What this type of data includes
Investment research data covers three categories: structured financial statements, in-depth industry research reports, and equity association graphs. Data sources include internal business systems of group subsidiaries, external regulatory disclosure documents, third-party industry databases, and public equity registration information.
Structured data is updated quarterly. Unstructured research reports and real-time news are updated daily. Equity association data is adjusted dynamically alongside transactions.
All documents include unified subject identification fields, content fields, and metadata fields. Structured fields have clear numerical units attached. Unstructured documents include classification and source labels.
Constraints on database and operations workflows
Multi-dimensional association of structured data requires composite indexes to reduce cross-table query latency. Long-form unstructured research reports must follow vector database segment storage rules to prevent oversized single vector data entries. Multi-source data synchronization requires scheduled verification tasks to maintain consistency between internal business data and external public data.
Unified storage rules for field units must be defined upfront to reduce subsequent data cleaning work. Many-to-many equity association relationships need optimized vector index structures to ensure efficient association retrieval.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_MAX_CONNECTIONS | 200–300 | Adapts to concurrent connection requirements for multi-source association queries, ensuring stable operation of investment research data retrieval and synchronization tasks |
MILVUS_COLLECTION_PARTITION_NUM | 4–6 | Matches the storage scale of multiple types of investment research data, reducing query pressure on single partitions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Covers the parsing duration of long-form industry research reports, avoiding parsing failures caused by overly long documents |
VECTOR_SEGMENT_LENGTH | 800–1200 characters | Adapts to the density of professional terminology in investment research documents, ensuring completeness and accuracy of semantic recall |
DATA_SYNC_CRON_EXPR | 0 0 2 * * * | Runs incremental synchronization during daily early morning non-business peak hours, reducing system resource usage |
MONGO_VERSION | 4.4.29 | Compatible with deployment environments that do not support AVX instruction sets, resolving version compatibility error issues |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. Testing on local samples is recommended before finalizing settings.
Three common configuration errors
- Symptom: A
mongo version incompatibleerror appears after deployment, indicating version incompatibility. Cause: The corresponding MongoDB image version was not selected based on the server's AVX instruction set support, or the version range supported by FastGPT was not matched. - Symptom: Investment research data synchronization tasks frequently time out, and database query response latency is too high. Cause: The
MONGODB_MAX_CONNECTIONSparameter was not adjusted, and the concurrent connection count was insufficient to support the concurrent requirements of multi-source data synchronization and retrieval. - Symptom: Unexpected resource usage spikes occur on database services, and core retrieval tasks freeze. Cause: The sandbox container was mistakenly included in the production environment database operation link, and its status as only a local development and testing component was not clarified.
How to verify correct configuration
- Execute a database connection test script to verify if the
MONGODB_MAX_CONNECTIONSconfiguration matches the current concurrent task scale, and adjust the value based on test results. - Import a single long-form industry research report, review the parsing log, and confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration covers the actual document parsing duration. - Run multiple sets of vector recall tests, compare recall results across different segment lengths, and adjust
VECTOR_SEGMENT_LENGTHto a value that meets business requirements. - Review the execution log of the scheduled synchronization task to confirm that the execution period specified by the
DATA_SYNC_CRON_EXPRconfiguration does not overlap with core business hours.
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.