Unleash CHATGPT Magic: Turbocharge Your Graph Database for Intelligent Conversations with LangChain and Neo4j

In the dynamic landscape of artificial intelligence, the ascent of generative AI models has ushered in a new era of possibilities. One of the pivotal elements in harnessing the full potential of these models is the integration of chat interfaces with internal data. This article delves into the crucial role of building chat interfaces that leverage internal data, with a focus on the integration of Neo4j, a powerful graph database.

Graph Databases: A Powerful Backbone for LLM Applications

Large Language Models (LLMs) thrive on their ability to comprehend and manage complex, interconnected data. This aligns seamlessly with the capabilities of graph databases, making them an invaluable asset for LLM applications. Notably, graph databases like Gemini Explore and Neo4j excel at representing and connecting heterogeneous data sources. This aligns perfectly with the contextual understanding and relationship management capabilities of LLMs.

By leveraging graph databases, such as Neo4j, LLMs can generate more accurate and context-aware responses. For instance, in the context of a customer support chatbot, integrating an LLM with Neo4j empowers the chatbot to provide personalized and contextually relevant solutions by drawing insights from the rich graph structure.

Furthermore, graph databases prove instrumental in representing and connecting diverse data sources. This enables LLMs to enhance knowledge graphs by extracting structured information from unstructured data. This enriched knowledge graph, powered by Neo4j, can be applied in various domains such as advanced analytics, medical research, and personalized patient care in healthcare.

Rapid Development with LangChain

To expedite the development of our chat interface, we'll leverage LangChain, a powerful tool that simplifies the creation of LLM applications. Let's start by installing LangChain:

pip install -U langchain-cli

Now, we'll create a new LangChain project using the Neo4j Cypher template:

langchain app new neo-test --package neo4j-cypher-ft

LangChain's templates streamline the code generation setup for your project.

Environment Setup

Before delving into the development, ensure that your environment variables are set up correctly. This includes credentials for both OpenAI and Neo4j:

export OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
export NEO4J_URI=<YOUR_NEO4J_URI>
export NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
export NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>

Ingesting Data

To populate your Neo4j database with test data, run:

python ingest.py

Configuring the Server

Add the following code to your server.py file:

from neo4j_cypher_ft import chain as neo4j_cypher_ft_chain

add_routes(app, neo4j_cypher_ft_chain, path="/neo4j-cypher-ft")

LangSmith Integration (Optional)

If you wish to use LangSmith for tracing and debugging, set up the following environment variables:

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>

Serving the App

Run the following command within your application directory:

langchain serve

For containerization, build and run the Docker image:

docker build . -t my-langserve-app
docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -e NEO4J_URI=$NEO4J_URI -e NEO4J_USERNAME=$NEO4J_USERNAME -e NEO4J_PASSWORD=$NEO4J_PASSWORD -p 8080:8080 my-langserve-app

Testing with Curl

Test your application using the following curl command:

curl -X POST -H "Content-Type: application/json" -d '{"input": {"question": "Who are the co-actors of Tom Cruise in any movie?"}}' http://localhost:8000/neo4j-cypher-ft/invoke

Feel free to experiment with more questions and use the LangChain playground for thorough testing.

In conclusion, the fusion of generative AI models, graph databases like Neo4j, and tools like LangChain represents a paradigm shift in the development of intelligent chat interfaces. The amalgamation of these technologies opens up a world of possibilities, from personalized customer support to advanced analytics in healthcare. LangChain's innovative approach to code generation significantly accelerates the development process, empowering developers to build sophisticated LLM applications effortlessly.

By embracing this holistic approach, businesses can position themselves at the forefront of technological innovation, delivering enhanced user experiences and making informed decisions backed by the power of generative AI and graph databases. The future of conversational AI is here, and it's powered by a harmonious integration of cutting-edge technologies.

Explore the complete codebase on GitHub: neotest - LangChain and Neo4j Integration