What This Category's Data Looks Like
Ledger data for state-owned capital operation companies comes from four main sources: state-owned assets supervision and reporting systems, internal financial accounting platforms, property right transaction registration systems, and investment project archives. Update cadence follows three patterns: daily entry of individual business ledgers, monthly aggregation of full-scope operating data, and quarterly updates of annual report drafts. Documents use standardized business fields: project number, investment entity name, transaction consideration, shareholding ratio, approval document number, ownership status, and update date. Supporting professional units include ten thousand yuan, percentage, square meter, and others. Some ledgers include scanned documents or PDF-format approval attachments.
Constraints Imposed on Vector Models and Indexing
The multi-field structured nature of state-owned capital ledgers requires preserving field relationships during vector embedding. This avoids destroying business logic via chunk splitting. The coexistence of daily individual updates and batch aggregation requires indexes to support both incremental update and full reindexing modes. Single ledger lengths vary widely. Ranges run from tens of characters for basic entries to thousands of characters for complete reports. This requires chunking parameters to support flexible adaptation. Professional terms such as "transfer of state-owned equity" and "capital injection" require vector models to have semantic understanding capabilities in the state-owned assets field. This avoids semantic bias from general-purpose models. Data scale grows continuously with new projects. This requires index architectures to support horizontal scaling to adapt to ledger data processing of different volumes.
Configuration Settings
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of a single ledger plus associated fields, avoids splitting that breaks core associated information such as project numbers and transaction entities |
| Overlap Length | 100–150 characters | Retains cross-chunk field association content, ensuring that cross-chunk business logic can be fully matched during retrieval |
maxToken | 8192 token | Adapts to the input upper limit of mainstream open-source vector models, while covering the length requirements of most summary reports |
| Similarity Threshold | 0.72–0.80 | Matches the professional term matching accuracy for state-owned capital ledgers, filters low-relevance retrieval results |
| Single Index Limit | 5000 entries per batch | Adapts to resource constraints of Docker deployments, avoids single index task timeouts or memory overflow |
defaultToken | 1500 token | Matches the average embedded token count of a single basic ledger, ensuring complete embedding of core business fields |
Common Misconfigurations
- A 503 status code appears, with the interface or logs indicating the embedding model group is unavailable. This occurs when a dedicated embedding model group is not configured, or the group's call quota is exhausted.
- Index tasks remain in a running state with no progress. This occurs when the chunk length setting exceeds the vector model's input limit, or insufficient Docker deployment memory quotas cause process blocking.
- Vector database connection fails, and index initialization cannot be completed. This occurs when the database's vector extension function is not enabled, or the access address and authentication information are configured incorrectly.
How to Verify Proper Configuration
- View vector embedding task logs in FastGPT v4.17.0. Confirm that the embedded token count for a single ledger does not exceed the
maxTokensetting. - Run a small-batch index test with fewer than 500 entries. Confirm that corresponding ledger entries appear in search results after the task finishes, and that retrieval result similarity falls within the preset threshold range.
- Check the vector database's index shard configuration. Confirm it matches the single index limit, with no data accumulation.
- Test the embedding model call flow. Confirm no 503 errors occur, and the model call returns normal vector data.
The values listed are common starting points and should be evaluated against sample data matching the actual use case.
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.