What data for this category looks like
Data for this category primarily comes from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, valuation firm reports and financial statements. The update frequency is monthly. Daily processed documents include property registration forms, asset valuation reports, property rights transfer plans, on-site transaction announcements and asset disposal approval slips. Fields cover specific business attributes such as asset ownership number, valuation amount, transaction status and approval process node. This data covers the registration, valuation, transfer and disposal stages of the full lifecycle of stock assets. It requires cross-verification against policy and regulatory basis.
What constraints these characteristics impose on vector models and indexing
The monthly update frequency means real-time incremental indexing is not required. Full index rebuilds can run on a weekly or monthly cycle to reduce computing resource consumption. Document lengths vary widely. Ranges from a few lines in property registration forms to dozens of pages in asset valuation reports. Flexible chunking rules are needed. These rules prevent over-splitting short documents and avoid semantic breaks in long documents. Multi-source heterogeneous data integration requires indexes to support unified mapping across cross-system data sources and retain metadata. This ensures corresponding business documents can be linked during Q&A. The presence of specific business fields requires vector models to retain field-level semantic features. This improves the accuracy of policy basis matching.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average semantic unit of property registration forms and asset valuation reports. Prevents single chunks from being too fragmented or overly long |
| Overlap Length | 100–150 characters | Connects adjacent semantic blocks. Prevents core clauses in long documents such as property rights transfer plans from being split apart |
maxToken | 4096 token | Fits the context limit of most open-source vector models. Matches the default call limit of FastGPT v4.17.0 |
| Similarity Threshold | 0.75–0.85 | Calibrated based on actual tests. Balances recall accuracy and recall rate for policy basis Q&A |
| Single Search Limit | Top 8 results | Fits the context length requirements of state-owned asset policy Q&A. Prevents redundant results from interfering with judgment |
defaultToken | 2048 | Fits the basic Q&A context configuration of FastGPT v4.17.0. Ensures effective integration of policy texts and business data |
Three common configuration errors
- Phenomenon: Expired asset disposal approval slips appear in search results. Cause: Index rebuild cycle is not configured according to the monthly update frequency. Old data is not cleaned up in a timely manner.
- Phenomenon: Vector model calls return format errors. Specified multi-modal vector models fail to load. Cause: Cross-model call interface mapping rules are not configured correctly. Parameter formats for non-general vector models are not adapted.
- Phenomenon: Index construction fails. Logs show chunk token limit exceeded. Cause: Chunk parameters are not adjusted for long documents. Long content such as property rights transfer plans exceeds the
maxTokenlimit.
How to verify correct configuration
- Check index construction logs. Confirm all configured data sources (state-owned assets supervision systems, asset management systems, etc.) are loaded successfully with no data missing.
- Submit a test query with policy-related keywords. Verify returned results include document types in this category such as property registration forms and asset valuation reports.
- Check vector model call logs. Confirm actual values of
defaultTokenandmaxTokenmatch configured settings. - Simulate cross-model call scenarios. Verify search results from different vector models can be unified into Q&A results.
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.