Neo4j, Ollama - Semantic Layer

This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using Mixtral as a JSON-based agent. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph database based on the user's intent. Learn more about the semantic layer template in the corresponding blog post and specifically about Mixtral agents with Ollama package.

"Neo4j Semantic Layer Workflow Diagram"

Tools

The agent utilizes several tools to interact with the Neo4j graph database effectively:

1. Information tool:
   • Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information.
2. Recommendation Tool:
   • Provides movie recommendations based upon user preferences and input.
3. Memory Tool:
   • Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions.
4. Smalltalk Tool:
   • Allows an agent to deal with smalltalk.

Environment Setup

Before using this template, you need to set up Ollama and Neo4j database.

1. Follow instructions here to download Ollama.

2. Download your LLM of interest:

   • This package uses mixtral: ollama pull mixtral
   • You can choose from many LLMs here

You need to define the following environment variables

OLLAMA_BASE_URL=<YOUR_OLLAMA_URL>
NEO4J_URI=<YOUR_NEO4J_URI>
NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>

Typically for a local Ollama installation:

export OLLAMA_BASE_URL="http://127.0.0.1:11434"

Populating with data

If you want to populate the DB with an example movie dataset, you can run python ingest.py. The script import information about movies and their rating by users. Additionally, the script creates two fulltext indices, which are used to map information from user input to the database.

As an aternative, you can use the demo neo4j recommendations database:

export NEO4J_URI="neo4j+s://demo.neo4jlabs.com"
export NEO4J_USERNAME="recommendations"
export NEO4J_PASSWORD="recommendations"
export NEO4J_DATABASE="recommendations"

Usage

To use this package, you should first have the LangChain CLI installed:

pip install -U "langchain-cli[serve]"

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package neo4j-semantic-ollama

If you want to add this to an existing project, you can just run:

langchain app add neo4j-semantic-ollama

And, from within the project, add the following code to your app/server.py file, replacing the add_routes(app, NotImplemented) section:

from neo4j_semantic_ollama import agent_executor as neo4j_semantic_agent

add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-ollama")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

If you are inside the top-level project directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/neo4j-semantic-ollama/playground

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-ollama")