**Article Summary: Handling Folders with No Shared Fields in RAG Systems**
When dealing with Retrieval-Augmented Generation (RAG) systems, the traditional approach of building an indexed table with rows as documents and columns as shared fields works well for homogeneous datasets. However, many real-world document collections—such as research folders containing security controls, technical specifications, academic papers, and market reports—lack common fields across files. This article explores how to handle such heterogeneous collections effectively without forcing an unnatural structure.
The key insight is that when no shared metadata fields exist across documents, the solution is not to build an index but to treat the entire folder as a single long document. The folder’s table of contents becomes the primary retrieval mechanism. The architecture relies on a two-level routing process: the first level selects relevant files based on summary descriptions, and the second level navigates inside selected files using their native outlines. This approach leverages what the documents already provide—their table of contents—rather than introducing external schemas.
The article outlines two critical questions to identify when a folder does not need indexing: whether documents refer to each other and whether a common, meaningful metadata field exists across all files. If both answers are negative, the collection is best treated as a long document. The preparation phase involves generating one summary line per file and using the parser’s native table of contents. Writing these summaries for routing—not documentation—is crucial, as they act as filters during query time.
At query time, the system first uses the file-level summaries to narrow down candidate documents, then descends into the selected file’s outline to locate the precise answer. This two-level retrieval keeps prompts manageable, improves accuracy, and avoids the noise of passing thousands of structureless rows in a single call. The method also highlights four common failure modes—poor summary lines, structureless long documents, flat lists that don’t scale, and the temptation to over-engineer solutions—and provides practical fixes for each.
The conclusion emphasizes that heterogeneous folders do not need relational indexing, ontologies, SQL querying, or entity extraction. Instead, they need concise summaries and intelligent routing. By embracing the folder’s natural hierarchy, RAG systems can deliver accurate, efficient, and maintainable retrieval without unnecessary complexity.
**FAQ**
**When should I use a two-level routing approach instead of building an index?**
Use two-level routing when documents in a folder share no common metadata fields and do not reference each other consistently. If users can’t name a column that meaningfully applies to all documents, forcing an index will lead to poor performance and complexity.
**What does the summary line for each file need to contain?**
The summary line should act like a schema definition for routing. Include the main subject using terms users will search for, describe the shape of answers the file provides (e.g., numbered controls vs. narrative), and clearly state what the file is not about to help the router eliminate irrelevant files early.
**Can this approach scale to thousands of files?**
A flat list of summaries works well up to a few hundred files. Beyond that, introduce an additional grouping layer—such as by source, topic, or year—to keep the level-0 prompt manageable while preserving the same routing logic.
**What happens if a document has no table of contents?**
Short files can be handled as indivisible units. Long files without structure may require reconstructing an outline from visible headings or page content before routing can occur. This is a parsing issue addressed before the retrieval pipeline begins.
**Does this approach replace traditional RAG components like embeddings or vector databases?**
Not entirely. The two-level routing method complements existing systems by reducing unnecessary document retrieval early. It works alongside embeddings and vector indices but avoids their limitations when documents lack shared structure.
**Is manual writing of summaries sustainable at scale?**
For folders of moderate size, summaries can often be generated automatically during ingestion and then reviewed like schema rather than content. In practice, automation combined with occasional manual refinement offers a good balance between accuracy and effort.
**Can same-document references or cross-file links be handled this way?**
This approach is designed for collections without meaningful inter-document relationships. If documents do refer to each other or need consistent cross-file queries, a different architecture centered on modeling those relationships is more appropriate.
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