What the Data for This Category Looks Like
Core data for this category comes from the mining, raw material, calcination, grinding, quality control, and energy departments of cement enterprises. Daily documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection logs, and energy reports. Data sources include online composition analysis, DCS systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Update frequencies include real-time, shift-based, and daily. Documents contain process parameter values, test results, operation records, and other content. Corresponding units include degrees Celsius, tons per hour, megapascals, and more.
Constraints Imposed on Vector Models and Indexing
Multi-source data with varied update frequencies requires indexes to support incremental updates. This avoids resource consumption from full reindexing. Document types include structured parameters and unstructured operation records. This requires vector models to adapt to semantic extraction for mixed content. High fault tolerance for process adjustments and quality control means retrieved documents must precisely match keywords. This prevents interference from irrelevant content. Exclusive documents from different departments need classification and indexing by business scenario. This enables precise retrieval of process specifications and technical standards for the corresponding link. High-frequency updates for real-time data require chunking strategies adapted to short-content documents. Daily ledger-style documents can use longer chunks to improve efficiency.
Configuration Settings
This document is based on FastGPT v4.17.0. The following vector model and indexing configurations apply to cement clinker and grinding scenarios:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-section content length of documents including raw material batching plans and kiln operation records, prevents cutting process parameter logic |
| Overlap Length | 100–150 characters | Covers scenarios where process parameters span chunks, such as continuous records of kiln temperature changes |
| Similarity Threshold | 0.75–0.85 | Meets the precision requirements for cement process parameters, avoids incorrectly retrieving irrelevant batching plans |
| defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
| maxToken | 8192 | Covers the total token limit during batch import, adapts to daily batch processing of laboratory reports and energy report data |
| Single Search Limit | Top 8 entries | Covers the number of related documents required for quality control and energy management, avoids redundant retrieval |
Three Common Misconfigurations
- Issue: An error prompt for incorrect parameters appears when adding indexes in bulk. Cause: Bulk import request parameters were not configured per the dedicated field format of cement clinker data, and process parameter fields in documents were not matched.
- Issue: The number of retrieved results does not match expectations. Cause: The single-document single-index configuration was used incorrectly. Index policies were not set per the classification needs of multi-department documents, leading to key documents not being retrieved.
- Issue: Direct management of vector database add/delete operations via MongoDB is not possible. Cause: FastGPT's external vector database connection permission was not enabled. The default permission of the open-source system was mistakenly treated as direct connection permission.
How to Confirm Configurations Are Set Correctly
- Check the running logs of bulk import tasks. Confirm that the chunk lengths of all cement clinker-related documents fall within the configured value range.
- Search for the keyword "rotary kiln thermal system adjustment". Verify that retrieved documents include category-specific documents such as raw material batching plans and kiln operation records.
- Check the vector database permission configuration. Confirm that non-root users can manage specified process specification and technical standard indexes according to preset rules.
- Upload a new laboratory report. Verify that the index updates automatically, and retrieval results can correctly include the new document.
The values provided here are common starting points. They should be evaluated against the reader's own samples.