What the data for this use case looks like
Data sources include engineering orders, sand and gravel raw material inspection records, mixing station operation control systems, and project laboratory test reports. Daily generated documents include mix proportion notices, opening identification forms, production and transport orders, raw material inspection sheets, test block records, and strength reports. Data updates are generated on a per-vehicle production, per-batch raw material delivery, and daily summary schedule. Fields include production batch number, raw material model, moisture content measurement value, test block compressive strength value, slump parameter, and others. Data is maintained jointly by mixing stations, project laboratories, and quality scheduling departments.
What constraints these characteristics impose on vector models and indexes
High-frequency small-batch data writes require indexes to support real-time incremental updates. This avoids time costs from full index reconstruction. Multi-source, multi-format data requires unified field mapping rules. This ensures consistency during embedding vector extraction, preventing misalignment of associated information. Single data volume is small, but overall data volume grows rapidly with production scale. Index strategies adapted for small-batch high-frequency writes are required. Lagging test data such as test block strength reports must be linked to corresponding production batches and transport links. Indexes must support cross-document multi-field combined retrieval to quickly trace the source links of quality problems.
How to set configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of single documents such as mix proportion notices and test block records, avoiding information fragmentation within chunks |
| Overlap Length | 100–200 characters | Covers cross-chunk associated fields like production batch numbers and raw material IDs, preventing key information from being truncated |
maxToken | 8192 token | Adapts to the input limit of mainstream embedding models, avoiding data truncation and loss of core production parameters |
defaultToken | Set based on actual measurement | Used to handle short fields without clear embedding requirements, adapting to differences in field lengths across data sources |
| Single Recall Limit | Top 20 entries | Matches the volume of historical data that needs to be linked across production, transport, and inspection links during quality traceability |
| Similarity Threshold | 0.75–0.85 | Distinguishes between same-batch production and different-batch data with similar parameters, preventing false associations |
Three Common Misconfigurations
- Phenomenon: When using chunk mode to call the pushdata API to upload data, the interface remains in the "indexing" state for a long time with no progress updates. Cause: No reasonable chunk length and overlap length are configured, causing single chunk data to exceed the embedding model's input limit, blocking embedding tasks.
- Phenomenon: An error is returned when calling the embedding model, prompting that the token does not have permission to use the target model. Cause: Model call permissions are not configured correctly, or a commercial embedding model not enabled under the current account is selected.
- Phenomenon: Retrieval results only return a small amount of relevant data, failing to cover the complete quality traceability chain. Cause: The single recall limit is set too low, not matching the retrieval requirements for multi-link associated data in ready-mixed concrete production.
How to Confirm Correct Configuration
- Upload a single mix proportion notice document, check if the indexing progress completes within a reasonable time frame with no long-term blocking.
- Enter search terms such as slump parameters and production batch numbers, verify that the associated fields of returned results match the business logic of ready-mixed concrete production.
- Check the embedding model call logs, confirm that no token permission errors or input length limit prompts appear.
- After adjusting chunk length and overlap length parameters, compare the completeness of retrieval results to confirm that associated information is not truncated.
The values provided are common starting points and should be evaluated against the reader's own samples.