What the data for this category looks like
This category of data comes from the regulatory and rule database, higher-level law database, departmental policy document database, and administrative normative document filing system, with irregular update frequency. Daily processed documents include normative document drafts, drafting explanations, legality review opinions, solicited opinion summaries, and filing reports. Fields cover formulation subject, text content, review basis, filing status, etc. The length of individual documents varies widely, from hundreds to tens of thousands of characters. Retrieval and recall logic must be adjusted for documents of different lengths.
What constraints these characteristics impose on the "Sharing and Embedding" link
Data sources are scattered and updates have no fixed cycle. In embedding scenarios, it is necessary to ensure that shared Q&A results always match the latest filing and review data. A regular synchronization mechanism must be configured. The wide range of document lengths requires controlling the total length of recalled text when sharing and embedding, to avoid exceeding the context window and causing critical compliance information to be truncated. Review-related data must strictly match formulation authority and higher-level law basis. Shared Q&A results must ensure that citation sources are traceable, to avoid compliance risks. In the party building learning Q&A scenario, users need to quickly obtain policy basis related to reviews. The post-embedding retrieval logic must prioritize matching knowledge base entries related to legality review, to avoid interference from irrelevant content.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
shareId | Bind the sharing ID of the exclusive compliance review knowledge base | Ensure that Q&A only calls the preset legality review-related dataset, avoiding irrelevant content interfering with the accuracy of party building learning Q&A |
| Single search limit | Top 8 results | Legality review of normative documents needs to cover multi-dimensional basis including higher-level laws, departmental rules, and review opinions. The top 8 results balance recall comprehensiveness and context length |
| Maximum context | 4000-6000 characters | Individual review documents and associated basis have wide length ranges. This range can accommodate complete regulatory clauses and review opinions, avoiding truncation of critical compliance information |
| Question optimization | Enabled | User questions in the party building learning Q&A scenario may be colloquial. After optimization, it can accurately match policy terms and compliance basis related to reviews |
| Data synchronization cycle | Calibrated based on actual testing | Since the update frequency of this category of data is irregular, adjust the synchronization cycle according to the update rhythm of the filing system, to ensure that Q&A results match the latest review status in real time |
Three Common Mistakes
- After calling the sharing API bound to
shareId, the returned result shows that no knowledge base content is found. Cause: Only the local temporary dataset is loaded in the debug page, and the formal knowledge base is not bound toshareId. The sharing link is not associated with the correct data source. - Images in the chatbot reply embedded on a web page can only be enlarged inside the iframe, and cannot be viewed in full screen externally. Cause: Custom front-end interaction logic is not configured. The default iframe limits the interaction range of images.
- A cross-origin connection block error pops up after embedding on a website, prompting that the target resource cannot be accessed. Cause: The allowed domain name whitelist is not configured in FastGPT's embedding settings. The browser blocks the cross-origin request.
How to Confirm the Configuration Is Complete
- Call the sharing interface bound to
shareId, enter test questions such as "What are the basis for legality review of normative documents", and check whether the returned results include regulatory clauses and review opinions in the preset knowledge base. - Open the web page embedded with the chatbot, send a test question containing images, and click the image in the reply to confirm that it can be enlarged or reduced in a browser pop-up window or new tab page.
- Log in to the FastGPT backend, check the knowledge base synchronization log corresponding to
shareId, and confirm that the most recent synchronization status is successful, with no data missing or failed records. - Embed the chatbot using a domain name not added to the whitelist, confirm that the browser pops up a cross-origin interception prompt, to verify that the whitelist configuration takes effect.
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.