What this category of data looks like
Party building learning Q&A data in the new energy development field mainly comes from internal party building work manuals of state-owned enterprises, party building specifications for new energy project construction, policy documents released by industry competent departments, and branch study records. The data is updated once per month with the latest policies and learning materials. Individual documents mostly follow a chapter-based structure, including fields such as document title, issuing unit, release date, and main body content. The character count of individual documents varies widely, measured in units of documents and MB.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Party building learning data mostly consists of official documents and standard materials. There are high requirements for the authority of retrieval results, so content strongly tied to the party building theme must be recalled first. The monthly update rhythm requires the retrieval system to support regular index synchronization, without frequent full index rebuilding. For documents with a chapter-based structure, cross-chunking that causes semantic disconnection must be avoided. When chunking, the association with chapter titles must be retained. For content that combines professional terminology and party building policies, a more precise semantic retrieval logic must be matched to reduce interference from irrelevant results.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the semantic rigor of official party building documents, filters low-relevance results |
limit | Top 3 entries | Party building Q&A requires precise focus; too many results will interfere with study reference |
searchMode | Semantic retrieval | Adapts to the semantic matching needs of party building policies and new energy professional terminology |
usingReRank | Enabled | Improves the sorting priority of highly relevant results, and strengthens the authoritative display of party building content |
datasetSearchUsingExtensionQuery | Disabled | Party building Q&A requires strict matching of original content, avoiding extended queries that deviate from the theme |
collectionIds | Specified by party building knowledge base ID | Only recall party building-related documents, exclude irrelevant non-party building content |
Three common mistakes
- Phenomenon: After mounting multiple knowledge bases, only answers from a single knowledge base are returned. Cause: The
collectionIdsparameter is not configured correctly, or only a single knowledge base ID is specified, causing the system to only load data from the specified single knowledge base. - Phenomenon: After importing knowledge base data via mongorestore, no results can be queried. Cause: The index rebuilding operation is not performed. Directly initiating a query results in no usable vector index being generated, making semantic matching impossible.
- Phenomenon: The proportion of low-relevance content in retrieval results is too high. Cause: The
similarityparameter is set too low, orusingReRankis not enabled, causing the sorting logic to fail and unable to filter highly matched content.
How to confirm the configuration is correct
- Check the knowledge base index status in the FastGPT backend, confirm that all data sets corresponding to the specified
collectionIdsshow completed indexing. - Initiate a test query related to party building, check that the number of returned results matches the setting of the
limitparameter. - Verify the multi-knowledge base mounting configuration, confirm that returned results include content from different specified knowledge bases.
- Adjust the
similarityparameter, observe changes in the relevance of retrieval results, confirm that the configuration takes effect.
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.