Technical guide · direct answer · primary references
Document pipelines with RAG
Principles for document ingestion, extraction, retrieval and auditing in production.
Direct answerA reliable document pipeline separates ingestion, normalization, extraction, indexing, retrieval and evaluation. RAG is only one stage: traceability, reprocessing and version control sustain the operation.
The essential stages
- Receive and identify every document.
- Normalize text and metadata.
- Extract relevant fields or segments.
- Index with source and version.
- Retrieve context and generate an answer.
- Evaluate, audit and reprocess when required.
Why traceability matters
When an answer is wrong, the team must discover whether failure came from the file, extraction, segmentation, search or generation. Persistent IDs and stage-level logs make that investigation possible.
RAG versus structured extraction
| Need | Approach |
|---|---|
| Answer open questions | RAG with sources |
| Capture defined fields | Structured extraction and validation |
| Regulated workflow | Combination with rules and auditing |
Frequently asked questions
Should every document go into a vector database?
No. The choice depends on queries, volume, structure and update requirements.
How does Docowling relate to this topic?
Docowling can support the conversion stage: it transforms popular formats into HTML, Markdown, JSON or the unified DoclingDocument representation. It does not replace pipeline indexing, retrieval, evaluation or auditing.