What the data for this category looks like
Financial report data for auto parts enterprises comes from periodic reports disclosed by domestic and overseas stock exchanges, production and sales tracking data released by industry associations, and investor relations announcements officially disclosed by enterprises. The update rhythm follows the quarterly and annual release cycles of periodic reports, and is also updated with temporary announcements triggered by major operating events. Documents are mostly a mix of structured financial tables and unstructured business analysis texts, with fields including segmented product revenue proportion, raw material procurement cost proportion, production capacity utilization rate, etc. Units are mostly RMB 10,000 yuan, percentage, units/sets.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The mixed structured and unstructured document structure requires the retrieval system to support both precise field matching and semantic similarity recall. Randomly triggered temporary announcement updates require the knowledge base to support incremental synchronization and incremental index construction, avoiding the time-consuming full index reconstruction. Specialized fields related to segmented products require the recall process to retain semantic consistency of professional terms to avoid ambiguity. The relatively long length of a single financial report document requires splitting by business modules when segmenting, ensuring the business integrity of retrieved fragments. The cross-document demand for supply chain related data requires retrieval to support associated recall across enterprise announcements, covering the operating information of supporting automobile manufacturers.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Auto parts financial reports contain long sections of business analysis and split structured text. This range can retain the semantic integrity of business modules and avoid context breakage caused by excessive splitting |
recall_top_k | Top 8–12 results | There are many financial report fields in this segmented category. A sufficient number of relevant fragments need to be recalled to cover multi-dimensional information such as product revenue, cost, and production capacity, while avoiding interference from redundant results |
similarity_threshold | 0.72–0.78 | There are many professional terms in the financial reports of this category. It is necessary to balance recall accuracy and coverage, avoiding missing segmented product data due to an overly high threshold, or introducing irrelevant industry reports due to an overly low threshold |
rag_refresh_mode | Incremental synchronization trigger | This category of financial reports has a large number of randomly updated temporary announcements. Incremental synchronization can reduce the time spent on index reconstruction and ensure data timeliness |
parse_structured_table | Enabled | Financial reports contain structured financial tables. After enabling, field-level information can be extracted to support precise field matching retrieval |
max_context | 6000 characters | Associated fragments of a single financial report need to retain sufficient contextual association, while complying with the context window limits of large models |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. 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
- Phenomenon: Retrieval results include ambiguous content with unclear references, such as only mentioning "supporting components" without specifying specific vehicle models or product models. Cause: The pre-question expansion and reference resolution process is not enabled, causing the retrieval query to fail to cover complete business-related information.
- Phenomenon: A large number of general financial report data unrelated to the auto parts industry appears in the recall results, and the number of recalled entries far exceeds the set value. Cause: The similarity threshold is set too low, failing to filter low-correlation general financial report fragments.
- Phenomenon: Retrieval still displays outdated financial report data after an incremental update. Cause: The trigger rules for incremental indexes are not configured, the full update mode is mistakenly used, or the update synchronization time interval is too long.
How to Confirm the Configuration is Correct
- Upload the quarterly financial report of a single auto parts enterprise, perform a retrieval test, and check whether the returned results contain relevant fragments of preset fields such as segmented product revenue and raw material costs.
- Trigger an incremental update, retrieve the latest disclosed temporary announcement content, and check whether the results contain updated information.
- Adjust the similarity threshold configuration, compare the number of recall results under different thresholds, and confirm that the balance between accuracy and coverage required by the business is met.
- View the knowledge base index log, confirm that the analysis results of structured tables have generated field-level indexes to support filtering retrieval by product dimension.
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.