What This Category’s Data Looks Like
The primary entities in this category are garment OEM/ODM factories and brand supply chain cutting and sewing workshops. Data sources include order and size charts, CAD patterns, raw and auxiliary material inspection records, hanging system operation data, needle detector logs, first sample sealing documents, and more. Daily generated documents include process sheets, layout plans, cutting orders, process quality sheets, packaging and size matching sheets, and others. Fields include piece number, batch number, process number, size specification, fabric gram weight, and more. Updates occur once per order, per style, per batch. Process documents for a single garment style are numerous and scattered.
Constraints Imposed on Knowledge Base Retrieval and Recall
This category has a large number of garment styles and strong seasonality, with high data update frequency. Knowledge bases must support high-frequency incremental updates and fast switching of batch data. Cutting pieces are irreversible and involve intensive manual work. Retrieval must accurately match unique identifiers such as piece number and batch number to avoid incorrect cross-batch or cross-style associations. Process documents for a single style are scattered across multiple materials. Retrieval must recall multiple relevant sections while controlling result length to stay within model context limits. The industry uses numerous abbreviations and specialized technical terms. Retrieval must balance structured field matching and semantic understanding.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | This category requires high matching precision for process descriptions and cutting piece features. This range filters irrelevant cross-style and cross-batch content, and can be fine-tuned based on actual measurements. |
limit | Top 8–12 results | Process documents for a single garment style are scattered across 8–12 sections. This value covers core process requirements and avoids excessive results exceeding context length limits. |
searchMode | mixedRecall | This category has both structured numbers for CAD patterns and unstructured text for process descriptions. Hybrid mode balances precise matching and semantic recall. |
usingReRank | Enabled | Document sections in this category have similar similarity scores. Reranking prioritizes higher-priority content such as first sample sealing documents and core processes. |
datasetSearchUsingExtensionQuery | Enabled | This category uses numerous industry abbreviations and specialized terms. Extended queries supplement associated process requirements not directly mentioned. |
collectionIds | Bind exclusive collections to order batches | Data in this category updates per order and batch. Exclusive collections avoid cross-batch data interfering with retrieval precision. |
Common Configuration Errors
- Symptom: Search returns the error
invalid configuration parameter name "hnsw.iter". Cause: A configuration item name not included in the official FastGPT parameter list was misused, leading to configuration verification failure. - Symptom: Search returns no results after uploading a PDF, only an error prompt. Cause:
searchModewas not correctly set to hybrid mode, or structured fields were not properly extracted from the uploaded PDF, causing both keyword retrieval and semantic recall to fail. - Symptom: Queries return incomplete results when the knowledge base is split into multiple sections, with only partial content returned. Cause:
datasetSearchUsingExtensionQuerywas not enabled, andlimitwas set too low, failing to cover scattered process sheet sections. - Symptom: No specified model appears in the text understanding model dropdown menu when creating a knowledge base. Cause: The indexing model was not correctly bound to the current dataset collection in model management, or the model channel was not configured as required.
How to Verify Correct Configuration
The process sheet and layout plan PDF for a single garment style are uploaded, a retrieval for "left sleeve cutting deviation" is executed, and the returned results are confirmed to include the process requirements for the corresponding process. The model management and knowledge base configuration pages are checked, the bound indexing model and language model are confirmed to appear in the corresponding dropdown menus, and the FastGPT v4.17.0 version identifier is confirmed to display at the top of the page. A single process sheet is split into multiple sections, a retrieval is executed, and the returned results are confirmed to cover all relevant sections without omissions. Cross-batch data retrieval is tested, and only document content from the current order batch is confirmed to be returned, with no cross-batch interference.
The values provided here are common starting points, and should be measured against the reader’s own samples.