💼 productive forces

AI-Data-Analysis-MultiAgent

AI-driven multi-agent data analysis system

#llm
#ai
#python
#agent
#langchain
#code-generation
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AI-Data-Analysis-MultiAgent

Product Details

AI-Data-Analysis-MultiAgent is an advanced AI-driven research assistant system that utilizes multiple professional agents to assist with tasks such as data analysis, visualization, and report generation. The system uses LangChain, OpenAI's GPT model and LangGraph to handle complex research processes, integrating diverse AI architectures to achieve optimal performance. Unique to this system is the integration of a dedicated Note Taker agent, which reduces computational overhead, improves context retention between different analysis stages, and enables more coherent analysis results by maintaining a concise and comprehensive record of the project.

Main Features

1
Hypothesis generation and verification
2
Data processing and analysis
3
Visual creation
4
Web Search and Information Retrieval
5
Code generation and execution
6
Report writing
7
Quality review and revision
8
The supervisory agent is responsible for supervising the analysis process
9
Chain thinking reasoning is used to solve complex problems
10
Criticizing agents for quality assurance and error checking
11
Innovative note-taking agent: Continuously records the current status of the project, providing a more efficient alternative to transmitting complete historical information and enhancing the system's ability to maintain context and continuity at different stages of analysis

How to Use

1
1. Clone the repository: Use the git clone command to clone the AI-Data-Analysis-MultiAgent repository.
2
2. Create and activate the Conda virtual environment: Use the conda create command to create a virtual environment named data_assistant and activate it.
3
3. Install dependencies: Use the pip install -r requirements.txt command to install the required dependencies.
4
4. Set environment variables: Rename .env Example to .env and fill in all necessary values.
5
5. Start Jupyter Notebook: Place the YourDataName.csv file in the data_storage directory.
6
6. Open the main.ipynb file and run all cells to initialize the system and create workflows.
7
7. In the last cell, customize the research task by modifying the userInput variable.
8
8. Run the last few cells to perform the study and view the results.

Target Users

The target audience is researchers and data scientists looking to improve workflow and productivity. The system helps them perform data analysis and report writing more efficiently by automating complex research processes, thereby saving time and resources.

Examples

✓

Researchers use the system for data analysis and visualization to support their research papers.

✓

Data scientists use the system to automate report writing and quickly generate project reports.

✓

Research teams use the system to conduct complex data exploration to discover new research hypotheses.

Quick Access

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Categories

💼 productive forces
› data analysis
› research tools

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