What This Category of Data Entails
This category of data comes from daily production and management documents of electroplating plants, electronic connector and hardware surface treatment workshops. Documents include bath solution analysis records, process parameter cards, fixture records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Data sources include customer surface treatment specifications, bath solution test results, rectifier operation data, online water quality monitoring records, and hazardous waste management ledgers. Update frequency is per bath cycle, per shift, or per special monitoring requirements. Documents contain basic fields such as tank number, process parameters, test values, and ledger identification numbers.
Constraints on Vector Models and Indexes
The high-frequency update nature of this category of data requires indexes to support incremental synchronization. This avoids business delays caused by full index rebuilding. The coexistence of multiple document types requires chunking logic adapted to different document structures. Use fine-grained chunking for process parameter cards to retain parameter details. Use ledger cycle-based chunking for hazardous waste ledgers to ensure complete compliance data. The strong binding between data and bid quotations requires indexes to prioritize association with core fields such as process parameters and compliance requirements. This prevents irrelevant data from interfering with retrieval results. Data sources cover production and compliance scenarios. Indexes must cover retrieval needs for both process accuracy and compliance verification.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category of documents includes long-text process descriptions and short-text test values. This range balances semantic integrity for both types of content |
| Overlap Length | 100–150 characters | Frequently updated bath solution data has cross-chunk association logic. Overlap length preserves context coherence |
| Chat Model Context Limit | Follow the selected model's documented context limit | Bid documents combine process and compliance data. Validate prompt length with representative retrieval results and the deployed chat model. |
| Similarity Threshold | 0.72–0.78 | This category of process parameters has high precision requirements. This threshold filters low-match irrelevant data. Adjust based on actual measurements |
| Single Search Limit | Top 8 results | Bid quotations need to balance core process and compliance requirements. 8 results cover most associated data and avoid interference from redundant information |
| Embedding Model Input Limit | Follow the selected embedding model's documented input limit | Input length varies by model. Align chunk size with the deployed model instead of assuming a universal FastGPT vector-model default. |
Three Common Configuration Errors
- Issue: The image-indexing model option is missing when creating a knowledge base in a local deployment. Check whether the selected distribution supports image indexing and whether its required authorization and model files are configured.
- Issue: The number of index retrieval results does not match the single search limit configuration. Cause: The index recall rules are not correctly associated, or core fields are truncated during data chunking.
- Issue: A large number of irrelevant hazardous waste ledger data appear in retrieval results. Cause: The similarity threshold is set too low, and non-core documents with low match rates are not filtered.
How to Confirm Proper Configuration
- Upload a bath solution analysis record document. Confirm that chunked text fragments retain core fields such as tank number and pH value.
- Enter a process query term related to the bid. Verify that the number of retrieval results matches the single search limit configuration.
- Check the index update log. Confirm that frequently updated bath solution data can be synchronized at the expected frequency.
- Compare retrieval results under different thresholds. Confirm that the match rate of business-related data meets business requirements.
The values provided are common starting points and should be evaluated against samples from the specific deployment.