What Data for This Category Looks Like
Typical data for this category comes from ERP/WMS systems, barcode/RFID equipment, production work orders, storage location records, AGV transport systems, and weighing equipment. Daily generated documents include receiving slips, putaway slips, material picking and return slips, inventory transfer slips, inventory count sheets, lot ledgers, first-in first-out records, and expiration date records. Data update frequencies are typically real-time (such as AGV operation data), daily (such as material picking and return records), and monthly inventory counts (such as inventory ledgers). Documents include fields such as lot number, storage location code, material code, expiration date, and weighing values, with units mostly being pieces, kilograms, days, and similar units.
Constraints on Vector Models and Indexes
The data for this category comes from scattered sources and has widely varying update rhythms. This requires indexes to support incremental updates and resumable uploads, to avoid resource waste caused by repeated full index execution. Core retrieval dimensions include lot, storage location, and material status. Chunk processing must retain these key metadata fields to prevent semantic association breaks. Long documents such as monthly inventory count sheets have large spans, so appropriate chunk granularity is required to ensure complete semantic integrity. Real-time updated equipment data requires index refresh frequencies to match business rhythms; otherwise, retrieval results will lag behind actual inventory status.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide range of document lengths in this category, from short texts like receiving slips to long texts like inventory count sheets, to ensure semantic integrity |
| Overlap Length | 50–100 characters | Retains key cross-chunk context such as lot numbers and storage locations, to avoid breaks in core retrieval information |
defaultToken | 2048 | Matches the average semantic unit length of documents in this category, to prevent truncation of key fields such as lot numbers and expiration dates |
maxToken | 4096 | Processes long documents containing multiple lot ledgers, to ensure complete semantic content is not truncated |
| Similarity Threshold | 0.75–0.85 | Differentiates retrieval results for similar material lots, to reduce mismatching risks |
| Single Search Limit | Top 10 results | Covers retrieval needs for multiple storage locations and lots in in-plant logistics scenarios, to facilitate manual verification |
Three Common Mistakes
- Issue: After a server crashes and restarts due to uploading a large number of inventory count sheets, the knowledge base status gets stuck and unresponsive, and automatic index completion fails. Cause: Incremental index trigger rules are not configured, full index tasks do not automatically recover after restart, and resumable upload parameters are not set.
- Issue: After updating FastGPT to v4.17.0, the original warehousing knowledge base cannot recall lot-related retrieval results. Cause: The chunk configuration of the old version index does not retain lot field metadata, and the new version vector model has adjusted metadata matching logic.
- Issue: When retrieving inspection questions for AGV transport systems, returned results include irrelevant finished goods warehouse inventory content. Cause: The similarity threshold is set too low, and document fragments from non-target scenarios are not filtered out.
How to Confirm Proper Configuration
- Upload a single lot ledger document, check whether the index generation progress bar completes normally with no error prompts.
- Initiate a retrieval containing the keywords "lot number" and "storage location", verify whether the returned results include document fragments from this category.
- Simulate a server restart operation, check whether the knowledge base automatically recovers index tasks without manual triggering.
- Adjust the similarity threshold, compare changes in the number of retrieval results, and confirm that the threshold matches business needs.
The values provided are common starting points and should be measured against the reader's own samples.