What the Data for This Category Looks Like
Data sources are party building learning materials archived by the propaganda department of the power grid enterprise party committee and various branches. These include party course materials issued by superior party committees, thematic education documents, branch study records, party member assessment archives, and more. Content is updated weekly with new additions, and revised official documents are updated in batches monthly. Documents are centered on structured paragraphs, including topic titles, main body content, publishing organization, and publishing date fields. Some long documents include multi-level subheadings and paragraph annotations. The content density and length of individual documents vary significantly.
What Constraints Do These Characteristics Impose on the "Knowledge Base Retrieval and Recall" Link?
The structured nature of party building data requires the retrieval chain to support precise title-based matching, to avoid basic retrieval confusing main body content under different level headings. Weekly updated incremental content requires the recall process to support incremental vector synchronization, to avoid resource consumption caused by full reconstruction. Party building content includes a large number of industry-specific terms and fixed expressions, such as "three meetings and one lesson", "thematic education", "democratic life meetings", and more. Semantic matching accuracy must be optimized to filter low-relevance retrieval results. Some documents come from non-standardized entry content from grassroots branches, leading to redundant formats and invalid characters in chunked text. Pre-filtering rules must be configured.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | There are many specialized terms in party building content. This range balances recall coverage and accuracy, filtering irrelevant results with low matching degrees |
limit | Top 8–12 results | Knowledge points for power grid enterprise party building learning are relatively concentrated. Too many recall results will exceed the context window limit |
searchMode | Prioritize title matching | Documents include multi-level heading structures. Prioritizing title matching improves the relevance of retrieval results |
usingReRank | Enabled | The semantic similarity of party building content is relatively high. Reranking further filters redundant results and improves the accuracy of final recommendations |
datasetSearchUsingExtensionQuery | Enabled | Party building learning questions often include prepositive context. Extended queries cover more relevant documents |
collectionIds | Specify after dividing collections by branch/department | Power grid enterprises have multi-level party building organizations. Dividing by collections enables permission isolation and precise recall |
Three Common Misconfigurations
- Phenomenon: An error is prompted when uploading party building knowledge base files, and the import process cannot be completed. Cause: The structured format of power grid party building documents is not adapted. Uploaded content includes unclosed multi-level headings, non-standard line breaks, or format tags from official document headers.
- Phenomenon: Server read-write overload occurs at fixed daily times, and vector database storage space grows rapidly. Cause: Incremental update rules are not configured. Full party building documents are synced each time, leading to concentrated read-write pressure spikes.
- Phenomenon: After deleting party building Q&A pairs in the knowledge base background, the content still appears in retrieval results. Cause: Real-time cache cleanup of the vector database is not triggered, or corresponding entries in the vector database are not updated synchronously.
How to Confirm the Configuration Is Correct
- Upload a test party building document, check whether the chunked text retains the original document's heading hierarchy and the corresponding relationship between headings and main body content, to confirm that the chunking rules are effective.
- Initiate a test question related to party building, check whether the number of retrieval results is within the configured
limitrange, and whether the similarity scores match the set range. - Perform an incremental update operation, check that only newly added documents are synced to the vector database, and no full reconstruction is triggered, to confirm that the update rules are effective.
- Delete a test Q&A pair, then initiate the same question for retrieval, confirm that the content is no longer included in the results, to confirm that the synchronization rules are effective.
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.