What the data for this category looks like
Business process data for power generation equipment manufacturing mainly comes from equipment operation and maintenance logs, production process specifications, spare parts requisition forms, and business approval process records. Data sources include on-site industrial control systems, ERP systems, and electronic documents submitted by front-line operation and maintenance personnel. Update cycles are split into two types: real-time (equipment operating parameters, fault alerts) and weekly updates (process optimization documents, annual spare parts lists). The structure of a single document includes unique equipment number, operation and maintenance timestamp, fault phenomenon, disposal steps, and associated spare part model. Field units include hours, units, megapascals, pieces, and other standard units.
What constraints do these characteristics impose on the vector model and indexing workflow
The characteristics of power generation equipment manufacturing business data impose clear constraints on the vector model and indexing workflow. High-frequency writes of real-time equipment alerts and operation and maintenance data require indexes to support low-latency incremental updates, to avoid data synchronization delays. Document content spans a wide range, from fault descriptions of tens of characters to process specifications of thousands of characters. This requires flexible chunking rules. Multiple fields carry dedicated units, so indexing workflows must retain field metadata to avoid semantic confusion across fields. Steps in business process flows have sequential relationships. Indexes must retain contextual semantics of process nodes, to avoid losing process logic after chunking.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to mixed content of process specifications and fault descriptions, avoids overly long or short single chunks |
| Overlap Length | 100–200 characters | Retains contextual connections for process steps, prevents chunking from breaking business logic |
| Similarity Threshold | 0.75–0.85 | Distinguishes similar fault descriptions and process steps across different scenarios, reduces false recalls |
defaultToken | 4096 token | Adapts to long-text process document indexing, meets the maximum length requirement for single indexing |
maxToken | 8192 token | Matches the context window for business process guidance, ensures recalled content can be fully spliced |
| Single Search Limit | Top 10 entries | Balances recall efficiency and result completeness, covers associated data for common business scenarios |
Three common mistakes
- Symptom: Knowledge base indexing progress stagnates for a long time, and the interface shows the "Indexing" status without updates. Cause: The correct vector model call timeout parameter was not configured when connecting to an external large model, causing the embedding generation request to time out without a response during the indexing process.
- Symptom: Search results show cross-field semantic confusion, such as incorrectly associating spare part model descriptions with operation and maintenance timestamps. Cause: Field metadata was not retained during the indexing workflow, causing vector embeddings to fail to distinguish semantic boundaries between different business fields.
- Symptom: The vector service call returns a 503 error, prompting that the model text-embedding is unavailable under the current default group. Cause: The vector model group configuration was not correctly bound to the index group of business data, causing request routing failure.
How to confirm the configuration is correct
- Upload a typical power generation equipment operation and maintenance document, check that the chunked text lengths fall within the preset chunk range, with no obvious truncated or overly short chunks.
- Initiate a retrieval related to a business process, verify that the similarity of the recalled results falls within the preset threshold range, with no excessive or insufficient recalls.
- View the write logs of the vector database, confirm that the update frequency of incremental data meets the synchronization requirements of real-time business data.
- Test retrieval requests for different fields, confirm that results only associate with content from the corresponding fields, with no cross-field confusion.
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.