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https://github.com/tomasonjo/llm-movieagent
Semantic layer on top of a graph database to provide an LLM with a set of robust tools to interact with the database
https://github.com/tomasonjo/llm-movieagent
agent function-calling langchain llm neo4j openai streamlit
Last synced: 7 days ago
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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
- Host: GitHub
- URL: https://github.com/tomasonjo/llm-movieagent
- Owner: tomasonjo
- License: mit
- Created: 2024-01-07T07:55:14.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-25T05:10:00.000Z (9 months ago)
- Last Synced: 2025-01-08T02:12:12.339Z (14 days ago)
- Topics: agent, function-calling, langchain, llm, neo4j, openai, streamlit
- Language: Python
- Homepage:
- Size: 211 KB
- Stars: 212
- Watchers: 9
- Forks: 38
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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=
NEO4J_URI=
NEO4J_USERNAME=
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 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!