What Data Looks Like for This Category
Data sources for glass industry investment research knowledge bases include public statistics from industry associations, spot quotes from commodity trading platforms, regular reports of listed companies, and customs import and export data. Update schedules follow multiple tiers: spot price data updates daily, regional inventory data updates weekly, industry production and sales data updates monthly, and capacity layout data updates quarterly.
Each single document includes structured specification parameter tables, text analysis of regional price spreads, and explanations of upstream and downstream transmission logic. Fields include glass category, thickness (millimeters), origin, ex-factory price (yuan/weight box), total inventory (weight box), production capacity scale (weight box/day), and other fields. No percentage-based statistical statements are included.
Constraints Imposed on Database and Operations Workflows
The multi-tier update frequencies, multi-dimensional fields, and long-document structure of glass industry investment research data create multiple constraints for database and operations workflows.
- High-frequency updated spot price data and low-frequency updated industry reports must be stored in separate databases to avoid read-write conflicts that impact overall performance.
- Multi-dimensional combined query requirements (such as filtering data by category, region, and time) require targeted indexes to prevent slow queries.
- Long documents with complex tables have significant segmentation differences after parsing. Adjust segmented storage thresholds to avoid breaking data relevance.
- Cross-data-source associated queries (such as linking price and downstream demand data) require optimized association logic to reduce database load.
Additionally, glass products have high specification similarity. Strictly control similarity thresholds during data recall to avoid interference from unrelated product categories.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Glass research reports often include high-definition charts and long tables, resulting in large single-document sizes. This setting accommodates large-file upload requirements |
ragChunkSize | 800–1200 characters | Glass data includes specification parameters and price tables. Excessively long segments lose contextual relevance, while excessively short segments break table logic. Adjust based on actual document structure testing |
dbReadConcurrency | 15 concurrent requests | Spot price data is updated frequently daily. Concurrent read operations must match data synchronization frequencies to avoid triggering interface rate limits |
similarityThreshold | 0.72–0.78 | Glass product specifications have high similarity (such as float glass with different thicknesses). The threshold must be slightly higher than general scenarios to avoid recalling unrelated product categories |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Research report documents with complex chart parsing require longer processing time. Extend the timeout to ensure complete parsing |
mongoCollectionShardKey | glassCategory, updateTime | Shard by glass category and update time to optimize performance for high-frequency regional and time-range queries |
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. Analyze specific issues individually, and test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When running multiple consecutive associated queries, returned results cannot link to the glass specification parameters from previous questions, resulting in irrelevant answers. Cause: The
maxContextparameter is not configured to limit the context window, or the window size does not match the multi-parameter association requirements of glass industry investment research. - Symptom: When calling the knowledge base query interface concurrently, a
429 Too Many Requestsstatus code is returned, and some requests fail. Cause: ThedbReadConcurrencyparameter is not adjusted based on the high-frequency update characteristics of glass spot data, and the concurrent request count exceeds the database connection pool limit. - Symptom: When executing combined queries for region, category, and time range, response latency increases significantly, and timeout errors may occur. Cause: No
glassCategory, region, updateTimecomposite index is created for PostgreSQL, causing multi-condition queries to miss the index and trigger full table scans.
How to Confirm Correct Configuration
- Upload a research report document that includes a glass specification table, check if the parsed segments retain complete row data from the table, and verify that the segment configuration matches the document structure.
- Send 10 concurrent spot price query requests, observe the interface return status codes, and confirm that the concurrency configuration does not trigger rate limits.
- Execute a combined query for region, category, and time range, check the database execution plan, and confirm that the preset composite index is being used.
- Run three consecutive associated queries (such as first querying Low-E glass prices, then querying regional inventory for that category, then querying downstream demand), check if each round of results links to previous parameters, and confirm that the context window configuration is active.
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.