In addition to performing database-related tasks, ChatMolData can utilize various tools within its Literature Module to carry out the collection of molecular information from unstructured data. As illustrated in Figure , the case demonstrates how ChatMolData extracts molecular structures and bioactivity data from a patent and ultimately stores them in a CSV file. In Figure , the user's query is "Extract the molecular structures and bioactivity information from 'WO2021252681.pdf' and save them as a CSV file.". ChatMolData initiates its reasoning process and identifies this task as a molecular data structuring task. The agent's Action1 involves using the Table_Detection tool to retrieve all images containing table information from each page (as shown in Figure ). Meanwhile, ChatMolData create new folds to save the images where tables are on the consecutive pages and the table column structures are same from each other. The output of the Observation includes the folder names. In Action 2, the system reads the files from the saved folder, and the Table2CSV tool interprets the table contents in image format, ultimately converting them into CSV files stored in the same folder . As shown in Figure , before processing each table saved in an image file, the tables are categorized into two groups: one group without molecular structures and the other group containing molecular structures. For both groups, the process involves converting the images into CSV files. The key difference is that the former directly copies the information from the image, while for the latter, the molecular structure images are processed using the DECIMER method, converting the molecular structure into SMILES format and saving it as a CSV file . Finally, the observation output remains the folder name where the data is stored as well. The above two operations realize the data extraction from the unstructured pdf file. However, data is scattered in different CSV files. In Action 3, the Y_Concat tool is used to concatenate the extracted table information along the Y-axis, achieving the structuring of cross-page table data. The observation confirms that tables across pages have been Concatenated (as shown in Figure ). In the final step, the X_Concat tool is applied to combine data from tables within different folders mentioned above, as shown in Figure . The system identifies identical columns across different tables and concatenates them. Ultimately, the structured molecular data from the patent is stored in a single table as the output of the multimodal agent.