What this type of data looks like
Data sources linked to the function entry include real-time user interactive input within the terminal, bound structured knowledge base documents, and pre-configured multi-turn dialogue node settings. User interactive data generates in real time. Knowledge base documents update per the configured synchronization cycle. Pre-configured settings only change when edited. The document structure of a single data entry includes: conversation identifier, current turn user input, context history, associated knowledge base recall entries, and current prompt template parameters. Fields include:
- Conversation ID (string type)
- Input text (measured in characters)
- Context turns (integer unit)
- Number of recalled segments (integer unit)
What constraints these characteristics impose on multi-turn conversations and prompt templates
Real-time user interactive data cannot accumulate indefinitely. Excessive context will exceed the model’s context window, so set reasonable context truncation rules. Bound structured knowledge base documents require prompt templates to accurately match the field formats of recalled entries. This prevents failure to correctly reference business information. Pre-configured multi-turn dialogue node settings require prompts to bind to node parameters. This ensures each turn of conversation context aligns with the business scenario. User interactions in financial scenarios often include sensitive information such as policy numbers and identity identifiers. Prompts must explicitly retain key context fields to prevent critical information from being truncated.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContextTurns | Last 3-5 turns | Users in financial scenarios typically complete business inquiries within 3 turns. Excessive turns will exceed the model's context window and increase inference costs |
promptVariableInjectMode | Real-time injection before conversation initiation | User input for the function entry generates in real time. Variables must match real-time parameters such as the current conversation's policy number and terminal ID |
recallTopK | Top 4-6 entries | Financial knowledge bases have a large number of entries. Too many will cause context overload. Too few will miss key business information |
contextTruncateThreshold | Calibrated to 70%-80% of the model's context window | This adapts to the context window limits of different large models and avoids triggering truncation errors |
responseLanguage | Auto-match based on user input language | Users in financial scenarios may mix Chinese and English. Output language must match input language |
variableNotFoundReply | Return preset business guidance | Missing variables will cause retrieval failure. Clear information supplementation guidance must be provided |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: After passing prompt variables via API call, the returned result does not replace the variables, which does not match the preset business logic. Cause: Real-time injection mode is not enabled in the
promptVariableInjectModeconfiguration. Variables are only loaded during template initialization and do not match real-time parameters of the current conversation. - Phenomenon: After a text content extraction failure occurs during multi-turn conversation, the system does not return a preset prompt immediately. It only responds after waiting for the user's second input. Cause: The extraction failure branch in the workflow is not directly connected to the specified reply node. The process still waits for the extraction operation to complete.
- Phenomenon: After configuring English prompts and an English knowledge base, the model only outputs Chinese. Cause: The
responseLanguageparameter is fixed to Chinese, and is not configured to automatically adapt to the user's input language.
How to confirm correct configuration
- Launch a test conversation, input test content that includes preset variables, and check whether variables in the returned result are correctly replaced.
- Simulate a text extraction failure scenario, and verify whether the system immediately returns a preset guidance prompt without waiting for additional input.
- Switch the input language to English, and check whether the model's output language matches the input language.
- View the conversation context log to confirm that only the specified number of turns of interactive content are retained, and the context window limit is not exceeded.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.