What the data for this category looks like
The core data for this category comes from daily office documents of government offices, government information disclosure offices and other relevant entities. It includes fixed-format documents such as government information disclosure application forms, supplementary notice letters, response letters, and more. Data sources cover government information disclosure platforms, official agency archive systems, document management systems, departmental business ledgers, and relevant specifications of the Regulations on Government Information Disclosure. Data updates occur irregularly, synchronized with the progress of application handling.
Core fields include application number, applicant information, application matters, basis for judging disclosure attributes, response content, and relief channels. The most time-consuming steps in the handling process are retrieving the information referenced in the application, judging disclosure attributes and third-party rights and interests, and drafting response letters with relief channels. Errors during handling may lead to overdue responses, administrative reconsideration corrections, or court orders to reissue responses.
This category centers on "whether a specific piece of government information should be disclosed", differing from general active information release. It handles individual requests from specific applicants.
Constraints imposed on multi-turn dialogue and prompt engineering
The data sources for this category are scattered but have fixed formats. Multi-turn dialogue must bind a unique application number as a context identifier to avoid mixing information from different applications. Data updates are irregular, so regular synchronization of the latest data from government information disclosure platforms and archive systems is required to ensure the timeliness of retrieved content.
Due to the serious consequences of handling errors, multi-turn dialogue prompts must embed legal disclosure judgment bases and document format requirements to ensure generated content complies with regulations. Additionally, the individual request characteristic requires each interaction round to focus on the specific demands of the current application, filtering redundant historical interaction content to avoid interfering with the large model's judgment.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId binding rule | Bind a unique government information disclosure application number | Ensure multi-turn dialogue is associated with the same individual application, avoiding context confusion |
messages history truncation strategy | Retain the most recent 5 valid interactions, truncate redundant prior history | Filter irrelevant content, focus on current handling steps |
variables configuration | Bind applicant information, application matters, legal disclosure judgment basis | Provide case-specific context variables for the large model |
| Single knowledge base retrieval limit | Top 6 matching results | Cover multi-source data from government information disclosure platforms and archive systems |
| Response timeout period | 600 seconds | Adapt to time-consuming requirements of cross-system retrieval |
| Total context window length | Latest 2500 characters | Adapt to information carrying requirements of multi-turn interactions |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- Phenomenon: The knowledge base retrieval returns an empty array during multi-turn dialogue, but the same search term successfully hits target documents when tested individually in the knowledge base test page. Cause: The application-specific context is not bound via
chatId, leading to redundant data from other applications being included during retrieval, or the associated archives and document data of the corresponding application are not correctly loaded. - Phenomenon: The dialogue flow triggers an error, returning 404 Invalid URL (POST /api/chat/completions). Cause: The calling path of the large model interface is not correctly configured, or the interface permission is not enabled for the current application's node.
- Phenomenon: The workflow ends directly after reaching the large model generation step, without triggering the subsequent document format verification step. Cause: The process trigger identifier is not configured in the system prompt of
messages, or the total context window length is set too small, causing subsequent nodes to not receive valid input.
How to Verify Configuration Correctness
- Initiate a simulated government information disclosure application, start a multi-turn dialogue by binding the application number via
chatId, and check whether the retrieval results include the associated documents of the corresponding application. - Call the interface to trigger the dialogue flow, confirm that the return status code is 200, with no 404 URL errors.
- Configure
variablesto bind application matters, ask the large model to generate a response letter, and check whether the output includes the preset legal judgment basis. - Trigger a timeout test, confirm that the retrieval and generation process is completed within 600 seconds, with no timeout interruptions.
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.