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GraphRAG with Neo4j & LangChain: Building AI Agents (Part 5)

Science & Technology

Introduction

Welcome back to our series on building a robust AI-powered Graph application from scratch using Neo4j and LangChain! In this fifth installment, we focus on the creation and management of AI agents within this system. Leaning heavily on previous discussions—from manual data pipelines to natural language processing—we'll dive deeper into developing custom AI tools that empower these agents.

Project Overview

As a brief recap, we've previously established a manual pipeline for ingesting data into our Neo4j graph database. We also built an automated pipeline and explored the fundamentals of LangChain, including how to convert natural language into Cypher queries. In today's video, we will take another significant step: constructing an AI agent equipped with a toolbox of custom tools.

What is an AI Agent?

AI agents can be viewed as digital assistants with a specially curated toolbox that enables them to interact with the external world. With a suite of tools, agents can execute a variety of functions, including calling APIs, performing internet searches, and more. In our case, these tools will enhance our agents' ability to perform similarity searches in our knowledge graph and execute Cypher codes.

This tutorial will focus on two primary tools:

  1. Employee QA Tool: This allows us to perform similarity searches about employee data using embeddings within our enriched knowledge graph.

  2. General QA Tool: This tool converts natural language questions into Cypher code, executing that code to retrieve relevant information from the graph.

Building Custom AI Tools

Employee QA Tool

First, let's create our Employee QA tool. The tool will leverage a function designed to interpret user questions about employee data. Below is a high-level outline of the steps involved in building this tool:

## Introduction

General QA Tool

Next, we’ll develop the General QA Tool that handles broader queries outside of employee-focused questions. The tool will convert the input natural language into Cypher code, execute that code, and return results.

## Introduction

Creating the Agent

Once we have our tools prepared, we can move on to developing the AI agent. This agent will utilize both tools efficiently, depending on the nature of the user's inquiry. Here’s a simplified breakdown of the creation process:

## Introduction

To initiate the agent, we will:

  1. Import the necessary libraries and modules.
  2. Load our tools.
  3. Setup the LLM (Language Model) to synthesize responses based on tool outputs.
  4. Create agent prompts and utilize them in conjunction with our tools.

Testing Our Setup

It's time to test our tools and agent to ensure they respond accurately to user queries. For instance, if we ask, "What is the address of Andrew?" the agent should utilize the Employee QA tool to fetch the correct data.

## Introduction

The agent's behavior can be observed as it cleverly chooses to use different tools based on the questions asked. This dynamic interaction demonstrates the practical capabilities of AI agents built with LangChain and Neo4j.

Conclusion

In this tutorial, we covered the foundational aspects of creating AI agents. You learned how to build two custom tools: the Employee QA tool for retrieving specific employee information and the General QA tool for broader queries. In the upcoming video, we will extend this knowledge by creating a FastAPI-based API, utilizing Docker for containerization, to expose our agents for seamless interaction with other applications.

Thank you for following along! Your continued support encourages more informative content. Don't forget to subscribe, like, and connect through various platforms for future updates and resources.

Keywords

Neo4j, LangChain, AI Agents, Employee QA Tool, General QA Tool, Knowledge Graph, Cypher Queries, API Development

FAQ

Q1: What tools have been created in this tutorial?
A1: The tutorial covers the development of two main tools: the Employee QA Tool for querying employee data and the General QA Tool for broader queries.

Q2: How do these AI agents work with the Neo4j graph?
A2: The agents utilize the tools to perform similarity searches and translate natural language into Cypher code, interacting with the knowledge graph to retrieve relevant information.

Q3: What is the purpose of the General QA Tool?
A3: The General QA Tool is designed to convert user natural language questions into Cypher queries, allowing for the retrieval of data from the knowledge graph.

Q4: What is the next step after building the agent?
A4: The next tutorial will focus on creating a FastAPI application that leverages these agents, allowing for API-based querying and data retrieval.