What the Data for This Category Looks Like
Ledger data for state-owned capital investment companies originates primarily from internal financial accounting systems, project investment management platforms, and property right registration databases. Update schedules are adjusted based on business milestones. Batch updates run during monthly settlement and quarterly reviews, while newly added ledgers sync in real time daily. Individual ledger documents are mostly structured tables, containing fixed fields such as project number, investment amount (unit: ten thousand yuan), investment subject, fund arrival time, approval document number, and risk level. Summary reports are generated by integrating multiple ledger datasets, are relatively lengthy, and often include nested detailed tables.
Constraints Imposed on Vector Models and Indexes
High proportions of structured fields and numerical fields with fixed units require index configurations to support field-level semantic differentiation, preventing vector matching deviations caused by differing numerical units. Long summary reports require long-text chunking strategies to avoid semantic breaks. Multiple batches of bulk updated data require indexes to support incremental synchronization, reducing full reconstruction time. Fixed field structures allow optimization of vector model input formats via preset field mappings, boosting retrieval recall accuracy. Additionally, the structured nature of individual data means indexes must balance mixed indexing of structured metadata and unstructured text, supporting both ledger summary and report generation use cases.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the text density of ledgers and summary reports for state-owned capital investment companies, avoid semantic breaks in single chunks |
| Overlap Length | 100–150 characters | Connect the semantics of adjacent chunks, prevent key information from being truncated |
defaultToken | 2000 token | Cover the core field information of a single ledger, balance the accuracy and time consumption of vector calculation |
maxToken | 4000–6000 token | Adapt to the chunking upper limit of long-length summary reports, retain complete analysis logic |
| Similarity Threshold | 0.72–0.80 | Match the semantic consistency of fields in state-owned capital investment ledgers, filter low-relevance retrieval results |
| Single Search Limit | Top 8 entries | Balance the recall of multi-dimensional information for summary reports, avoid excessive redundant data |
Three Common Errors
- Phenomenon: After importing ledger data, the dataset status remains stuck in building index for more than 4 hours. Cause: The chunk length was not adjusted for long-length summary reports, causing the token count of a single chunk to exceed the model limit, triggering multiple retries or timeouts.
- Phenomenon: Vector calculation scores are abnormally high, and multiple datasets have identical scores. Cause: Vector mapping between structured fields and unstructured text was not differentiated, and numerical fields with fixed units were not semantically normalized, causing the model to fail to recognize field differences.
- Phenomenon: The vectorization process stalls after uploading ledger attachments in PDF format. Cause: PDF text structured parsing configuration was not enabled, causing a large number of nested tables to be recognized as unstructured text, increasing the load of vector calculation.
How to Verify Correct Configuration
- View the index construction logs of the dataset, confirm the matching degree between chunk length and
maxToken, with no token overflow errors. - Randomly select 3 ledger datasets to perform retrieval, check that the similarity distribution of recall results matches the preset threshold interval.
- Perform an incremental update test, confirm that the index only synchronizes newly added data without triggering a full reconstruction.
- Check the vector mapping configuration of structured fields, confirm that semantic tags for corresponding fields have been associated.
Note: The values listed above are common starting points and 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.