Deployment and Upgrade for General Comprehensive Investment

What Data for This Category Looks Like

Data sources for general comprehensive investment research knowledge bases include public industry research report libraries, regulatory disclosure documents, industry association statistical data, corporate public financial reports, and third-party non-standardized news content. Update frequencies vary significantly. Regulatory announcements are updated in real time. Most industry research reports are updated weekly or daily. Corporate financial reports are updated quarterly or annually. Document structures range from dozens-of-page long-form research reports and structured financial report tables to short-form public opinion news and more. Fields include publishing institution, release time, content category, industry dimension, revenue unit, and other items. Some data uses custom unit formats.

Constraints Imposed on Deployment and Upgrade by These Characteristics

Scattered and mixed-format data sources include long-form research reports, structured financial report tables, real-time regulatory announcements, and more. The deployment phase must support multi-format parsing and custom metadata extraction capabilities. High proportions of long documents and varied update frequencies require upgrades to support incremental sync cycles configured per data source. This prevents excessive cluster resource usage from full syncs. Structured data includes multi-dimensional fields and different unit formats. Deployments require custom field mapping and unit normalization rules to ensure consistency for subsequent retrieval and analysis. Cross-category data association requires the knowledge base’s vector model to support multimodal hybrid retrieval logic.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Adapts to the parsing time of a single long research report, avoiding task interruption due to timeout mid-process
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Supports uploading large single research reports or batch files, meeting multi-source data import requirements
`maxContext`	`800–1200 characters`	Retains sufficient context association information while controlling vector database storage and retrieval overhead
`Recall Count`	`Top 8–12 results`	Balances coverage and retrieval accuracy for multi-source data recall, avoiding redundant results or insufficient coverage
`Incremental Sync Cycle`	Configured per data source	Adapts to update rhythms of different data sources; set regulatory announcements to real-time sync, financial reports to quarterly sync
`VECTOR_SIMILARITY_THRESHOLD`	`0.75–0.85`	Balances matching accuracy for structured and unstructured data, filtering low-quality matching results

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Issue: After deployment, calling the model interface returns 404 Not Found, and no new entries appear in the platform’s model list. Cause: The ONEAPI_BASE_URL parameter is not configured correctly, or the service was not restarted after configuration to apply changes.
Issue: Long document parsing fails, with timeout error logs displayed in the console. Cause: The PARSE_FILE_TIMEOUT_SECONDS value is set lower than the actual parsing time, failing to adapt to the parsing requirements of long research reports.
Issue: The number of knowledge base recall results is far lower than expected, with only 1-2 pieces of content returned. Cause: The Recall Count configuration value is too low, or the VECTOR_SIMILARITY_THRESHOLD is set too high, filtering out valid matching results.

How to Confirm Proper Configuration

Run the docker ps command, confirm all deployed containers are in the Up state, with no abnormally exited records.
Upload a typical long-form research report, wait for parsing to complete, and check if metadata fields are fully extracted.
Initiate a retrieval request, verify that the number of recall results matches the configured Recall Count parameter.
Enter the model management page, confirm that the configured third-party models have loaded normally and appear in the available list.

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.