Reference only; verify against the target FastGPT release and deployment documentation.
What this category's data looks like
The core data for this category comes from the national student funding management information system, school student status system, civil affairs low-income and special hardship data, and fiscal payment system. Daily documents include family economic hardship assessment forms, aid application forms, scholarship and grant review forms, payment lists, and public announcement materials. Fields cover student identity, family economic status, student status, fund payment information, and more. Data is updated on a semester cycle, with a single update covering all policy documents and business data from the current semester. Core business processes focus on hardship assessment, student status verification, and list cross-checking. Data accuracy directly relates to the compliance of aid payments.
Constraints on the knowledge base retrieval and recall link
Multi-source data and fixed update cycles require the retrieval system to support unified indexing of cross-system documents, and complete full knowledge base synchronization on a semester cycle to avoid mixing old and new data. Document types cover business forms and policy texts, requiring matching both precise field and semantic association query needs, rather than relying solely on a single retrieval mode. The long data update cycle means the retrieval system does not need real-time synchronization, but must complete a full reindex of all data within the update window to ensure the timeliness of retrieval results. The high compliance requirements of business processes mean retrieval results must have high confidence, to avoid irrelevant or incorrect policy basis being recalled.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Retrieval of policy and regulatory basis requires high matching accuracy. This range filters low-correlation non-policy documents to ensure the compliance of recalled content |
limit | Top 10 entries | Core clauses of policy basis are usually concentrated in a small number of documents. 10 results cover the full policy reference scope and avoid redundant information interfering with the review process |
searchMode | hybrid | Balances semantic matching and keyword matching, adapting to both precise queries for professional policy terms and semantic retrieval for colloquial business questions |
usingReRank | Enabled | When the number of policy documents is large, reordering optimizes the result ranking logic, returning the most relevant policy clauses first and improving review efficiency |
datasetSearchUsingExtensionQuery | Enabled | Business staff often use colloquial expressions for queries. This configuration automatically expands query terms to match professional terms in policy texts, improving recall accuracy |
collectionIds | Bind exclusive policy and business document collections for this category | Restrict the retrieval scope to knowledge base collections related to student funding, avoiding interference from documents of other categories and ensuring compliance |
Three common mistakes
- A prompt stating "No available channel for model gpt-4o-mini under current group default" appears during search. This occurs when the knowledge base retrieval channel for the corresponding model is not configured, or the retrieval task is not bound to the correct group.
- Unable to clarify parameter filling rules when selecting a knowledge base using variable references. This happens when the identification specification for knowledge base collections is not defined in advance, and the requirement to pass a valid
collectionIdsparameter value is not specified in the variable configuration. - Image links uploaded to the knowledge base display as "input an image" in AI replies. This occurs when the accessible link rendering configuration for knowledge base images is not enabled, or the retrievable display address of the image is not correctly extracted during retrieval.
How to confirm proper configuration
- Upload documents corresponding to this category, such as family economic hardship assessment forms and scholarship and grant policy texts, to the FastGPT knowledge base, and check if
collectionIdsincludes the unique identifier of the corresponding collection. - Initiate a test query, enter "Application requirements for financial aid for students with family economic difficulties", and check if the
similarityscores of returned results fall within the preset range, and if the number of results matches thelimitsetting. - After enabling
usingReRank, compare the ranking of retrieval results before and after enabling to confirm that more relevant policy documents are returned first. - Test a query using colloquial expression, enter "How to apply for a grant when the family has financial difficulties", and check if
datasetSearchUsingExtensionQuerycorrectly matches the corresponding policy documents.
The values provided here 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.