What this category of data looks like
Multi-financial investment research data covers multiple sources, including public fund quarterly position announcements, private fund filing information, real-time derivative trading quotes, industry-specific research reports, regulatory compliance documents, and more. Update rhythms vary significantly: derivative quotes are pushed in real time, public fund quarterly reports are updated quarterly, industry research reports are updated weekly or daily, and regulatory documents have no fixed release cycle. Document structures include structured tables and unstructured long texts, with fields such as product code, net value unit, position detail entries, regulatory document number, release date, and others, with notable format differences.
Constraints these characteristics impose on database and operations
The multi-source nature and varied update rhythms of multi-financial data require databases to support both high-concurrency writes for structured data and vector storage for unstructured long texts. The low-latency demand for real-time quote data requires operations teams to configure sufficient connection pools and concurrent processing capacity. Mixed-format data requires databases to support mixed invocation of structured queries and vector retrieval. Regulatory documents and research reports with no fixed update cycle require operations to support flexible incremental sync scheduling, avoiding full syncs that consume excessive resources. Additionally, compliance requirements for investment research data mean databases must have complete access logging and retention capabilities to meet regulatory audit needs.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_BATCH_SIZE | 50-100 items/batch | Balances batch upload speed and database write stability, adapts to batch processing scenarios for multi-category documents in multi-financial investment research |
MAX_CONCURRENT_PARSE | 8-12 concurrent | Prevents server resource exhaustion from long text parsing, matches average parsing time for research documents |
DB_CONNECTION_POOL_SIZE | 20-30 connections | Covers concurrent query and write demands from multi-source data, prevents request queuing caused by exhausted connections |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing full texts of research reports or announcements exceeding 100,000 words, avoids mid-parsing interruptions for long documents |
VECTOR_DIMENSION | 1536 dimensions | Matches standard output dimensions of general embedding models, supports vectorization needs for multi-financial texts and structured data |
MONGODB_VERSION | 5.0.25 and above | Fixes known security vulnerabilities, fits research data storage scenarios with high compliance requirements |
The parameter values provided on this page are common recommendations used as starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and testing against suitable samples is recommended prior to finalizing settings.
Three common configuration errors
- The symptom is a database write error
ETIMEDOUT. The cause is failure to adjustUPLOAD_BATCH_SIZEto meet multi-financial batch upload requirements, with per-batch data volume exceeding the database's carrying limit. - The symptom is that the number of vector recall results does not match the configured value. The cause is failure to match
VECTOR_RECALL_TOPKto the multi-source data of the investment research knowledge base, with the default value failing to cover recall demands for multi-category multi-financial data. - The symptom is a MongoDB connection error
MongoSocketReadException. The cause is use of the vulnerablemongo:5.0.18version without upgrading to the stable version with official fixes.
How to confirm proper configuration
- Run a batch upload test task, verify that the write rate in the database write log matches the configured
UPLOAD_BATCH_SIZE. - Check vector database connection logs to confirm that the connection pool count matches the configured
DB_CONNECTION_POOL_SIZE. - Search historical investment research data to verify that the vector recall dimension matches the configured
VECTOR_DIMENSION. - Run the official vulnerability scanning tool to confirm that the MongoDB version is the stable version with official fixes.
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.