What data looks like for this category
Data related to service consultation for power generation equipment manufacturing comes primarily from four sources: equipment technical manuals, operation and maintenance procedures, quality inspection reports, and service process guides. Data update rhythm falls into two categories: sudden updates when new models enter mass production, quarterly updates to service guides, and annual updates to quality inspection ledgers. Document structures include parameter fields with units such as equipment model, rated power, and rotor speed, plus content such as fault codes, maintenance steps, and service approval processes. Single document lengths range from tens of characters of service prompts to thousands of characters of operation and maintenance manuals.
What constraints these characteristics impose on vector models and indexes
First, the data includes device parameters with clear units. Vector models must retain the associated semantics of numerical values and units, avoiding semantic fragmentation caused by splitting parameter units during chunking. Second, there are two update rhythms for data: sudden and scheduled. Indexes must support flexible switching between incremental synchronization and full reconstruction to adapt to different update frequency requirements. Third, single document lengths vary significantly. Chunking strategies must balance the integrity of short documents and the semantic coherence of long documents. Fourth, fields include precise retrieval items such as equipment models and fault codes. Indexes must associate field metadata to narrow the recall scope and improve retrieval accuracy.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length differences of power generation equipment documents, from short prompts to long operation and maintenance manuals, and retains complete semantics of parameters and context |
| Overlap Length | 150–200 characters | Prevents chunking from splitting continuous device parameters or fault codes, ensuring semantic coherence |
| Similarity Threshold | 0.72–0.78 | Balances the accuracy of power generation equipment parameter matching and recall coverage, filtering low-correlation results |
| Single Search Limit | Top 8 entries | Adapts to the limited equipment types involved in a single round of consultation, avoiding excessive results interfering with context integration |
maxToken | 8000 | Adapts to the default context limit of FastGPT v4.17.0, accommodating the content length of long document recalls |
| Maximum Context | 6000 tokens | Balances the information completeness of responses and the input constraints of the model |
Three common configuration errors
- Phenomenon: After using chunk mode to call the pushdata API to upload documents, the interface permanently displays the "Indexing" status. Cause: The chunk length setting exceeds the single-processing limit of the vector model, causing a backlog in the indexing task queue.
- Phenomenon: The last set of indexes after question-and-answer splitting fails to complete construction. Cause: The character count of the last chunk set does not meet the minimum requirement for valid embedding generation, or fails to associate core field metadata such as equipment model.
- Phenomenon: When using a third-party embedding model, the recall results have insufficient matching degree with the original document. Cause: The input preprocessing of the embedding model is not adjusted for the unitized parameters of power generation equipment, resulting in loss of numerical semantics.
How to verify correct configuration
- Upload a single short document containing parameters such as rated power and rotor speed, check whether the chunked fragments retain complete parameters and units, and confirm that the chunk length setting is reasonable.
- Initiate a consultation involving a specific equipment model, check whether the number of recall results conforms to the single search limit configuration, and confirm that the similarity threshold takes effect.
- Upload an updated operation and maintenance document, check whether the index completes synchronization within a reasonable period, and confirm that the incremental index configuration takes effect.
- Check the FastGPT v4.17.0 system logs to confirm that the
maxTokenparameter configuration value matches the actual used context length, with no overflow errors.
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.