What Data for This Category Looks Like
This category typically includes raw material warehouses, line-side warehouses, finished product warehouses, and logistics material control departments. Daily generated documents include receiving orders, putaway orders, material picking and return orders, warehouse transfer orders, inventory checklists, lot ledgers, and expiration date records. Data comes primarily from ERP/WMS systems, barcode or RFID devices, production work orders, warehouse location codes, AGV operation logs, and weighing data. Update frequencies include real-time, daily synchronization, and monthly inventory counts. Most data is structured or semi-structured, with unified core identifier fields: lot number, warehouse location code, material code, inspection status, expiration date, weight, and similar content.
Constraints Imposed on Vector Models and Indexes
The data characteristics of this category create multiple constraints for the vector model and index workflow. First, multi-source heterogeneous structured and semi-structured data formats require indexes to support filtered retrieval of fields like lot numbers and warehouse location codes. Core metadata must be retained to ensure retrieval accuracy. Second, mixed update rhythms—real-time, daily synchronization, and monthly inventory counts—require index configurations to support both incremental refresh and full reconstruction. This prevents inconsistencies between book and physical data. Third, core business focuses on lot flow and book-physical consistency. Vector retrieval recall results must directly link to warehouse process documents, and prioritize material information related to lot flow and book-physical consistency.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of warehouse documents such as lot ledgers and material picking/return orders, avoids splitting lot-related information, and aligns with the chunking recommendation rules of FastGPT v4.17.0 |
| Overlap Length | 100–150 characters | Retains cross-chunk core metadata such as lot numbers and warehouse location codes, ensuring that material lot flow can still be fully associated after chunking |
| Similarity Threshold | 0.75–0.85 | Accurately matches core identifiers such as lots and warehouse locations, avoids incorrectly recalling irrelevant warehouse data, and meets the high accuracy requirements for quality traceability |
| Single Search Limit | Top 10 results | Quickly locates warehouse documents associated with a lot, reducing redundant information for subsequent manual analysis |
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 | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
Three Common Misconfigurations
- Symptom: Vector retrieval returns empty results, and no warehouse documents for the corresponding lot can be found. Cause: Core fields such as lot numbers and warehouse location codes were not retained during chunking, preventing vectors from associating with key identifiers in warehouse scenarios.
- Symptom: An embedding request reports an input-token limit error. Check the selected embedding model/service limit and inspect the individual input sent to it. For LLM context-limit errors, inspect retrieved text, prompt, history, and the selected LLM context window separately; embedding
maxTokenis not the total context limit. - Symptom: Locally deployed vector model cannot index normally. Cause: The API address and port of the vector model were not correctly configured in FastGPT, or the remote access permission of the model was not enabled.
How to Verify Correct Configuration
- Upload a single lot ledger document, enter a known lot number for retrieval, and check whether the returned results include all associated documents for the corresponding lot.
- Compare the configured embedding-model
maxTokenwith the selected model/service documentation, then review call logs for rejected or oversized inputs. - Simulate a book-physical difference scenario, retrieve material data for the corresponding warehouse location, and confirm that the number of retrieval results matches the single search limit setting.
- Test real-time updated AGV operation logs, confirm that the data can be indexed and retrieved within the preset time after submission.
The values listed here are common starting points. Adjust them based on your own sample data.