From Raw Documents to Searchable Chunks
You'll learn to
- -Understand the document ingestion pipeline end to end
- -Identify the formats and edge cases real ingestion has to handle
- -Explain where OCR and metadata extraction fit into the pipeline
A law firm has 2 million documents: PDFs, Word files, scanned contracts, emails. Their current system only handles clean text files, which means it is silently ignoring roughly 60% of the firm's actual knowledge. This is the unglamorous but absolutely load-bearing first stage of every RAG system: before you can retrieve anything, you have to get real-world documents into a form a model can search over at all.
The Pipeline, Stage by Stage
Every RAG system starts here, long before the first user query ever arrives.
- -Document Loader: format-specific extraction. A PDF, a DOCX, an HTML page, and a plain text file all need different parsers, and a naive PDF text extractor frequently mangles multi-column layouts, tables, and headers/footers into unreadable noise.
- -Text Cleaner: strips boilerplate (page numbers, repeated headers, navigation menus scraped from HTML), normalizes whitespace and encoding, and removes content that would only pollute retrieval.
- -Metadata Extractor: pulls out structured facts about the document itself, source filename, author, creation date, document type, page number, that get attached to every chunk derived from it. This is what makes filtering ("only search contracts from 2023") and citations (the next tier's topic) possible at all.
- -OCR (Optical Character Recognition): for scanned images and photographed documents with no underlying text layer at all, OCR converts pixels into text before any of the above steps can even begin. It is usually the least accurate stage in the pipeline, and its error rate deserves explicit tracking rather than being assumed away.
Batch Processing at Real Scale
Two million documents cannot be ingested one at a time in a request-response loop. Real ingestion pipelines run as batch jobs: pull a large set of documents, process them in parallel across many workers, and write results incrementally so a crash partway through does not mean starting over from document one. This is the same batch-processing instinct covered for cost control back in Tier 7, applied here to a different kind of workload.
Ingestion quality is a ceiling on everything downstream. A chunk built from garbled, poorly-extracted PDF text cannot be rescued by a better embedding model, a better reranker, or a better prompt, the retrieval and generation stages can only work with what ingestion actually handed them. Treating ingestion as a solved, boring problem is one of the most common reasons a RAG system underperforms for reasons that look like a retrieval bug but are actually a parsing bug.
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