What the data for this category looks like
Internal data sources for pharmaceutical distribution include enterprise internal GSP compliance files, pharmaceutical purchase-sales-stock ledgers, cold chain transport logs, employee position manuals, scanned vendor qualification documents, and more. Update frequencies vary by data type: purchase-sales ledgers update daily, compliance files update quarterly per regulatory requirements, and employee manuals are revised annually. Document structures include structured fields (such as generic drug name, approval number, outbound order number, temperature control duration) and unstructured documents (PDF compliance guides, PPT training slides, Word operation manuals). Some data includes multimodal content such as cold chain temperature control screenshots and actual photos of drug packaging.
Constraints on knowledge base retrieval and recall from these characteristics
Fixed fields in structured purchase-sales ledgers require retrieval to support a combination of precise field matching and semantic recall, to avoid mixing irrelevant results. Multimodal temperature control screenshots and drug packaging photos require enabling multimodal recall capabilities to cover mixed text-image query needs. Daily updated purchase-sales data requires configuring incremental synchronization logic to avoid full imports occupying system resources. Professional compliance terms must retain their original expressions to ensure matching for regulatory-related queries. Position manuals are divided by department to narrow retrieval scope, improve the accuracy of employee queries, and avoid interference from off-post irrelevant information.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Professional terminology in pharmaceutical distribution has high recognition. A threshold that is too low will introduce irrelevant compliance or purchase-sales data, while a threshold that is too high will miss relevant position operation documents |
limit | Top 8 entries | Employee queries mostly cover position operations or compliance requirements. Keeping results within 8 entries covers common scenarios and avoids overly long context |
searchMode | Hybrid retrieval | Balances structured purchase-sales ledger field matching and unstructured semantic recall, adapting to multiple data types |
usingReRank | Enabled | There are many professional terms in pharmaceutical distribution. Reranking improves the sorting priority of relevant results and reduces manual screening costs |
datasetSearchUsingExtensionQuery | Enabled | Employees often use colloquial questions such as "how to check drug expiration dates". Extended queries can match structured ledger fields |
collectionIds | Bind corresponding knowledge base collections by department | Narrow the retrieval scope, improve the accuracy of employee queries, and avoid returning off-department irrelevant data |
Three common configuration errors
- Phenomenon: After uploading PPT-format position training documents, retrieval results only include text content, with no recall results related to images or charts. Cause: Multimodal data parsing configuration is not enabled, only the text layers in the PPT are extracted, and embedded visual content is not recognized.
- Phenomenon: When retrieving pharmaceutical purchase-sales ledgers, multiple highly similar entries appear, and duplicate results are not automatically merged. Cause: Reasonable similarity filtering rules are not configured, or the duplicate result merging logic for retrieval results is not enabled, leading to redundant results being returned.
- Phenomenon: After importing real-time purchase-sales data obtained via HTTP interfaces into the knowledge base, corresponding data cannot be recalled via natural language queries. Cause: Structured data returned by the HTTP interface is not converted into retrievable text chunks, or the corresponding data parsing template is not configured, resulting in data not being properly vectorized.
How to confirm correct configuration
- Upload a training document containing both text and images, initiate a query related to the image content, and verify that the document can be recalled.
- Import a batch of structured purchase-sales ledger data, initiate a query containing specific field keywords, and verify that corresponding entries can be accurately matched.
- Initiate a colloquial position query such as "how to operate cold chain transport", and verify that corresponding position manual content can be recalled.
- View the retrieval result list, confirm that similar entries have been properly integrated or sorted.
The values provided here are common starting points, and should be evaluated against your own specific 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.