What the Data for This Category Looks Like
Data for pharmaceutical distribution declaration materials and case reviews comes from four primary sources: pharmaceutical business qualification declaration documents, cold chain transportation full-process case files, pharmaceutical circulation ledgers, and supplier qualification certificates. Qualification documents are updated annually, while ledgers and transportation case files are updated in real time with each batch of circulation business. Documents include two structural types: structured fields and unstructured scanned files. Structured fields include enterprise name, National Pharmaceutical Approval Number, drug generic name, transportation temperature, approval document number, and other items. Unstructured content is mostly multi-page PDF approval reports and on-site inspection records.
Constraints Imposed on Vector Models and Indexing by These Characteristics
Annual updates to qualification documents require support for incremental indexing to avoid resource consumption from full reindexing. Real-time updated ledgers and case files need low-latency index synchronization capabilities. Multi-page PDF unstructured content must retain upstream and downstream business associations after chunking, otherwise semantic matching accuracy will be compromised. Precise codes such as National Pharmaceutical Approval Number in structured fields require vector models to balance both semantic matching and precise field matching, to avoid misjudgments during compliance reviews. Additionally, ledger data from different batches has relatively uniform formats, and vector embedding of fixed fields can improve retrieval efficiency.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Pharmaceutical distribution declaration materials often contain long approval descriptions and ledger records. This length can cover complete business semantic units |
| Overlap Length | 100–200 characters | Prevents loss of upstream and downstream business associations after chunking long texts, and adapts to the continuous approval process of case files |
| Similarity Threshold | 0.75–0.85 | Distinguishes precise matching for qualification materials and semantic matching for business descriptions, and adapts to compliance review requirements for pharmaceutical distribution |
| maxToken | 8192 token | Adapts to long text input of batch numbers and temperature records in pharmaceutical distribution declaration materials |
| defaultToken | Calibrated based on actual testing | Adapts to semantic consistency verification for qualification documents and ledger data from different batches |
| Single Search Limit | Top 10 entries | Covers the associated review needs of multiple case files, and avoids missing cross-batch cross-compliance information |
Three Common Misconfigurations
- After uploading using the pushdata API's chunk mode, the interface remains in the "indexing" status without updates for extended periods. Cause: The chunk length is set too large, exceeding the maxToken limit supported by the model, causing index tasks to pile up and fail to complete.
- The interface returns the error "This token is not authorized to use the model: text-embedding-3-large". Cause: defaultToken is not configured correctly, or the bound model has not been granted corresponding permissions under the current account.
- Cannot connect to the specified embedding model (such as bce-embedding-v1). Cause: Did not switch to the non-one API model call path in system settings, and did not correctly fill in the model access address and secret key.
How to Verify Correct Configuration
- Upload a single typical pharmaceutical distribution declaration material, check that the index completion time has no abnormal pileup and meets business expectations.
- Test embedding the specified embedding model, verify that the returned vector data format conforms to the standard requirements of FastGPT v4.17.0.
- Retrieve indexed case file data, confirm that the similarity of the returned results falls within the preset threshold range.
- Check the defaultToken and maxToken configuration parameters to match the official documentation of the currently used embedding model.
The values provided here are common starting points, and should be validated against local sample data.
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.