What the Data for This Category Looks Like
Research reports for the construction machinery industry target financial asset management scenarios. Their sources include publicly available reports from construction machinery industry associations, official technical documents from original equipment manufacturers, regular research outputs from third-party industry consulting firms, and internal industry tracking minutes from financial institutions. Reports are updated on a monthly basis, plus ad-hoc supplements when major industry policies are released, new products launch, or corporate earnings reports are issued. Most documents include industry prosperity analysis, corporate revenue forecasts, core parameter comparison tables, and working condition adaptation plans. Fields cover revenue growth rate, market share, rated lifting capacity, operating radius, and similar metrics. Common units include percentage, units, kilonewtons, and meters. Some documents include corporate production capacity data and construction case calculation tables.
Constraints Imposed on Deployment and Upgrade
Construction machinery research reports for financial asset management scenarios have three key characteristics: multi-source data, numerous structured fields, and flexible update cadence. These traits impose multiple constraints on deployment and upgrade workflows.
Structured parameters in research reports cover core investment analysis indicators. Configure strict parsing and verification rules during deployment. This prevents incorrect data from entering the vector database.
Ad-hoc updates are sometimes required. Design upgrade processes to support incremental data synchronization. Full re-runs consume excessive resources and disrupt business continuity, so avoid them.
Research reports from different sources may use inconsistent units. Configure parsing plugins with unit normalization logic. Verify field extraction consistency after upgrades.
Some research reports include internal corporate calculation data. Enable parsing support for the corresponding format during deployment. Re-verify sensitive data processing rules after upgrades.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Construction machinery research reports often contain long text and structured tables, resulting in long parsing times. The default value is insufficient for complete parsing. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some complete industry research reports include multiple attachments and case data, so upload limits must be relaxed to support full document imports. |
maxContext | 8000–12000 characters | Core analysis paragraphs and parameter comparison content in research reports are lengthy, so long-context retrieval and generation requirements must be accommodated. |
similarity threshold | 0.72–0.85 | Retrieval for financial asset management scenarios requires high precision to avoid retrieving low-relevance non-target research reports. |
PARSE_STRUCTURED_TABLE | Enabled | Parameter comparison tables and revenue calculation tables in research reports are core retrieval content, so structured parsing must be enabled to extract fields. |
VECTOR_STORE_BATCH_SIZE | 50 items/batch | Vector generation for structured fields requires stable batch processing resources to avoid overloading deployment nodes from overly large batches. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Scenario: A
TypeError: Cannot read properties of undefinederror occurs after upgrading to version 4.8.20 via Docker deployment. Cause: The existing database volume was not mounted during the upgrade, resulting in loss of historical configuration and vector data, and the field mapping rules of the parsing plugin were not migrated synchronously. - Scenario: An interface error occurs when calling the deepseek model via a locally deployed Ollama instance. Cause: The local access address and port for
OLLAMA_BASE_URLwere not configured correctly, or the API access permission for the corresponding model was not enabled. - Scenario: Some structured parameters cannot be retrieved correctly after upgrading from version 4.8.9. Cause: The field extraction rules of the parsing plugin were not updated synchronously, resulting in a mismatch between the old version's parameter mapping logic and the new document format.
How to Confirm Correct Configuration
- Upload a construction machinery research report containing a structured parameter table, review the parsed field extraction results, and confirm that the
PARSE_STRUCTURED_TABLEconfiguration is correctly enabled. - Initiate a retrieval for construction machinery industry revenue growth rate, verify the relevance of the returned results, and confirm that the
similarity thresholdconfiguration meets business requirements. - Perform a Docker container upgrade operation, log in to the database management interface, and verify that the vector data and configuration parameters of historical research reports have not been lost.
- Call the locally deployed model interface, confirm that normal question-and-answer results related to research reports can be generated, and verify that the model access configuration is correct.
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.