Document Extraction

Document extraction converts PDFs, images, and scanned files into structured, searchable content that can be indexed and used in RAG workflows within IntraLLM AI.

Document Extraction in IntraLLM AI

IntraLLM AI provides document extraction capabilities that convert unstructured files into structured, machine-readable content suitable for indexing and Retrieval Augmented Generation (RAG). This step is critical for turning documents into searchable knowledge that language models can use reliably.

What is document extraction?

Document extraction is the process of automatically identifying and extracting text and relevant data from supported file formats, including:

  • PDFs: text-based PDFs and scanned PDFs
  • Images containing text: screenshots, photos, scans
  • Handwritten documents: where supported by the configured extraction method
  • Other formats: depending on the extraction engines enabled in your deployment

Why it matters

With effective extraction, IntraLLM AI can:

  • Convert image-based content into searchable text (via OCR where required)
  • Preserve structure and layout signals (headings, sections, tables) when supported by the parser
  • Extract structured data for downstream workflows (summaries, classification, reporting)
  • Support multilingual recognition when the extraction engine is configured with the appropriate language settings

Available extraction methods

IntraLLM AI can be configured with multiple extraction engines to match different document types and quality levels. Each method has different strengths (for example, accuracy on scanned PDFs, table extraction, or multilingual OCR).

To choose the best method for your use case:

  • Prefer structure-preserving parsers for well-formed PDFs and Office documents.
  • Enable OCR for scanned documents or images that contain text.
  • Validate extraction output quality before large-scale ingestion into your knowledge base.

Next steps

Review the documentation for each extraction method available in your deployment to learn:

  • How to configure the service endpoint (if applicable)
  • Supported file types and languages
  • Quality considerations (scan quality, compression, handwriting support)
  • Recommended settings for your RAG workload