{"id":997686,"date":"2024-01-11T09:00:00","date_gmt":"2024-01-11T17:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=997686"},"modified":"2024-01-11T06:55:46","modified_gmt":"2024-01-11T14:55:46","slug":"taskweaver-a-code-first-agent-framework-for-efficient-data-analytics-and-domain-adaptation","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/taskweaver-a-code-first-agent-framework-for-efficient-data-analytics-and-domain-adaptation\/","title":{"rendered":"TaskWeaver: A code-first agent framework for efficient data analytics and domain adaptation"},"content":{"rendered":"\n
\"TaskWeaver<\/figure>\n\n\n\n

The advent of large language models (LLMs) has revolutionized human-machine interactions, particularly in natural language understanding and generation applications. These AI- or LLM-backed virtual assistants hold the promise of serving as intelligent agents <\/em>capable of autonomously reasoning, observing, and performing tasks articulated in natural language. However, it is still challenging for most agent frameworks to efficiently handle complex data structures (e.g., DataFrame), which are prevalent in data analytics tasks and domain-specific scenarios.\u00a0<\/p>\n\n\n\n

To address these challenges, we introduce TaskWeaver<\/a> \u2013 a code-first agent framework which can convert natural language user requests into executable code, with additional support for rich data structures, dynamic plugin selection, and domain-adapted planning process. Now publicly available as an open-source framework, TaskWeaver leverages LLMs\u2019 coding capability to implement complex logic and incorporates domain-specific knowledge through customizable examples and plugins. TaskWeaver empowers users to easily build their own virtual assistants that understand diverse domain questions, follow examples, and execute customizable algorithms on complex data structures efficiently.<\/p>\n\n\n\n

Motivating example \u2013 Anomaly detection on time-series data<\/h2>\n\n\n\n

Scenario<\/h3>\n\n\n\n

Amy is a business analyst who wants to identify anomalies on a time series of sales data stored in a SQL database. She would like to get help from an AI assistant for the task with natural language interactions. Moreover, Amy would like to apply her own definition and interpretation of anomalies in the context of sales data, including a customized anomaly detection algorithm. Figure 1 shows the desired conversation between the user and the AI assistant \u2013 the AI assistant should be able to first pull the data from target database, then apply desired algorithms, and return the visualized results.<\/p>\n\n\n\n

\"Figure
Figure 1. Sample chat between the user and the AI assistant<\/figcaption><\/figure>\n\n\n\n

Requirements for an agent framework<\/h3>\n\n\n\n

To accomplish the task above, we identify several key requirements that current agent frameworks may lack:<\/p>\n\n\n\n