Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images, especially for use with large language models (LLMs). It helps improve data extraction results, particularly for complex layout documents with large tables, by balancing LLM and standard Python data extraction methods.
Key Features of Sparrow
– Supports data processing with ML and LLM
– Enables the creation of independent LLM agents that can be called through an API to handle specific tasks
– Includes an unstructured processor, markdown processor, and HTML extractor
– Converts table data to HTML using pandas dataframes for better processing
– Allows for the implementation of different agents with various LLM functionalities that can be run separately or called from one another
Agent Implementation in Sparrow
To implement a new agent in Sparrow:
1. Set up the necessary configuration properties in the config file
2. Create a pipeline class that extends the pipeline interface and implements the abstract run pipeline method
3. Write the agent’s logic inside the run pipeline method
This flexibility allows for the creation of agents with different LLM-related processing capabilities within Sparrow.
Sparrow is still a work in progress, with the code for the enhanced extractor component not yet available on GitHub. The project aims to advance the understanding of how to train agents to be safer and more useful, ultimately contributing to the development of safer and more useful artificial general intelligence (AGI).
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