This document is compatible with FastGPT v4.17.0.
What this category's data looks like
Core data for this category comes from garment OEM/ODM factories, brand supply chains, and cutting and sewing workshops. Daily stored documents include process sheets, CAD patterns, layout plans, cutting orders, first sample seals, process quality sheets, and packaging and size matching sheets. Data sources cover order and size charts, surface and accessory inspection records, hanging system data, needle detector logs, and customer sealed samples. Update cycles adjust per order, per style, per batch. Single-batch data links to exclusive patterns, process requirements, and packaging ratios.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Data for this category updates dynamically per order, per style, per batch. Single-batch data binds to exclusive patterns, processes, and packaging requirements. Multi-turn dialogue must strictly limit context to only conversation content related to the current style number, to avoid interference from cross-batch data. Most document fields include precise identifiers such as size, color number, and process number. Prompts must clearly bind the current order’s style number and size range to ensure Q&A matches exclusive process requirements. Cut pieces are irreversible and processes are dense, so multi-turn dialogue must retain historical verification records. This prevents duplicate or conflicting process instructions and ensures process matching accuracy.
How to set the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current order style number, in the format order number-style number | Ensure multi-turn sessions only associate with the current batch’s process data, avoiding interference from cross-order data |
messages | Retain the first 3-5 conversation records | Processes for this category are dense. Historical dialogue must cover previous process verification results to avoid repeated questions or missing key constraints |
variables | Inject three custom variables: current style number, size range, and fabric type | Match fields of exclusive process sheets, layout plans, and other documents to improve Q&A accuracy |
| Maximum context length | 800-1200 characters | Document fields for this category are compact and closely linked. Overly long contexts dilute key process information, calibrated based on actual testing |
| Number of context entries for chat history extraction nodes | First 5 entries | Cover key information such as previous fabric inspection and process disassembly, meeting full requirements for process procedure Q&A |
Three common mistakes
- Phenomenon: After connecting a text content extraction node to the workflow and configuring the number of chat records, multi-turn dialogue cannot be triggered, and no error logs are generated. Cause: The
chatIdis not bound to the exclusive identifier of the current session, causing the node to fail to associate historical conversation records and only handle single-turn questions. - Phenomenon: When calling a multimodal model to process fabric pattern images, the prompt format is incorrect or the service hangs. Cause: The style number and order information associated with the image are not injected via
variables, so the model cannot match exclusive layout plans or process sheet data, leading to format verification failure. - Phenomenon: In an online dialogue scenario, Q&A requests remain in the knowledge base search state without a response. Cause: The number of context entries for
messagesis not limited, causing a large amount of historical data to flood in and cause context overflow, making matching impossible.
How to confirm the configuration is correct
- Launch a conversation that includes previous process verification results, verify that the model can automatically associate the process requirements of historical dialogue without repeated questions.
- Enter the current order’s style number and size range, confirm that the returned process procedure content matches the exclusive document fields of this style.
- Test multimodal image queries, confirm that the model can generate cutting process suggestions that meet the current style’s requirements based on the uploaded pattern images.
- Check the running logs of workflow nodes, confirm that no binding errors or overflow occur for chat records and context parameters.
The values provided here are common starting points and should be evaluated against the reader’s own samples.