What the data for this category looks like
Daily data sources include concentrate and auxiliary material analysis reports, operating data such as furnace temperature and tank pressure collected by the DCS system, quality inspection documents issued by the laboratory, and energy and environmental monitoring records. Daily documents include batching lists, smelting records, electrolytic cell status records, anode and cathode quality ledgers, environmental emission logs, and more. Each individual document contains fields such as raw material composition parameters, furnace status indicators, tank voltage and current values, and energy consumption data. Update frequency changes in real time with production batches, furnace runs, or tank conditions, and some ledgers are archived per furnace or tank cycle.
What constraints these characteristics impose on the vector models and indexes workflow
The data for this category has high update frequency and strong real-time performance, requiring vector indexes to support incremental synchronization and low-latency refresh to avoid ledger data lagging behind production progress. Documents contain a large number of professional process parameters and terms, so chunk length must be adapted to the field density of individual documents to avoid splitting that destroys process logic connections. Field differences in multi-source data will lead to scattered vector features, so reasonable index dimensions must be configured to adapt to the embedding needs of multiple types of fields. The accuracy of production ledgers is directly linked to production compliance and metal recovery rates, so the precision of index recall must match business verification requirements to reduce false recall risks.
How to configure the settings
The following configuration scheme is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the field density of individual smelting ledger documents, avoid splitting that destroys process logic connections |
| Overlap Length | 50–80 characters | Connect process parameters across adjacent chunks, retain cross-chunk logical connections |
defaultToken | 8192 | Cover the complete field content of individual ledger documents, adapt to the embedding needs of professional terms |
maxToken | 16384 | Support batch embedding of multiple ledger documents across batches, meet context requirements for summary scenarios |
| Similarity Threshold | 0.72–0.85 | Filter low-match non-process-related documents, ensure business relevance of recall results |
| Single Search Limit | Top 10 entries | Limit the number of recall results, adapt to multi-source data correlation analysis for production ledgers |
Three common configuration errors
- Issue: After batch re-embedding multilingual ledgers, the recall rate of professional terms fails to meet business requirements. Cause: The word segmentation rules of the embedding model were not adjusted for professional terms in the smelting industry, leading to deviations in vector features of multilingual terms.
- Issue: Manually inserted environmental protection ledger indexes disappear automatically after several hours. Cause: The persistent storage strategy for incremental indexes was not configured, and the temporary index cache was not correctly written to the persistent storage directory.
- Issue: After connecting a specified embedding model, the status of uploaded ledger files continues to show "Indexing". Cause: The input token limit of the embedding model does not match the chunk length of ledger documents, causing embedding tasks to time out without completion.
How to confirm the configuration is working correctly
- Randomly select 3 documents of smelting records and electrolytic cell status records from different batches, perform an embedding task, and check whether the chunked content retains complete process parameter connections.
- After triggering a batch embedding operation, check the embedding progress in the system logs, confirm that the
defaultTokenandmaxTokenconfigurations do not trigger text truncation error messages. - Input query statements related to smelting processes, verify that the similarity scores of recall results fall within the preset threshold range, and that there are no unrelated non-smelting documents.
- Wait 24 hours and check the index storage directory, confirm that manually inserted ledger indexes have not been automatically cleaned up, and that the persistent configuration has taken effect.
The values provided here are common starting points and should be measured against the reader's own samples.