What the data for this category looks like
The data for e-commerce service intelligent due diligence reports comes primarily from e-commerce platform backend transaction reports, supply chain management ledgers, brand compliance filing documents, and third-party quality inspection reports. The data update cadence covers three scenarios: real-time transaction streams, daily order summaries, and monthly supply chain inventories.
Document formats include multi-header Excel files (with fields such as SKU codes, order numbers, payment amounts, logistics tracking numbers), Word due diligence templates with embedded tables, and PDF-format qualification certificates. Field units are mostly physical measurement units such as Chinese yuan, pieces, and kilograms. Some fields include nested sub-tables and attachment references.
What constraints these characteristics impose on the document parsing and chunking workflow
The varied update cadence of e-commerce due diligence data requires the parsing workflow to support both batch rapid processing and offline batch parsing modes. The large volume of row data in multi-header Excel files and nested sub-tables can cause the default chunking logic to incorrectly merge related business data across headers, or cause chunk volume to exceed vector model limits.
Embedded tables and attachment references in Word documents require retention of original layout associations, otherwise business relevance in due diligence reports will be disrupted. Mixed-format document inputs require the parsing module to support extraction logic for multiple field types, to avoid field misalignment or content loss.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The text volume of single business units (such as individual order details, single SKU ledgers) for e-commerce due diligence data mostly falls within this range. This avoids chunk volume exceeding vector model limits while retaining business relevance |
chunk_overlap | 100–150 characters | Retains cross-chunk business association information, such as cross-chunk links between order numbers and corresponding payment amounts, to avoid context breaks |
PARSE_EXCEL_MULTI_HEADER | true | E-commerce due diligence Excel files often include multi-level headers. Enabling this setting correctly identifies header hierarchies, avoiding field misalignment or incomplete content extraction |
UPLOAD_FILE_MAX_SIZE | 500 MB | Covers the conventional need for batch importing multiple Excel and Word attachments for e-commerce due diligence reports, preventing large file uploads from being blocked |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Provides sufficient parsing duration for processing 10,000-row Excel files and 100,000-character Chinese Word documents, avoiding mid-process timeout interruptions |
VECTOR_MODEL_MAX_TOKEN | Configured per official parameters of the selected model | Matches chunk length to ensure chunked content can be correctly encoded by the vector model, avoiding vector generation failures |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: When processing a 15,000-row e-commerce transaction Excel file, the number of returned chunk results is far lower than expected, and individual chunks include unrelated data across headers. Cause: The
PARSE_EXCEL_MULTI_HEADERconfiguration is not enabled. The default parsing logic incorrectly identifies multi-level headers as a single level, leading to merging of content across business units. - Symptom: When uploading a 100,000-character Chinese Word due diligence report, the system returns a
413 Request Entity Too Largeerror. Cause: TheUPLOAD_FILE_MAX_SIZEconfiguration is not adjusted. The default upper limit is insufficient to accommodate large documents, or the document is not split into multiple sub-files. - Symptom: Vector generation fails, with logs showing a
token limit exceedederror. Cause: Chunk length does not match theVECTOR_MODEL_MAX_TOKENparameter. Individual chunk content exceeds the maximum token count supported by the vector model, leading to encoding failure.
How to confirm configurations are set correctly
- Upload a single 10,000-row e-commerce transaction Excel file, review the parsed chunk list, and verify that each chunk contains complete single business units with no unrelated data across headers. Adjust parsing logic via
PARSE_EXCEL_MULTI_HEADERif needed. - Upload a single 50,000-character Chinese Word document, review the parsing progress, and confirm no timeout errors occur. Adjust parsing duration thresholds via
PARSE_FILE_TIMEOUT_SECONDSif needed. - Review vector generation logs to confirm no
token limit exceededrelated errors are present. Adjust chunk parameters by matchingchunk_sizeandVECTOR_MODEL_MAX_TOKENif needed. - Batch upload three or more e-commerce due diligence documents, confirm that upload and parsing processes are not blocked. Adjust upload upper limits via
UPLOAD_FILE_MAX_SIZEif needed.
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.