What this category of data looks like
Data related to medical supply chain service inquiries and responses comes from internal enterprise ERP systems, public data from drug regulatory platforms, and materials submitted at offline service windows. Data types include drug business qualification documents, drug traceability data, cold chain logistics records, purchase and sales documents, service guidelines, approval process descriptions, and industry policy documents. Update frequencies vary significantly: drug qualification documents update quarterly per regulatory requirements, cold chain data syncs hourly, and service guideline documents update irregularly alongside policy adjustments. Document structures include both structured fields and unstructured text. Structured fields include drug generic names, approval numbers, circulation batches, and cold chain temperature records. Unstructured content includes policy documents and transcribed PDFs of service guidelines. Fields often include circulation links, approval status, trace code numbers, and more. Units include degrees Celsius, batch numbers, and standard date-time formats.
Constraints imposed on vector models and indexing by these characteristics
The coexistence of structured and unstructured data requires vector models to adapt to both short text fields and long text content. Short text fields include approval status and approval numbers. Long text content includes policy documents and service guidelines. Varying update frequencies require the indexing system to support both incremental and full update modes. This supports real-time sync of cold chain data and quarterly batch updates of qualification documents. The precision requirements of service inquiry scenarios require the index to support field filtering. These filters can associate structured fields such as drug batches and circulation links. This avoids returning irrelevant non-compliant content. Large differences in data length require flexible chunking strategies. These strategies adapt to text lengths ranging from a few characters of approval status to thousands of characters of policy documents.
How to set configurations
| Configuration Item | Recommended Setting | Rationale | |
|---|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the common paragraph length of medical supply chain service documents, balancing long text splitting and semantic integrity | |
| Overlap Length | 100–150 characters | Prevents semantic breaks across chunks, adapting to long sentence structures of drug traceability processes and policy documents | |
| Similarity Threshold | 0.75–0.85 | Matches the precision requirements of medical compliance inquiries, filtering low-relevance non-compliant content | |
| Single Search Limit | Top 8 results | Covers multi-step process information common in service inquiries, avoiding result redundancy | |
maxToken | 4000 token | Adapts to context splicing requirements for long documents, aligning with the default parameter limit of FastGPT v4.17.0 | |
defaultToken | 2000 seconds | Calibrated via actual testing | Adapts to incremental sync of medical data and index construction duration |
Three common errors
- Phenomenon: Repeated submission of the same service inquiry request results in high system response delay or long vector database query time. Cause: Query cache configuration is not enabled, so a full vector retrieval process runs for every request.
- Phenomenon: Drug qualification documents added via collections fail to return in service inquiry searches. Cause: The temporary storage of collection-added data is confused with the formal index construction process of training jobs. Service inquiry documents must complete vectorization and index generation via training jobs.
- Phenomenon: After uploading images of drug cold chain temperature records, search results do not link to the corresponding batch’s service approval information. Cause: Text extracted via OCR from the images is not bound to drug basic data fields. Only the OCR text is vectorized separately.
How to verify correct configuration
- Initiate an inquiry request for a drug traceability process. Check if the chunk length of search results matches the original document’s splitting rules.
- Submit two identical service inquiry requests. Compare the time difference between the two responses to confirm if the cache mechanism is active.
- Upload a drug qualification document and submit a training job. Check if the index generation status completes normally with no error prompts.
- Upload a drug packaging image and initiate a related inquiry. Confirm if search results include service guidelines for the corresponding drug.
The values listed here are common starting points, and should be evaluated against the user’s own samples.
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.