What the Data for This Category Looks Like
Daily documents for this category include process sheets, garment patterns, layout plans, cutting orders, first-piece sample seals, process quality sheets, and packaging and size matching lists. Data sources include order and size charts, CAD patterns, fabric and accessory inspection records, hanging system data, needle detector logs, and customer sample seals. Data is updated per order, per style, per batch. Different styles have notable differences in fit, structure, and technical requirements. Fields include size parameters, fabric specifications, process requirements, and packaging ratios, with common units including centimeters, gram weight, and piece count.
Constraints on Knowledge Base Retrieval and Recall
This category has high data update frequency and strong per-style customization requirements. The knowledge base must support high-frequency incremental updates to prevent retrieval of outdated process or size data. Document types cover unstructured process descriptions, structured size charts, and layout plans, so retrieval must support multi-format content. Given the large number of styles and strong seasonality, retrieval must accurately match the category and size range of the current bidding project to avoid retrieving irrelevant past batch data. Additionally, the irreversible nature of cut fabric pieces requires that retrieval results strictly correspond to fabric dye batches and cut piece matching requirements, to reduce error risks.
Configuration Settings
Adjust configurations in FastGPT v4.17.0 to match the characteristics of this category, as detailed below:
| Configuration Item | Recommended Range | Rationale | Corresponding Parameter |
|---|---|---|---|
| Similarity threshold | 0.75–0.85 | Higher matching accuracy is required for garment category process and size requirements to avoid low-relevance results being included in bidding document preparation. | similarity |
| Single search upper limit | 8–12 results | Balance retrieval comprehensiveness and context window usage, avoid excessive redundant content interfering with document writing. | limit |
| Extended query character upper limit | 800–1200 characters | When expanding style and size keywords for bidding projects, control the extended length to cover relevant technical terms. | datasetSearchUsingExtensionQuery |
| Search mode | Hybrid vector + keyword | Adapt to the retrieval needs of mixed structured size charts and unstructured process documents for this category. | searchMode |
| Reranking toggle | Enabled | Sort multi-style retrieval results by relevance to ensure core process and size requirements are prioritized. | usingReRank |
| Knowledge base collection ID | Bind to the dedicated knowledge base collection for the current bidding project | Isolate data from different projects to avoid retrieving irrelevant past batch content. | collectionIds |
| Knowledge base capacity upper limit | 10 GB | Adapt to the storage needs of multi-style documents for this category, avoid retrieval failures due to insufficient capacity. |
Three Common Misconfigurations
- Issue: Generated bidding documents do not include source identifiers for knowledge base documents, making it impossible to confirm whether content originates from the knowledge base. Cause: Knowledge base source tracing configuration is not enabled, or the prompt does not explicitly require source labeling.
- Issue: The number of results returned by a single knowledge base retrieval is far higher or lower than the preset single search upper limit, or a large number of past batch garment process documents irrelevant to the current bidding project are returned. Cause: The dedicated knowledge base collection for the current bidding project is not bound, or the
limitparameter is configured incorrectly leading to cross-category retrieval. - Issue: Retrieved process requirements or size data do not match the latest version of the current bidding project, or retrieval time exceeds business processing thresholds. Cause: Knowledge base data is not synchronized at the per-order and per-style update frequency, or
usingReRankis not enabled to optimize retrieval results.
How to Confirm Successful Configuration
- Access the knowledge base management interface in FastGPT v4.17.0, check whether the currently bound knowledge base collection is the dedicated collection for the current bidding project, and verify that the
collectionIdsparameter is configured correctly. - Initiate a simulated retrieval, enter core keywords for the bidding project (such as size and process requirements for a specific garment style), and verify that the relevance and quantity of returned results meet preset requirements.
- Check the source tracing identifiers of retrieval results, confirm that each retrieved piece of content is labeled with the document information of the source knowledge base, and verify that the source tracing configuration is effective.
- Check the knowledge base update log, confirm that the latest order and style data have been synchronized to the currently bound knowledge base collection, and verify that the incremental update configuration is correct.
The values provided here are common starting points and should be evaluated against the reader's own samples.