What the data for this category looks like
Data sources are public financial report announcements of black home appliances listed companies from domestic and overseas stock exchanges, and operational briefings officially released by enterprises. Updates are triggered in line with quarterly and annual financial report disclosure cycles, while temporary operational announcements are updated synchronously with events such as new product launches and channel adjustments. Each individual document contains structured report paragraphs and unstructured operational analysis content. Fields include operating values corresponding to revenue amounts, shipment quantities, and channel categories for each product line. Units include RMB yuan, units, ten thousand yuan, and the like. No fixed fields for cross-category cross-statistics are included.
What constraints these characteristics impose on the vector models and indexing link
Documents from scattered sources with large format differences require vector models to support unified encoding of multi-source text, and also require pre-built text cleaning rules to align field formats. The update rhythm combining regular and temporary updates requires the index to support parallel configuration of incremental updates and batch updates, to avoid resource consumption caused by full reconstruction. Documents containing structured reports and unstructured analysis content require the index to support both paragraph-level and table block-level vector recall, and also require exclusive vector mapping configured for the operating fields of segmented product categories, to avoid interference from irrelevant fields on recall accuracy. Documents with a high proportion of long text require a segmentation strategy adapted to the chapter structure of financial reports, to avoid truncation of core operating data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the long paragraph structure of black home appliances financial reports, retains complete operating descriptions of segmented product lines, and avoids truncation of core information |
recall_top_k | Top 8–12 entries | Covers the recall requirements for multi-segment operating data, avoids excessive irrelevant content interfering with analysis, or insufficient entries leading to missing information |
vector_model_api_timeout | 60–120 seconds | Matches the time interval required for long text vector encoding, avoids index failure caused by timeouts |
enable_duplicate_removal | Disabled | Retains the order of document chunks after custom splitting, avoids automatic deduplication destroying the preset index structure |
api_key_auth_type | Custom key verification | Adapts to the authentication rules of most vector models, ensures the legality of API calls |
index_batch_size | 20–30 entries per batch | Balances memory usage and index update efficiency, adapts to the scenario of batch processing of financial report documents |
The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the settings.
Three common mistakes
- A
401 Unauthorizederror code is returned when calling the vector model, because the API key of the vector model is not configured correctly, or the key permissions do not cover the vector model call scope. - Requests initiated through a custom vector model channel are incorrectly routed to the large language model link, because the exclusive request path and request header identifier for the vector model are not specified in the channel configuration.
- The order of custom-split document chunks stored in the knowledge base is disrupted, because automatic deduplication configuration is enabled, causing duplicate document chunks to be automatically deleted and destroying the preset index structure.
How to confirm the configuration is correct
- Upload a single test segment of a black home appliances financial report, check the vector encoding log, and confirm that the request carries correct authentication information.
- Initiate a recall test, verify that the returned order of document chunks matches the custom splitting result, and confirm that automatic deduplication configuration is disabled.
- Adjust the segmentation length parameter and re-upload the test document, compare the encoded text fragments, and confirm that the segmentation rules meet the preset requirements.
- Trigger an incremental indexing task, check the task execution log, and confirm that there are no timeout or authentication failure error reports.
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.