This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph databas based on the user's intent. Learn more about the semantic layer template in the corresponding blog post.
The agent utilizes several tools to interact with the Neo4j graph database effectively:
- Information tool:
- Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information.
- Recommendation Tool:
- Provides movie recommendations based upon user preferences and input.
- Memory Tool:
- Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions.
You need to define the following environment variables
Populating with data
If you want to populate the DB with an example movie dataset, you can run
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.
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-layer
If you want to add this to an existing project, you can just run:
langchain app add neo4j-semantic-layer
And add the following code to your
from neo4j_semantic_layer import agent_executor as neo4j_semantic_agent
add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-layer")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
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-layer/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-layer")