What this category of data looks like
Ledger data for non-ferrous metal smelting comes primarily from production control systems, raw material warehouse systems, and finished product quality inspection systems. Real-time production data updates every 15 minutes. Daily summary ledgers for raw material consumption and finished product output update at early morning each day. Single ledger documents include fields such as furnace number, production batch, collection time, raw material ratio, energy consumption value, product purity, and qualification rate. Units include tons, kilowatt-hours, grams per liter, percentage, and others. Most single documents range from 300 to 800 characters. Bulk imported ledger files are mostly in CSV or structured Excel format.
What constraints these characteristics impose on vector models and indexing
Real-time production ledgers update every 15 minutes. Indexing must support incremental synchronization to avoid performance overhead from full reindexing. There are many structured fields with multiple unit types. Chunking must retain field identifiers to avoid semantic confusion. Bulk imported CSV and Excel files may contain empty fields or inconsistent formats. Indexing must add field validation logic to filter invalid data. Single documents are moderately sized, but bulk data scale is large. Vector storage density after chunking must be controlled to ensure index query response speed.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single ledger documents, retains semantic integrity of field data |
| Overlap Length | 100–200 characters | Prevents loss of cross-chunk context association, adapts to the structured logic of ledgers |
| Similarity Threshold | 0.75–0.85 | Filters low-match retrieval results, adapts to the strongly structured characteristics of ledger data |
| Maximum Search Results per Request | Top 8 | Balances retrieval recall rate and query performance, matches the summary needs of bulk ledgers |
defaultToken | 4096 | Adapts to the total token length of ledger fields and retrieval queries, avoids truncating critical information |
maxToken | 8192 | Supports vector storage and context splicing for long documents, adapts to the summary needs of multi-field ledgers |
Three common errors
- The interface permanently shows "Indexing" status with no updates. The cause is that incremental synchronization trigger rules are not configured. Bulk ledger data is imported in full without splitting batches, causing index tasks to occupy resources for a long time and block processing.
- No valid matches appear in retrieval results after custom index configuration. The cause is that structured field identifiers such as furnace number and production batch are not retained during chunking. Vectors cannot associate core business attributes of ledgers, leading to semantic matching failure.
- Retrieval results are empty after connecting a local m3e vector model. The cause is that the output dimension of the vector model is not aligned with the preset dimension of the index library. Generated vectors cannot be properly recognized and matched by the index library.
How to confirm configurations are correct
- Check index task logs to confirm that the number of chunked documents matches the chunking rules of the original ledger data.
- Run a single retrieval test to verify that the field identifiers of returned results match the business attributes of the original ledgers per preset requirements.
- Verify the incremental synchronization function: add a test ledger, then confirm that the index task triggers automatically and updates the retrieval results.
- Check the vector model access configuration to confirm that the return format of model calls is compatible with the storage format of the index library.
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.