What This Type of Data Looks Like
The ledger data for urban investment platforms mainly comes from project approval documents, financing contracts, detailed debt repayment ledgers, municipal public utility operation records, and similar materials. Update frequency follows business milestones: real-time updates when financing is received, debt matures, or project progress changes. Monthly summary ledgers are updated per calendar month. Documents use structured tables as their core carrier, including fields such as project ID, project name, financing amount, repayment term, maturity date, and guarantee subject. They also include transcribed text from unstructured approval documents and scanned contract attachments. Field units are mostly ten thousand yuan, months, and calendar days. Some fields use enumeration values such as project status.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The high proportion of structured tables and enumeration fields requires preserving field associations during chunking. Do not split core information from the same business unit, as this will cause semantic fragmentation after vectorization. The coexistence of multiple update rhythms requires the index to support both incremental update and full reconstruction modes. This adapts to the different needs of monthly batch updates and real-time business changes. Scenarios mixing unstructured text and structured fields require the vector model to adapt to both the semantics of structured fields and free text. This avoids adaptation bias from using a single model. The presence of field units and enumeration values requires the index to associate field semantics with units during recall. This prevents recall results where the amount does not match its unit.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Aligns with the length of core business units for individual ledger entries, avoids splitting associated fields such as repayment amount and maturity date |
| Overlap Length | 100–200 characters | Preserves field associations between adjacent chunks, prevents key business information from being truncated by chunking |
| maxToken | 8192 | Adapts to the input upper limit of most mainstream vector models, meets the vectorization requirements of single-chunk text |
| defaultToken | 2048 | Used as the default context truncation threshold, balances information integrity and response speed for report generation |
| Similarity Threshold | 0.75–0.85 | Filters entries with high matching degrees for ledger queries, avoids introducing irrelevant historical data |
| Single Search Limit | Top 10 entries | Matches the number of core entries for batch ledger queries, prevents result overload from impacting report generation |
Three Common Misconfigurations
- Phenomenon: A large number of irrelevant entries appear in vector recall results, and core business entries rank lower in results. Cause: The similarity threshold was not adjusted for urban investment platform ledgers. Using a generic threshold leads to insufficient screening accuracy.
- Phenomenon: Vectorization processing tasks throw timeout errors, and batch task failure rates are elevated. Cause: Chunk length is set too large, exceeding the input limit of the vector model. This causes single-chunk processing time to exceed the threshold.
- Phenomenon: Only a single text slice can be passed during API calls, and the batch processing mode of the vectorization service cannot be used. Cause: Batch vectorization configuration was not enabled in FastGPT v4.17.0. The default setting only enables single-slice transmission, which does not match the batch processing requirements of the vectorization service.
How to Confirm Proper Configuration
- Review chunked text fragments to confirm that core associated fields such as project amount and repayment date are not split across different chunks.
- Initiate a ledger query, and verify that the number of recall results matches the single search limit setting, and that similarity scores fall within the threshold interval.
- Call the FastGPT vectorization API interface to confirm support for passing multi-slice arrays for batch processing.
- Check the input token count of the vector model to confirm it does not exceed the
maxTokensetting value.
The values provided 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.