What the data for this category looks like
Data for the warehousing, lot tracking and in-plant logistics scenario comes from ERP/WMS systems, barcode or RFID collection devices, purchase work orders, storage location archives, AGV transport systems and weighing equipment. Daily generated documents include receiving slips, putaway slips, material issue and return slips, inventory transfer slips, inventory count forms, lot ledgers, first-in first-out and expiration date control records. Data update frequencies cover three categories: real-time (such as AGV scheduling data), daily (such as material issue and return statistics) and monthly (such as full physical and book inventory reconciliation). Core fields include material code, lot number, storage location number, expiration date and inspection status. There is no fixed unified unit, and it varies according to material type and management requirements.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The data sources for this scenario are scattered and update cycles vary significantly. Multi-turn dialogue must sequentially distinguish real-time scheduling data from historical ledger information to avoid confusing material status across different time dimensions. Core documents include sensitive control fields such as lot numbers and expiration dates. Prompts must clearly specify extraction scope to prevent irrelevant information from interfering with bid document and quotation generation. Consistent physical and book inventory and lot traceability are core management goals. The dialogue flow must verify the matching between material identity and lot number to ensure the generated bid content aligns with actual warehousing status.
Configuration Recommendations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the single-page content length of documents such as material issue and return slips and lot ledgers, avoids breaking field integrity across document chunks, and complies with FastGPT v4.17.0 document chunking rules |
| Single Search Limit | Top 5 entries | Limits returned warehousing data entries to prevent excessive historical data from interfering with material verification for current bid quotations |
| Context Retention Duration | 600 seconds | Covers the full dialogue cycle of bid document preparation, ensuring key information such as lot numbers and storage locations is not lost during multi-round communication |
| Knowledge Base Association Scope | Within 1000 MB | Limits the size of associated warehousing documents to avoid loading redundant data and affecting dialogue response speed |
variables | Predefined list of material fields | Pre-defines core fields to be extracted such as material code and lot number, unifies extraction standards for multi-turn dialogue |
messages | Store historical interactions per session | Retains material verification records for each dialogue round, ensuring correct association of lot and storage location information during subsequent quotation generation |
Three Common Misconfigurations
- Phenomenon: When calling text content extraction during dialogue, core fields such as lot number and storage location number cannot be identified, and empty values are returned. Cause: The prompt fails to clearly specify extraction of specified fields from warehousing documents, and does not associate the document template for this category.
- Phenomenon: The model output language does not match the preset English prompt, and only returns Chinese content. Cause: No language constraint parameter is configured in
variables, or the knowledge base does not correctly associate English warehousing documents, causing the model to prioritize Chinese content from training data for generation. - Phenomenon: Parsing errors are triggered after uploading inventory count forms. Cause: Dedicated text extraction rules are not configured for category-specific documents such as material issue and return slips and lot ledgers, and the general chunking logic damages the table field structure of inventory count forms.
How to Verify Proper Configuration
- Initiate a test dialogue, input "Generate bid quotation documents for XX work order", check whether the dialogue process sequentially confirms material code, lot number and storage location information, and whether interaction records are fully retained in
messages. - Trigger the text extraction function, upload a simulated receiving slip, confirm that core fields are completely extracted without omissions, and verify the adaptability of chunk length and document structure.
- Configure an English prompt and initiate a dialogue, confirm that the model output language meets preset requirements, and verify that the language constraint parameter in
variablestakes effect. - View the session log, confirm that
chatIdis correctly associated with the current session, and that the context retention duration covers the full bid document preparation process.
The values listed above are common starting points and should be evaluated against the reader's own samples.