# neo4j-generation This template pairs LLM-based knowledge graph extraction with Neo4j AuraDB, a fully managed cloud graph database. You can create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve). When you initiate a free database instance, you'll receive credentials to access the database. This template is flexible and allows users to guide the extraction process by specifying a list of node labels and relationship types. For more details on the functionality and capabilities of this package, please refer to [this blog post](https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/). ## Environment Setup You need to set the following environment variables: ``` OPENAI_API_KEY= NEO4J_URI= NEO4J_USERNAME= NEO4J_PASSWORD= ``` ## Usage To use this package, you should first have the LangChain CLI installed: ```shell pip install -U langchain-cli ``` To create a new LangChain project and install this as the only package, you can do: ```shell langchain app new my-app --package neo4j-generation ``` If you want to add this to an existing project, you can just run: ```shell langchain app add neo4j-generation ``` And add the following code to your `server.py` file: ```python from neo4j_generation.chain import chain as neo4j_generation_chain add_routes(app, neo4j_generation_chain, path="/neo4j-generation") ``` (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](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-generation/playground](http://127.0.0.1:8000/neo4j-generation/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/neo4j-generation") ```