llm-movieagent
# 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).  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!