What the data for this category looks like
Data sources include product technical manuals from consumer building materials manufacturers, compliance reports from third-party testing institutions, bidding response documents for engineering projects, and supply settlement documents from upstream and downstream supply chains. Update frequency varies by document type: product manuals are updated with new product iterations, testing reports are updated upon completion of batch inspection, bidding documents are generated and archived per individual project cycles, and supply ledgers are updated with monthly settlements. Most documents mix structured tables, technical parameter description paragraphs, and attachments embedded as scanned images. Some documents contain complex tables with merged cells or cross-page parameter lists. Fields include product specification models, implementation standard numbers, physical performance parameters such as flexural strength, thermal conductivity, supply unit price, delivery cycle, and more. Some parameters have dedicated units: for example, thermal conductivity is measured in W/(m·K), and flexural strength is measured in MPa.
What constraints do these characteristics impose on the "document parsing and chunking" link?
The mixed structure of consumer building materials due diligence documents requires the parsing link to first distinguish between structured table data and unstructured description paragraphs, to avoid splitting product parameters and supporting technical descriptions during chunking. Complex merged cells and cross-page parameter tables may cause conventional parsing logic to lose cell association relationships, requiring adapted dedicated table parsing rules. Embedded scanned image attachments require OCR recognition before chunking, otherwise handwritten or printed testing data within the scanned images cannot be extracted. Parameter fields bound to dedicated units require retaining the association between parameters and units during chunking, to avoid mismatches between parameters and units during retrieval. Supply ledgers updated monthly need to be split into chunk units by time dimension, to facilitate subsequent retrieval of supply data for corresponding periods by project cycle.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | The parameter paragraphs and table blocks in consumer building materials due diligence documents mostly fall within the 600-1000 character range. This interval preserves the integrity of parameters and supporting descriptions |
chunk_overlap | 100–150 characters | Prevents cross-block parameter descriptions from being split after chunking, and retains contextual association between adjacent blocks |
parse_table_mode | detailed | Consumer building materials documents contain a large number of parameter tables with merged cells. This mode preserves the hierarchical association and original format of cells |
enable_ocr | Enabled | Some due diligence documents include embedded PDF scanned images, and OCR recognition is required to extract testing data from the scanned images |
split_delimiter | [newline][newline], ###, ## | Consumer building materials due diligence documents mostly use section titles to separate parameter content of different products. This delimiter can split chunk units by product dimension |
parse_merge_cell | Enabled | Product parameter tables for consumer building materials often use merged cells to mark specification ranges. This configuration correctly parses the content of merged cells |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The uploaded consumer building materials due diligence report table displays correctly in the knowledge base preview, but table content is missing or displayed as plain text in retrieval results. Cause: The detailed parsing mode of
parse_table_modeis not enabled, only the text surrounding the table is extracted, and the table structure is not retained. - Phenomenon: In the chunked document, parameter data from merged cells is split into multiple scattered entries, and complete specification information cannot be associated. Cause: The
parse_merge_cellconfiguration is not enabled. Conventional parsing logic directly splits the content of merged cells, losing the binding relationship between parameters and specification ranges. - Phenomenon: Document data generated using custom chunking logic locally cannot match expected retrieval results after being uploaded to the server. Cause: Chunking granularity and embedding dimensions vary between vector models. The text units chunked locally do not match the embedding requirements of the server-side vector model, resulting in failure to correctly associate with target content during retrieval.
How to confirm the configuration is correct
- Upload a consumer building materials product manual containing complex merged tables, check the table parsing effect in the knowledge base preview, and confirm that the table structure is complete and free of missing content.
- Perform a retrieval test, enter keywords containing product parameters and units, and check whether the returned results retain the binding relationship between parameters and units.
- Upload a test report with embedded PDF scanned images, check whether the parsed text includes testing data from the scanned images, and confirm that the OCR configuration is effective.
- Compare the locally chunked text units with the server-side chunking results, and confirm that the chunking granularity and delimiter settings are consistent.
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.