## neo4j-semantic-ollama

This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using Mixtral as a JSON-based agent. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph database 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) and specifically about [Mixtral agents with Ollama](https://blog.langchain.dev/json-based-agents-with-ollama-and-langchain/).

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.
4.  **Smalltalk Tool**:
    *   Allows an agent to deal with smalltalk.

## Environment Setup[​](#environment-setup "Direct link to Environment Setup")

Before using this template, you need to set up Ollama and Neo4j database.

1.  Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama.
    
2.  Download your LLM of interest:
    
    *   This package uses `mixtral`: `ollama pull mixtral`
    *   You can choose from many LLMs [here](https://ollama.ai/library)

You need to define the following environment variables

```
OLLAMA_BASE_URL=<YOUR_OLLAMA_URL>NEO4J_URI=<YOUR_NEO4J_URI>NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
```

## Populating with data[​](#populating-with-data "Direct link to 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[​](#usage "Direct link to Usage")

To use this package, you should first have the LangChain CLI installed:

```
pip install -U "langchain-cli[serve]"
```

To create a new LangChain project and install this as the only package, you can do:

```
langchain app new my-app --package neo4j-semantic-ollama
```

If you want to add this to an existing project, you can just run:

```
langchain app add neo4j-semantic-ollama
```

And add the following code to your `server.py` file:

```
from neo4j_semantic_layer import agent_executor as neo4j_semantic_agentadd_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-ollama")
```

(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

```
export LANGCHAIN_TRACING_V2=trueexport 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:

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-ollama/playground](http://127.0.0.1:8000/neo4j-semantic-ollama/playground)

We can access the template from code with:

```
from langserve.client import RemoteRunnablerunnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-ollama")
```