by tomasonjo · Agent Tool · ★ 244
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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. To start the project, run the following command: Open in your browser to interact with the agent. Tools The agent utilizes several tools to interact with the Neo4j graph database effectively: Information tool: Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information. Recommendation Tool: Provides movie recommendations based upon user preferences and input. 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 file. OPENAIAPIKEY= NEO4JURI= NEO4JUSERNAME= NEO4JPASSWORD
| Stars | 244 |
| Forks | 40 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 49.2/100 |
| Open Issues | 3 |
| Last Updated | 2024-04-25 |
| Created | 2024-01-07 |
| Platforms | python |
| Est. Tokens | ~14k |
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llm-movieagent is Semantic layer on top of a graph database to provide an LLM with a set of robust tools to interact with the database. It is categorized as a Agent Tool with 244 GitHub stars.
llm-movieagent is primarily written in Python. It covers topics such as agent, function-calling, langchain.
You can find installation instructions and usage details in the llm-movieagent GitHub repository at github.com/tomasonjo/llm-movieagent. The project has 244 stars and 40 forks, indicating an active community.
llm-movieagent is released under the MIT license, making it free to use and modify according to the license terms.
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