What the Data for This Category Looks Like
Semiconductor financial report data comes primarily from public annual reports, quarterly reports, monthly industry association statistics, and in-depth securities firm research reports. The core update cycle is quarterly, with annual reports as the full cycle. Temporary announcements are updated at any time alongside production capacity adjustments or large order releases. Individual document lengths vary widely, from several thousand-word announcements to tens of thousands-word annual financial reports. Fields include revenue, attributable net profit, R&D investment ratio, wafer production capacity, unit product cost, and more. Common units are hundreds of millions of yuan, percentages, and ten thousand wafers per month.
Constraints on Vector Models and Indexing
Semiconductor financial report documents have large variations in length. Fixed segment lengths may split the association between core fields. Implement dynamic segment logic instead. Multi-field financial report data requires vector models to understand both numerical values and textual semantics. This avoids bias from single-dimensional semantic encoding. Frequently updated quarterly data and temporary announcements require indexes to support incremental refreshes. Full reindexing would lengthen processing time. Production capacity and order data for some market segments are unstructured text. Add additional entity extraction steps to ensure vector encoding covers core business metrics.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the length of core information per segment in semiconductor financial reports, avoids splitting associated fields such as revenue and production capacity |
chunk_overlap | 100–150 characters | Retains field association information across segments, prevents key financial report metrics from being truncated by segmentation |
embedding_model | Determined via actual testing | Must support multimodal semantic encoding, adapts to mixed expressions of numerical fields and business terminology |
index_refresh_interval | 15 minutes | Matches the update frequency of temporary announcements for quarterly financial reports, ensures latest data is covered by indexes in a timely manner |
top_k | Top 10–15 results | Covers recall requirements for multi-dimensional financial report metrics, avoids missing segmented business data |
similarity_threshold | 0.72–0.80 | Filters low-match irrelevant financial report fragments, improves accurate recall rate |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- The interface displays a file status as "Indexing" for more than 2 hours with no progress updates. The cause is that when
embedding_modelis set toqwen3-embedding-8b, the corresponding model call address is not configured in the FastGPT environment. This causes model inference timeouts that block the indexing process. - Recall results only include text fragments, and do not match core fields such as revenue and production capacity. The cause is that
chunk_sizeis set too small. This splits core metrics of a single financial report into multiple independent segments, causing vector encoding to lose field associations. - Duplicate financial report fragments appear after incremental indexing. The cause is that
index_refresh_intervalis set too short. Full index scans do not filter processed historical documents, causing duplicate encoding and storage.
How to Verify Proper Configuration
- Upload a single ten-thousand-word semiconductor annual financial report. Check whether the index completion time meets expectations. Confirm that the
chunk_sizeandindex_refresh_intervalconfigurations are active. - Initiate a query containing "2024 wafer production capacity". Verify whether the recall results include associated fragments of the corresponding fields. Check the filtering effect of the
similarity_threshold. - Submit a temporary announcement document. Check whether it can be retrieved within 15 minutes after indexing is complete. Confirm that the incremental indexing configuration is working correctly.
- Check the FastGPT backend model call logs. Confirm that there are no errors in the
embedding_modelcall requests. Verify that the model configuration is correct.
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.