What Data for This Category Looks Like
This category’s data comes primarily from government portals, internal document management systems, government information disclosure directories, policy document repositories, and press conference materials. Updates occur daily. Commonly processed documents include policy documents, policy interpretation drafts, public directory lists, information release approval forms, press releases, and website column update records. Document fields include document number, public attribution, confidentiality level, release deadline, corresponding column ID, and more. Individual document lengths vary widely, from short notices of a few hundred words to complete policy texts of tens of thousands of words.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering Workflows
Daily high-frequency updates require multi-turn dialogue chains to deliver fast responses. This avoids delayed public content caused by data latency. Wide-ranging document types require prompts to adapt to inputs of different formats. Prompts must also impose special constraints for policy document confidentiality attributes and public directory column correspondence. Fixed document fields require dialogue flows to explicitly extract corresponding field values. This prevents missing required items. In cross-department collaboration scenarios, multi-turn dialogue must retain context to transmit approval status, modification opinions, and other information. This ensures process continuity.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph structure of policy documents and interpretation drafts, avoid truncating key policy clauses |
| Overlap Length | 100–150 characters | Retain contextual association between chunks, prevent logical breaks in policy interpretations |
| Single Search Limit | Top 8 results | Cover multi-source recall results from public directories and policy repositories, ensure comprehensive information |
| Maximum Context | 8000 tokens | Adapt to context transmission for multi-turn dialogue approval processes, retain historical modification opinions |
| Question Optimization | Calibrated based on actual testing | Address automatic optimization needs for policy terminology, adjust optimization rules based on business scenarios |
variables | Bind public attribution and column ID fields | Extract fixed fields from document metadata for verification and triggering in dialogue flows |
Three Common Configuration Mistakes
- Symptom: Policy content retrieved from the knowledge base is truncated, only showing partial clauses. Cause: Chunk length is set too small, or insufficient overlap length leads to lost context, failing to fully extract core policy content.
- Symptom: A 500 error is returned when importing public directory or policy documents. Cause: Mismatch between the embedding model deployment environment and FastGPT version, or parsing failure caused by non-standardized metadata fields in documents.
- Symptom: Historical approval opinions are not correctly called during multi-turn dialogue. Cause:
variablesfields are not properly bound, or completemessagescontext is not transmitted, leading to loss of historical interaction information.
How to Verify Correct Configuration
- Upload a single policy document and public directory document, check that chunk results cover the full text with no key clauses truncated.
- Initiate a simulated dialogue with a multi-turn approval workflow, confirm that historical interaction context is correctly loaded.
- Configure the
variablesfield to bind metadata such as public attribution and column ID, check whether corresponding fields are automatically extracted and verified during the dialogue flow. - Trigger the question optimization function, confirm that policy terminology and standardized public expressions are correctly adjusted.
- Check that the
chatIdof the dialogue chain is correctly generated, ensuring correct context attribution for multi-turn dialogue.
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.