What this category of data looks like
Party building learning Q&A data for the refined oil sales sector comes primarily from internal party building study manuals, study ledgers from frontline gas stations, and thematic education materials issued by higher-level party committees. Updates follow a set schedule: weekly updates for current study content, and monthly updates for supporting assessment question banks. Each single document includes a title, issuing unit, release date, main content, and applicable position field. The main body of content ranges from 1000 to 3000 characters. No unified fixed format exists across documents, and some documents include embedded study point annotations.
Constraints on multi-turn dialogue and prompt engineering
Ununified document formats and varying single-document lengths require prompts for multi-turn dialogue to include structure adaptation guidance. This ensures the model correctly extracts core points from different documents. Weekly and monthly update cycles require regular cleanup of expired study content in dialogue context. This prevents the model from using outdated data. The applicable scenario of frontline sales positions requires prompts to tie to refined oil sales business scenarios. This combines party building requirements with service specifications. Large variation in document lengths requires context truncation for multi-turn dialogue to filter by content priority. This avoids redundant information interfering with model outputs.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Session ID Binding | Bind chatId to the current session's unique identifier | Ensures conversation records returned by the API can correctly associate user questions with AI replies |
| Dialogue History Truncation | Truncate the most recent 3 turns of dialogue via messages | Adapts to the short context requirements of party building learning Q&A, avoids interference from redundant information |
| Global Variable Configuration | Bind variables to party building study material IDs | Ensures fixed study data can be called within the same conversation, supports future updates |
| Context Window Size | 800–1200 characters | Adapts to the common length of single party building documents, aligns with model processing limits |
| Temperature Parameter Setting | 0.6–0.8 | Balances the rigor of party building content with the flexibility of responses |
| Question Record Retrieval | Extract each round of user input per session | Ensures all content from three questions can be obtained |
Three common misconfigurations
- Phenomenon: Data returned by the "Get Conversation Record List" API has no clear correspondence between AI replies and user questions. It is impossible to distinguish which reply matches which question. Cause: Failed to bind
chatIdto the current session's unique identifier, resulting in historical conversation data not being grouped and associated by session. - Phenomenon: The initial global variable value passed for the first time displays normally in two components, but modification of this variable value by the model within the same conversation does not take effect. Cause: Did not enable variable edit permission in the
variablesconfiguration, and the prompt did not explicitly state that the model can update global variable content. - Phenomenon: After three consecutive questions, only the content of the last question can be obtained. The user questions from the first two rounds cannot be retrieved. Cause: Did not configure the parameter to extract each round of user input via
messages, only truncated the last round of dialogue fragments.
How to confirm the configuration is correct
- Call the "Get Conversation Record List" API, check if the returned data contains the
chatIdfield, confirm that each AI reply and user question are bound to the same session identifier. - Enter two different party building learning questions in sequence in the same conversation, check if the model outputs match the corresponding questions respectively, and do not confuse context content.
- Modify the initial value of the global variable, trigger the variable update operation in the conversation, confirm that the model output uses the updated variable value.
- View the conversation history record, confirm that each round of user questions is fully retained, and the first two rounds of question content can be retrieved through the history.
The values provided here 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.