Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract
Amazon Textract integrated with Amazon Bedrock enables accurate extraction of structured and unstructured content from complex, multi-page utility bills in various formats (PDF, DOCX, TXT, HTML, XLSX, PNG) Raw document ingestion into RAG pipelines leads to incomplete data extraction, hallucinations, and inconsistent performance across varying document formats Preprocessing with Textract provides advanced text extraction, data cleaning/enrichment, and contextual tagging to make document content L
Analysis
TL;DR
- Amazon Textract integrated with Amazon Bedrock enables accurate extraction of structured and unstructured content from complex, multi-page utility bills in various formats (PDF, DOCX, TXT, HTML, XLSX, PNG)
- Raw document ingestion into RAG pipelines leads to incomplete data extraction, hallucinations, and inconsistent performance across varying document formats
- Preprocessing with Textract provides advanced text extraction, data cleaning/enrichment, and contextual tagging to make document content LLM-friendly
- The solution transforms manual document searching into programmatic querying, enabling scalable, accurate customer service responses
- A shell script deployment approach is provided with full code available on GitHub
Why It Matters
This addresses a critical pain point for AI practitioners building RAG systems: raw document ingestion often fails with complex, real-world documents. The integration of a dedicated OCR/extraction service (Textract) with a generative AI platform (Bedrock) demonstrates a practical pattern for improving retrieval quality in production document-processing pipelines.
Technical Details
- Architecture: Amazon Textract serves as the preprocessing layer, extracting and structuring content from documents before feeding it into Amazon Bedrock's RAG pipeline for retrieval and generation
- Supported formats: PDF (multi-page with complex layouts and embedded images), DOCX (with tables and embedded objects), TXT, HTML (structured tag data), XLSX (cell contents and table data), and PNG (image-based text via OCR)
- Key problems solved: Incomplete data extraction (missing due dates, payment amounts, account numbers), LLM hallucinations producing incorrect/irrelevant information, and inconsistent performance across format variability
- Data pipeline: Textract performs text extraction → data cleaning and enrichment (noise removal) → contextual labeling and table tagging → ingestion into Bedrock for LLM-based querying and response generation
- Deployment: Shell script-based deployment with source code available on GitHub
Industry Insight
- Preprocessing is non-negotiable for production RAG: The case study confirms that raw document ingestion into LLMs is insufficient for complex, real-world documents; investing in dedicated extraction/preprocessing layers (like Textract, Docling, or custom parsers) is essential for reliability
- Hybrid extraction + generation architectures are the emerging standard: Combining purpose-built document intelligence services with generative AI platforms delivers better accuracy than end-to-end LLM approaches, particularly for regulated or high-stakes domains like billing and finance
- Format variability demands flexible pipelines: Supporting six distinct document formats in a single pipeline highlights the need for abstraction layers that can normalize heterogeneous inputs before they reach the LLM, a pattern that will become increasingly important as document volumes grow
Disclaimer: The above content is generated by AI and is for reference only.