What the data for this category looks like
The data for specialized chain intelligent due diligence reports mainly comes from store POS terminals, inventory management systems, regular operation reports submitted by franchisees, and regional market research datasets. There are three types of data update rhythms: real-time store transaction data is updated daily, franchisee summary reports are updated weekly, and regional market research data is updated monthly. The document structure takes individual stores as the basic unit, and includes fields such as store number, business address, monthly revenue, per-square-meter daily efficiency, inventory turnover days, and member repurchase rate. The units for these fields are as follows: per-square-meter daily efficiency is in yuan/square meter/day, inventory turnover days is in days, and revenue is in ten thousand yuan.
What constraints these characteristics impose on deployment and upgrade
Multi-store data for specialized chains has a large volume, and data update rhythms are inconsistent. The deployment link must support both incremental sync and full sync configuration modes to avoid excessive server resource usage from full pull operations. Data with different update frequencies corresponds to different scheduled task cycles, so separate sync trigger rules must be configured. Document fields include specific unit identifiers, so field format validation rules must be preset during deployment to prevent parsing errors. Some chain store edge nodes lack public network access permissions, so adaptive configuration for offline Docker deployment must be supported. The upgrade link must support gray-scale batch push to avoid interruptions to store data sync caused by bulk upgrades, which would disrupt offline business operations.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Specialized chain due diligence reports contain multi-store operation data and attachments, with long parsing durations, to avoid task interruptions from mid-task timeouts |
UPLOAD_FILE_MAX_SIZE | 800 MB | Adapt to the common volume upper limit of packaged multi-store reports, research images and other attachments |
LOG_DIR | /var/log/fastgpt | Centralize storage of running logs for multi-store data sync and document parsing, to facilitate problem troubleshooting |
SYNC_WEEKLY_TASK_CRON | 0 2 * * 0 | Match the weekly update cycle of franchisee weekly reports, execute sync tasks during off-peak hours |
RECALL_THRESHOLD | 0.72 | Filter low-relevance due diligence data fragments, ensure that recalled content matches current due diligence requirements |
GRAYSCALE_UPDATE_RATE | 20% | Perform batch upgrades of deployment nodes in stages, avoid interruptions to store data sync caused by bulk upgrades |
The parameter values given on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Files uploaded in the conversation scenario cannot be read, while files uploaded to the knowledge base can be parsed normally. Cause: File parsing permissions for the conversation scenario are not configured, only the file upload switch for the knowledge base module is enabled.
- Phenomenon: FastGPT running logs cannot be viewed after Docker deployment. Cause: The
LOG_DIRparameter is not set correctly, or the log directory inside the container is not mounted to the host machine, causing logs to only be stored inside the container and not directly accessible. - Phenomenon: In the open source version 4.8.17, developed plugins cannot find their corresponding entry in the interface. Cause: The plugin package is not placed in the system's preset plugin loading directory, or the service is not restarted to trigger the plugin scanning process.
How to confirm the configuration is correct
- Upload a standard due diligence report containing multi-store data, check if the fields in the parsing result match the preset business fields, and adjust the field validation rules based on actual business needs.
- Manually trigger a full data sync task, check the log files in the
LOG_DIRdirectory to confirm there are no abnormal errors in the sync process, and adjust the scheduled sync interval parameters based on task execution results. - Select a small number of deployment nodes for gray-scale upgrade, verify the running status of the service after upgrade, and adjust the gray-scale upgrade ratio based on test feedback.
- Upload a test file in the conversation scenario, confirm that the file can be loaded normally and used for the question and answer link, and verify that the conversation scenario file upload configuration takes effect correctly.
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.