Data Profile for This Category
Sources of black home appliance investment research data include industry association monthly monitoring reports, publicly available supply chain documents from brands, e-commerce platform product parameter pages, energy efficiency reports from third-party testing institutions, and patent application documents. Update cycles cover real-time e-commerce data, monthly industry reports, and quarterly supply chain updates. Document structures include structured parameter tables, long-text analysis chapters, and standardized testing data fields. Common fields include screen size (inches), standby power consumption (watts), energy efficiency rating, shipment volume (ten thousand units), and more. Some documents contain multi-level nested tables and image annotations.
Constraints for Parsing and Chunking
The multi-dimensional characteristics of black home appliance documents create multiple constraints for the parsing and chunking process. Nested supply chain tables can be split by conventional chunking logic, breaking parameter associations and preventing complete SKU information matching during subsequent retrieval. Frequently updated e-commerce data requires short-cycle incremental processing to avoid re-parsing full historical files. Image-embedded parameters in energy efficiency labels and testing reports lose visual positioning links when parsed as plain text, making full extraction of annotated values impossible. Document formats vary significantly across brands, with custom fields and non-standard layouts. General parsing templates cannot cover all valid information. Long-text industry analysis chapters are bound to corresponding parameter tables. Chunking must retain contextual associations to avoid separating analysis content from its associated parameters.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
parse_mode | structured+image_ocr | Black home appliance documents contain nested tables and energy efficiency label images. Structured parsing preserves field associations, while OCR extracts parameter text from images |
chunk_size | 800–1200 characters | Analysis chapters of industry research reports are bound to parameter tables. This length retains complete context for a single set of SKU parameters and their corresponding analysis content |
chunk_overlap | 100–150 characters | Prevents contextual breaks between parameters and analysis content after chunking, ensuring cross-chunk information association |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Large supply chain documents contain multi-page SKU details, which take longer to parse. The default timeout duration is insufficient to complete parsing |
enable_incremental_parse | true | E-commerce data and industry reports are updated frequently. Incremental parsing reduces duplicate processing overhead |
max_table_parse_depth | 3 levels | Nested tables in black home appliance supply chain documents typically do not exceed 3 levels, avoiding over-parsing redundant content |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Common Misconfigurations
- Issue: PDF parsing tasks return
500 Internal Server Error, with logs showing doc2x call timeout. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. Large supply chain PDF documents for black home appliances contain multi-page SKU tables, and the default timeout duration is insufficient to complete parsing. - Issue: When uploading a file directly to a large model node, the node receives the original binary file instead of parsed text. Cause: The
parse_before_invokeparameter was not configured. Pre-parsing is not enabled by default, and directly uploading original files cannot be understood by large models. - Issue: When creating a file collection via API, the returned knowledge base content lacks structured fields. Cause: The
parse_modeparameter was not included in the API request. The default parsing mode cannot extract nested table parameters from black home appliance documents.
How to Verify Correct Configuration
- Upload a single black home appliance supply chain document containing nested tables. Confirm the parsed text retains complete hierarchical field associations, and verify preset custom parameter fields are extracted.
- Submit an e-commerce data document for incremental update. Confirm the knowledge base synchronization log only processes new files, with no records of duplicate parsing of historical files.
- Call the API to create a file collection, add the
parse_modeparameter to the request body, and confirm the returned knowledge base entries contain structured parameter fields. - After configuring pre-parsing for the node, pass the uploaded file to the large model node. Confirm the node output contains parsed text content, and does not include original file metadata.
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.