Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
Data sources for power generation group work inquiries and responses include internal work guideline documents, archived customer service tickets, officially released work-related policy documents, and historical conversations from offline consultations. The data update cadence falls into two categories: policy documents are synchronized quarterly, while daily work tickets and consultation records are synchronized incrementally on a daily basis.
Each individual work document has a fixed structure, including fields such as item name, handling channel, required materials, handling time limit, consultation response template, and more. The handling time limit field uses "working days" as the standard unit, and some cross-regional items will add supplementary notes on calendar days.
What Constraints Do These Characteristics Impose on the "Multi-turn Dialogues and Prompts" Link
Decentralized data sources and inconsistent update rhythms require multi-turn dialogues to support cross-source context integration, and require regular synchronization of the knowledge base to avoid using expired policy data. Fixed document structures and fields require prompts to clearly define the recall scope, only calling documents corresponding to the current consultation item. Differences in the units of handling time limits require prompts to unify expression to avoid confusion between working days and calendar days. The progressive questioning logic of multi-turn dialogues requires identifying the current consultation item category through historical context, accurately associating the corresponding data documents, and preventing invalid information from different categories from interfering with responses.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique business identifier of the current session | Multi-turn dialogues must associate historical context and recalled work data via chatId to ensure coherent dialogue logic |
messages | Retain the last 10 rounds of conversation content, with no more than 1200 characters per round of input | Power generation group work inquiries are mostly progressive questions, and the last 10 rounds can cover the complete demand chain. Controlling character count avoids exceeding the model context limit |
variables | Preset {"business_scope": "power generation group work items", "time_unit": "working days"} | Pass business scope and unit information, define the recall scope of prompts, and unify the expression specifications for handling time limits |
| Question Optimization | Enabled, add item classification tags to consultation questions | Power generation group work items have clear classifications, and optimization can improve the probability of accurately recalling corresponding work documents |
| Maximum Context Length | 8000–12000 token | Power generation group work documents contain multi-field details, requiring sufficient context to carry complete data for multi-turn dialogues and recall |
| Global Variable Binding | Allocate per session | Ensure that prompts and knowledge base calls within the same session use unified business parameters, avoiding confusion of data across sessions |
Three Common Misconfigurations
- Issue: The content returned after calling the knowledge base does not match the current consultation question, with irrelevant answers. Cause: The recall scope is not limited through
variablesor preset parameters, resulting in the recall of work documents from other business lines. - Issue: When configuring prompts in the workflow, global variables cannot be called correctly. Cause: Global variables are not bound per session, resulting in variables not being passed to the current dialogue node.
- Issue: Unclosed reference mark garbled characters appear at the end of AI output content, which is automatically corrected to normal quotation marks afterwards. Cause: The prompt does not clearly specify the output format, and no escape processing is performed for special characters in work documents.
How to Verify Proper Configuration
- Initiate a single work consultation question, check whether the conversation context retains the last 10 rounds of historical interactions, and confirm that the configuration logic of the
messagesparameter takes effect. - In the prompt editing interface, check whether the preset
variablesparameter content has been loaded, and confirm that the business scope and unit parameters have been correctly bound to the current session. - Construct a test question with no matching work documents, check whether the
chat:LLM_model_response_emptyerror is triggered, and verify that the context length configuration matches the current test requirements. - Generate multiple consultation responses, check whether there are unclosed special symbol garbled characters in the output content, and confirm that the prompt has added format constraint rules.
The values listed above are common starting points and should be evaluated 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.