What the data for this category looks like
Papermaking industry intelligent due diligence report data sources include China Paper Association public statistical documents, listed papermaking enterprises' annual and semi-annual disclosure reports, real-time data from raw and auxiliary material trading markets, and public indicators from environmental protection regulatory authorities. Update cadences include monthly production data updates, quarterly industry prosperity report updates, annual enterprise annual report updates, and irregular updates to environmental protection policies. Most documents use multi-chapter structured formats, with core fields such as production capacity scale, raw and auxiliary material prices, unit energy consumption, and environmental emissions. Units adopt industrial standard measurement methods including ten thousand tons, yuan/ton, mg/m³, and similar standards.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Data sources cover multiple public channels with significantly different update frequencies. This requires knowledge bases to support multi-source scheduled synchronization and incremental update mechanisms to avoid data lag or duplicate redundancy. Documents contain multiple structured fields, and field names and units from different sources have minor differences. This requires the retrieval link to support field mapping and unit normalization to ensure accurate query matching. Single due diligence reports are lengthy with strong logical connections between content modules. This requires segmented recall to retain cross-segment contextual associations, avoiding damage to logical links between indicators. Industry policy updates occur frequently and must be synchronized to the knowledge base in a timely manner. This requires the recall link to prioritize matching the latest policy-related entries.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Individual papermaking due diligence reports are lengthy and contain multi-chapter data, with longer parsing times than general documents |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports batch uploading of large documents such as industry statistical yearbooks and enterprise annual reports |
maxChunkSize | 1000–1200 characters | Structured fields and associated content in papermaking reports must be retained in a single segment to avoid splitting that breaks logical connections between indicators |
recallTopK | Top 10 results | Due diligence reports require coverage of multi-dimensional data, so a sufficient number of candidate results must be retrieved for subsequent screening |
similarityThreshold | 0.72–0.78 | Minor differences exist in the wording of papermaking industry indicators, so the threshold should not be set too high to avoid missing relevant content, nor too low to introduce irrelevant results |
rerankTopN | Top 5 results | Retain core relevant entries after reranking recall results to adapt to the multi-module retrieval needs of due diligence reports |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: In FastGPT 4.8.9, an
embedding erroroccurs when uploading large documents such as papermaking industry annual reports. Cause: TheUPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSparameters are not adjusted, resulting in large document parsing timeouts or exceeding upload limits. - Phenomenon: Knowledge base recall results simultaneously include papermaking indicator data with different units, such as some entries marked yuan/ton and others marked yuan/kilogram. Cause: Field mapping and unit normalization rules are not configured, and original document fields are used directly for retrieval matching.
- Phenomenon: When calling the knowledge base plugin via variables, recall results mix due diligence content from non-papermaking categories. Cause: Industry classification fields are not bound in the retrieval configuration, causing the screening condition to not take effect.
How to Verify Proper Configuration
- Upload a typical papermaking due diligence report, check the parsing progress and final segmented results to confirm no timeouts or errors occur during the parsing process.
- Enter a typical papermaking industry query, check whether the units of fields in the recall results are unified, and verify that the field mapping and normalization rules are effective.
- Configure variable screening conditions, trigger plugin calls, and verify that returned results only include due diligence content from the papermaking category.
- Adjust the similarity threshold, test the number of recall results under different query scenarios, and confirm that the results meet business screening needs.
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.