What the data for this category looks like
Data for this category primarily comes from public regulatory disclosure documents, third-party credit agency datasets, due diligence materials submitted by target enterprises, and publicly available industry research reports. Update rhythm varies by data source type: regular financial reports are updated quarterly and annually, regulatory announcements are synchronized in real time, and dynamic information for target enterprises is updated on demand. Documents include two parts: structured fields and unstructured attachments. Structured fields include target entity unified social credit code, report generation date, attributable net profit, compliance filing number, risk level score. Units for amount fields are uniformly ten thousand yuan or hundred million yuan. Date fields use the YYYY-MM-DD format. Risk scores are integers between 0 and 100. Unstructured attachments mostly include compliance materials such as enterprise official seal scans and original financial statements.
What constraints do these characteristics impose on deployment and upgrade
The above data characteristics impose multiple constraints on the deployment and upgrade process: First, multi-source heterogeneous data access requirements require configuring data synchronization modules compatible with multiple formats and protocols during deployment, supporting connection to public APIs, local file import, and third-party data interfaces. Second, differentiated update rhythms require flexible configuration of incremental synchronization trigger rules during upgrades to avoid excessive server resource usage from full data pulls. Third, precise mapping requirements for structured fields require completing alignment configuration between vector database fields and standard due diligence report fields during deployment to avoid errors in amount unit and date format parsing. Fourth, compliance attachment parsing requirements require pre-installing OCR-enabled document parsing plugins during deployment to ensure scan-based materials can be properly indexed and retrieved.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Due diligence reports often contain multi-page financial statements and compliance attachments, with long parsing times. 600 seconds covers the parsing needs of most long documents |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Unstructured attachments of individual due diligence reports (such as annual financial report PDFs) may exceed the size of conventional documents. 2000 MB meets the upload needs of most scenarios |
VECTOR_STORE_BATCH_SIZE | 50 items/time | Due diligence reports have many structured fields and unstructured content. When batch inserting into the vector database, balance write efficiency and stability. 50 items/time avoids single request timeouts |
SYNC_INCREMENTAL_INTERVAL | 3600 seconds | Public regulatory data is often updated hourly. Hourly incremental synchronization ensures data timeliness while avoiding excessive resource occupation |
PGVECTOR_VERSION | 0.7.4-pg15 | Most current deployment environments are based on PostgreSQL 15. This version has stable compatibility with PG15 and supports high-dimensional vector indexing required for due diligence reports |
DOCKER_BUILD_PLATFORM | linux/amd64,linux/arm64 | Deployment environments of investment platforms may cover x86 and ARM architectures. Multi-platform building ensures images run properly on different architectures |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, 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
- Symptom: Images built with
docker build --platform linux/amd6throw architecture incompatibility errors when deployed on AMD architecture servers. Cause: The platform parameter format in the build command is incomplete, not correctly specified aslinux/amd64, and multi-platform build support is not enabled, resulting in images only adapted to partial architecture environments. - Symptom: Vector retrieval function is abnormal after deployment, with a prompt of PGVECTOR version incompatibility. Cause: The corresponding PGVECTOR version is not matched according to the PostgreSQL version. For example, installing the non-adapted
0.7.4-pg17version when using PostgreSQL 15 causes index creation failure. - Symptom: Frequent
bad_response_status_codeerrors are returned when executing due diligence report parsing tasks after local deployment. Cause: Reasonable timeout parameters are not configured, or the latest stable version is not updated in time, leading to status code exceptions triggered by timeouts when parsing long documents, or unresolved interface compatibility issues in older versions.
How to confirm configuration is correct
- A typical due diligence report containing compliance attachments is uploaded, and the extraction results of structured fields in the parsing log are checked to confirm that field mapping and unit parsing meet expectations.
- An incremental synchronization task is executed, and the number of new data and update times in the synchronization log are checked to confirm that the synchronization interval configuration matches the data source update rhythm.
- The built image is pulled on test servers of different architectures, the container is started and the availability of core interfaces is verified, confirming that the multi-platform build configuration takes effect.
- The matching status between the PGVECTOR plugin version and the PostgreSQL version is checked, confirming that a stable version adapted to the current database version has been installed.
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.