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llm-movieagent

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Semantic layer on top of a graph database to provide an LLM with a set of robust tools to interact with the database

// repository documentation

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# llm-movieagent

This project is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling.
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. Read more in the [blog post](https://towardsdatascience.com/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49).

![Workflow diagram](https://raw.githubusercontent.com/tomasonjo/llm-movieagent/main/api/packages/neo4j-semantic-layer/static/workflow.png)

To start the project, run the following command:

```
docker-compose up
```

Open `http://localhost:8501` in your browser to interact with the agent.

## Tools

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.

## Environment Setup

You need to define the following environment variables in the `.env` file.

```
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
NEO4J_URI=<YOUR_NEO4J_URI>
NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
```

## Docker containers

This project contains the following services wrapped as docker containers

1. **Neo4j**:
   - Neo4j, a graph database, is used to store the information about actors, movies, and their ratings.
2. **API**:
   - Uses LangChain's `neo4j-semantic-layer` template to implement the OpenAI LLM and function calling capabilities.
3. **UI**:
   - Simple streamlit chat user interface. Available on `localhost:8501`.

## Populating with data

If you want to populate the DB with an example movie dataset, you can run [`ingest.py`](api/ingest.py).
The script imports information about movies and their rating by users. 
To run within the api docker container (recommended) do the following:
```bash
# access container shell
docker exec -it <container id for llm-movieagent-api> bash
# run script
python ingest.py
```
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.
The dataset is based on the MovieLens dataset, which is also available as the `Recommendation` project in [Neo4j Sandbox](https://sandbox.neo4j.com/?usecase=recommendations).

## Contributions

Contributions are welcomed!

llm-movieagent

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