the Neo4j driver to get the database schema and return it as a string.

Determine what tool to use

The agent will use the tool’s name (Get-graph-database-schema), and docstring (Get the schema of the graph database.) to determine whether it should execute the tool to resolve a user’s query.

Creates a react (Reasoning and Acting) agent using the model and tools.

python

# Create the agent with the model and tools
agent = create_agent(
    model, 
    tools
)

Runs the agent, passing the query and streams the response.

python

# Run the application
query = "Summarise the schema of the graph database."

for step in agent.stream(
    {
        "messages": [{"role": "user", "content": query}]
    },
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

When you run the agent, you will see:

The messages between Human, AI, and Tool
The context of the database schema
The agent’s final response

Experiment

Experiment with agent, modify the query to ask different questions, for example:

"What questions can I answer using this graph database?"
"How are concepts related to other entities?"
"How does the graph model relate technologies to benefits?"

Lesson Summary

In this lesson, you reviewed an agent which can answer questions about a Neo4j database schema.

In the next lesson, you will add a vector + cypher tool to the agent to enable semantic search capabilities.

Neo4j and Generative AI Workshop

Generative AI

Knowledge Graph Construction

Retrieval

Agents

LangChain Agent

Overview

Agent

Review

Experiment

Lesson Summary

Chatbot