What the data for this category looks like
Engineering consulting yield and market daily report data comes primarily from three sources: public cost monitoring platforms operated by housing and urban-rural development authorities, project revenue statistical reports released by industry associations, and archived engineering consulting project cost and revenue ledgers. The data updates daily, covering industry revenue changes from the previous day. Each daily report is a structured entry that includes the project’s affiliated building sub-sector, statistical cycle, benchmark investment return parameters, regional reference parameters, and unit cost indicators. Benchmark investment return parameters use unit investment return amount as their unit. Regional reference parameters use regional benchmark values as their unit. Unit cost indicators use per-square-meter cost indicators as their unit.
Constraints on Deployment and Upgrade from Data Characteristics
The data characteristics of this category impose multiple constraints on deployment and upgrade workflows. Deployments must support mixed import of public APIs and local files to meet multi-source data access needs. This accommodates different access methods for housing and urban-rural development platforms, industry associations, and internal ledgers. Stable scheduled pull tasks must be configured to match the daily data update rhythm. During upgrades, task scheduling logic must be retained to avoid interrupting data synchronization. Deployment configurations must support flexible field mapping rules to accommodate custom structured document fields. This adapts to field differences across regions and sub-sectors. Data permission isolation rules must be configured to protect sensitive attributes in internal ledgers. These rules limit the scope of data source access for different teams.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Engineering consulting daily reports are mostly structured batch data with many fields to parse, requiring longer parsing time |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Batch-imported regional cost ledger files usually contain multi-region, multi-cycle data, requiring support for large file uploads |
SCHEDULER_INTERVAL | 86400 seconds | Data is updated once daily, matching the scheduled synchronization cycle of daily reports |
RECALL_TOP_K | Top 8 entries | Data sources for sub-sectors are relatively vertical; excessive recall will introduce irrelevant regional or project data |
SIMILARITY_THRESHOLD | 0.85–0.90 | Structured professional data requires high matching accuracy to avoid introducing information from non-corresponding sub-sectors |
RE_RANK_TOP_N | Top 5 entries | Professional consulting scenarios require streamlined results to avoid excessive entries interfering with decision-making |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes During Deployment and Upgrade
- After upgrading a Docker deployment to version V4.14.7.1 or later, the console returns a "plugin pull failed" error, and the container remains in a restart loop. The cause is that the access address of the default plugin source is updated after the upgrade, and no domestic mirror proxy is configured, resulting in pull timeout.
- After upgrading, executing the same query shows that the knowledge base retrieval module logs take several times longer than older versions. The cause is that the recall count parameter is not adjusted according to the structured data volume; excessive candidate entries increase the calculation load of retrieval and re-ranking.
- After local deployment runs for a period of time, the storage directory accumulates a large number of redundant chat record files that cannot be automatically cleaned. The cause is that the chat record expiration parameter is not configured, and the system retains all historical records by default.
How to Confirm Proper Configuration
- Upload a single structured daily report test file, check the parsing log to confirm that all preset fields are correctly identified and mapped to system fields.
- Manually trigger a scheduled synchronization task, verify that the number of imported data matches the actual number of entries in the data source.
- Submit a test query targeting a specific region and sub-sector, verify that the number of recall results matches the configured recall count requirement.
- Check the container runtime logs to confirm that there are no abnormal errors in plugin pulling, service startup, and data synchronization links.
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.