What Data Looks Like for This Category
This category’s data typically originates from ERP, WMS, barcode/RFID equipment, purchase work orders, and AGV conveyance systems. Daily documents include receiving slips, putaway slips, material picking and return slips, lot ledgers, and expiration date records. Covered fields include material code, lot number, storage location number, inspection status, arrival quantity, and expiration date. Data updates occur at three frequencies: real-time (such as AGV position updates and scanned warehouse intake), daily (such as inventory changes), and monthly inventory reconciliation updates.
Constraints for Vector Models and Indexes
Multiple heterogeneous data sources and varied update frequencies require indexes to support incremental synchronization and compatibility with multiple data sources, to avoid performance loss from full reindexing. Document fields include strongly linked business identifiers such as lot number, expiration date, and storage location. Vector chunking must retain semantic integrity of core fields to prevent loss of material identity association information during retrieval. Real-time scanned warehouse intake data triggers high-frequency index writes, so the system must adapt to low-latency index update logic. Full data updates from monthly inventory reconciliations must not disrupt daily retrieval services.
Configuration Settings
This configuration is adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the field density of documents such as lot ledgers and receiving slips, avoiding loss of key business association information in individual chunks |
| Overlap Length | 100–200 characters | Retains continuous semantics across chunks such as lot number and expiration date, preventing broken association logic during retrieval |
defaultToken | 1024 token | Carries the text content of a single material picking or return slip, meeting context requirements for basic Q&A |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Distinguishes similar codes and valid matches for material lots, preventing error prevention failure caused by mismatching |
| Single Search Limit | Top 10 results | Covers multi-material lot information required for work order kitting and delivery, avoiding missing associated materials |
maxToken | 8000 | Carries long context including multiple lot ledgers and storage location information, supporting complex process specification Q&A requirements |
Three Common Misconfigurations
- Interface prompts indicate vector access failure, and checking API key configuration shows no available channels. The cause is failure to configure a dedicated group for multi-source data in warehousing scenarios, and the default group does not have call permissions for the corresponding embedding model.
- Retrieval results show lot number mismatching, returning other materials with similar codes. The cause is a similarity threshold set too low, which cannot distinguish highly similar material codes and lot identifiers.
- Q&A response times out, and re-ranked retrieval takes too long. The cause is an excessively large chunk length, leading to vector calculation volume for a single lot context exceeding the system's carrying limit, or an excessively high single search limit.
How to Verify Proper Configuration
- Import a single lot ledger document, check the vector generation status, and confirm no field loss or chunking anomalies.
- Input questions such as "Find the lot expiration dates for materials required for the current work order", and verify that returned lot number and storage location information matches the work order.
- Simulate incremental data synchronization for real-time scanned warehouse intake, and check that index updates complete within the preset time frame.
- Adjust the similarity threshold, verify retrieval result matching rates across different thresholds, and confirm alignment with business error prevention requirements.
The values provided are common starting points, and should be measured against the reader's own samples.