# neo4j-semantic-layer 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](https://medium.com/towards-data-science/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49). ![Diagram illustrating the workflow of the Neo4j semantic layer with an agent interacting with tools like Information, Recommendation, and Memory, connected to a knowledge graph.](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-semantic-layer/static/workflow.png "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. ## Environment Setup You need to define the following environment variables ``` OPENAI_API_KEY= NEO4J_URI= NEO4J_USERNAME= NEO4J_PASSWORD= ``` ## 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](https://neo4j.com/docs/cypher-manual/current/indexes-for-full-text-search/), which are used to map information from user input to the database. ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U "langchain-cli[serve]" ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package neo4j-semantic-layer ``` If you want to add this to an existing project, you can just run: ```shell langchain app add neo4j-semantic-layer ``` And add the following code to your `server.py` file: ```python 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. You can sign up for LangSmith [here](https://smith.langchain.com/). If you don't have access, you can skip this section ```shell export LANGCHAIN_TRACING_V2=true export LANGCHAIN_API_KEY= export LANGCHAIN_PROJECT= # if not specified, defaults to "default" ``` If you are inside this directory, then you can spin up a LangServe instance directly by: ```shell langchain serve ``` This will start the FastAPI app with a server is running locally at [http://localhost:8000](http://localhost:8000) We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) We can access the playground at [http://127.0.0.1:8000/neo4j-semantic-layer/playground](http://127.0.0.1:8000/neo4j-semantic-layer/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-layer") ```