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Understanding What Matters for LLM Ingestion and Preprocessing – Unstructured

Apr 21, 2024 - unstructured.io
The article discusses the importance of data ingestion and preprocessing for Language Learning Models (LLMs) and Retrieval Augmented Generation (RAG) architectures. It outlines the process of transforming unstructured and semi-structured data into a machine-readable format, which involves steps like extracting, partitioning, structuring, cleaning, chunking, summarizing, and generating embeddings. The article also highlights the need for a robust preprocessing solution that can handle a continuous flow of data from various sources, process it, and write it to one or more destinations.

The article then introduces Unstructured, a tool that supports a wide range of file types, offers low-latency pipelines, detects reading order and language, and provides element coordinates and predicted bounding boxes. Unstructured also supports GPU and CPU tiering, extracts images, forms, and tables, and offers smart-chunking. It also provides scheduling and workflow automation, making it a comprehensive solution for LLM ingestion and preprocessing. The article concludes by inviting readers to try Unstructured's open-source Python library, prebuilt containers, free API, or its end-to-end platform.

Key takeaways:

  • The article discusses the importance and challenges of preprocessing and structuring unstructured data for Language Learning Models (LLMs) and Retrieval Augmented Generation (RAG) architectures.
  • Key steps in making documents RAG-ready include Transforming (extracting, partitioning, structuring), Cleaning, Chunking, Summarizing, and Generating embeddings.
  • For effective data preprocessing, it's crucial to have a robust solution that can handle various file types, support low-latency pipelines, detect reading order and language, and extract images, forms, and tables.
  • Unstructured offers a comprehensive solution for LLM ingestion and preprocessing, supporting a wide range of file types, offering smart-chunking, and providing end-to-end capabilities for full preprocessing workflows.
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