What the data for this category looks like
Data sources include publicly disclosed periodic reports of domestic listed companies, operation briefings released by industry associations, and operating data officially disclosed by enterprises. Update cycles are monthly, semi-annual, and annual, corresponding to the release cycles of different data sources. Document structures cover modules such as main business breakdown, raw material cost composition, production capacity and operating rate, inventory and export data. Fields include revenue amount, production capacity scale, raw material procurement proportion, and more. Units are RMB yuan, ten thousand tons, and decimals ranging from 0 to 1 respectively.
What constraints these characteristics impose on vector models and indexing
Multi-source, heterogeneous data formats require indexes to support metadata tagging and partitioned storage for multiple data sources. This prevents mixing of financial report data from different sources. Differing update frequencies require configuration of differentiated incremental indexing scheduled tasks. This distinguishes update logic for monthly industry data, semi-annual reports, and annual reports. Multi-module document structures require semantic segmentation by business segment. This prevents cross-module semantic associations from interfering with recall results. Text dense with specialized terminology requires vector models to adapt to semantic features of industrial subfields. This improves recall accuracy for professional content. Multiple field types coexisting requires indexes to support precise filtering by field attributes. This narrows the recall scope.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Professional terminology is dense in chemical fiber financial reports. Excessive length causes semantic fragmentation, while insufficient length loses contextual connections |
chunk_overlap | 150–200 characters | Retain contextual cohesion after long text segmentation, and avoid professional terms being truncated at segment boundaries |
INDEX_INCREMENTAL_UPDATE_CRON | 0 0 2 1 * *, 0 0 4 30 6,12 *, 0 0 6 1 1 * * | Match the monthly, semi-annual, and annual update cycles of industry data |
similarity_threshold | Calibrated based on actual testing | Adapt to the professional semantic features of chemical fiber financial reports, and filter irrelevant recall results |
search_top_k | 10–15 entries | Cover relevant business segments of multi-module documents, and avoid missing core information |
filter_field | report_type, update_time | Perform precise filtering by financial report cycle and data source type, to narrow the recall scope |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After switching the vector model, the similarity values returned by search exceed the 0-1 range, reaching over 10000. Cause: The similarity normalization configuration is not enabled. The original output of some vector models is not range-calibrated, leading to abnormal values.
- Phenomenon: After importing the financial report dataset, the interface displays an "indexing" status that lasts longer than reasonable. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. Individual chemical fiber financial report texts have long lengths, and the default timeout period is insufficient to complete parsing and index construction. - Phenomenon: After configuring multi-replica deployment, index data duplication or inconsistent search results occur. Cause: The global synchronization mechanism for distributed indexes is not configured. Multiple replicas simultaneously trigger incremental indexing tasks, leading to data write conflicts.
How to confirm correct configuration
- Perform parsing and indexing testing for a single financial report, and check whether the segmentation results cover core modules such as main business and raw material costs, and that segment boundaries do not truncate professional terms.
- Initiate a similarity search test, verify that the similarity values of returned results are within a reasonable range, and that the filtering rules take effect, only recalling specified types of financial report data.
- Configure the incremental indexing scheduled task, manually trigger the task once, and check whether the index update status is completed within the preset time, with no error logs.
- Deploy multiple replica instances, initiate a batch indexing task, and verify that there are no issues of data duplication or inconsistent search results.
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.