What the data for this category looks like
Seasoning industry research report data primarily comes from brokerage firm industry reports, public reports from food and beverage industry associations, and annual and quarterly financial reports from leading seasoning enterprises. Updates follow official report release timelines, with regular quarterly and annual updates. Temporary supplementary documents are also released during major corporate operational changes or raw material price fluctuations. Most documents use a multi-chapter structure, including fields such as overall industry scale, production and sales data for segmented categories like soy sauce, oyster sauce, sauces, channel share, cost breakdown, and more. Data units include tons, yuan per kilogram, percentage, and other standard units. Some documents contain nested tables and chart descriptions.
What constraints do these characteristics impose on document parsing and chunking?
The multi-chapter nested tables and chart structures in seasoning research reports require the parsing process to identify nested content boundaries. This prevents breaks in data associations caused by tables split across multiple chunks. The mixed fields and units in the data require chunking to retain the binding relationship between each field and its corresponding unit. This prevents loss of data semantics. Temporary supplementary documents require the parsing process to support incremental identification and chunking, to adapt to non-standard document structures. Core production and sales data for segmented categories is a key focus for research report retrieval. Chunking rules must prioritize retaining these core data blocks, to avoid dilution of retrieval relevance from irrelevant content.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_NESTED_TABLE | Enabled | A large number of nested tables exist in seasoning research reports. Enabling this setting allows complete extraction of associated data within tables |
CHUNK_SIZE | 800–1200 characters | Core data blocks in seasoning research reports mostly fall within this length range, balancing semantic completeness and retrieval accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large research report documents have more content, so reserving sufficient time completes full parsing |
KEEP_FIELD_UNIT | Enabled | Data units in seasoning research reports are mixed. Enabling this setting ensures accurate data semantics |
ENABLE_INCREMENTAL_PARSE | Enabled | Adapts to the non-standard update rhythm of temporary supplementary documents for seasoning research reports |
RECALL_PRIORITIZE_CORE_DATA | Enabled | Segmented category production and sales data is a key focus for retrieval, so this setting prioritizes retaining core data blocks |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, 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 errors
- After importing seasoning research reports saved in PPT or DOC format, the parsing result is empty or only extracts a small amount of text. Cause: General parsing parameters for non-PDF documents are not configured, and only PDF enhanced parsing is enabled by default.
- Parsed chunks have table content split across multiple independent blocks, with fields separated from their corresponding units. Cause: Nested table parsing and field unit retention configurations are not enabled. The default chunking rules do not adapt to the nested table structure of research reports.
- After configuring knowledge base question-answer pairs, retrieval returns content that includes additional generated text and does not strictly match the knowledge base original text. Cause: The generative answer switch is not disabled, or the
RETRIEVE_ONLYparameter is not enabled, causing the system to call generative logic instead of directly recalling original text.
How to confirm correct configuration
- Upload a test seasoning research report document that includes nested tables, then check if the parsing result completely extracts all table content with no cross-chunk splitting.
- Randomly select chunks of text after parsing, and verify that fields and their corresponding units are fully bound with no loss of data semantics.
- Submit a retrieval request related to core production and sales data, and confirm that core data blocks are recalled first, with the proportion of irrelevant content meeting expectations.
- After enabling the
RETRIEVE_ONLYparameter, submit a question-answer retrieval request, and confirm that returned content strictly matches the knowledge base original text with no additional generated content.
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.