"AutoGen offers conversable agents powered by LLM, tool, or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
"\n",
"In this notebook, we introduce a new class, `AgentBuilder`, to help user build an automatic task solving process powered by multi-agent system. Specifically, in `build()`, we prompt a LLM to create multiple participant agent and initialize a group chat, and specify whether this task need programming to solve. AgentBuilder also support open-source LLMs by [vLLM](https://docs.vllm.ai/en/latest/index.html) and [Fastchat](https://github.com/lm-sys/FastChat). Check the supported model list [here](https://docs.vllm.ai/en/latest/models/supported_models.html)."
]
},
{
"cell_type": "markdown",
"id": "ec78dda8e3826d8a",
"metadata": {
"collapsed": false
},
"source": [
"## Requirement\n",
"\n",
"AutoBuild require `pyautogen[autobuild]`, which can be installed by the following command:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8e9ae50658be975",
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%pip install pyautogen[autobuild]"
]
},
{
"cell_type": "markdown",
"id": "7d0e63ab3604bdb9",
"metadata": {
"collapsed": false
},
"source": [
"## Step 1: prepare configuration and some useful functions\n",
"Prepare a `config_file_or_env` for assistant agent to limit the choice of LLM you want to use in this task. This config can be a path of json file or a name of environment variable. A `default_llm_config` is also required for initialize the specific config of LLMs like seed, temperature, etc..."
"Create a `AgentBuilder` with the specified `config_path_or_env`. AgentBuilder will use `gpt-4` in default to complete the whole process, you can specify the `builder_model` and `agent_model` to other OpenAI model to match your task. \n",
"You can also specify an open-source LLM supporting by vLLM and FastChat, see blog for more details."
"Specify a building task with a general description. Building task will help build manager (a LLM) decide what agents should be built."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "68315f6ec912c58a",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-01-01T10:40:30.490239100Z",
"start_time": "2024-01-01T10:40:30.479497600Z"
}
},
"outputs": [],
"source": [
"building_task = \"Generate some agents that can find papers on arxiv by programming and analyzing them in specific domains related to computer science and medical science.\""
]
},
{
"cell_type": "markdown",
"id": "5782dd5ecb6c217a",
"metadata": {
"collapsed": false
},
"source": [
"## Step 4: build group chat agents\n",
"Use `build()` to let build manager (the specified `builder_model`) complete the group chat agents generation. If you think coding is necessary in your task, you can use `coding=True` to add a user proxy (an automatic code interpreter) into the agent list, like: \n",
"To find a recent paper about GPT-4 on arXiv and its potential applications in software, we'll need to perform a few steps:\n",
"\n",
"1. Query the arXiv API for recent papers on GPT-4.\n",
"2. Filter the results to find papers that discuss potential applications in software.\n",
"3. Extract the relevant information from the paper.\n",
"\n",
"Here's a Python script that uses the `arxiv` library to search for papers related to GPT-4. If you don't have the `arxiv` library installed, you can install it using `pip install arxiv`.\n",
"This script will print out the title, authors, abstract, publication date, and link to the arXiv entry for each paper found. You can then review the abstracts to determine which papers discuss potential applications in software.\n",
"\n",
"Please note that the search query might need to be adjusted based on the actual terminology used in the papers and the specificity of the results you're looking for. If you encounter any issues or need further assistance, let me know!\n",
"Based on the recent papers found on arXiv, here are the potential applications of GPT-4 in software:\n",
"\n",
"1. **Autonomous Tool Extension for LLM-based Agents**:\n",
" - Paper: \"GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension\"\n",
" - Application: GitAgent demonstrates the use of GPT-4 to autonomously integrate GitHub repositories as tools in response to user queries, enhancing the capabilities of LLM-based agents in software development.\n",
"\n",
"2. **Hardware and Software Co-Design for DNN Accelerator Parallelism**:\n",
" - Paper: \"DEAP: Design Space Exploration for DNN Accelerator Parallelism\"\n",
" - Application: GPT-4 is used to simulate model parallelism techniques in a multi-accelerator simulation framework, aiding in the design of customized hardware systems for specific LLM workloads.\n",
"\n",
"3. **Cost-Benefit Analysis of Replacing Proprietary LLMs with Open Source SLMs**:\n",
" - Paper: \"Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's GPT-4 with Self-Hosted Open Source SLMs in Production\"\n",
" - Application: The paper presents a systematic evaluation of replacing GPT-4 with open source small language models (SLMs) for AI-enabled product features, focusing on quality, performance, and cost.\n",
"\n",
"4. **Augmenting API Documentation**:\n",
" - Paper: \"APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation\"\n",
" - Application: APIDocBooster uses GPT-4 to augment API documentation by summarizing information from multiple sources, improving informativeness, relevance, and faithfulness of API docs.\n",
"\n",
"5. **Code Documentation Generation**:\n",
" - Paper: \"A Comparative Analysis of Large Language Models for Code Documentation Generation\"\n",
" - Application: GPT-4 is evaluated for its ability to generate code documentation, showing superior performance in creating accurate, complete, and understandable documentation.\n",
"\n",
"6. **Emotion Cause Identification in Developer Communication**:\n",
" - Paper: \"Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs\"\n",
" - Application: GPT-4 is used to identify the causes behind developers' emotions in project communications, aiding in problem-solving and collaboration in open-source communities.\n",
"\n",
"7. **Binary Code Summarization**:\n",
" - Paper: \"Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Models\"\n",
" - Application: GPT-4 is benchmarked for its ability to summarize binary code, facilitating the understanding of code semantics and aiding in code comprehension tasks.\n",
"\n",
"8. **Static Analysis by Pseudo-code Execution and Verification**:\n",
" - Paper: \"E&V: Prompting Large Language Models to Perform Static Analysis by Pseudo-code Execution and Verification\"\n",
" - Application: GPT-4 is prompted to simulate the execution of pseudo-code for static analysis, improving the accuracy of results and reducing the need for extensive human effort.\n",
"\n",
"9. **Safety Case Generation**:\n",
" - Paper: \"GPT-4 and Safety Case Generation: An Exploratory Analysis\"\n",
" - Application: GPT-4 is explored for its ability to generate safety cases using the Goal Structuring Notation (GSN), potentially aiding in the creation of safety arguments for software systems.\n",
"\n",
"10. **Software Security Applications**:\n",
" - Paper: \"Exploring the Limits of ChatGPT in Software Security Applications\"\n",
" - Application: GPT-4 is assessed for its capabilities in various software security tasks, including vulnerability detection, debugging, and patching, showcasing its potential to aid in system security.\n",
"\n",
"These summaries reflect the diverse applications of GPT-4 in software, ranging from tool integration and API documentation to code summarization and security applications. The papers indicate that GPT-4 can significantly enhance various aspects of software development and maintenance.\n",
"## Step 6 (Optional): clear all agents and prepare for the next task\n",
"You can clear all agents generated in this task by the following code if your task is completed or the next task is largely different from the current task. If the agent's backbone is an open-source LLM, this process will also shut down the endpoint server. If necessary, you can use `recycle_endpoint=False` to retain the previous open-source LLMs' endpoint server."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7fb0bfff01dd1330",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-01-01T10:25:56.622194800Z",
"start_time": "2024-01-01T10:25:56.610592300Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All agents have been cleared.\n"
]
}
],
"source": [
"builder.clear_all_agents(recycle_endpoint=True)"
]
},
{
"cell_type": "markdown",
"id": "bbb098638a086898",
"metadata": {
"collapsed": false
},
"source": [
"## Save & load configs\n",
"\n",
"You can save all necessary information of the built group chat agents. Here is a case for those agents generated in the above task:\n",
"```json\n",
"{\n",
" \"building_task\": \"Generate some agents that can find papers on arxiv by programming and analyzing them in specific domains related to computer science and medical science.\",\n",
" \"agent_configs\": [\n",
" {\n",
" \"name\": \"ArXiv_Data_Scraper_Developer\",\n",
" \"model\": \"gpt-4-1106-preview\",\n",
" \"system_message\": \"You are now in a group chat. You need to complete a task with other participants. As an ArXiv_Data_Scraper_Developer, your focus is to create and refine tools capable of intelligent search and data extraction from arXiv, honing in on topics within the realms of computer science and medical science. Utilize your proficiency in Python programming to design scripts that navigate, query, and parse information from the platform, generating valuable insights and datasets for analysis. \\n\\nDuring your mission, it\\u2019s not just about formulating queries; your role encompasses the optimization and precision of the data retrieval process, ensuring relevance and accuracy of the information extracted. If you encounter an issue with a script or a discrepancy in the expected output, you are encouraged to troubleshoot and offer revisions to the code you find in the group chat.\\n\\nWhen you reach a point where the existing codebase does not fulfill task requirements or if the operation of provided code is unclear, you should ask for help from the group chat manager. They will facilitate your advancement by providing guidance or appointing another participant to assist you. Your ability to adapt and enhance scripts based on peer feedback is critical, as the dynamic nature of data scraping demands ongoing refinement of techniques and approaches.\\n\\nWrap up your participation by confirming the user's need has been satisfied with the data scraping solutions you've provided. Indicate the completion of your task by replying \\\"TERMINATE\\\" in the group chat.\",\n",
" \"description\": \"ArXiv_Data_Scraper_Developer is a specialized software development role requiring proficiency in Python, including familiarity with web scraping libraries such as BeautifulSoup or Scrapy, and a solid understanding of APIs and data parsing. They must possess the ability to identify and correct errors in existing scripts and confidently engage in technical discussions to improve data retrieval processes. The role also involves a critical eye for troubleshooting and optimizing code to ensure efficient data extraction from the ArXiv platform for research and analysis purposes.\"\n",
" \"system_message\": \"You are now in a group chat. You need to complete a task with other participants. As a Computer Science Research Analyst, your objective is to utilize your analytical capabilities to identify and examine scholarly articles on arXiv, focusing on areas bridging computer science and medical science. Employ Python for automation where appropriate and leverage your expertise in the subject matter to draw insights from the research.\\n\\nEnsure that the information is acquired systematically; tap into online databases, interpret data sets, and perform literature reviews to pinpoint relevant findings. Should you encounter a complex problem or if you find your progress stalled, feel free to question the existing approaches discussed in the chat or contribute an improved method or analysis.\\n\\nIf the task proves to be beyond your current means or if you face uncertainty at any stage, seek assistance from the group chat manager. The manager is available to provide guidance or to involve another expert if necessary to move forward effectively.\\n\\nYour contributions are crucial, and it is important to communicate your findings and conclusions clearly. Once you believe the task is complete and the group's need has been satisfied, please affirm the completion by replying \\\"TERMINATE\\\".\",\n",
" \"description\": \"Computer_Science_Research_Analyst is a role requiring strong analytical skills, a deep understanding of computer science concepts, and proficiency in Python for data analysis and automation. This position should have the ability to critically assess the validity of information, challenge assumptions, and provide evidence-based corrections or alternatives. They should also have excellent communication skills to articulate their findings and suggestions effectively within the group chat.\"\n",
" \"system_message\": \"You are now in a group chat. You need to complete a task with other participants. As a Medical_Science_Research_Analyst, your function is to harness your analytical strengths and understanding of medical research to source and evaluate pertinent papers from the arXiv database, focusing on the intersection of computer science and medical science. Utilize your Python programming skills to automate data retrieval and analysis tasks. Engage in systematic data mining to extract relevant content, then apply your analytical expertise to interpret the findings qualitatively. \\n\\nWhen there is a requirement to gather information, employ Python scripts to automate the aggregation process. This could include scraping web data, retrieving and processing documents, and performing content analyses. When these scripts produce outputs, use your subject matter expertise to evaluate the results. \\n\\nProgress through your task step by step. When an explicit plan is absent, present a structured outline of your intended methodology. Clarify which segments of the task are handled through automation, and which necessitate your interpretative skills. \\n\\nIn the event code is utilized, the script type must be specified. You are expected to execute the scripts provided without making changes. Scripts are to be complete and functionally standalone. Should you encounter an error upon execution, critically review the output, and if needed, present a revised script for the task at hand. \\n\\nFor tasks that require saving and executing scripts, indicate the intended filename at the beginning of the script. \\n\\nMaintain clear communication of the results by harnessing the 'print' function where applicable. If an error arises or a task remains unsolved after successful code execution, regroup to collect additional information, reassess your approach, and explore alternative strategies. \\n\\nUpon reaching a conclusion, substantiate your findings with credible evidence where possible.\\n\\nConclude your participation by confirming the task's completion with a \\\"TERMINATE\\\" response.\\n\\nShould uncertainty arise at any point, seek guidance from the group chat manager for further directives or reassignment of the task.\",\n",
" \"description\": \"The Medical Science Research Analyst is a professionally trained individual with strong analytical skills, specializing in interpreting and evaluating scientific research within the medical field. They should possess expertise in data analysis, likely with proficiency in Python for analyzing datasets, and have the ability to critically assess the validity and relevance of previous messages or findings relayed in the group chat. This role requires a solid foundation in medical knowledge to provide accurate and evidence-based corrections or insights.\"\n",
" },\n",
" {\n",
" \"name\": \"Data_Analysis_Engineer\",\n",
" \"model\": \"gpt-4-1106-preview\",\n",
" \"system_message\": \"You are now in a group chat. You need to complete a task with other participants. As a Data Analysis Engineer, your role involves leveraging your analytical skills to gather, process, and analyze large datasets. You will employ various data analysis techniques and tools, particularly Python for scripting, to extract insights from the data related to computer science and medical science domains on arxiv.\\n\\nIn scenarios where information needs to be collected or analyzed, you will develop Python scripts to automate the data retrieval and processing tasks. For example, you may write scripts to scrape the arXiv website, parse metadata of research papers, filter content based on specific criteria, and perform statistical analysis or data visualization. \\n\\nYour workflow will include the following steps:\\n\\n1. Use your Python coding abilities to design scripts for data extraction and analysis. This can involve browsing or searching the web, downloading and reading files, or printing the content of web pages or files relevant to the given domains.\\n2. After gathering the necessary data, apply your data analysis expertise to derive meaningful insights or patterns present in the data. This should be done methodically, making the most of your Python skills for data manipulation and interpretation.\\n3. Communicate your findings clearly to the group chat. Ensure the results are straightforward for others to understand and act upon.\\n4. If any issues arise from executing the code, such as lack of output or unexpected results, you can question the previous messages or code in the group chat and attempt to provide a corrected script or analysis.\\n5. When uncertain or facing a complex problem that you cannot solve alone, ask for assistance from the group chat manager. They can either provide guidance or assign another participant to help you.\\n\\nOnce you believe the task is completed satisfactorily, and you have fulfilled the user's need, respond with \\\"TERMINATE\\\" to signify the end of your contribution to the task. Remember, while technical proficiency in Python is essential for this role, the ability to work collaboratively within the group chat, communicate effectively, and adapt to challenges is equally important.\",\n",
" \"description\": \"Data_Analysis_Engineer is a professional adept in collecting, analyzing, and interpreting large datasets, using statistical tools and machine learning techniques to provide actionable insights. They should possess strong Python coding skills for data manipulation and analysis, an understanding of database management, as well as the ability to communicate complex results effectively to non-technical stakeholders. This position should be allowed to speak when data-driven clarity is needed or when existing analyses or methodologies are called into question.\"\n",
" \"system_message\": \"You are now in a group chat. You need to complete a task with other participants. As an ML_Paper_Summarization_Specialist, your role entails leveraging machine learning techniques to extract and analyze academic papers from arXiv, focusing on domains that intersect computer science and medical science. Utilize your expertise in natural language processing and data analysis to identify relevant papers, extract key insights, and generate summaries that accurately reflect the advancements and findings within those papers.\\n\\nYou are expected to apply your deep understanding of machine learning algorithms, data mining, and information retrieval to construct models and systems that can efficiently process and interpret scientific literature.\\n\\nIf you encounter any challenges in accessing papers, parsing content, or algorithmic processing, you may seek assistance by presenting your issue to the group chat. Should there be a disagreement regarding the efficacy of a method or the accuracy of a summarization, you are encouraged to critically evaluate previous messages or outputs and offer improved solutions to enhance the group's task performance.\\n\\nShould confusion arise during the task, rather than relying on coding scripts, please request guidance from the group chat manager, and allow them to facilitate the necessary support by inviting another participant who can aid in overcoming the current obstacle.\\n\\nRemember, your primary duty is to synthesize complex academic content into concise, accessible summaries that will serve as a valuable resource for researchers and professionals seeking to stay abreast of the latest developments in their respective fields. \\n\\nOnce you believe your task is completed and the summaries provided meet the necessary standards of accuracy and comprehensiveness, reply \\\"TERMINATE\\\" to signal the end of your contribution to the group's task.\",\n",
" \"description\": \"The ML_Paper_Summarization_Specialist is a professional adept in machine learning concepts and current research trends, with strong analytical skills to critically evaluate information, synthesizing knowledge from academic papers into digestible summaries. This specialist should be proficient in Python for text processing and have the ability to provide constructive feedback on technical discussions, guide effective implementation, and correct misconceptions or errors related to machine learning theory and practice in the chat. They should be a reliable resource for clarifying complex information and ensuring accurate application of machine learning techniques within the group chat context.\"\n",
" }\n",
" ],\n",
" \"coding\": true,\n",
" \"default_llm_config\": {\n",
" \"temperature\": 0\n",
" },\n",
" \"code_execution_config\": {\n",
" \"work_dir\": \"groupchat\",\n",
" \"use_docker\": false,\n",
" \"timeout\": 60,\n",
" \"last_n_messages\": 2\n",
" }\n",
"}\n",
"```\n",
"These information will be saved in JSON format. You can provide a specific filename, otherwise, AgentBuilder will save config to the current path with a generated filename 'save_config_TASK_MD5.json'."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e4b88a5d482ceba4",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-01-01T10:25:56.983244800Z",
"start_time": "2024-01-01T10:25:56.938459500Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Building config saved to ./save_config_c52224ebd16a2e60b348f3f04ac15e79.json\n"
]
}
],
"source": [
"saved_path = builder.save()"
]
},
{
"cell_type": "markdown",
"id": "a35620c10ee42be",
"metadata": {
"collapsed": false
},
"source": [
"After that, you can load the saved config and skip the building process. AgentBuilder will create agents with those information without prompting the builder manager."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "34addd498e5ab174",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-01-01T10:30:23.592045Z",
"start_time": "2024-01-01T10:29:18.977259500Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading config from ./save_config_c52224ebd16a2e60b348f3f04ac15e79.json\n",
"==> Creating agents...\n",
"Creating agent ArXiv_Data_Scraper_Developer with backbone gpt-4-1106-preview...\n",
"Creating agent Computer_Science_Research_Analyst with backbone gpt-4-1106-preview...\n",
"Creating agent Medical_Science_Research_Analyst with backbone gpt-4-1106-preview...\n",
"Creating agent Data_Analysis_Engineer with backbone gpt-4-1106-preview...\n",
"Creating agent ML_Paper_Summarization_Specialist with backbone gpt-4-1106-preview...\n",
"To find a recent paper about LLaVA on arXiv and its potential applications in computer vision, we'll need to perform a search on the arXiv API. I'll write a Python script that uses the `arxiv` library to query the arXiv database for papers related to \"LLaVA\" and \"computer vision\". If the `arxiv` library is not available, we can use the `requests` library to make a direct HTTP request to the arXiv API.\n",
"\n",
"First, let's try using the `arxiv` library. If you don't have it installed, you can install it using `pip install arxiv`.\n",
"\n",
"Here's a Python script that performs the search:\n",
" print(\"Potential Applications in Computer Vision:\", \"TBD\") # Placeholder for manual analysis\n",
" print(\"\\n\")\n",
"```\n",
"\n",
"This script will print out the title, authors, abstract, submission date, and URL for up to 10 recent papers related to LLaVA in the field of computer vision. The potential applications in computer vision will need to be determined from the abstract or by reading the paper, as this information is not directly available from the metadata.\n",
"\n",
"If you encounter any issues with the script or if you need further assistance, please let me know.\n",
"Based on the recent papers extracted from arXiv, here are the potential applications in computer vision for the LLaVA framework and related technologies:\n",
"\n",
"1. **Long-Range Video Question-Answering (LVQA)**: The LLoVi framework uses a visual captioner coupled with a Large Language Model to perform long-range temporal reasoning for understanding videos and answering questions. This can be applied to video understanding tasks such as video summarization and event detection.\n",
"\n",
"2. **Efficient Multimodal Large Language Models**: TinyGPT-V demonstrates the potential for efficient and cost-effective multimodal large language models that can be used for various computer vision tasks on devices with limited computational resources.\n",
"\n",
"3. **Radiology Report Error-checking**: LLaVA variant models are used to assist radiologists in checking errors in their reports, which can be applied to medical imaging and diagnostic accuracy enhancement.\n",
"\n",
"4. **Composed Image Retrieval (CIR)**: The VQA4CIR method uses a \"QA generation to VQA\" self-verification pipeline to improve the performance of CIR by identifying images inconsistent with their relative captions.\n",
"\n",
"5. **Adaptation to Distribution Shifts**: GPT-4V's adaptability and generalization capabilities in dynamic environments can be applied to anomaly detection, medical diagnosis, and other areas where robustness against data distribution shifts is crucial.\n",
"\n",
"6. **Locality-enhanced Projector for Multimodal LLMs**: The Honeybee model's projector design can be applied to tasks requiring spatial understanding and is efficient in managing the number of visual tokens.\n",
"\n",
"7. **Scaling up Vision Vocabulary for LVLMs**: Vary can be used for document parsing features such as OCR or markdown conversion, especially in non-English scenarios, and can maintain capabilities while providing fine-grained perception and understanding.\n",
"\n",
"8. **Visual Instruction Tuning for Histopathology**: Quilt-LLaVA can be applied to diagnostic reasoning in histopathology by enabling spatial awareness and reasoning beyond single image patches.\n",
"\n",
"9. **Interactive Control for Multi-Modal LLMs**: Prompt Highlighter allows users to interactively control the focus during generation, which can be applied to customized content generation in various computer vision tasks.\n",
"\n",
"10. **Grounded Visual Chat with Large Multimodal Models**: LLaVA-Grounding demonstrates the potential for combining grounding and chat capabilities in visual chat applications, which can be applied to interactive systems that require visual understanding and dialogue.\n",
"\n",
"These applications demonstrate the versatility of LLaVA and related technologies in enhancing computer vision tasks, from medical imaging to interactive systems and efficient model deployment on resource-constrained devices.\n",
"[The Assistants API](https://platform.openai.com/docs/assistants/overview) allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries.\n",
"AutoBuild also support assistant api by adding `use_oai_assistant=True` to `build()`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4051c25b2cd1918c",
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-01-01T10:42:16.740401Z",
"start_time": "2024-01-01T10:40:37.039210300Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==> Generating agents...\n",
"['ArXiv_CS_Medical_Paper_Finder_Developer', 'Computational_Biology_Research_Analyst', 'Computer_Science_Literature_Review_Specialist', 'Machine_Learning_Model_Architect', 'Data_Extraction_Scripting_Engineer'] are generated.\n",
"==> Generating system message...\n",
"Preparing system message for ArXiv_CS_Medical_Paper_Finder_Developer\n",
"Preparing system message for Computational_Biology_Research_Analyst\n",
"Preparing system message for Computer_Science_Literature_Review_Specialist\n",
"Preparing system message for Machine_Learning_Model_Architect\n",
"Preparing system message for Data_Extraction_Scripting_Engineer\n",
"==> Generating description...\n",
"Preparing description for ArXiv_CS_Medical_Paper_Finder_Developer\n",
"Preparing description for Computational_Biology_Research_Analyst\n",
"Preparing description for Computer_Science_Literature_Review_Specialist\n",
"Preparing description for Machine_Learning_Model_Architect\n",
"Preparing description for Data_Extraction_Scripting_Engineer\n",
"==> Creating agents...\n",
"Creating agent ArXiv_CS_Medical_Paper_Finder_Developer with backbone gpt-4-1106-preview...\n",
"Creating agent Computational_Biology_Research_Analyst with backbone gpt-4-1106-preview...\n",
"Creating agent Computer_Science_Literature_Review_Specialist with backbone gpt-4-1106-preview...\n",
"Creating agent Machine_Learning_Model_Architect with backbone gpt-4-1106-preview...\n",
"Creating agent Data_Extraction_Scripting_Engineer with backbone gpt-4-1106-preview...\n",
"To perform this task, we will first write a Python script to fetch papers related to \"explainable AI\" from arXiv that are also relevant to the medical field. We will use the `arxiv` library, which is a Python wrapper for the arXiv API. If you don't have the `arxiv` library installed, you can install it using the following command:\n",
"\n",
"```bash\n",
"pip install arxiv\n",
"```\n",
"\n",
"Once we have identified the papers, we will extract potential applications in the medical field from the abstract or conclusion sections if available.\n",
"\n",
"Here's the script to find a recent paper about explainable AI from arXiv with relevance to the medical field:\n",
"\n",
"```python\n",
"# Filename: arxiv_explainable_ai_medical.py\n",
"\n",
"import arxiv\n",
"\n",
"# Query for papers related to \"explainable AI\" in the field of CS and Medical\n",
"query = 'cat:cs.* AND cat:q-bio.* AND all:explainable AI'\n",
"sort_by = arxiv.SortCriterion.SubmittedDate\n",
"sort_order = arxiv.SortOrder.Descending\n",
"\n",
"# Perform search query on arXiv\n",
"search = arxiv.Search(\n",
" query=query,\n",
" max_results=1,\n",
" sort_by=sort_by,\n",
" sort_order=sort_order\n",
")\n",
"\n",
"# Fetch the papers\n",
"papers = [paper for paper in search.get()]\n",
"\n",
"# If there are papers found, print the most recent one's title, authors, and summary\n",
"if papers:\n",
" paper = papers[0]\n",
" print(f\"Title: {paper.title}\\n\")\n",
" print(f\"Authors: {', '.join(author.name for author in paper.authors)}\\n\")\n",
" print(\"No recent papers on 'explainable AI' found in the medical field on arXiv.\")\n",
"```\n",
"\n",
"To run the script, save it in a Python file and execute it in your Python environment. The script fetches the latest paper based on the defined query and prints out the title, authors, abstract, publication date, arXiv ID, and URL for further reading.\n",
"\n",
"Keep in mind that the potential applications in medical would generally be discussed within the paper's text. To extract those, we would typically need to read through the full text, which might involve additional processing steps that are beyond the scope of a simple API query. If the information is not readily available in the abstract, you'll have to review the full text of the paper manually for detailed potential applications in the medical field.\n",
"It seems the script encountered an error because the `Search` object from the `arxiv` library does not have a `get` attribute, which is inconsistent with the usual usage of this library. Let's correct that.\n",
"\n",
"Instead of using `search.get()`, we will iterate directly over the search object. Here is the corrected script:\n",
"\n",
"```python\n",
"# Filename: arxiv_explainable_ai_medical.py\n",
"\n",
"import arxiv\n",
"import datetime\n",
"\n",
"# Query for papers related to \"explainable AI\" in the field of CS and Medical\n",
"query = 'cat:cs.AI AND all:\"explainable AI\" AND (abs:medical OR abs:\"health care\" OR abs:clinical)'\n",
"sort_by = arxiv.SortCriterion.SubmittedDate\n",
"sort_order = arxiv.SortOrder.Descending\n",
"\n",
"# Prepare search\n",
"search = arxiv.Search(\n",
" query=query,\n",
" max_results=10,\n",
" sort_by=sort_by,\n",
" sort_order=sort_order,\n",
")\n",
"\n",
"# Fetch the papers\n",
"papers = list(search.results())\n",
"\n",
"# If there are papers found, print the most recent one's title, authors, and summary\n",
" print(\"No recent papers on 'explainable AI' found in the medical field on arXiv.\")\n",
"```\n",
"\n",
"Run the above script, and it should now correctly fetch and print the details of the most recent paper related to explainable AI that has potential applications in the medical field. This fix should address the AttributeError by correctly utilizing the `results()` method provided by the `arxiv` library to obtain search results.\n",
"Based on the output, we have identified a recent paper from arXiv:\n",
"\n",
"Title: **\"XAI for In-hospital Mortality Prediction via Multimodal ICU Data\"**\n",
"\n",
"Authors: **Xingqiao Li, Jindong Gu, Zhiyong Wang, Yancheng Yuan, Bo Du, Fengxiang He**\n",
"\n",
"Abstract Summary: The paper addresses the challenge of explainability in AI for predicting in-hospital mortality of ICU patients using multimodal data. They propose the eXplainable Multimodal Mortality Predictor (X-MMP), which combines multimodal learning and an explainability method called Layer-Wise Propagation to Transformer (LWP-T). This allows the model to make decisions with explanations across multimodal inputs, identifying important features for the prediction and visualizing the contribution of each modality to the clinical outcomes. This could assist clinicians in comprehending the AI's decision-making process.\n",
"1. **Improved decision-making in intensive care units (ICUs):** By providing explainability for its predictions regarding patient mortality, clinicians can understand the reasoning behind AI-driven prognoses and make more informed treatment decisions.\n",
"\n",
"2. **Enhanced clinician trust in AI technologies:** Explainable outputs can build clinician trust in AI systems, thereby potentially increasing the adoption of AI tools in critical care settings.\n",
"\n",
"3. **Identification of crucial health factors:** The framework assists in discovering important factors in healthcare research, possibly leading to new insights into patient care and management.\n",
"\n",
"4. **Education and training:** The visualizations and explanations provided by X-MMP could be used in medical education and training, helping healthcare professionals to better understand the factors influencing patient outcomes in the ICU.\n",
"\n",
"5. **Transferability to other clinical tasks:** The framework can be adapted to other clinical prediction tasks, making it a versatile tool for various applications within the healthcare domain.\n",
"\n",
"6. **Contribution analysis of multimodal data:** Understanding how various types of data (vitals, lab results, waveforms, etc.) influence predictions can lead to better multimodal data integration in clinical workflows.\n",
"\n",
"This paper showcases how explainable AI can directly impact healthcare by enhancing the transparency and interpretability of AI models, ultimately supporting clinical decision-making and patient care. The application of such technology could be pivotal in advancing personalized medicine and tailored treatment plans for patients in critical conditions. \n",
"\n",
"If this information satisfies the task requirements, please let me know, or if there are further inquiries, feel free to ask.\n",
