Model Access and Configuration for Traditional Chinese Medicine Marketing Content

What the data for this category looks like

This category’s data comes from multiple sources: the People’s Republic of China Pharmacopoeia, traditional Chinese medicine decoction pieces and proprietary Chinese medicine information filed by the National Medical Products Administration, official product manuals released by pharmaceutical companies, medical insurance traditional Chinese medicine reimbursement catalogs, as well as product detail pages and user review tags from e-commerce channels. Core fields include product name, nature, taste and meridian tropism, functions and indications, dosage and administration, approval number, manufacturing enterprise, selling price, inventory and marketing copy tags. The dosage and administration field uses fixed units such as grams per use and grams per day. The approval number is a fixed-format string. Pharmacopoeia data is revised every 5 years. Internal pharmaceutical product data is updated with new product launches. E-commerce channel data is synchronized daily.

What constraints these characteristics impose on model access and configuration

Different sources of traditional Chinese medicine data have format differences. Validation must be performed for fields with fixed formats such as nature, taste and meridian tropism and dosage and administration to avoid parsing errors. Authoritative filed data and e-commerce marketing data must be stored and recalled separately. This prevents non-compliant content from mixing into marketing outputs. The real-time synchronization requirement of e-commerce channels means connected data sources must support incremental pulling. Otherwise, data lag will occur. For scenarios where a single document contains multiple sets of traditional Chinese medicine information, longer parsing timeout and larger file upload limits are required.

How to set configurations

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 specific analysis. It is recommended to test on your own samples before finalizing.

Three common mistakes

• Phenomenon: Setting Recall count to more than 5 entries results in marketing content returned that includes efficacy descriptions beyond the scope of the pharmacopoeia. Cause: The Similarity threshold is not set, or the threshold is set too low, introducing non-authoritative and non-compliant marketing content.
• Phenomenon: A 400 Bad Request error occurs when connecting to a third-party reasoning framework, with the log showing invalid model name. Cause: MODEL_NAME is not configured as the model identifier supported by the framework, or the base address of the reasoning interface is not correctly configured.
• Phenomenon: After synchronizing e-commerce platform data, the "dosage and administration" field of some traditional Chinese medicines is empty. Cause: No format validation is performed on the fields of the e-commerce detail page, and the correct data source fields are not mapped, resulting in non-standard unit text that cannot be parsed.

How to confirm the configuration is complete

• Upload a single traditional Chinese medicine product manual. Check whether the parsed fields include core fields such as nature, taste and meridian tropism and functions and indications, and whether the field formats comply with pharmacopoeia specifications.
• Initiate a test query, enter "functions and indications of a certain traditional Chinese medicine". Check whether the number of background knowledge recall entries matches the configured Recall count.
• View the connection logs of the reasoning service. Confirm that MODEL_NAME and the interface address are configured correctly, with no invalid model name or connection timeout errors.
• Adjust the Similarity threshold. Test the recall content under different thresholds, and confirm that the filtering effect meets expectations.

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.