Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Four types of systems provide coal power integration ledger data: coal mine production scheduling, power operation monitoring, fuel procurement management, and equipment maintenance records. Systems generate one shift-level ledger per shift, compile daily ledgers via daily summaries, and produce full-cycle ledgers each month. Each ledger document includes fields such as equipment number, shift time, tunneling footage, power generation, coal consumption rate, and operating duration, with dedicated units attached such as meters, kilowatt-hours, grams of standard coal per kilowatt-hour, and hours. Documents are arranged in chronological order, with individual entries mostly falling within the several hundred character length range.
Constraints on vector models and indexes from these characteristics
The multi-field structured nature of coal power ledgers requires vector models to retain semantic associations between fields and units, preventing generic text vector models from splitting field information. High-frequency shift-level updates require indexes to support incremental writes, avoiding performance losses from full index rebuilding. Long-cycle ledger report generation requires access to several months of historical data, so vector indexes must support large-span context recall and adapt to token length limits for multi-field concatenation in single batches.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the core information length of a single ledger, to avoid splitting associated fields such as equipment number and shift time across chunks |
| Overlap Length | 50–80 characters | Retains shift connection information between adjacent ledger entries, to prevent loss of contextual association from chunking |
| Similarity Threshold | 0.72–0.85 | Matches ledger entries for the same equipment and period, to filter invalid associated results across devices or shifts |
| Single Search Limit | Top 10 entries | Controls the recall scope, to fit the reasonable data scale of single-batch queries for coal power ledgers |
defaultToken | 4096 | Adapts to the token length limit for single-batch ledger summary, to meet text requirements after multi-field concatenation |
maxToken | 8192 | Covers the context window for long-cycle monthly ledger report generation, to avoid truncation of critical data |
Three Common Misconfigurations
- Phenomenon: Vectorization interface request logs only show a single text slice, and the batch length of results returned by the vectorization service is 1. Cause: Batch chunking configuration is not enabled. FastGPT submits requests for single text by default, which does not match the batch processing requirements of the vectorization service.
- Phenomenon: The interface prompts that the Alibaba multimodal-embedding-v1 vector model cannot be loaded, and the model status shows abnormal. Cause: Correct interface address and authentication parameters are not filled in the custom model configuration, and the input format of the multimodal vector model is not adapted.
- Phenomenon: Calling the chunked index query API returns an empty array, and historical ledger index data cannot be obtained. Cause: Persistent storage for chunked indexes is not enabled. Index content is only cached in memory, and historical data is lost after service restart.
How to Verify Correct Configuration
- A single complete ledger text is submitted to the vectorization interface, and the batch length of the returned results is checked to match the number of submitted slices.
- The Alibaba multimodal-embedding-v1 model is added on the model management page, correct interface address and authentication parameters are filled in, and the model loading status is verified as normal.
- The chunked index query API is called, the specified equipment number and time range are passed in, and the returned index entries are checked to include core fields of the corresponding ledger.
- The vector index monitoring panel is viewed, incremental update tasks are confirmed to execute normally at shift frequency, with no failed logs.
The values provided 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.