What the data for this category looks like
Data for this category comes from social insurance handling systems, personal benefit records, labor relations and work injury identification systems, and the electronic social security card platform. Daily generated and used documents include benefit application forms, payment duration certificates, retirement approval forms, work injury identification materials, and unemployment insurance benefit application materials. Most documents are structured forms or certification files, containing fields such as participant identity identifiers, payment duration, benefit calculation basis, and eligibility judgment conditions. Field units include years, months, yuan, and other units. Data update frequency is daily.
What constraints do these characteristics impose on knowledge base retrieval and recall?
The data characteristics of this category impose multiple constraints on the knowledge base retrieval and recall process. Data updates daily, so the knowledge base must synchronize the latest social insurance handling, personal benefit record, and other system data each day. This ensures recalled content matches current business data. Documents include structured forms and certification files, and fields have specific units such as years, months, and yuan. Retrieval must accurately match field units to avoid search result deviations caused by mismatched units. Data sources across multiple systems require retrieval to associate documents from different sources. This ensures that recalled payment duration, work injury identification material, and other data come from accurate information in corresponding business systems, guaranteeing the accuracy of benefit verification. At the same time, errors in benefit verification directly affect fund security and public rights and interests. Retrieval results must have high confidence to avoid recalling incorrect or outdated information.
How to set up configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the precision requirements of social security business scenarios, filters low-relevance documents |
limit | Top 8 entries | Covers commonly used benefit verification-related documents, controls context length |
searchMode | Hybrid retrieval | Combines vector retrieval and full-text retrieval, adapts to structured and unstructured documents |
usingReRank | Enabled | Improves the relevance of recalled documents, filters irrelevant content |
datasetSearchUsingExtensionQuery | Enabled | Optimizes business question phrasing to match social security professional terminology |
collectionIds | Specify the corresponding social security benefit document collection | Limits retrieval scope to avoid recalling irrelevant documents |
Three common errors
- Phenomenon: Some PDF documents cannot be recognized by the knowledge base. Cause: PDF parsing dependencies are not configured correctly, or document formats do not meet specifications, resulting in failed vector generation.
- Phenomenon: text-embedding-ada-002 is configured, but the knowledge base returns an error stating "no available content". Cause: The dataset’s embedding model parameters are not configured correctly, or the dataset has not completed the vector generation process.
- Phenomenon: Using different models for embedding and querying leads to reduced relevance of search results. Cause: The models used for embedding and querying are inconsistent, resulting in mismatched vector spaces that cannot effectively recall relevant documents.
How to confirm the configuration is properly set up
- Manually enter business questions related to pension benefit verification, work injury identification materials, and unemployment insurance benefit applications. Check if the recalled documents include daily documents within this category such as payment duration certificates and retirement approval forms.
- View the knowledge base’s synchronization logs to confirm whether the dataset has completed daily updates, ensuring the timeliness of recalled data.
- Check the parameter values of configuration items, confirm that parameters such as
similarityandlimitfall within the set value ranges. - Test the retrieval process to confirm that the relevance of returned results meets the requirements of the business scenario.
Note: The values provided here are common starting points, and should be evaluated 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.