What the data for this category looks like
Construction machinery intelligent due diligence data sources include equipment factory archives, on-site operation and maintenance ledgers, second-hand circulation records, and maintenance reports. Update rhythms fall into two categories: static and dynamic. Factory archives and basic equipment parameters are updated statically. Operation logs and maintenance records are updated per single operation or daily.
The document structure centers on a single device as the core unit, including structured fields and associated attachments. Structured fields include equipment ID, rated lifting capacity, cumulative operating hours, and next maintenance threshold, with units of string, ton, hour, and day respectively. Associated attachments include PDF-format maintenance reports, Excel-format operation and maintenance ledgers, and scanned lease contracts.
What constraints these characteristics impose on deployment and upgrade
The batch import capability of static factory archives requires presetting standardized data mapping rules during deployment, to prevent disordered device field formats from different sources.
High-frequency updates for dynamic operation data require adjusting the vector database refresh interval parameter during upgrade, to ensure real-time retrieval of data.
The need to parse multiple types of attachments requires configuring parsing plugins for corresponding formats during deployment, covering parsing rules for PDF maintenance reports and Excel ledgers.
The requirement for unified field units requires presetting unit mapping logic during upgrade, to prevent mixing of tons and kilograms in retrieval results.
Unstructured text from second-hand circulation records requires configuring an entity extraction model during deployment, to extract core fields such as equipment transaction price and service life.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Construction machinery due diligence data includes multi-page maintenance PDFs and complex Excel ledgers; 300 seconds covers most large file parsing scenarios |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Full due diligence archives for a single device include multiple attachments; 2000 MB meets the single-batch file size requirement for batch imports |
VECTOR_DB_REFRESH_INTERVAL | 3600 seconds | Dynamic operation data is updated hourly; this interval ensures the vector database sync frequency matches business requirements |
RECALL_TOP_K | Top 10 entries | Due diligence reports need to cover multi-dimensional data across the entire device lifecycle; 10 retrieved entries balance retrieval efficiency and information completeness |
PGVECTOR_VERSION | 0.7.4-pg15 | Official stable version compatible with PostgreSQL 15; avoids retrieval exceptions caused by version compatibility issues |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Images built for ARM architecture fail to start on AMD architecture servers. Cause: Target architecture parameters were not specified in the Docker build command, resulting in a mismatch between the image architecture and deployment environment.
- Phenomenon: The
bad_response_status_codeerror appears repeatedly during local deployment. Cause: The parsing file timeout configuration was not adjusted; parsing of large construction machinery maintenance reports times out and triggers this error. - Phenomenon: Vector retrieval functionality malfunctions after upgrading to version 4.8.12. Cause: The pgvector version was not updated to the 0.7.4-pg15 version compatible with PostgreSQL 15, causing database dependency incompatibility.
How to confirm configurations are correct
- Upload the largest single-size construction machinery due diligence archive, check if parsing completes within the preset timeout period. Adjust the timeout configuration if parsing does not complete.
- Run the Docker image build command with the target architecture parameter specified, verify that the image starts normally on servers of the corresponding architecture.
- Connect to the PostgreSQL database, query the pgvector version, confirm it is compatible with the current FastGPT version.
- Import test data for multiple devices, retrieve device fields, check if units are unified. Adjust field mapping rules if units are not unified.
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.