What Data Looks Like for This Category
Data sources for this category include online composition analysis systems, DCS distributed control systems, laboratory test data, kiln and grinding equipment operation logs, raw fuel ledgers, and energy metering reports. Data update frequencies cover real-time (such as kiln temperature, feed rate), shift-based (such as inspection records), and daily (such as test reports, quality notices).
Daily documents include raw material batching plans, kiln operation records, clinker test reports, equipment inspection sheets, and energy consumption reports. Most fields have clear units, such as temperature (℃), feed rate (t/h), and compressive strength (MPa). Some ledgers include material batch and incoming time identifiers.
Constraints on Vector Model and Indexing Workflow
Multiple data sources, varied update frequencies, and diverse document structures create multiple constraints for vector indexing. Real-time DCS and online composition data require incremental indexing to avoid excessive system resource usage from full reindexing. Shift-based equipment inspection sheets and daily test reports or quality notices need tiered indexing strategies configured by update frequency to ensure retrieval timeliness.
Documents include structured fields (such as equipment ID, temperature readings, material batches) and unstructured operation descriptions. Text splitting must retain the binding relationship between fields and their content to avoid losing critical associated information after splitting. Additionally, associations between raw fuel ledgers and kiln or grinding operation data must be completed during indexing to ensure maintenance Q&A can match equipment issues under corresponding working conditions.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of raw material batching plans and equipment inspection sheets for this category, prevents chunk loss |
| Overlap Length | 100–150 characters | Retains contextual association for continuous working condition descriptions, fits long-text scenarios like kiln operation records |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Matches the accuracy of fault descriptions and historical maintenance records, adapts to semantic differences in equipment issues for this category |
| Single Search Limit | Top 6–10 results | Covers the typical volume of associated data for this category, avoids interference from redundant information |
| Maximum Context | 3000–4000 characters | Meets the context length requirements for equipment inspection Q&A, aligns with the default adaptation range of FastGPT v4.17.0 |
| Maximum Tokens per Chunk | 8192 | Fits embedding requirements for long documents in this category, such as complete kiln system operation logs |
| Default Embedding Tokens | 2048 | Fits embedding requirements for routine inspection sheets and test reports, balances accuracy and resource usage |
Three Common Configuration Mistakes
- Issue: Setting chunk length to 3000 characters results in lost text blocks for some long documents. Cause: No reasonable overlap length configured, leading to missing critical start and end information during long text splitting, or exceeding the maximum embedding limit for system chunks.
- Issue: After batch uploading daily test reports and energy reports, the knowledge base indexing status remains stuck in "indexing" with no progress. Cause: No tiered indexing strategy configured based on document update frequency, full indexing tasks consume excessive resources, and no reasonable task sharding rules are set.
- Issue: Unable to generate document embedding vectors normally when configuring the
m3evector model. Cause: Incorrect configuration of the vector model call address, confusing path formats for different proxy services, leading to service connection failures.
How to Confirm Proper Configuration
- Upload a single typical document, such as a complete kiln operation record, and check that the number of split text blocks matches the expected document length to confirm chunking and overlap configurations are active.
- Trigger a batch indexing task, view indexing progress in system logs to confirm that tasks execute according to configured rules with no prolonged stagnation.
- Submit a test question related to equipment inspection, verify that the number of retrieved results falls within the configured single search limit range to confirm the similarity threshold and search limit are active.
- Verify the vector model call link: After uploading a small document, check embedding generation logs to confirm normal service connection with no errors.
The values provided are common starting points and should be measured against the reader's own samples.