What the data for this category looks like
Marketing content data for plastics and rubber primarily comes from internal enterprise production batch records, raw material purchase ledgers, quality inspection reports, and public industry supply and demand dynamic documents. Update rhythms vary by module: production and purchase data updates alongside actual business operations, while industry documents update weekly. Document structures typically include four modules: basic attributes, production parameters, quality inspection indicators, and transaction information. Most fields include physical units, for example: density uses g/cm³, tensile strength uses MPa, and inventory uses tons.
What constraints these characteristics impose on deployment and upgrade
Fields with multiple physical units require configuring unit validation rules during deployment, to prevent parsing failures caused by mismatched input units. Dynamically updated production data requires adapting to newly added batch fields during upgrades; hardcoding fixed field lists is not permitted. Weekly updated industry documents require configuring scheduled pull task intervals during deployment to match the update frequency. Multi-module document structures require configuring module-specific recall rules during deployment, to prevent irrelevant content from being mixed into marketing materials.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_MAX_SIZE | 2000 MB | Single files for plastics and rubber quality inspection reports and production ledgers are typically large, so this setting must accommodate large file parsing needs |
maxContext | 8000–12000 characters | Individual plastics and rubber product documents contain multiple sets of physical parameters, so sufficient context is needed to hold complete field information |
Recall Count | Top 6 entries | Marketing content must accurately match product attributes queried by users; limiting recall count avoids interference from redundant information |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large production ledgers takes significant time, so extending the timeout prevents parsing interruptions |
Similarity Threshold | 0.75–0.85 | Accurate matching of product physical parameter characteristics is required to support precise recall for this category's content |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After sharing marketing content via a no-login window on the overseas SaaS version, the content cannot respond to queries normally. Cause: No cross-origin whitelist was configured to allow the no-login sharing domain, so requests are blocked.
- Symptom: A docker-deployed instance throws a token encoder error and restarts indefinitely. Cause: The permission scope of the API key was not configured correctly, so the encoder interface for the specified model cannot be called.
- Symptom: Parsed documents lack physical unit information. Cause: The unit recognition parsing switch was not enabled, so units such as g/cm³ and MPa in the documents are automatically filtered out.
How to Confirm Configurations Are Correct
- Upload a real plastics and rubber quality inspection report, and check if the parsed fields fully include the preset physical parameters and their corresponding units.
- Initiate a query for product parameters, and verify that the number of returned results matches the configured recall count requirement.
- Simulate a no-login sharing scenario, and confirm that the shared link loads normally and can respond to query requests.
- Check the service running logs, and confirm that there are no error messages related to file parsing timeouts or token encoder issues.
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.