AI Agents for Smarter Data Input: DocETL (Berkeley)

DocETL is an advanced ETL framework optimized for document-centric tasks, integrating large language models (LLMs) to execute nuanced data transformations within unstructured text. It introduces a modular set of operators, such as Map, Reduce, Resolve, and Split-Gather, each tailored to perform specific ETL functions.

Map and Parallel Map allow document-specific context extraction and augmentation, while Reduce aggregates information based on user-specified attributes, enabling cross-document grouping. Resolve ensures entity standardization across documents, and Split-Gather manages large texts by dividing them into chunks while preserving contextual integrity, enhancing the quality of downstream analysis. This operator design allows DocETL to flexibly handle complex transformations while maintaining context in large document collections.

To optimize these processes, DocETL employs rewrite directives and two types of LLM-driven agents: generation and validation. Rewrite directives are abstract strategies that outline how operators can be decomposed or optimized in stages. Generation agents configure operators based on these directives, setting parameters such as chunk sizes and prompt structures to create candidate plans.

Validation agents then assess the outputs, iterating and refining configurations to ensure high accuracy and relevance. This iterative agent-based approach, termed “gleaning,” allows DocETL to dynamically adapt transformations based on data characteristics, enhancing its scalability and precision in ETL tasks that require a high degree of document-specific context and standardization.

All rights w/ authors:
DocETL: Agentic Query Rewriting and Evaluation
for Complex Document Processing
https://arxiv.org/pdf/2410.12189v1

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00:00 The problem w complex documents
04:46 UC Berkeley Pre-print DocETL
05:14 Our Operators for unstructured data
11:37 Rewrite Directives
17:51 2 new AGENTS for DocETL
20:03 Optimization process DocETL
20:57 Terms explained
26:54 Performance data
28:15 CODE DocETL GitHub repo