What the data for this category looks like
Policy and regulatory basis data for the pharmaceutical distribution sector is sourced primarily from official announcements of the National Medical Products Administration, Ministry of Commerce, provincial and municipal medical insurance bureaus, and industry regulatory authorities. Updates occur in real time alongside policy releases, with quarterly updates accompanying policy revisions and new regulation implementations.
Document structure includes fields such as policy document number, issuing authority, effective date, scope of application, and clause details. Some files include chapter numbers and clause serial numbers, with "article", "paragraph", and "subparagraph" as the smallest granular units. Special documents will additionally mark applicable scenarios such as cold-chain pharmaceuticals and special pharmaceutical distribution.
What constraints these characteristics impose on knowledge base retrieval and recall
Policy texts are highly normative. Document numbers and effective dates are core identification fields, so retrieval must support precise metadata matching. Policy documents are lengthy; a single document may contain dozens of clauses. When chunking content, retain contextual connections between clauses to avoid breaking policy logic.
Policies at different levels and for different scenarios have priority differences, so retrieval must support filtering by issuing authority and effective time period. Data update frequency varies widely. When new regulations are issued, they must be quickly synced to the knowledge base, so the retrieval link must support incremental update verification.
How to configure the settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Pharmaceutical distribution policy texts have precise semantics. This range filters low-relevance non-policy content while retaining the possibility of recalling policies in the same domain. |
limit | Top 8–12 results | Policy clauses are densely packed. Too many recall results will exceed the context window (typically 4096 tokens). This range covers core relevant policies. |
searchMode | Semantic + full-text hybrid retrieval | Balances precise matching of metadata such as policy document numbers and semantic recall of long-text clauses, adapting to the retrieval needs of policy documents. |
usingReRank | Enabled | Policy texts are lengthy. The re-ranking step corrects ranking biases from initial semantic retrieval and improves the recall priority of core clauses. |
datasetSearchUsingExtensionQuery | Disabled | Policy texts use standardized wording. No need to supplement semantics via extended queries, which avoids introducing irrelevant retrieval results. |
collectionIds | Bind to the dedicated pharmaceutical distribution policy dataset | Isolate knowledge base content from other domains, ensuring the retrieval scope only covers policies related to pharmaceutical distribution. |
Three common mistakes
- Symptom: Semantic retrieval fails to recall the target policy, but full-text retrieval works normally. Cause: The similarity threshold is set too high, filtering content that semantically matches the target policy, or the embedding model has insufficient encoding effect for long policy texts.
- Symptom: A slow operation error occurs when the knowledge base parses policy files. Logs show the response time exceeds the threshold. Cause: The chunk length is set too large, exceeding FastGPT's processing capacity, or MongoDB connection parameters are not adapted to the needs of large-text parsing.
- Symptom: Retrieval results do not include policy files from the specified issuing authority. Cause: No filtering rule by issuing authority is configured, or the dataset binding is incorrect, causing the retrieval scope to cover non-target domain content.
How to verify the configuration is correct
- Upload one pharmaceutical distribution policy file to the dedicated dataset of FastGPT v4.17.0, trigger semantic retrieval, and check whether the recall results include relevant clauses of the target file.
- Enter a specific policy document number, check whether the retrieval results accurately match the policy file of the corresponding number, to verify that the metadata retrieval function is active.
- View the retrieval logs to confirm that the
similarityandlimitparameters are configured correctly, and the number of recalled results matches the preset value. - Switch the search mode, compare the differences between semantic retrieval and hybrid retrieval recall results, to verify that the search mode configuration is active.
The values provided are common starting points and should be measured against the reader'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.