add figure and method

README.md

@@ -58,6 +58,8 @@ analysis_session.save_results()
 
 [Play with our Colab demo! ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17DnAiE6EdAtLfX7xjdxvMEV5HUtBKTq6?usp=sharing)
 
+[Video walk-through of our system ](https://youtu.be/APP0uvU3QAQ?si=B4bVy646wH1WkMxX)
+
 ### ✅ Current Features 🌟
 
 - **Auto Analytics in Local Env:** The coding agent have access to a local python kernel, which runs code and interacts with data on your computer. No more concerns about file uploads, compute limitations, or the online ChatGPT code interpreter environment. Leverage any Python library or computing resources as needed.
@@ -66,6 +68,17 @@ analysis_session.save_results()
 - **Vision Analytics:** Integration with Vision API enables the data analytics agent to generate and understand the meaning of plots in a closed loop.
 - **Versatile Report Export:** After automated data analysis, a Jupyter notebook is generated, combining code, results, and visuals into a narrative that tells the story of your data. Exports are available in Jupyter notebook, PDF, and HTML formats for your review and reproduction.
 
+### How does it work? 
+![](docs/Schematics.png)
+<details>
+  <summary>Click to expand!</summary>
+
+  **System Overview**. Our high-level idea is to emulate the workflow in research labs: we will assign roles to AI agents, such as research supervisor or student coding agent and let them work in a closed loop. The supervisor, equipped with broader background knowledge, is tasked to set an overarching research goal, and then break it down into detailed code-solvable tasks which are sent to the student. Then, the student coding agent, equipped with coding tool and vision capability, will tackle the tasks one-by-one, synthesizing their findings into reports for the supervisor's review. This iterative process allows the supervisor to refine their understanding and possibly adjust the research agenda, prompting further investigation. Through this collaborative effort, both parties converge to a unified conclusion, culminating in a final report with code, figures and text interpretations. 
+
+  **Internal Coding Loop**. At the core of our system lies the coding agent, an LLM agent who is tasked for conducting interactive data analysis. This agent will take in task objective, and then analyze datasets by outputting code snippets, which are executed in a local IPython kernel. The results of code execution (including error message) will be sent back to LLM in the form of text strings. Notably, when figures are generated, they will also be turned into text descriptions by a multimodal LLM prompted to interpret the figure .
+
+</details>
+
 ### 🌐 What's Next?
 - [ ] Better report summarization.
 - [ ] Enhanced report presentation. (filtering, formating etc.)