What Data for This Category Looks Like
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints for Multi-turn Dialogue and Prompt Engineering
Government and enterprise business handling data has many structured fields and standardized units. Multi-turn dialogue must accurately match field names to avoid process guidance errors from vague retrieval. The lack of a fixed update cycle requires prompts to support dynamic calling of latest process data, without relying on static preset content. A single document containing multiple business items requires preserving field integrity during chunking. This prevents splitting complete process descriptions across nodes, which would cause logical breaks. Most government and enterprise user inquiries are colloquial process questions. Prompts must convert natural language into structured field queries to ensure retrieved content matches the current handling item.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the field density of government and enterprise business documents, avoid splitting complete descriptions across business nodes |
| Overlap Length | 150–200 characters | Retain context from adjacent chunks, prevent truncation of process node logic |
chatId | Generated as a unique session identifier | Distinguish business handling sessions of different users, avoid context confusion |
messages | Retain the last 10 rounds of interactions | Focus on dialogue history for the current handling process, reduce interference from redundant information |
variables | Bind business item coding variables | Dynamically call process data for corresponding items, support multiple business scenarios |
| Question Optimization | Enabled | Convert colloquial user inquiries into structured queries, align with field matching rules |
Common Misconfigurations
- Phenomenon: When requesting to repeat content from an XLSX file in the knowledge base during a dialogue, a prompt indicating inability to read the file is returned. Cause: The target worksheet or field is not explicitly specified in the prompt, or structured header information from the file is lost during knowledge base chunking.
- Phenomenon: During multi-turn dialogue, the model deviates from the current business handling process and references irrelevant descriptions of historical items. Cause: The
messagesparameter is not restricted to only retain interaction records from the current session, introducing redundant context from old sessions. - Phenomenon: After creating a knowledge base via the API, initiating a dialogue fails to retrieve content from that knowledge base. Cause: The unique identifier for the target knowledge base is not passed in the
variablesfield of the dialogue request, preventing the model from locating the correct data source.
How to Verify Proper Configuration
- Upload a test XLSX file for government and enterprise business handling, send a request to repeat the file’s content, and confirm the returned content matches the fields and node information in the file.
- Send two or more rounds of business handling inquiries, and confirm the
messagesparameter used by the model only includes interaction records from the current session, with no redundant information from old sessions. - Check the request parameters for the dialogue API, confirm
chatIdis the unique identifier for the current session, and thatvariablesare bound to the correct knowledge base association information. - Adjust the chunk length parameter, and confirm the chunked document does not split complete descriptions across business nodes.
The values provided here are common starting points and should be evaluated against sample data relevant to the specific use case.
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.