What This Category of Data Looks Like
The core data for this category comes from government information disclosure platforms, official agency archive systems, document management systems, departmental business ledgers, and relevant specifications for government information disclosure. Daily processed documents include standard forms such as government information disclosure application forms, supplementary notice letters, third-party opinion solicitation letters, reply letters, service receipts, and administrative reconsideration response materials. Data update frequency is irregular, changing with individual application cases and archive filing schedules. Core data fields center on applicant subject, application matters, processing milestones, and reply content. There is no unified batch field format; all content revolves around individual requests from a single applicant.
Constraints Imposed by These Characteristics on the "Multi-turn Dialogue and Prompting" Link
The documents in this category are standardized individual case materials, and data sources are scattered. Multi-turn dialogue must accurately bind the context of a single application to avoid cross-session confusion. Data updates are irregular, so the knowledge base's on-demand synchronization logic must be adapted to prevent expired data from interfering with retrieval. Core processing steps rely on associated information for specific applications, so prompts must accurately extract individual case-specific fields and cannot use generalized processing. Errors directly relate to administrative remedies, so the accuracy of information in multi-turn dialogue must strictly match official document content, and no information deviations are allowed.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of standardized documents in this category, preserves semantic integrity, and prevents key information from being truncated |
| Context Window Upper Limit | 80%–90% of the model's native window size | Reserves space to carry the messages historical content of multi-turn dialogue, avoiding loss of context due to insufficient window size |
| Session ID Binding | Bind chatId | Ensures continuous context for multi-turn dialogue in the same scenario, preventing cross-application session confusion |
| Single Recalled Document Count | Top 3–5 entries | Focuses on the core retrieval needs of this category, reducing interference from irrelevant documents on model judgment |
| Prompt Variable Configuration | Bind application number and applicant information from variables | Accurately associates individual case-specific data, ensuring reply content matches the specific requirements of a single application |
| Conversation Log Storage Upper Limit | 1000 MB (calibrated based on actual testing) | Adapts to the document storage requirements of single sessions in this category, avoiding excessive occupation of storage resources |
Three Common Misconfigurations
- Phenomenon: Multi-turn dialogue provides irrelevant answers starting from the second question. Cause:
chatIdis not correctly bound, so session context is not retained, or the context window upper limit is set too low to carry historicalmessagescontent. - Phenomenon: Key information is truncated in uploaded standardized documents. Cause: Chunk length settings do not match the paragraph structure of documents in this category, causing core processing milestone information to be split and lost.
- Phenomenon: Conversation logs cannot be automatically cleaned according to the administrative processing cycle. Cause: No retention duration rules matching the application cycle of this category are configured, or the session's application status logic is not associated.
How to Verify Proper Configuration
- Initiate a simulated government information disclosure application conversation, ask 3 consecutive related questions, and verify that the model can answer by associating historical application information.
- Upload a standardized document of this category, check that no key paragraphs are truncated after chunking.
- View the
messagesfield records of the session, confirm that historical messages of the same session are fully retained and thechatIdis consistent. - After configuring prompt variables, initiate a test, verify that the model can correctly extract and use bound specific information such as the application number.
The values provided are common starting points and should be evaluated against local sample data.
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.