What the Data for This Category Looks Like
Cement clinker and grinding business data sources include online composition analysis, DCS systems, laboratory records, kiln and mill equipment sensor data, raw and fuel ledgers, and energy metering reports. Data update cycles are divided into three categories: real-time (equipment sensor and DCS data), per shift (equipment patrol records and kiln operation logs), and daily (laboratory reports and quality control notices). Daily documents include raw meal batching plans, kiln operation logs, clinker strength test reports, equipment inspection sheets, and energy consumption reports. Fields include raw and fuel composition values, equipment operating parameters, and quality indicator readings. There is no standardized document structure with a fixed, unified format.
What Constraints These Characteristics Impose on Vector Models and Indexing
Multi-source heterogeneous data sources require indexes to support mixed import of structured and semi-structured data, to avoid losing field associations from raw and fuel ledgers. Real-time updated equipment sensor data needs an incremental indexing strategy to avoid the time cost of full indexing. Documents with different update frequencies need corresponding index refresh cycles to ensure abnormal reports can be retrieved in a timely manner. Document fields include key quality indicators and thermal parameters. When chunking, field context must be retained to prevent loss of critical associated information after splitting, ensuring accurate recall of abnormal reports.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-section content length of raw meal batching plans and kiln operation records, avoiding splitting across key parameters |
| Overlap Length | 100–150 characters | Retains field associations between adjacent chunks, such as the connection of front and rear parameters in batching plans |
| Similarity Threshold | 0.72–0.80 | Matches the recall accuracy for key indicators such as cement strength and thermal control systems, adapting to feature matching for abnormal reports |
| Single Search Limit | Top 8 entries | Covers the typical number of entries in per-shift patrol records and daily laboratory reports, avoiding redundant recall |
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. |
defaultToken | 1024 | Basic output length when drafting process documents, matching the typical length of a single report |
Three Common Errors
- Phenomenon: Indexing takes significantly longer than expected, with the status showing "processing" for more than 30 minutes. Cause: No incremental indexing strategy configured for real-time updated DCS data, and full indexing scanned all historical data.
- Phenomenon: No corresponding ollama qwen2.5 option appears in the text understanding model dropdown when creating a knowledge base. Cause: The model channel's language model was not linked to the knowledge base's text understanding link, only the indexing model was configured.
- Phenomenon: Key abnormal report entries are missing from recall results, and the number of results is far lower than expected. Cause: Chunk length was set too small, splitting paragraphs containing equipment patrol abnormal descriptions into multiple unrelated fragments, causing similarity matching to fail.
How to Verify the Configuration Is Correct
- Perform a small-batch test index, select 10 typical raw meal batching plans and abnormal reports, and check whether the index completion time meets business expectations.
- On the knowledge base's model configuration page, confirm that the added ollama qwen2.5 and bge-m3 models are displayed in the corresponding dropdown menus.
- Enter an abnormal report keyword, and check whether the number and content matching degree of the recall results meet the needs of the business scenario.
- View the index log to confirm that the incremental index only processed documents updated later than the last index, and did not repeatedly scan full historical data.
The values listed above are common starting points, and should be evaluated against the reader's own samples.