What the Data for This Category Looks Like
State-owned industrial fund party building learning data mainly comes from superior party committee policy documents, internal party lecture courseware, compliance study manuals, and special documents integrating investment research and party building. Updates follow two modes: fixed cycle and temporary trigger. Fixed cycle updates include monthly interpretations of superior party building guidelines, and quarterly updates of internal party lecture materials. Temporary updates trigger when new party building policies are released, or internal party building activities are carried out. All documents use a unified structure with five fields: document title, issuing unit, release date, policy document number, and main text. Character counts per document vary widely. Storage is measured in megabytes (MB).
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Party building content has a policy-focused, serious tone. Retrieval results must precisely match both semantics and keywords. The recall link must balance semantic relevance and keyword filtering. The dual-cycle update mode requires the knowledge base to support both incremental and full synchronization. Full synchronization uses excessive resources, so this dual support avoids that issue. Single documents have widely varying character counts. Chunking strategies must adapt to different document lengths. Overly small chunks cause semantic fragmentation. Overly large chunks reduce recall accuracy. The fixed document field structure allows precise filtering for specific fields during retrieval. This narrows the recall scope.
Configuration Settings
These configurations are designed for FastGPT v4.17.0, and adapt to the retrieval and recall needs of party building learning knowledge bases for state-owned industrial funds.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Party building content for state-owned industrial funds has high semantic consistency requirements. This threshold filters low-match irrelevant results while retaining valid recall content |
limit | Top 6 results | Party building documents have moderate average length. Too many recall results exceed the large model context limit. 6 results balance coverage and context capacity |
searchMode | hybrid | Party building content includes both policy keywords and semantic associations. Hybrid search matches both keywords and semantics to improve recall accuracy |
usingReRank | Enabled | For multi-document recall scenarios, re-ranking re-sorts results based on semantic relevance to optimize final output relevance |
datasetSearchUsingExtensionQuery | Enabled | Party building has a large number of specialized terms. Extended queries cover synonyms and related expressions, improving recall success rates for obscure party building questions |
collectionIds | Specify dedicated party building knowledge base collection IDs | Isolate data from other categories to avoid non-party building content in retrieval results, ensuring query targeting |
Three Common Misconfigurations
- Symptom: Knowledge base search takes longer than 10 seconds, with noticeable result delay. Cause:
usingReRankis not enabled, andlimitis set to more than 10 entries. This causes a large number of redundant recall results to participate in sorting calculations. - Symptom: Knowledge base query returns disorganized results, and the large model fails to sort and output content effectively. Cause:
searchModeis only configured for semantic search, keyword filtering is not enabled, anddatasetSearchUsingExtensionQueryis not enabled. This leads to recall results with insufficient matching to the query. - Symptom: The AI returns irrelevant or fabricated content even when no matching content exists in the knowledge base. Cause: The
similaritythreshold is set below 0.7, or no silent return logic is configured for no matching results. This causes low-match results to be incorrectly recalled.
How to Verify Proper Configuration
- Access the knowledge base management page in the FastGPT backend. Check the configuration items for the dedicated party building collection. Confirm the
similaritythreshold falls within the 0.75–0.85 range. - Submit a test query related to party building. Check if the number of returned results is 6 or fewer, and if single query latency is below 3 seconds.
- Upload a new party building policy document. Verify that incremental update functions properly, and the new content can be retrieved normally.
- Input a party building question not included in the knowledge base. Confirm the AI does not return any irrelevant or fabricated information.
The values listed above are common starting points. They should be evaluated against the user’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.