What the data for this category looks like
Data sources for packaging and printing research reports include brokerage light manufacturing team research reports, industry association public reports, and regular reports of listed packaging and printing companies. Update schedules include regular monthly in-depth reports, weekly industry dynamic reports, and temporary reports added when policies change. Document structures include industry supply and demand data, production process parameters, supply chain cost breakdown, and policy interpretation modules. Fields include single factory production capacity (tons/year), printing accuracy (dpi), order delivery cycle (days), and others. Most units are physical and time units.
What constraints these characteristics impose on deployment and upgrade
Packaging and printing research reports contain many specialized process parameters. Custom industry word segmentation and entity extraction dictionaries must be loaded during deployment to prevent incorrect splitting of professional terms. The flexible update schedule requires configurable synchronization task scheduling rules, supporting manual triggering of temporary updates to meet rapid synchronization needs for temporary reports. Long documents and large volumes of table content require adjustment of segment length and table parsing parameters to avoid truncation of key data. The diversity of field units requires configuration of entity extraction unit matching rules to ensure accurate association during retrieval. During upgrades, compatibility with migration of old version industry dictionaries is required to prevent configuration failures.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
custom_entity_dict_path | ./fastgpt/data/printing_entity_dict.json | Stores custom entity dictionaries for packaging and printing industry-specific terms, facilitating dictionary migration during version upgrades |
maxContext | 8000–12000 characters | Adapts to the average length of individual research reports, retains context association after splitting, and avoids truncation of key information |
PARSE_TABLE_MODE | accurate | Research reports contain a large number of process parameter tables; the high-precision parsing mode fully retains field and unit information |
sync_cron | 0 0 2 * * * | Adapts to the update frequency of regular weekly or monthly reports, supports manual triggering of temporary synchronization tasks |
similarity_threshold | 0.75–0.85 | Meets retrieval accuracy requirements for matching professional terms, filters low-relevance retrieval results |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Adapts to the common size of individual in-depth research report PDF files, avoids file truncation during upload |
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. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Scenario: After deploying version V4.9, when configuring an online model, the configuration entry for the deepseek model cannot be found. Cause: The interface address and key for deepseek are not configured in
model_config.json, or the plugin for the corresponding model is not enabled. - Scenario: After upgrading to version 4.9.1, a non-closable full-screen overlay appears on the login interface, preventing subsequent operations. Cause: Front-end static resource cache was not cleared during the upgrade process. The overlay style of the new UI conflicts with the Nginx cache rules from the previous deployment.
- Scenario: TRACE request warning messages appear in remote WWW service logs, or retrieval requests return a 500 status code. Cause: TRACE request support for the built-in FastGPT service is not disabled. Such requests occupy additional connection resources and interfere with normal retrieval processes.
How to confirm the configuration is complete
- Run a test extraction for the custom entity dictionary, input packaging and printing professional terms, confirm that word segmentation and entity recognition results conform to industry standards. Adjust the dictionary content based on recognition accuracy.
- Trigger a scheduled synchronization task, check the update status of research report data sources, confirm that newly released reports have been successfully pulled and parsed. Adjust the synchronization cycle configuration based on update delay.
- Initiate a retrieval request containing professional process parameters, confirm that the returned result fields and units match accurately. Adjust the similarity threshold and entity extraction rules based on matching accuracy.
- Run a curl command to test the remote WWW service, confirm that TRACE requests are rejected. Adjust the HTTP request configuration of the service based on test results.
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.