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# 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=<YOUR_OPENAI_API_KEY>
NEO4J_URI=<YOUR_NEO4J_URI>
NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
NEO4J_PASSWORD=<YOUR_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=<your-api-key>
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:
```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")
```
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