What Data Looks Like for This Category
This category’s data comes from customer color swatches, formula libraries, dye chemical batch records, dye vat control systems, colorimeters, setting machines, and environmental monitoring equipment. Daily documents include dyeing formulas, chemical weighing records, dye vat operation curves, proofing confirmation sheets, color difference reports, setting process sheets, and sewage ledgers. Data updates occur per vat, per batch, or per shift. Each single record includes fields such as process parameters, test results, and batch identifiers. Some parameters have clear units: temperature is measured in degrees Celsius, color difference in ΔE, and sewage concentration in mg/L.
Constraints on Vector Models and Indexes
This category has high-frequency, real-time updated data. Indexes must support incremental synchronization to avoid resource consumption from full reconstruction. This category has mixed structured and semi-structured documents of multiple types. Indexes must adapt to vector extraction logic for different fields. This avoids vector alignment deviation between unstructured text and structured parameters. Single vat data links to multiple related documents. For example, dye vat curves correspond to dyeing formulas, and color difference reports correspond to proofing records. Indexes must support cross-document associative recall. This ensures complete associated data can be retrieved during Q&A. Batch data requires unique identification. Indexes must perform precise indexing on batch identifier fields. This avoids recalling invalid data from non-corresponding batches.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category’s documents include long sections of dye vat curve data and process text. 800–1200 characters can fully cover a single segment of process logic while avoiding overly fragmented chunks |
| Overlap Length | 100–150 characters | Prevents process logic across chunks from being split, ensuring vector continuity for continuous data such as dye vat heating curves |
| Similarity Threshold | 0.72–0.80 | This category has high precision requirements for process parameters. A threshold that is too low will recall irrelevant batch data, while a threshold that is too high may miss valid associated formulas |
| Single Search Limit | Top 6 results | Data associated with a single vat typically includes 3-5 related documents. The top 6 results can cover all valid associated content while avoiding redundant recall |
maxToken | 4096 token | Q&A for this category requires integrating dye vat parameters, color difference reports, and process sheets. 4096 token can carry complete associated context |
defaultToken | 512 token | Used for context completion when processing user questions, ensuring basic questions can be parsed correctly |
Three Common Mistakes
- No search results after custom index configuration: The interface returns an empty result array, or a 200 status code with no matching data. The cause is that the batch identifier field was not added to the index mapping, making it impossible to associate process data for the corresponding vat run.
- Vector search timeout: The interface returns a 504 timeout error. The cause is that incremental indexing was not configured. Full index reconstruction occupies too many resources, making real-time queries unresponsive.
- Mixing general-purpose vector models: Search results have mismatched color difference parameter values. The cause is that a vector model adapted to industrial process parameters was not used, leading to vector representations of structured parameters that do not match actual business logic.
How to Confirm Proper Configuration
- Upload the dyeing formula and dye vat curve for a single vat run. Perform an inspection Q&A for the corresponding vat run. Verify that returned results include the complete process parameters for that vat run.
- Simulate batch data with delayed updates. Trigger incremental index synchronization. Verify that newly uploaded sewage ledgers can be retrieved quickly.
- Adjust the similarity threshold. Observe changes in the matching accuracy of search results. Confirm that the threshold falls within a reasonable range that meets business requirements.
- View the index monitoring panel in FastGPT v4.17.0. Confirm that incremental synchronization time is within an acceptable range. Check that there are no abnormal full reconstruction tasks.
The values given are common starting points and should be measured against the reader's own samples.