What Data Looks Like for This Category
Typical entities include garment OEM/ODM manufacturers, brand supply chains, and cutting and sewing workshops. Daily documents include process sheets, patterns, layout plans, cutting orders, first sample seals, process quality sheets, and packaging size matching sheets. Data sources include orders and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector records, and customer sample seals. Update frequency follows per order, per style, per batch. Core fields include size specifications, fabric color batches, cutting piece matching relationships, process quality inspection results, and packaging ratio.
Constraints Imposed by These Characteristics on Source Citation and Traceability
This category has a wide variety of styles and strong seasonality. Cutting pieces are irreversible, and manual processes are labor-intensive. Traceability requires precise binding of exclusive data for individual orders, individual styles, and individual batches. General documents cannot be mixed and matched. Daily document types are diverse and updated frequently. Knowledge bases must be managed separately by order dimension, to avoid retrieving invalid data across batches. Core fields such as size, color batch, and cutting piece matching relationships must fully match the original documents. Otherwise, inspection and audit errors will occur. Multi-source data requires cross-verification to ensure consistency of traceability information.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Suggested Value | Basis for This Setting |
|---|---|---|
quoteMaxToken | 800–1200 token | The effective content length of core documents such as garment process sheets and layout plans mostly falls within this range, covering key information required for traceability |
responseData | Enabled | Metadata of cited documents such as order numbers, cutting piece batches, and process numbers must be returned to facilitate inspection personnel's verification and traceability |
detail | Enabled | Specific content of cited fragments must be displayed, such as appearance descriptions of first sample seals and cutting piece quantities on cutting orders, to ensure accurate audit basis |
| Maximum Context Rounds | First 3 rounds of conversation | Garment processing procedures are sequential. Previous cutting and sewing records can be used as reference for current inspection, avoiding repeated questions |
| Maximum Number of Citations | Top 3 entries | Core inspection documents for a single order usually do not exceed 3. Limiting the number avoids redundant information interfering with audit judgments |
Three Common Misconfigurations
- Unable to select knowledge base citations in node configuration: The cause is that only a general document library is bound, not a garment processing-specific knowledge base categorized by order or style.
- Output only shows citation list without body content: The cause is that the prompt word only configures retrieval logic, with no instruction to generate reply content, resulting in only retrieval results being returned.
- Unable to associate multi-turn conversation context: The cause is that context call configuration is not enabled, or the context round limit is set to 0, making it impossible to call process records from previous conversations.
How to Verify Successful Configuration
- Upload a garment process sheet to the corresponding knowledge base, send a test request, and check if the retrieval result includes the document's metadata.
- Send a test request containing cutting process descriptions, and check if the returned result includes cited fragments within the
quoteMaxTokenlimit. - Send two test conversations, and verify if the second round can associate the cutting piece batch information mentioned in the first round.
- Check the
responseDatafield in the returned result, and confirm that the metadata of the cited document has been correctly returned.
The values provided here are common starting points, and should be evaluated against the user's own samples.