What the data for this category looks like
Data sources include industry public research reports, supermarket retail monitoring data, public corporate financial reports, and supply chain raw material quotation data. Update frequencies vary by type: financial reports are released quarterly, retail monitoring data is updated weekly, and raw material quotations are updated daily. Documents include structured entries and unstructured analysis content. Structured fields include product SKU, ex-factory unit price, channel sales volume, raw material cost as a percentage of revenue, and capacity utilization rate. Units include yuan/kilogram, ton, monthly sales volume, and others. Unstructured content includes industry trends, analyst comments, and similar material.
What constraints do these characteristics impose on the database and operations link?
Multi-source heterogeneous data structures require support for both structured storage and unstructured indexing. Separate storage pools must be allocated to match different data types. High-frequency updated retail monitoring and raw material quotation data generate continuous write pressure. Adjust read-write separation strategies to avoid query blocking. Rich SKUs and multi-dimensional association requirements require targeted joint indexes to optimize multi-table join query efficiency. Varying-length document content affects vector index construction efficiency. Set layered index rules for documents of different lengths. Additionally, differences in field names and formats across data sources require pre-configured field mapping to avoid format conflicts during data import.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some long industry research report documents take longer to parse. 600 seconds covers parsing requirements for most seasoning-related documents |
vectorStore.batchInsertSize | 200-300 items/batch | Seasoning data includes structured and unstructured entries of varying lengths. This batch size range balances write efficiency and stability |
retrieval.similarityThreshold | 0.75-0.85 | Investment research scenarios require precise matching of industry data. This threshold filters low-correlation results and avoids invalid recall |
db.readReplicaCount | 2-3 replicas | High-frequency updated retail monitoring and raw material quotation data generate a large number of read requests. Multiple read-only replicas distribute query pressure |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some seasoning industry research reports include multi-page charts and attachments. 1000 MB covers most upload requirements |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing values.
Three Common Mistakes
- Phenomenon: A 429 Too Many Requests error is returned during concurrent requests, and interface calls are restricted. Cause: Interface rate limit parameters are not configured, or the limit threshold is set too low, which cannot adapt to the high-frequency query scenario of seasoning industry data.
- Phenomenon: Response lag occurs during multiple consecutive rounds of investment research queries, and the number of returned query results is insufficient. Cause: Database performance does not adapt to multi-round association query requirements, causing query link blocking.
- Phenomenon: Deadlocks occur in PostgreSQL databases, or continuous lag occurs in MongoDB collection writes. Cause: Reasonable indexes are not set for multi-source heterogeneous data, and field mapping conflicts lead to frequent full table scans, increasing database load.
How to Confirm Proper Configuration
- Upload a typical seasoning industry research report, check whether the parsing status is normal, and verify the adaptability of document parsing configuration.
- Initiate multiple concurrent query requests, observe the read-write load on the database monitoring panel, and confirm that read-write separation and replica configurations can distribute request pressure.
- Test multiple consecutive rounds of investment research-related queries, check context association and the relevance of recall results, and verify the rationality of retrieval configuration.
- View the database slow query log, confirm that there are no frequent full table scans or deadlock events, and verify the effectiveness of index configuration.
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.