Document Parsing and Chunking for Railway and Highway

What the data for this category looks like

Marketing content data targeting the railway and highway category from financial, insurance, and wealth management institutions primarily comes from monthly passenger flow reports, quarterly marketing review documents, offline promotional materials, online campaign effect statistics spreadsheets, and customer survey feedback files.

Update cycles follow monthly and quarterly schedules, with additional new documents tied to temporary marketing campaigns. Document formats include structured tables, long-form text summaries, and mixed-media materials. Common fields cover passenger trips, operating mileage, advertising budget, route numbers, and station names. Typical units are trips, kilometers, and ten thousand yuan.

Constraints on document parsing and chunking

Marketing documents for railway and highway clients from financial, insurance, and wealth management institutions present specific traits that create constraints for parsing and chunking:

Monthly passenger flow reports contain multi-row nested tables with real-time passenger flow detail fields. Parsing must retain table hierarchy to avoid breaking structured information during chunking.
Long-form marketing review documents often include logically connected content across pages. Chunking processes must identify context continuity.
Temporary promotional materials are typically mixed-format, with embedded images and accompanying text descriptions. Parsing must extract text linked to images simultaneously.
A large volume of specialized field combinations such as route number + station name appear in documents. Chunking must avoid splitting these proper noun combinations to prevent semantic breaks.

How to set configurations

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`900 seconds`	Railway and highway marketing documents often contain large nested tables and long-text reviews. The default timeout duration is insufficient for complete parsing
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Single packaged promotional materials and quarterly reports can reach 1.5 GB or larger in size. This setting adapts to large file upload requirements
`chunk_size`	`800–1200 characters`	Documents contain proper noun combinations and long logical sentences. This range avoids splitting technical terms while ensuring complete chunk semantics
`table_parse_strategy`	`structured`	Most passenger flow and budget tables in documents are nested. Structured parsing retains field correspondence and avoids disordered table content
`chunk_overlap`	`100–150 characters`	Long review documents have logically connected content across pages. Overlapping chunks preserve context continuity
`ENABLE_GPU_PARSE`	`Enabled`	Parsing large PDF and DOCX documents requires GPU acceleration to adapt to high-concurrency parsing scenarios

The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

When uploading a quarterly passenger flow report PDF larger than 10 MB, the interface returns the timeout of 900000ms exceeded error. This occurs because the PARSE_FILE_TIMEOUT_SECONDS configuration is not adjusted. Using the default timeout duration causes parsing of large documents to disconnect before completion.
After importing a document into a knowledge base, nested marketing budget tables only display the first row of content, with all remaining fields empty. This happens because table_parse_strategy is not set to structured. The default parsing mode cannot recognize nested table structures.
After starting a parsing service deployed via Docker, an out-of-memory error is displayed and the model cannot be loaded. This occurs because GPU memory usage is not limited, and the CUDA version does not match the PDF parsing plugin version, resulting in failure to correctly call hardware resources.

How to verify configurations are correctly set

Upload a typical monthly passenger flow report PDF. Check if the parsing log shows parse completed with no timeout errors, and confirm that the timeout configuration matches the document's actual parsing time.
Import a marketing budget document containing nested tables. Verify that all table fields are complete in the chunking results, and confirm that the table parsing strategy has taken effect.
View the GPU monitoring panel. Confirm that GPU memory usage does not exceed the preset threshold after the parsing task starts, and that the CUDA driver and plugin versions match.
Randomly extract text fragments from the chunked results. Check that proper nouns such as route numbers and station names are not split, and confirm that the chunk length setting is reasonable.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.