What This Category of Data Looks Like
Chemical fiber investment research data primarily comes from raw material quotes in the upstream petroleum and petrochemical industry chain, such as PTA and ethylene glycol. It also includes supply and demand data for downstream weaving and terminal apparel, monthly operating rate and inventory reports released by industry associations, and detailed import and export data from customs authorities.
Data update cycles vary significantly. Raw material spot prices are updated in real time. Operating rate and inventory data is updated weekly. Industry research reports and import and export data are updated monthly or quarterly.
Three types of document structures are supported: structured tabular data with clear fields and units, semi-structured industry analysis snippets, and unstructured long-form research reports. Fields include spot price, operating rate, inventory days, import and export volume, and more. Some data requires cross-data source association and matching.
Constraints on Database and Operations Workflows
The characteristics of chemical fiber investment research data impose multiple constraints on database and operations workflows:
- Real-time raw material price data requires low-latency write and query pipelines to avoid database connection blocking.
- Data sources with varying update cycles require tiered scheduling of update tasks to prevent resource contention during peak hours.
- Large volumes of structured data with units require unified metadata management to ensure accurate field matching during retrieval.
- Cross-cycle historical data queries require support for complex SQL joins, which places higher demands on database query performance and timeout configurations.
- Mixed storage of multiple document types requires dual support for structured queries and vector retrieval, requiring proper allocation of resources between databases and vector databases.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
VECTOR_STORE_BATCH_SIZE | 20-30 items/batch | Single structured data snippets and research report sentence segments in the chemical fiber industry are moderate in length. Excessively large batches risk triggering database write timeouts, while excessively small batches increase database connection overhead |
SQL_QUERY_TIMEOUT | 600 seconds | Investment research scenarios require cross-cycle association of historical production capacity, price, and inventory data. Sufficient timeout time is needed to avoid query interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Large annual supply and demand reports and customs import and export detailed files in the chemical fiber industry have large file sizes, requiring support for large file uploads |
MONGO_CONNECTION_POOL_SIZE | 50-80 | Multi-concurrent investment research query scenarios require a sufficient connection pool to support simultaneous reading and writing of business data and vector retrieval data |
DB_BACKUP_CRON | 0 2 * * * | Avoids daily 9-17 peak investment research business hours to reduce the impact of backups on normal services |
PARSE_TABLE_FIELD_PRESERVE | Enabled | Chemical fiber data contains a large number of structured tables with units, such as yuan/ton and operating rate percentage. Retaining field metadata improves retrieval matching accuracy |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: The system returns a
504 Gateway Timeouterror when calling the investment research knowledge base query interface. Cause: TheSQL_QUERY_TIMEOUTparameter was not adjusted for long-cycle historical data queries in the chemical fiber industry, causing cross-table join queries to time out and be blocked by the gateway. - Symptom: The service fails to start normally after deleting the database mount directory and redeploying. Cause: Persistent volume remnants inside the container were not cleaned, or data path parameters were not configured correctly, causing the database to still read old abnormal configuration files.
- Symptom: The database connection structure returned by the data source plugin fails to generate expected SQL execution results. Cause: Structured data from the data source was not mapped to the field association rules required for investment research, or database SQL query permissions were not enabled, preventing the plugin from executing normal query logic.
How to Verify Proper Configuration
- Upload a structured price table file from the chemical fiber industry, verify that the parsed data retains relevant fields and unit information, and confirm that the field parsing configuration is active.
- Initiate a historical data query spanning multiple cycles, verify that the query execution duration meets the preset timeout requirements, and confirm that the query timeout configuration is reasonable.
- View database backup logs, confirm that daily backup tasks run during non-business peak hours, with no backup alerts during business hours, and verify that the backup scheduling configuration is correct.
- Initiate multiple concurrent investment research query requests, observe database connection count changes, confirm that the connection count does not exceed the configured limit, and that no connection overflow related alerts are triggered.
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.