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Understanding What Matters for LLM Ingestion and Preprocessing
Unstructured effortlessly extracts and transforms complex data for use with every major vector database and LLM framework.
Historically, data scientists have had to hard code hundreds or thousands of regular expressions or integrate custom Python scripts into preprocessing pipelines to clean their data—a laborious approach prone to breaking if document layouts or file formats change. For these production use cases, developers require a robust preprocessing solution—a solution that systematically fetches all of their files from their various locations, ushers them through the document processing stages described above, and finally writes them to one or more destinations. In addition to abstracting away the infrastructure management, these services also host enhanced models for precise table extraction, advanced chunking capabilities and document hierarchy detection, and early access to fresh features.
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