It is a code-first agent framework designed for seamlessly planning and executing data analytics tasks. TaskWeaver interprets user requests through code snippets and coordinates various plugins, implemented as functions, to execute data analytics tasks in a stateful manner. Unlike traditional agent frameworks that primarily track chat history with large language models (LLMs) in text, TaskWeaver preserves both chat history and code execution history, including in-memory data. This capability enhances its expressiveness, making it particularly effective for handling complex data structures, such as high-dimensional tabular data.
TaskWeaver requires Python 3.10 or higher and can be installed via a command-line interface. It supports multiple LLMs, including OpenAI, and requires configuration through a `taskweaver_config.json` file. By default, TaskWeaver operates in container mode, meaning code execution occurs within a Docker container, necessitating Docker installation and dependency management. The framework can be interacted with via a command-line interface or a WebUI for demonstration purposes. Additionally, TaskWeaver can be integrated into existing projects as a library.
The framework includes plugins like `sql_pull_data`, which retrieves data from databases and returns it as a DataFrame, leveraging Langchain for implementation. TaskWeaver also supports advanced use cases, such as forecasting financial data (e.g., QQQ’s price) and anomaly detection. However, its planning and execution rely on LLMs, meaning results may vary based on user prompts and model behavior.
TaskWeaver is an open-source project under Microsoft, and users are encouraged to contribute to its development. It includes disclaimers regarding trademarks, third-party licenses, and liability, emphasizing compliance with Microsoft’s guidelines and third-party policies. For research purposes, users are advised to cite the associated paper. Documentation, examples, and further details are available on the TaskWeaver website and GitHub repository.
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