What This Category of Data Looks Like
Data sources for this category include government websites, document management systems, government information disclosure directories, policy document repositories, and press conference materials. The update frequency is daily. Commonly processed documents include policy documents, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. Document fields include policy document numbers, issuing authorities, public attribute tags, approval status, column affiliation, and more. The length of individual documents varies widely, ranging from several hundred characters for column update records to tens of thousands of characters for complete policy documents.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Scattered data sources and daily updates require the multi-turn dialogue process to support incremental data synchronization and context integration across multiple source documents, to avoid missing newly published policy content. The wide range of document lengths requires adapting input from short entries to long files. Multi-turn dialogue context management needs to set reasonable truncation rules to prevent exceeding model capacity limits. Documents include dedicated fields such as public attribute and approval status. Prompt engineering must explicitly specify verification logic for these fields to ensure the review process accurately identifies compliance requirements. Additionally, public release requirements mandate that dialogue-generated content strictly matches public disclosure specifications, to avoid including unapproved information.
Configuration Settings
The following configuration items are optimized for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to documents of varying lengths including policy files and short entries, balances context completeness and retrieval efficiency |
| Overlap Length | 100–200 characters | Prevents context breaks between adjacent document chunks after splitting, ensures logical consistency of retrieval results |
| Maximum Single Search Results | Top 5 entries | Controls the number of returned knowledge base results, meets the precision requirements of government review scenarios |
| Context Window | Calibrated based on actual testing | Adapts to the capacity limits of different models, prevents exceeding model processing capabilities during multi-turn dialogue |
| Prompt Template | Includes public attribute verification, approval status confirmation, and public disclosure specification instructions | Aligns with the review requirements of this category, ensures generated content complies with government public disclosure rules |
| Conversation History Storage | Enabled | Supports context continuity for multi-turn dialogue, meets the traceability requirements of file review |
Three Common Misconfigurations
- Symptom: After calling the dialogue API, no conversation history record corresponding to
chatIdis found in the database. Cause: ThechatIdfield is not correctly carried in the request parameters, or the conversation history storage configuration is not enabled, resulting in session data not being persisted. - Symptom: When debugging knowledge base search in a workflow, the returned results are normal, but the retrieved knowledge base content is not referenced in the AI dialogue step. Cause: The prompt template does not explicitly specify the rule for referencing retrieval results, or the retrieved document fragments are not correctly injected into the
messagesparameter. - Symptom: After entering a single review-related query, the preset multi-turn continuous questioning process is not triggered, and only a single-round reply is returned. Cause: The trigger condition for continuous questioning is not configured in the
variablesparameter, or the prompt engineering does not explicitly require multi-turn iteration logic.
How to Verify Proper Configuration
- A test dialogue covering policy review and public attribute verification is executed. The returned results are reviewed to confirm whether they reference matching document content in the knowledge base, verifying that the retrieval and reference logic operates correctly.
- The conversation history storage module is reviewed to confirm that the session data corresponding to
chatIdhas been correctly saved, verifying that the persistence configuration is effective. - The public attribute field of the test document is adjusted. The dialogue-generated content is checked to confirm whether it adjusts the review prompt based on this field, verifying the adaptability of the prompt template.
- Multiple consecutive review-related queries are sent. The conversation context is confirmed to continue correctly, verifying that the multi-turn dialogue configuration is effective.
The values provided 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.