What the data for this category looks like
Marketing content data for construction machinery in the finance/insurance/wealth management sector primarily comes from manufacturer official product manuals, technical parameter documents, dealer regional adaptation script libraries, offline exhibition materials, after-sales common question sets, and financial institution exclusive financing and insurance product script libraries for construction machinery. Update frequency aligns with new model releases, compliance policy adjustments, and financial product iterations, with no fixed cycle. Documents are mostly categorized by model number, and a single document typically includes fields such as model number, rated lifting capacity, working radius, applicable working conditions, configuration options, compliance certification numbers, and more. Parameter fields are accompanied by clear units such as tons and meters, and are linked to information such as rates and terms of corresponding financial products.
What constraints these characteristics impose on multi-turn conversation and prompt engineering
As marketing content in the finance/insurance/wealth management sector, it must match both construction machinery parameters and financial product terms. There are many model categories, and parameter fields have clear units. This requires multi-turn conversations to first guide users to clarify specific model numbers, usage scenarios, and funding needs, to avoid generic responses. Documents have no fixed update cycle, so a knowledge base version verification logic must be added to the prompt to ensure the called marketing content uses the latest version. Differences in regional adaptation scripts require multi-turn conversations to confirm the service area upfront, matching corresponding dealer scripts and regional financial product policies. Long documents contain many parameter details, so the prompt must limit the recall scope to model-related parameters and associated financial product information mentioned by the user, to avoid irrelevant content interfering with the conversation flow.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Construction machinery parameters and financial product-related documents are lengthy. Multi-turn conversations need to retain sufficient context for model parameters and financial terms to avoid losing critical information |
RECALL_TOP_N | Top 6–8 results | Construction machinery marketing content in the finance/insurance/wealth management sector needs to cover multiple dimensions such as model parameters, applicable working conditions, and financial product terms. Excessive recall will disrupt conversation logic |
SIMILARITY_THRESHOLD | 0.75–0.85 | High precision is required for construction machinery parameters and financial products. Low-match irrelevant documents must be filtered out, while matching for regional adaptation scripts and product variants must be retained |
PARSE_CHUNK_SIZE | 1200–1500 characters | Parameters in single product manuals and financial-related documents are concentrated. Too long segments will reduce recall accuracy, while too short segments will add context redundancy |
WORKFLOW_MODEL_SELECTOR | Specialized model with fine classification capability | Accurate identification of detailed information such as user-mentioned models, working conditions, and funding needs, to adapt to question classification and demand matching requirements |
CONVERSATION_TIMEOUT | 120 seconds | Users need to gradually provide multi-turn information such as models, working conditions, and funding needs. Timeouts will interrupt valid conversation flows |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The question classification field returned by the workflow node is empty, or the classification result does not match actual model parameters and financial products. Cause: The selected model is not adapted to mechanical parameters with clear units, detailed model classifications, and financial product keywords, and cannot accurately identify professional terminology.
- Phenomenon: Variables such as model number or funding needs set in the previous round cannot be called in subsequent conversations. Cause: The scope of global variables is not configured as session-level, causing variables to only take effect in a single request.
- Phenomenon: After uploading product manuals or financial-related documents via API, no recall results for corresponding parameter fields are returned. Cause: The uploaded file format does not match the preset parsing rules, or the uploaded file size exceeds the configured limit.
How to confirm successful configuration
- Initiate a test conversation that includes specific model numbers, usage scenarios, and funding needs, and check whether multi-turn context retains the previously entered relevant information.
- Trigger the question classification node of the workflow, and verify that the classification result matches the test-entered construction machinery-related keywords and financial product types.
- Upload a standard-format product manual and financial-related documents via API, and check whether the parsed fields include construction machinery-specific parameters and associated financial product information.
- View session logs to confirm that global variables are passed as expected during multi-turn conversations, with no losses.
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.