What the data for this category looks like
Data sources include official vehicle model configuration databases, dealer retail management systems, and brand marketing material central platforms. Update cadence: full refresh upon vehicle model generation updates, weekly sync for regular configuration parameters, and monthly updates for marketing script assets. Document structure: one structured document per single vehicle model. Fields include vehicle identification code, body dimensions (unit: millimeters), power parameters, official suggested retail price (unit: Chinese Yuan), standard/optional equipment list, official promotional script text, with some fields linked to real-time inventory status.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
The large number of structured fields with clear units requires that prompt engineering must specify parameter unit matching rules to avoid confusing size or price descriptions. Multi-turn dialogue must track the specific vehicle model specified by the user to prevent cross-model parameter confusion. Marketing script generation must link to real-time inventory status, requiring the context window to retain association information between vehicle models and inventory. Single vehicle model documents have significant length, so the context recall volume for single model data must be limited to avoid exceeding the model's context threshold.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The average length of a single vehicle marketing document is approximately 3000 characters. Combined with multi-turn dialogue context, retaining 2-3 rounds of vehicle parameter dialogue requires over 8000 characters to avoid critical parameter truncation |
relevanceThreshold | 0.75–0.85 | Vehicle parameters are high-distinction structured data. A threshold that is too low will recall irrelevant vehicle data, while a threshold that is too high will miss matching sub-model configurations |
rerankTopN | Top 3 entries | Single vehicle parameter data is concentrated. Reranking too many entries will increase context redundancy. Prioritize recalling the most matching vehicle parameters for user queries |
promptTemplate | "Please generate marketing scripts based on the structured parameters of the specified vehicle model, strictly match field units, and confirm the specific vehicle model mentioned by the user in multi-turn dialogue. If using an inference model, replace the inference process tags with a format recognizable by FastGPT" | Adapt to structured data and multi-turn tracking requirements for the vehicle category, clarify unit matching, vehicle model tracking, and inference tag replacement rules |
responseMaxTokens | 1500–2000 characters | A single marketing script must cover core selling points and parameters of the vehicle model. Excessive length will exceed user reading limits, while insufficient length will fail to deliver complete information |
fileParseChunkSize | 1000–1500 characters | Split single vehicle documents to retain complete parameter blocks, avoid parameter information fragmentation caused by splitting, and adapt to context recall for multi-turn dialogue |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The configured system prompt does not take effect, generated marketing scripts do not match parameter units, 400 Bad Request errors are returned, or inference tags are not replaced as required. Cause: The prompt was not placed in the system prompt input field, or the prompt does not clearly specify unit matching, vehicle model tracking, and inference tag replacement rules.
- Phenomenon: Multi-turn dialogue fails to correctly associate the vehicle model mentioned by the user, and generated content confuses parameters of different vehicle models. Cause: Context tracking configuration is not enabled, or
maxContextis set too small, causing vehicle model information to be truncated. - Phenomenon: The dialogue application cannot connect normally with integrated plugins, triggering 500 Internal Server Error. Cause: Plugin call parameters are not configured correctly, or plugin trigger nodes are not added to the dialogue workflow.
How to Verify Proper Configuration
- Upload a single vehicle model structured document, initiate a test dialogue, and check whether the generated marketing script matches the parameter units in the document.
- Initiate a multi-turn dialogue, ask for parameters of two different vehicle models in sequence, and check whether the system can correctly distinguish and output content corresponding to each vehicle model.
- Switch the AI model configuration, and check whether marketing script generation complies with prompt requirements across different models.
- View the context recall log, check whether the recalled parameter data comes from the specified vehicle model's document and does not exceed the character range set by
maxContext.
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.