What this category of data looks like
Data sources for this category include online composition analysis equipment, DCS control systems, laboratory testing systems, kiln and grinding operation equipment, raw fuel ledgers, and energy metering systems. Daily documentation covers raw material batching plans, rotary kiln operation records, clinker test reports, quality control notices, equipment inspection records, and energy consumption reports. Data update frequencies fall into three categories: real-time (equipment operating parameters, online composition data), shift-based (inspection records, energy reports), and daily (batching plans, quality control documents). Fields include raw fuel composition ratios, equipment operating temperatures, clinker strength indicators, batching ratios, and other relevant metrics.
What constraints these characteristics impose on vector models and indexes
High-frequency updates of real-time equipment and online composition data require indexes to support incremental synchronization mechanisms. This prevents full reindexing from consuming excessive computing resources. Multi-source heterogeneous data types include numerical parameters, text records, and structured reports. Unified field mapping rules are needed to maintain consistency across vector embedding spaces for data from different sources. Update frequencies divided by production cycles require indexes to split data by batch or time dimension. This allows quick location of quality issues for corresponding production links. Core quality indicators such as clinker strength and free calcium oxide content need field weight settings during vector recall. This improves the accuracy of defect analysis. Long-text kiln operation records need appropriate chunking lengths. This avoids loss of key thermal regime information during embedding.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for long texts such as raw material batching plans and kiln operation logs, and avoids breaking key information |
| Overlap Length | 100–150 characters | Ensures semantic connection between adjacent chunks, and prevents key context of long texts from being split |
defaultToken | 4096 | Adapts to the embedding interface specification of FastGPT v4.17.0, and matches the embedding requirements of single-batch quality data |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Similarity Threshold | 0.72–0.85 | Filters low-relevance retrieval results, and adapts to subtle difference recognition for clinker quality indicators |
| Single Search Limit | Top 10 entries | Controls the number of returned retrieval results, and avoids redundant information interfering with defect analysis |
Three common mistakes
- Symptom: A 401 status code is returned when calling the vector model, or a model deployed via Ollama is rejected by FastGPT. Cause: The access key for the vector model is not configured, or the key permissions do not cover production data retrieval scenarios.
- Symptom: Duplicate document chunks appear after the knowledge base index is generated, and the custom split document order does not match the index storage order. Cause: The index deduplication function is not disabled, or the deduplication logic does not retain the split order of the original documents.
- Symptom: The number of returned retrieval results does not match the single search limit setting, or core quality indicators are not prioritized in recall. Cause: Field weights are not set correctly, or retrieval parameters are not bound to clinker quality-related field rules.
How to confirm correct configuration
- Run a curl command to test the vector model interface, and confirm that the returned vector data format meets FastGPT's embedding requirements.
- Upload a single raw material batching plan document, and check that the number of index-generated chunks matches the set chunk length.
- Submit a simulated quality issue query, and verify that the number of returned retrieval results matches the single search limit setting.
- View index logs, and confirm that the incremental synchronization function triggers normally when updating online composition data, with no full reindexing errors.
The values provided here are common starting points, and should be evaluated against the reader's own samples.