What data for this category looks like
Data related to business processes for state-owned capital operation companies comes primarily from filings submitted to state-owned assets supervision departments, internal approval manuals, transaction declaration materials, and annual operation reports. Data updates follow three cycles: monthly updates for business rules, quarterly updates for regulatory requirements, and annual updates for operation summaries. Document structures include fixed fields such as approval node serial numbers, responsible departments, material lists, and approval time limits. Units used are workdays, copies, and serial numbers. Most documents are long text paragraphs with continuous step-by-step business process instructions. Typical document lengths range from several thousand characters.
Constraints on vector models and indexing
The structured fields in the data — such as fixed approval nodes and responsible departments — require vector indexes to retain field associations. Do not use chunking operations that damage field integrity. The staggered update cycles (monthly, quarterly) require support for incremental index synchronization. This avoids excessive time spent on full index rebuilds. Most documents contain continuous business step text. Limit single chunk length to prevent splitting content across approval nodes. Documents involve state-owned assets regulatory materials. Select vector models adapted to government compliance texts to ensure accurate semantic matching.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-node text length of business process documents, avoids splitting across approval steps |
| Overlap Length | 100–200 characters | Retains semantic connections between adjacent chunks, prevents splitting process nodes |
| Similarity Threshold | 0.72–0.85 | Matches the semantic similarity distribution of state-owned assets business texts, filters low-relevant process rules |
| Single Recall Limit | Top 10 entries | Controls the number of returned process nodes, prevents context overload |
maxToken | 4096 token | Adapts to the input length limit of mainstream vector models, ensures complete vectorization of long process texts |
defaultToken | Calibrated via actual testing | Adapts to the special field encoding requirements of documents in this category, prevents loss of field information |
Three common configuration errors
- Symptom: Index creation tasks get stuck during processing of the final set of files. The interface displays "index synchronization timeout". Cause: Incremental index synchronization is not configured. Full synchronization of large documents such as state-owned assets annual operation reports exceeds the processing limit set by
defaultToken. - Symptom: Knowledge base creation tasks get stuck during index construction, with no progress updates for extended periods. Cause: Chunk length is set beyond the single-node length of business documents. This leads to excessively long chunking operations that fail to complete vectorization.
- Symptom: After switching to a custom embedding model, index creation succeeds but search tests return a "semantic matching failed" error. Cause: The
maxTokenparameter is not adjusted to match the input length limit of the new model. This leads to incomplete truncation of long process text during vectorization.
How to confirm configurations are correct
- Review index creation logs to confirm all chunk lengths fall within the preset 800–1200 character range.
- Trigger an incremental index synchronization task. Verify that only updated business process files are included in the new index, and no full rebuild is triggered.
- Submit test queries. Confirm that returned process nodes exactly match the approval steps in the business documents.
- After switching to a custom embedding model, verify that the
maxTokenparameter has been updated to match the input length limit of the new model.
The values listed above are common starting points. They should be evaluated 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.