What This Category of Data Looks Like
This category of data originates from customer color swatches, formula libraries, dye chemical batch records, dye vat operation logs, colorimeter outputs, setting machine parameters, and environmental protection monitoring ledgers. Daily documents include dyeing formulas, chemical material weighing records, dye vat heating and heat preservation curves, proofing confirmation slips, color difference reports, setting process sheets, and sewage discharge ledgers. Data updates follow a per-vat, per-batch, per-shift schedule. Individual documents contain fields such as color difference values, heating rates, feeding quantities, and heat preservation durations, with units including degrees Celsius, kilograms, minutes, and other standard units.
Constraints Imposed by These Characteristics on Vector Models and Indexes
The process data for this category consists of batch-oriented, highly time-series wet chemical processing records. Updates occur frequently, and individual documents contain multi-dimensional process parameters. These traits require indexes to support incremental updates to accommodate rapid data synchronization per vat and per batch. The chunking strategy must balance compatibility with long time-series curves and short parameter documents. Vector embedding accuracy must be maintained to meet the high precision requirements for color matching and formula scaling. Additionally, mixed indexing of multi-source heterogeneous data must support filtering and recall by document type, to prevent irrelevant data from compromising the accuracy of process Q&A.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the content length of individual documents such as dye vat curves and dyeing formulas, avoiding exceeding token limits for single chunks |
| Overlap Length | 100–150 characters | Retains process-related information across chunks, such as continuous node data for heating curves |
| Similarity Threshold | 0.75–0.85 | Matches the accuracy requirements for dyeing and finishing process parameters, filtering low-correlation historical data |
| Single Search Limit | Top 6 results | Adapts to the number of associated parameters for a single vat process, balancing recall accuracy and response speed |
defaultToken | 4096 | Adapts to the default configuration of FastGPT v4.17.0, meeting the single-chunk token requirements for most dyeing and finishing documents |
maxToken | 8192 | Supports context stitching requirements for batch processing of multi-vat process data |
Three Common Errors
- Symptom: The interface prompts "No available channels". Cause: The index model was not added to the default group, causing the system to fail to recognize available vector model channels.
- Symptom: Vector embedding task times out. Cause: The chunk length was not adjusted for long time-series documents such as dye vat curves, causing single-chunk content to exceed token limits and embedding to fail.
- Symptom: Recall results deviate significantly from actual process parameters. Cause: The similarity threshold was set incorrectly. An overly high threshold filters valid parameters, while an overly low threshold introduces irrelevant historical data.
How to Confirm Correct Configuration
- Upload a typical dyeing formula document, and confirm that the chunk preview aligns with the set chunk length and overlap length requirements.
- Initiate a test Q&A targeting a color difference report, and check that the number of returned recall results matches the single search limit setting.
- Switch between different index models, and confirm that embedding completes normally and Q&A results consistent with process logic are returned.
- Review system operation logs, and confirm that no error messages such as vector embedding failures or unavailable channels are present.
The values listed here are common starting points, and should be evaluated against relevant samples.