What the data for this category looks like
Data related to pharmaceutical circulation business handling comes from pharmaceutical procurement approval forms, inbound acceptance records, cold chain transportation ledgers, GSP compliance inspection documents, and business handling process manuals. Updates occur in real time aligned with business operations: procurement forms and inbound records are updated daily, while compliance documents are revised quarterly or annually. Document structure includes fields such as generic drug name, approval number, circulation batch number, storage temperature requirements, and handling node descriptions, among others. Most units used are ℃, box, case, batch, and similar. Individual documents are mostly step-by-step process instructions, with lengths ranging from 300 to 1500 characters.
Constraints Imposed on Knowledge Base Retrieval and Recall
Pharmaceutical circulation data contains unique identifier fields such as approval numbers and batch numbers, requiring precise matching of core business fields during retrieval to avoid compliance risks caused by fuzzy recall. High-frequency, real-time updated data requires the retrieval link to support incremental synchronization configuration, ensuring that recalled content matches the latest business status. Step-by-step process documents must be recalled in the order of handling nodes; disrupting the sequence will reduce the availability of business guidance. Fields with units such as storage temperature require unit matching to prevent invalid recall caused by missing units.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Pharmaceutical circulation data has precise matching requirements for compliance numbers, batch numbers and other fields. This threshold can filter irrelevant recall content |
limit | Top 8 entries | The steps of a single business handling process do not exceed 10. Too many recalled entries will exceed the context length limit |
searchMode | Precise matching mode | Matches exclusive terminology and unique identifier fields in pharmaceutical circulation business to avoid interference from generic content |
usingReRank | Enabled | Sorts recalled process steps by relevance, aligning with the logical order of business handling |
datasetSearchUsingExtensionQuery | Disabled | Business terminology in pharmaceutical circulation is fixed, no need for extended queries that match non-business-exclusive content |
collectionIds | Bind exclusive business document collection ID | Distinguishes pharmaceutical circulation business data from other category data to avoid interference from cross-category recall |
Three Common Configuration Errors
- When creating a new knowledge base in a locally deployed FastGPT v4.17.0 instance, the image understanding model option does not appear. This occurs because the function relies on a specific third-party model interface, and relevant configuration is not included by default in the open-source package.
- The number of retrieved results does not match the configured
limitparameter. This happens because the exclusivecollectionIdsare not correctly bound, resulting in recall of irrelevant documents from other collections. - A large volume of general medical popular science content appears in retrieval results. This is caused by enabling
datasetSearchUsingExtensionQuery, where extended queries match non-business-exclusive general documents.
How to Verify Correct Configuration
- Enter the FastGPT knowledge base management page, verify that the bound
collectionIdsmatch the ID of the pharmaceutical circulation business document collection. - Submit a query related to business handling, and check whether the similarity of returned results falls within the configured
similarityrange. - Count the number of retrieved results, confirm that it matches the value set for the
limitparameter. - Review the sorting of retrieval results, confirm that after
usingReRankis enabled, results are arranged in the order of business steps.
The values provided here are common starting points and should be validated against sample data from the target deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.