What the data for this category looks like
Data sources for plastic and rubber due diligence reports include real-time quotes from commodity trading platforms, monthly statistics from industry associations, outgoing ledgers from production factories, and third-party compliance test reports. Update rhythms vary across sources: trading data updates daily, industry statistics data updates weekly or monthly, and compliance test data updates with each inspection batch. Document structures are mostly multi-page composite formats, including basic parameter pages, transaction detail pages, and compliance test pages. Exclusive fields include brand number, density, melt index, and tensile strength, with corresponding units: no unit, g/cm³, g/10min, and MPa respectively.
What constraints these characteristics impose on the model access and configuration link
Dispersed data sources and inconsistent update rhythms require configuring multi-source data synchronization trigger rules to avoid duplicate or delayed loading. The multi-page composite document structure requires configuring segmented parsing parameters during model access to ensure exclusive fields on different pages are correctly identified. The fixed units of exclusive fields require adding unit verification prompt configurations during model access to prevent unit errors in output results. The presence of a large number of numeric fields requires adjusting batch processing parameters for embedding models to balance parsing efficiency and memory usage.
How to set the configurations
| Configuration Item | Recommended Value | Basis for This Value |
|---|---|---|
maxContext | 8000–12000 characters | Plastic and rubber due diligence reports include multi-dimensional exclusive fields and multi-page attachments; overly long context will cause the model to truncate critical compliance data |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | A single due diligence report may include multiple attachments such as test reports and customs documents, resulting in long parsing time |
EMBEDDING_BATCH_SIZE | 16–32 | Plastic and rubber data contains a large number of numeric fields; batch embedding requires balancing memory usage and processing speed |
SIMILARITY_THRESHOLD | 0.72–0.85 | It is necessary to distinguish parameter differences of plastic and rubber products with the same brand number but different batches; a threshold that is too low will introduce irrelevant data, while a threshold that is too high will miss valid information |
RECALL_TOP_K | Top 6–10 entries | Due diligence reports need to cover multiple types of data including transaction, production, and compliance; too few recalled entries will lose critical dimensions |
PROMPT_TEMPLATE | "Based on the provided plastic and rubber industry data, generate a compliant due diligence report, and strictly match the field units, such as density unit g/cm³, melt index unit g/10min" | It is necessary to clarify the model's verification requirements for the units of category-exclusive fields to avoid unit errors in output |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration mistakes
- The symptom is that the model call returns "No available channel for model whisper-1 under current group default". The cause is that the available channel for the corresponding model is not bound in the model group configuration, or the channel call quota has been exhausted.
- The symptom is that the generated TTS broadcast content has incorrect terminology pronunciation, or the specified voice cannot be loaded. The cause is that the voices parameter does not match the pronunciation rules of plastic and rubber industry exclusive terms, or the parameter format does not meet the model requirements.
- The symptom is that API logs show continuous receiving of call requests at night, and the account balance is quickly consumed. The cause is that API call frequency limits and reasonable cycles for scheduled tasks are not configured, resulting in invalid calls during non-working hours.
How to confirm the configuration is complete
- Upload a single plastic and rubber due diligence report attachment, check if the parsed fields include exclusive fields such as brand number, density, melt index, and that the units match the category requirements.
- Trigger a single model call, check if the returned results follow the configured similarity threshold and number of recalled entries, with no redundant or missing valid data.
- View the API call logs, confirm that the call frequency complies with the configured restriction rules, and there are no abnormal high-frequency requests at night.
- After modifying the configuration parameters, compare the model output results before and after to confirm that the parameter adjustment has the expected impact on the output content.
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.