AI-Data-Analysis-MultiAgent
A
AI Data Analysis MultiAgent
Overview :
AI-Data-Analysis-MultiAgent is an advanced AI-driven research assistant system that utilizes multiple specialized agents to assist with tasks such as data analysis, visualization, and report generation. The system employs LangChain, OpenAI’s GPT model, and LangGraph to handle complex research workflows while integrating a diverse array of AI architectures for optimal performance. A key feature of the system is its dedicated Note Taker agent, which maintains concise and comprehensive project records to reduce computational overhead, improve contextual continuity between different analysis phases, and deliver more coherent and consistent analytical outcomes.
Target Users :
The target audience consists of researchers and data scientists looking to enhance their workflows and productivity. This system streamlines complex research processes through automation, allowing for more efficient data analysis and report generation, ultimately saving time and resources.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 61.8K
Use Cases
Researchers use the system for data analysis and visualization to support their research papers.
Data scientists leverage the system to automate report writing, quickly generating project reports.
Research teams utilize the system for complex data exploration to uncover new research hypotheses.
Features
Hypothesis generation and validation
Data processing and analysis
Visualization creation
Web searching and information retrieval
Code generation and execution
Report writing
Quality review and revision
Supervisory agents responsible for overseeing the analysis process
Chain-of-thought reasoning for complex problem-solving
Critique agents for quality assurance and error checking
Innovative note-taking agent: Continuously records the project's current state, providing a more efficient alternative to transmitting complete historical information, thus enhancing the system's ability to maintain context and continuity across various analysis phases.
How to Use
1. Clone the repository: Use the git clone command to clone the AI-Data-Analysis-MultiAgent repository.
2. Create and activate a Conda virtual environment: Use the conda create command to create a virtual environment named 'data_assistant' and activate it.
3. Install dependencies: Use the pip install -r requirements.txt command to install the required dependencies.
4. Set environment variables: Rename .env Example to .env and fill in all necessary values.
5. Launch Jupyter Notebook: Place the YourDataName.csv file in the data_storage directory.
6. Open the main.ipynb file and run all cells to initialize the system and create the workflow.
7. Customize the research task by modifying the userInput variable in the final cell.
8. Run the last few cells to execute the research process and view results.
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