What the data for this category looks like
Data sources for dairy industry research reports include public monitoring reports from domestic dairy industry associations, specialized food and beverage research reports from securities firms, production and sales data officially disclosed by leading dairy enterprises, and terminal sales data from third-party consumer retail monitoring institutions. Update frequencies cover monthly regular monitoring, quarterly, half-yearly, and annual industry analysis, as well as temporary emergency nodes such as raw material price fluctuations and policy adjustments. Each report usually includes core data summaries, production and sales data tables by category, market competition pattern analysis, and upstream and downstream industry chain linkage content. Some reports include excerpts from original monitoring ledgers. Core quantitative fields include raw milk purchase unit price (unit: yuan/kg), total liquid milk shipment volume (unit: 10,000 tons), single product terminal monthly sales volume (unit: 10,000 units). Some documents include financial data related to corporate operating cash flow (unit: 10,000 yuan).
What constraints these characteristics impose on the knowledge base retrieval and recall link
The multi-source, multi-cycle update characteristics of dairy industry research reports require the knowledge base retrieval and recall link to support incremental update mechanisms classified by data source. This prevents result deviations caused by mixing data of different timeliness. Research reports contain a large number of structured table data. Ordinary text parsing cannot fully retain the field association relationships in tables. Separate structured extraction and indexing are required for table content. This ensures that specific indicators in tables can be accurately retrieved. There are differences in indicator calibers for segmented product categories. For example, the shipment volume statistics dimensions of liquid milk and infant formula differ. Retrieval must match the caliber rules corresponding to each category to avoid confusion of cross-category data. Temporary emergency reports have higher timeliness requirements. The recall link needs to configure targeted time decay weights to prioritize returning recent emergency research report content.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Dairy industry research reports contain a large number of structured table data. Enabling this option fully extracts fields and associated content within tables |
TEXT_SPLITTER_CHUNK_SIZE | 800–1200 characters | The table content and text paragraph lengths of dairy industry research reports are balanced. This segment length retains complete context for indicators |
UPLOAD_FILE_MAX_SIZE | 500 MB | Single PDF collections of dairy industry research reports usually contain multiple industry reports. 500 MB covers the storage needs of conventional collections |
RECALL_TOP_N | Top 8 entries | Dairy industry research reports have a large number of segmented indicators. Returning 8 entries covers core retrieval needs and avoids excessive results interfering with judgment |
TIME_DECAY_WEIGHT | 0.95 every 7 days | The timeliness cycle of dairy industry data is approximately 1-2 months. This decay coefficient balances the weights of historical data and new data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large dairy industry research report PDFs contain multiple pages of tables and attachments. 600 seconds ensures complete parsing without timeout |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: After calling the API to upload a PDF of dairy industry research reports, fields such as raw milk prices and sales volume in the table are not correctly extracted. Cause: The
PARSE_TABLE_ENABLEconfiguration item is not enabled, or the associated rules for table structured extraction are not configured. - Phenomenon: After importing dairy industry research reports in Excel format, some numeric fields display garbled characters or cannot be retrieved. Cause: The numeric type parsing switch for Excel tables is not enabled, and the encoding format of imported files is not unified.
- Phenomenon: When attempting to delete uploaded research report files from the knowledge base, a
404 Not Foundstatus code is returned. Cause: The uploaded file is not correctly bound to the specified knowledge base, or the query parameter format of the file ID is incorrect.
How to confirm the configuration is correct
- Upload a dairy industry research report PDF that contains structured tables. Check the parsed text content to confirm that fields and values in the table have been fully extracted.
- Call the retrieval interface, enter core keywords of dairy industry research reports, and verify that the number of returned results matches the
RECALL_TOP_Nconfiguration value. - Upload a batch of dairy industry research report data in Excel format, and check whether the imported field mapping matches the preset rules.
- Call the file deletion interface, use the correct file ID to perform the deletion operation, and confirm that the interface returns a successful status code.
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.