What Data for This Category Looks Like
Data for this category covers daily documents from cement enterprise mines, raw material, calcination, grinding, quality, and energy departments. Included documents are raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data sources include online composition analysis systems, DCS control systems, laboratory equipment, kiln and mill equipment ledgers, raw and fuel registration books, and energy metering devices. Update frequencies include real-time equipment operation data, shift-by-shift inspection and operation records, daily laboratory reports, and daily energy reports. Most documents are structured tables and paragraph-style records. Fields include raw and fuel composition, kiln temperature parameters, clinker strength indicators, and corresponding units such as tons, degrees Celsius, megapascals, and others.
Constraints Imposed on Vector Models and Indexing
The multi-source heterogeneous data, varied update frequencies, and specialized parameter characteristics of this category impose multiple constraints on the vector models and indexing process. Format differences between real-time data from online composition analysis and DCS systems, paper inspection records, and manual ledgers require unified format preprocessing before indexing to avoid deviations in vector embeddings. The mixed update frequencies of real-time, shift-based, and daily data require separate processing logic for incremental indexing and batch indexing. Mixed execution will slow overall indexing efficiency. Documents contain a large number of industrial specialized parameters and numeric fields. Vector models must adapt to the semantic and numerical correlations of these parameters, otherwise they cannot accurately match the logical requirements of cross-review. The association attributes of cross-department documents also require indexing to support cross-document associated retrieval to cover complete cross-review scenarios.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Most documents in this category are paragraph-style business records and structured tables. This range preserves the integrity of individual business logic |
| Overlap Length | 100–150 characters | Prevents chunking from breaking cross-paragraph specialized parameter associations, ensuring semantic coherence of vector embeddings |
| maxToken | 8192 token | Adapts to long-paragraph kiln operation records and batching plan documents in this category, avoiding truncation of critical parameter content |
| defaultToken | 4096 token | Balances vector embedding efficiency and content coverage requirements for most short documents |
| Similarity Threshold | 0.72–0.85 | Distinguishes matching precision for industrial specialized parameters, avoiding mismatched or missed associated documents for cross-review |
| Single Search Limit | Top 10 entries | Covers most association scenarios for cross-department documents in this category, meeting retrieval needs for cross-review |
Three Common Errors
- Phenomenon: Indexing tasks take significantly longer than expected, and background logs show no progress updates for chunk processing over extended periods. Cause: Failure to distinguish incremental indexing and batch indexing by update frequency; mixed processing of real-time data and daily batch data causes resource contention.
- Phenomenon: When creating a knowledge base, the text understanding model dropdown menu does not display the added
ollama qwen2.5model. Cause: Failed to complete the association configuration between the language model and indexing model, or the model deployment path was not correctly synchronized to the system. - Phenomenon: Cross-review results contain a large number of irrelevant document fragments that do not match business logic. Cause: Chunk length settings do not align with the business logic length of documents in this category, leading to broken semantic associations of specialized parameters.
How to Confirm Proper Configuration
- Access the indexing management page of FastGPT v4.17.0, check task progress, and confirm that indexing tasks for real-time data and batch data execute at preset frequencies.
- Verify on the model configuration page that the added
ollama qwen2.5model andbge-m3model are displayed in their corresponding configuration items. - Input a query containing clinker strength parameters, and check whether returned results include relevant documents such as laboratory reports and quality control notices.
- Review system logs to confirm no error messages about vector embedding failures or indexing timeouts appear.
The values provided are common starting points and should be measured against the reader's own samples.