[Question]: DeepDoc + “Parse as Paper” Generates Only OCR Text; Missing LLM-Based Graph Descriptions (Works in General Parsing)

[nikhilmgeorge7]

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Describe your problem

When using DeepDoc with the “Parse as Paper” option, the output generated in the graph view contains only OCR-extracted text from the PDF.
However, no semantic / LLM-generated description is created for the graph nodes.

In contrast, when the exact same file is processed using “Parse as General”, the system successfully generates:

OCR extraction

and an LLM-generated description / summary for each graph node

This means that Paper parsing mode appears to disable or skip the LLM description step, resulting in graph nodes that are incomplete and less useful for retrieval or downstream reasoning.

At the moment, “Parse as Paper” = OCR only, while “Parse as General” = OCR + LLM description.

This behavior is unexpected because Paper mode is typically used for structured scientific documents, where semantic descriptions are even more important.

Steps to Reproduce

Upload any PDF with structured content (e.g., a research paper).

Choose DeepDoc as the parser.

Select “Parse as Paper.”

After parsing completes, open the Graph View.

Observe that:

Each node contains only OCR text chunks.

The field where a semantic description normally appears is missing or empty.

Re-upload or re-parse the same file using “Parse as General.”

In Graph View:

Each node now includes not only OCR text but also the LLM-generated description, as expected.

Expected Behavior

DeepDoc + Parse as Paper should produce both:

OCR-extracted raw text

LLM-based description / summarization, exactly like General parsing
unless there is a setting explicitly disabling it.

The graph should contain a semantic description field that enhances retrieval quality and document understanding.

Actual Behavior

Parse as Paper output:

Only plain OCR text in graph nodes

No LLM summary or description generated

Graph nodes appear incomplete compared to the General mode

Parse as General output:

OCR text + full LLM description

Graph nodes are enriched and more usable

This issue affects:

Document comprehension

RAG quality (embeddings rely heavily on semantic descriptions)

Accuracy of downstream QA

Graph-based retrieval usefulness

Especially for research papers, losing LLM descriptions significantly reduces the value of DeepDoc’s processing.

[dosubot]

The reason DeepDoc's "Parse as Paper" mode only outputs OCR text (without LLM-based semantic descriptions for graph nodes) is due to its default parser configuration: semantic enrichment steps like RAPTOR and Graphrag are disabled by default in this mode. In contrast, "Parse as General" mode enables these steps, so you get both OCR and LLM-generated summaries in graph nodes.

To enable LLM-based descriptions in "Parse as Paper" mode, you need to update the parser configuration for your dataset or document to set use_raptor: true (and optionally use_graphrag: true). This activates the semantic enrichment pipeline, so graph nodes will include LLM-generated summaries just like in General mode. This change is not currently exposed in the UI—you'll need to update the config via the API or database directly. See the HTTP API reference for details on the relevant fields.

Enabling these flags will improve document comprehension, retrieval quality, and downstream QA by enriching graph nodes with semantic context, which is especially valuable for structured scientific PDFs.

To reply, just mention @dosu.

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