# neo4j-vector-memory This template allows you to integrate an LLM with a vector-based retrieval system using Neo4j as the vector store. Additionally, it uses the graph capabilities of the Neo4j database to store and retrieve the dialogue history of a specific user's session. Having the dialogue history stored as a graph allows for seamless conversational flows but also gives you the ability to analyze user behavior and text chunk retrieval through graph analytics. ## 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 some example data, you can run `python ingest.py`. The script process and stores sections of the text from the file `dune.txt` into a Neo4j graph database. Additionally, a vector index named `dune` is created for efficient querying of these embeddings. ## 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-vector-memory ``` If you want to add this to an existing project, you can just run: ```shell langchain app add neo4j-vector-memory ``` And add the following code to your `server.py` file: ```python from neo4j_vector_memory import chain as neo4j_vector_memory_chain add_routes(app, neo4j_vector_memory_chain, path="/neo4j-vector-memory") ``` (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-vector-memory/playground](http://127.0.0.1:8000/neo4j-parent/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/neo4j-vector-memory") ```