What Data for This Category Looks Like
This category’s data covers multiple types of business documents from cement enterprise mining, raw material, calcination, grinding, quality, and energy departments. These include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data sources include online composition analysis equipment, DCS systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Update frequencies cover three levels: real-time, shift-based, and daily. Most fields include material composition, equipment parameters, energy consumption values, and quality indicators, with units such as tons and kilowatt-hours.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
The data characteristics of this category impose multiple constraints on the vector models and indexes link. Heterogeneous data from multiple sources includes online real-time data, shift-based inspection records, and daily report ledgers, which require adaptation to different incremental and batch indexing logic. Document types cover structured laboratory data and unstructured operation texts, so chunking rules must be designed for different content to avoid truncating long texts and losing key thermal parameters or quality indicators. Different update frequencies require indexing strategies to distinguish between real-time incremental updates and scheduled full builds, while also adapting to the import processing of 100,000-level data per batch to avoid excessive resource usage.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single documents in this category, avoiding truncation of complete thermal parameter segments in kiln operation records |
| Overlap Length | 100–150 characters | Covers key connecting content between chunks, preventing continuous logic of raw material batching plans from being split |
maxToken | 4096 token | Matches the input limit of mainstream API models, adapting to multi-field combined text in laboratory reports |
defaultToken | 512 token | Configures the default vector generation length for short single texts, adapting to the brief content of equipment inspection records |
| Similarity Threshold | 0.75–0.85 | Distinguishes similar entries in raw and fuel ledgers, avoiding mismatching material data from different batches |
| Single Recall Limit | Top 20 entries | Covers key abnormal items in shift-based inspection records, meeting the need for full reference during audits |
Three Common Misconfigurations
- Symptom: After importing 100,000 entries of raw and fuel ledger CSV data, only more than 90,000 entries remain after vectorization, with thousands missing. Cause: No chunking filter rules are configured, causing some laboratory report data with special formats to fail correct parsing, or duplicate equipment inspection record entries to be automatically merged.
- Symptom: Creating a document collection returns a success prompt, but the page index status always shows "Not Ready", and retrieval cannot be initiated. Cause: The batch data update frequency for this category is not adapted, and the index building task is suspended in the background due to excessive resource usage, or the documents contain non-standard text formats that cannot be parsed by the model.
- Symptom: Vector retrieval results meet expectations in a local environment, but after packaging as a Docker image and running, all text vector scores are completely identical. Cause: Model call parameters are not correctly configured in the Docker container, causing model parameters to be uniformly reset, or model inference exceptions occur due to container resource restrictions.
How to Confirm Proper Configuration
- Randomly select 10 documents of different types (such as kiln operation records, laboratory reports), perform a vector import operation, and check whether the number of imported entries matches the number of source data entries.
- Initiate a retrieval request, check whether the sorting logic of returned results matches the business scenario, and adjust the similarity threshold to a range that meets audit requirements.
- View the index building logs of FastGPT v4.17.0, and confirm whether real-time data incremental update tasks are triggered normally according to the preset shift-based or daily schedule.
- Test vector retrieval results after Docker container deployment, and confirm that reasonable differences exist in the scores of different texts, with no completely identical cases.
The values provided here are common starting points, and should be evaluated against the reader's own samples.