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AI Driven Research Assistant
Overview :
The AI-Driven Research Assistant is an advanced system leveraging AI-driven agents to assist with tasks such as data analysis, visualization, and report generation. This system employs LangChain, OpenAI's GPT models, and LangGraph to manage complex research workflows, integrating various AI architectures for optimal performance.
Target Users :
This product is suitable for researchers and data scientists seeking to enhance their work efficiency and research quality through automation tools.
Use Cases
Researchers utilize the system for analyzing complex datasets, quickly generating and validating hypotheses.
Data scientists leverage the reports and visualizations generated by the system to support their research papers.
Research teams effectively track project progress and decision-making processes through the system’s automated note-taking feature.
Features
Hypothesis generation and validation
Data processing and analysis
Visualization creation
Web search and information retrieval
Code generation and execution
Report writing
Quality review and revision
Innovative note-taking agents: continuously record the current state of projects, providing a more efficient way of information transfer to enhance the system's ability to maintain context and continuity across different analysis stages
Adaptive workflows: dynamically adjust analysis methods based on data and tasks
How to Use
Clone the code repository to your local environment
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Create and activate a Conda virtual environment
Install the dependencies
Set the environment variables
Launch Jupyter Notebook
Place data files in the data_storage directory
Open the main.ipynb file
Run all cells to initialize the system and create workflows
In the last cell, customize your research task by modifying the userInput variable
Run the final cells to execute the research process and view the results
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