What data for this category looks like
Paint and ink industry research reports primarily come from public statistics released by national coatings industry associations, regular announcements of listed coating enterprises, and special research documents from third-party chemical consulting institutions. Most documents are in PDF format, with fields including raw material unit prices, production capacity data, downstream application structure, compliance indicators, etc. Commonly used units are ton, kilogram, ppm, and yuan per kilogram. Update cycles follow industry standards: industry-wide reports are updated quarterly, while enterprise dynamic research reports are updated monthly. Individual document lengths vary widely. Confirm based on internal sample statistics or actual measurement before setting values.
Constraints on citation source and traceability
Clear marking of the publishing organization and release time during citation is required for multiple public report sources, to avoid confusion between data with different statistical calibers. Individual documents are long and contain specialized segmented fields. During traceability, specific chapters or paragraphs must be located to ensure the cited content fully matches the marked source. Differences in update frequency require a regular synchronization mechanism for the knowledge base, to ensure retrieved research reports are the latest versions. Some research reports include compliance indicators. During traceability, original text expressions from documents must be retained to avoid translation deviations. Unit labeling rules across different sources must also be unified, to prevent unit mismatch issues.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_top_k | Top 10 entries | Paint and ink research reports contain many specialized segmented fields. A sufficient recall volume is needed to cover segmented retrieval needs, while avoiding introducing excessive redundant content |
similarity_threshold | 0.65–0.75 | Professional research reports have high accuracy requirements. A too low threshold will introduce irrelevant general industry content, while a too high threshold will miss effectively matched segmented field content |
source_cite_format | [Document Name] Publishing Organization: Release Time, Page Number/Chapter | Core source information must be clearly marked to meet the requirement of quickly locating the original text during traceability |
parse_chunk_size | 800–1200 characters | Individual research reports are long and contain specialized paragraphs. A segment length within this range can retain content integrity while improving retrieval accuracy |
knowledge_refresh_interval | 7 days | Industry-wide reports are updated quarterly, while monthly dynamic reports require more frequent synchronization. A 7-day interval balances update costs and data timeliness |
rerank_top_n | Top 5 entries | The most relevant research report content must be retained, while controlling the amount of information displayed in citations to avoid page overload |
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 case-by-case analysis. Confirm based on actual testing on in-house samples before finalizing.
Three Common Mistakes
- Phenomenon: When retrieving multi-category research reports together, non-paint and ink content cannot be filtered out. Cause: No data source classification tag filtering rules are configured, resulting in retrieved content including research reports from unrelated categories.
- Phenomenon: Directly writing the
knowledge_idvariable or hardcoding the knowledge base ID in the prompt word triggers a parameter parsing error when deploying the application. Cause: FastGPT's knowledge base reference syntax is not used correctly, or the association relationship between the application and the target knowledge base is not bound. - Phenomenon: After adjusting
similarity_thresholdto the minimum andrecall_top_kto the maximum, the number of retrieved content entries remains fixed. Cause: The knowledge base is configured with a maximum number of retrieved paragraphs per document, or global deduplication rules are enabled, causing duplicate content to be filtered out, making it impossible to further increase the number of retrieved entries.
How to Confirm Proper Configuration
- Enter the application debugging interface, enter professional paint and ink keywords, and check whether the citation sources of the returned results are marked with the document name, publishing organization, and release time.
- Adjust the value of
similarity_threshold, observe the correlation change of the retrieved content, and confirm that the configuration parameters take effect normally. - Manually trigger the knowledge base refresh operation, wait for the refresh to complete, retrieve the same keyword, and confirm that the update time of the retrieved content meets expectations.
- Check the application running logs to confirm that the display format of the cited content is consistent with the preset
source_cite_formatrules.
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.