What the data for this category looks like
Party building learning data for state-owned and state-holding enterprises in the pharmaceutical distribution sector comes from four main sources. These include monthly learning materials issued by the enterprise party committee office, quarterly branch meeting minutes, industry-specific party building policy documents, and individual party member learning records. The data update rhythm follows two cycles. New learning materials are added each month. Industry policy documents are updated quarterly. Most documents use PDF or Word format. Their structure includes title hierarchies, chapter divisions, issuing unit and date fields. The length of a single document ranges from 5000 to 15000 characters. Additional fields include tags such as document number, topic classification, and applicable positions.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Scattered data sources require unified dataset classification and binding before retrieval. This prevents interference from cross-category documents. Monthly updated materials require incremental synchronization rules. This avoids wasting computing resources on full retrieval. Long document structures require retaining chapter associations during text chunking. This prevents semantic breaks that reduce retrieval accuracy. Topic classification and release date fields can act as filtering conditions. These narrow the retrieval scope and improve matching efficiency. Party building content in the pharmaceutical distribution sector may include party building requirements for drug circulation links. Retrieval must therefore associate relevant business keywords. This avoids disconnecting general party building content from business scenarios.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Similarity threshold (similarity) | 0.75–0.85 | Matches semantic relevance of pharmaceutical distribution party building texts, filters low-interference content |
Single search limit (limit) | Top 6–10 results | Adapts to the average length of pharmaceutical distribution party building documents, avoids redundant recall consuming token quota |
Search mode (searchMode) | Semantic priority | Adapts to the abstract semantic expression of party building texts, outperforms basic keyword matching |
Enable re-ranking (usingReRank) | Enabled | Filters semantically similar duplicate learning materials, improves result concentration |
Query optimization (datasetSearchUsingExtensionQuery) | Enabled | Supplements synonyms related to pharmaceutical distribution party building, expands query coverage |
Specify knowledge base collection (collectionIds) | Bind exclusive pharmaceutical distribution party building dataset | Isolates documents from other categories, avoids recalling irrelevant content |
Three common configuration errors
- Phenomenon: No automatic knowledge base call is triggered after Q&A is initiated, only general party building responses are output. Cause: Automatic trigger configuration for knowledge base retrieval is not enabled, or only manual retrieval trigger rules are set.
- Phenomenon: The token count for a single response far exceeds the set limit. Logs show excessive documents are recalled. Cause: Single search limit is set to 2000, and no reasonable adjustment is made based on the average length of pharmaceutical distribution party building documents.
- Phenomenon: Knowledge base retrieval results are displayed directly on the interface, and duplicate outputs appear alongside AI-generated responses. Cause: The default display switch for retrieval results is not turned off, or both retrieval output and AI generation output links are configured in the workflow.
How to confirm the configuration is properly set
Upload an exclusive test document for pharmaceutical distribution party building. Initiate a question related to the document’s core content. Verify that the recall results include the document’s key information. View the system retrieval logs. Confirm that the number of recalled documents matches the set single search limit. Trigger the full Q&A workflow. Confirm that only the final AI-generated response appears on the interface, with no duplicate retrieval results. Adjust the similarity threshold. Verify that the relevance of recall results shifts reasonably in line with the threshold change.
The configurations on this page apply to FastGPT v4.17.0. The values provided here are common starting points, and should be evaluated against the specific samples of the deployed system.
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.