Grain Reserve Declaration Material and File Review: Document Parsing and Chunking

What this category of data looks like

Data for grain reserve declaration material and file review comes from rotation declaration forms, inventory check ledgers, supervision inspection files, and quality inspection reports from reserve grain depots at all levels. Update cycles align with reserve rotations, quarterly inspections, and annual audits, with no fixed schedule. Document types include Excel inventory details, official PDF declaration files, and Word file records. Some documents use table structures with merged cells. Fields include grain reserve variety, storage quantity, depot number, inspection batch number, rotation plan number, and more. Quantity fields have fixed units such as tons and kilograms.

What constraints do these characteristics impose on the document parsing and chunking workflow

Tables in grain reserve declaration materials often contain merged cells. Parsing must preserve cell associations, otherwise field misalignment or data loss will occur. Single file records may include multiple attachments and have a long total length. Chunking must preserve cross-page context connections, to avoid breaking cross-page plan or detail logic. Fields have fixed units. Chunking cannot split combined numerical values and units, otherwise subsequent data retrieval will fail to match. Temporarily uploaded emergency materials have inconsistent formats. Parsing must adapt to multiple file types, to avoid losing key data due to format differences.

How to set the configurations

Reference only; verify against the target FastGPT release and deployment documentation.

| chunkSplitter | Recognize by paragraph + table header | Preserves the correspondence between table headers and content in documents, adapts to structured data in Excel and PDF |

| customPdfParse | Enable dynamic table recognition | Identifies merged cell tables in declaration materials, avoids losing field data of inventory details |

| UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading multiple file attachments in a single batch, adapts to the demand for bulk documents from annual audits |

| PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Reserves sufficient time for parsing long file records, avoids task failure due to parsing timeout |

| chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types, adapts to the fixed field structure of grain reserve documents |

Three common errors

• Symptom: After uploading an Excel inventory file, merged cell fields in the parsing result are empty. Cause: The dynamic table recognition mode of customPdfParse is not enabled. The default parsing logic fails to identify merged cell structures, leading to field misalignment or data loss.
• Symptom: After uploading a PDF file record to the knowledge base, the vectorization link takes too long. Cause: The chunkSize setting is too small, resulting in an excessive number of chunks, or PARSE_FILE_TIMEOUT_SECONDS is not adjusted to a reasonable duration, triggering repeated parsing.
• Symptom: Triggering the document parsing process during a query breaks the logic of only retrieving existing chunked data. Cause: The default automatic parsing configuration in the knowledge base settings is not disabled, leading to unintended parsing operations.

How to confirm the configuration is correct

• Upload an Excel inventory table that contains merged cells, check that the fields corresponding to the merged cells in the parsing result are fully displayed, with no blanks or misalignment.
• Upload a PDF annual audit file with more than 20 pages, view the execution logs of the parsing task, confirm that no timeout errors occur, and the number of chunks meets expectations.
• Test uploading a bulk document package larger than 500 MB, confirm that the upload and parsing processes start normally, with no file size limit prompts.
• Adjust chunkSize to different ranges, upload the same declaration document separately, confirm that the chunked text does not split the combined fields of varieties and quantities.

The values provided are common starting points and should be measured against the reader’s own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.