What the data for this category looks like
Data sources include party building policy documents issued by the group party committee, learning ledgers from each workshop branch, annual party building work summaries and meeting minutes. Update cadence follows monthly addition of learning points issued by the party committee that month, and quarterly updates of branch learning records and assessment archives. Most document structures are structured policy clauses, Q&A-style learning manuals, and meeting minute snippets. Some ledgers include fields such as participants and assessment scores.
What constraints these characteristics impose on knowledge base retrieval and recall
Party building policy documents are mostly long texts with industry-specific terms. This can lead to false recalls caused by similar terms during vector retrieval. Learning ledgers from each branch are mostly short fragments with scattered fields. Precise matching of topic classification fields is required to retrieve valid content. Monthly newly added learning points have strong timeliness. Regular synchronization of the knowledge base is needed to avoid retrieving outdated content. Some ledgers contain internal assessment information. Filtering rules must be configured during the retrieval link to ensure public content is retrievable and sensitive content is not disclosed.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
| Similarity Threshold | 0.75–0.85 | Adapt to the term density of party building texts, filter low-correlation recall results |
| Single Search Limit | Top 6 entries | Match the context length of single-round party building Q&A, avoid exceeding the model window |
| Search Mode | semantic | Prioritize semantic matching for mixed scenarios of long-text policies and short-text Q&A |
| Reranking | Enabled | Perform secondary sorting on initial recall results to correct false recalls caused by similar terms |
| Query Optimization | Enabled | Generate extended queries based on the original question to cover synonymous expressions in the party building field |
| Dataset Binding | Exclusive party building dataset ID | Isolate party building data for power generation equipment manufacturing from other business data to avoid cross-domain recall |
Three Common Misconfigurations
- Symptom: Retrieval results have high semantic similarity scores, but the actual content is unrelated to the party building question. Cause: The similarity threshold is not configured, or the threshold is set too low, resulting in low-correlation content being recalled.
- Symptom: An error occurs when using an application variable when configuring the knowledge base ID reference variable. Filling in the ID directly works normally. Cause: The variable is not correctly bound to the dataset configuration item, or the variable scope does not cover the current retrieval link.
- Symptom: After importing party building learning ledgers in Excel format, retrieval results cannot match the Q&A corresponding to the fields. Cause: No field mapping rules are configured, or the column names of the Excel table do not match the retrieval matching fields.
How to Verify Successful Configuration
- Input party building-related test questions, check whether the number of retrieval results matches the configured single search limit.
- Check whether the similarity scores of retrieval results fall within the preset similarity threshold interval.
- Verify that the bound dataset only contains party building-related data for power generation equipment manufacturing, with no cross-domain business content.
- After triggering the reranking function, check whether the result sorting conforms to the relevance logic of the question.
The values provided here are common starting points, and should be evaluated against the samples relevant to the actual deployment scenario.
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.