What the Data for This Category Looks Like
Data sources for this category include government information disclosure platforms, agency archive systems, document management systems, departmental business ledgers, and standard documents related to the Government Information Disclosure Regulations. Daily processed documents include government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, reply letters, service receipts, and administrative reconsideration response materials. Fields covered include applicant information, application content, reply document number, disclosure attribute, relief channels, and more. Update frequency is irregular, with no fixed cycle.
Constraints Imposed on the Sharing and Embedding Link
Data sources are scattered and involve government-sensitive information. Data from different business scenarios must be strictly isolated during sharing and embedding to prevent cross-department or cross-applicant information leaks. Document structures are diverse, and updates have no fixed cycle. The embedded knowledge base must support flexible synchronization configuration to adapt to irregular document updates. The core of this category is individual requests from specific applicants. The embedded Q&A robot must limit the context scope to ensure only compliant information related to the current query is returned, avoiding interference from irrelevant data.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Embedded page whitelist | Only allow domain names with the *.gov.cn suffix | Prevent non-government platforms from accessing sensitive government data |
shareId | Bind a dedicated sharing ID for the party building learning + government information application disclosure knowledge base | Isolate knowledge base data from different business scenarios to avoid data confusion |
| Maximum context length | 8000–12000 characters | Adapt to the average length of documents in this category, avoiding context overflow |
| Maximum recall results | Top 3 entries | Focus on accurately matched government information application disclosure and party building learning content, reducing redundant data |
| Knowledge base synchronization trigger timing | Trigger on file update events, or once weekly | Adapt to the irregular update characteristics of data in this category |
| Error prompt text | Standardized government reply language, such as "Please submit an application through official government channels" | Comply with government disclosure specification requirements |
Three Common Misconfigurations
- Phenomenon: No page display after iframe embedding. Console shows cross-origin blocking or element height of 0. Cause: Embedded page whitelist not configured, or the current domain name is not included in the whitelist.
- Phenomenon: Empty retrieval results or model incompatibility error returned. Console shows vector dimension mismatch. Cause: A model with a vector dimension incompatible with the knowledge base chunking was used, and model compatibility was not verified in advance.
- Phenomenon: Embedded page loading timeout or slow interface response. Cause: No reasonable context window length set, or no limit on the number of single recall results, leading to loading excessive document data.
How to Verify Successful Configuration
- In FastGPT v4.17.0, copy the generated sharing link, embed the iframe tag on the page with the configured whitelist domain name, and check whether the chat window loads normally.
- Call the interface corresponding to
shareId, and verify that the returned knowledge base data includes government information application disclosure documents and party building learning content from this category. - Submit a test question such as "How to apply for government information disclosure", and check that the retrieval result matching meets expectations.
- Simulate loading multiple embedded windows, check that system resource usage is normal, and no obvious lag or timeout occurs.
The values given 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.