What the data for this category looks like
The data sources for the condiment category mainly include batch quality inspection reports issued by production enterprises, dealer inventory ledgers, offline supermarket POS sales data, and publicly available monitoring data from industry associations. Data update frequencies vary by source: quality inspection reports are released with production batches, inventory data is updated daily, and industry monitoring data is updated monthly. A single due diligence document typically includes batch numbers, raw material ratios, physical and chemical test indicators such as amino acid nitrogen and sodium chloride content, production dates, shelf lives, production addresses, and dealer filing information. The fields fall into three categories: string-type category names, numeric test values with units like g/100ml and mg/kg, and date-type timestamps.
What constraints these characteristics impose on vector models and indexing
Multi-source heterogeneous data with varying update frequencies requires the indexing system to support batch incremental writes. This avoids resource consumption from full index rebuilds. Fields include numeric test items with attached units. Vector models must retain the binding semantics of fields and units, to prevent confusion between similar test values with different units. Single due diligence documents contain associated fields; for example, batch numbers appear in both quality inspection reports and inventory ledgers. When splitting indexes, the association between fields must be preserved, to avoid losing contextual connections after splitting. Some documents are in structured table format. The system must support vector extraction of table content, rather than limiting processing to plain text only.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the mixed content of structured tables and long-text test reports in condiment due diligence documents, avoiding loss of field associations after splitting |
chunk_overlap | 100–150 characters | Retains associated fields such as batch numbers and test indicators between adjacent segments, preventing contextual breaks |
recall_top_k | Top 8–12 results | Matches the multi-source association query requirements of condiment due diligence data, balancing recall accuracy and response speed |
similarity_threshold | 0.72–0.78 | Distinguishes similar semantic content across different batches and test indicators, avoiding confusion across batches |
incremental_index_enable | Enabled | Adapts to daily updated dealer inventory data, reducing resource usage from full index rebuilds |
embedding_model | Calibrated via actual testing | Adapts to numeric fields with units and structured tables, prioritize models that support multimodal encoding |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test using samples specific to the deployment before finalizing settings.
Three common mistakes
- Symptom: A
401 Unauthorizederror is returned when calling the vector model, or the interface prompts "No available channels". Cause: The API key for the vector model is not configured correctly, or the model corresponding to the key is not authorized under the currentdefaultgroup. - Symptom: Irrelevant test data across batches appears in vector recall results, and the number of recall results does not match the configured
recall_top_kvalue. Cause: Thechunk_overlapconfiguration is not enabled, or the segment length is set too small, causing associated fields to lose contextual connections after splitting. - Symptom: Old batch test data is not synchronized for update after incremental updates. Cause: The
incremental_index_enableswitch is not configured correctly, or the incremental update trigger condition is not bound to the batch number field.
How to confirm the configuration is complete
- The vector model configuration page is accessed, and the
embedding_modelis verified to match the currently used model provider. The API key is confirmed to be correctly filled in. - A condiment batch quality inspection report is uploaded to trigger a vector embedding task. The segmentation results are checked to confirm retention of the association between batch numbers and corresponding test indicators.
- A due diligence-related query is initiated. The number of recall results is verified to match the configured
recall_top_kvalue, and similarity scores fall within the preset range. - New inventory data is added to trigger incremental indexing. The indexing task logs are checked to confirm only new data was processed, with no full index rebuild performed.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.