GraphAgent
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Graphagent
Overview :
GraphAgent is an automated agent pipeline designed to manage explicit graphic dependencies and implicit semantic interdependencies to suit real-world data scenarios involving predictive tasks (e.g., node classification) and generative tasks (e.g., text generation). It consists of three key components: a graphical generation agent that constructs knowledge graphs reflecting complex semantic dependencies; a planning agent that interprets various user queries and formulates corresponding tasks; and an execution agent that efficiently performs planned tasks while automating tool matching and invocation. GraphAgent reveals intricate relational information and data semantic dependencies by integrating language and graphical language models.
Target Users :
The target audience for GraphAgent includes data scientists, machine learning engineers, and researchers who need to handle complex graphical data and semantic dependencies for predictive and generative tasks. This product enhances their efficiency and accuracy by automating and integrating advanced language models.
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Use Cases
Using GraphAgent for node classification to identify key influencers within social networks.
Utilizing GraphAgent to generate technical documentation by comprehending complex interrelations among documents to create summaries.
Applying GraphAgent in recommendation systems to enhance recommendation accuracy through analysis of user behavior and relationships between products.
Features
Knowledge graph construction: The graphical generation agent of GraphAgent can build knowledge graphs to reflect complex semantic dependencies.
Task planning: The task planning agent interprets diverse user queries and formulates corresponding tasks.
Task execution: The task execution agent efficiently performs planned tasks while automating tool matching and invocation.
Language model integration: GraphAgent integrates language models and graphical language models to reveal complex relational information and data semantic dependencies.
Multi-task processing: Suitable for both predictive and generative tasks such as node classification and text generation.
Automated tool matching and invocation: Automatically matches and calls appropriate tools in response to user queries.
How to Use
1. Clone the repository: Use the git clone command to clone the GraphAgent repository.
2. Create an environment: Use conda to create a new Python environment and activate it.
3. Install dependencies: Use pip to install the required dependencies for GraphAgent inference.
4. Obtain pre-trained models: Download pre-trained models from Hugging Face and replace or download them automatically.
5. Set up the planner and API token: Configure the default planner and API key in the run.sh file.
6. Run inference: Start GraphAgent by executing the bash script and provide user instructions or file paths for inference.
7. View results: Based on the input commands or file paths, observe how GraphAgent processes tasks and outputs results.
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