Add neo4j vector memory template (#12993)

templates/neo4j-vector-memory/README.md

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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. 
+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=<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")
+```