What Data Looks Like for This Category
Data comes from dyeing and finishing mills and fabric enterprises’ dyeing, setting, laboratory, and quality departments. It covers documents including dyeing formulas, chemical dosing records, dye vat curves, proofing confirmations, color difference reports, setting process sheets, and sewage ledgers. Data sources include customer color samples, formula libraries, dye chemical batches, dye vat control equipment, colorimeters, setting machines, and environmental monitoring systems. Update frequency is per vat, per batch, or per shift. Each single data entry includes fields such as process parameters, test results, and batch numbers. Units include temperature, concentration, time, and grammage.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
High-frequency updates per vat or per batch require the retrieval and recall link to support incremental index synchronization. This prevents retrieval of expired dyeing formulas or dye vat parameters. Mixed multi-type documents covering process, testing, and compliance dimensions require parsing logic adapted to different fields. This ensures data like color difference reports and setting process sheets can be accurately split and associated. Each single data entry includes unit-bearing fields such as temperature, concentration, and batch number. Retrieval must retain field relevance to support process descriptions and quotation basis in bid documents. Environmental monitoring and sewage ledger data must be tied to compliance requirements. Recall results must match query needs across both process and compliance dimensions.
How to Set the Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Dyeing formula and other process data require high matching accuracy to avoid low-similarity irrelevant data from being included. FastGPT v4.17.0 optimizes matching logic for process data, and this range meets the needs of this category |
limit | Top 8 entries | Bid document and quotation preparation requires multi-dimensional process references, while controlling context length to avoid exceeding model input limits |
searchMode | embedding | Structured process data is more suitable for vector retrieval, which can accurately associate fields such as batch number and temperature, improving the relevance of retrieval results |
usingReRank | Enabled | When mixing multiple types of documents, reranking optimizes the sorting logic of retrieval results and filters out low-correlation non-target data such as sewage ledgers |
datasetSearchUsingExtensionQuery | Enabled | Bid queries often include composite needs of process + compliance. Extended queries can cover more associated data and improve retrieval comprehensiveness |
collectionIds | Exclusive collection IDs divided by department | Distinguish data from dyeing, setting, lab, and other departments to avoid irrelevant cross-department data being recalled. This ensures retrieval results fit the current business scenario |
Three Common Misconfigurations
- After calling the
pushDataAPI to batch upload dye vat curve and dyeing formula data, only a small number of entries are successfully indexed. The upload parameters do not follow the API’s specified per-batch data limit. Exceeding the allowed number of data groups per batch causes the API to fail to fully process the uploaded content. - External web links can normally extract text, but internal Confluence pages cannot be parsed to retrieve content. Internal access permissions or proxy rules are not configured. This prevents the retrieval module from connecting to the internal Confluence server and pulling page data.
- When retrieving process requirements related to bids, a large number of irrelevant historical test reports are returned. The
similaritythreshold is set too low, and the matching degree is not adjusted to match the accuracy requirements of dyeing and finishing category data. This causes irrelevant low-matching data to be recalled.
How to Verify Successful Configuration
- Check the index status of the knowledge base collection. Confirm that all documents updated per vat or per batch have completed incremental index synchronization.
- Initiate a test query containing dyeing formula keywords. Verify that the returned results include category-specific parameters such as batch number, temperature, and concentration.
- Call the
pushDataAPI to upload 200 test data groups per batch. Confirm that the API returns a successful status and the data can be retrieved normally. - Compare the sorting of retrieval results with
usingReRankenabled and disabled. Confirm that the reranking logic optimizes result relevance.
The values provided are common starting points and should be measured against the reader's own samples.