What the data for this category looks like
Automated equipment investment research data comes primarily from official manufacturer product specifications, industrial control industry standard documents, on-site operation and maintenance logs, research patent literature, and industry technical journals. Product specifications and industry standard documents are updated annually or during major product iterations. Operation and maintenance logs and real-time operation data are synchronized per collection cycles. Patent and technical journal content is updated quarterly.
Document structures include parameter detail sheets, installation wiring diagrams, fault code tables, and maintenance cycle guides. Fields cover rated power, response time, communication protocols, and similar items. Units typically use kW, ms, and protocol identifiers. Fault codes use four-digit numerical identifiers.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-dimensional parameters and standardized fields of automated equipment data require prompts to clearly define unit verification and code mapping rules. This prevents confusion between power units or incorrect fault code matching. Real-time operation data and multi-turn related questions require retaining context associations. This ensures subsequent questions can bind to the currently discussed device model. Long documents and dense fields restrict context window size. This prevents model overflow caused by overly long input. Industry-specific terms must be calibrated in prompts. This avoids ambiguity between general terms and industrial control industry terminology.
How to Set Configurations
| Config Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | Last 8-12 turns of dialogue | Multi-turn questions for automated equipment investment research usually revolve around multi-dimensional parameters of a single device. 8-12 turns can cover 3-4 consecutive related questions, avoiding irrelevant content introduced by excessive historical context |
prompt_template | Custom template including "device parameter unit verification, fault code mapping" | Automated equipment data contains a large number of unit-bearing parameters and standardized fault codes. Clearly calibrating units and code meanings in prompts ensures reply accuracy |
recall_top_k | Top 6-10 recall results | Technical documents for automated equipment have dense fields. Excessive recall leads to context overload. 6-10 results cover core parameters, installation requirements, and fault handling content |
similarity_threshold | 0.75-0.85 | Device parameters have high similarity differentiation. A threshold that is too low introduces documents from unrelated devices. A threshold that is too high misses matching detailed parameter documents |
global_variables | Configure device model, operating conditions, data collection time | Investment research often requires adjusting analysis logic for specific models and operating conditions. Global variables simplify parameter transfer in multi-turn dialogue, avoiding repeated input |
mcp_trigger_condition | Trigger based on device model keywords in user input | Investment research questions for automated equipment are usually tied to specific models. MCP calls exclusive knowledge base content for the corresponding model, improving reply accuracy |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: Context fields returned by dialogue nodes are empty. AI replies do not associate with knowledge base content. Cause: The
recall_top_kparameter is not configured correctly. Recall results are not written to context variables. - Phenomenon: When a user asks for the rated power of a specific model of servo motor, the AI replies with parameter information for another model of device. Cause: The similarity threshold is set too high. Target model parameter documents are not recalled, or current device model information is not bound in the prompt.
- Phenomenon: Corresponding logic is not triggered after MCP is configured, or irrelevant content is returned. Cause: The MCP trigger condition is not configured with automated equipment model keywords, leading to failed trigger rule matching.
How to Confirm Configuration is Complete
- Initiate a test dialogue with multiple consecutive related questions. For example, first ask for the input voltage of a specific model of PLC, then ask for its corresponding maintenance cycle. Check if the AI reply associates with parameters of the same model.
- View context fields in the dialogue log. Confirm that the historical questions of this dialogue and recalled knowledge base document content are included.
- Configure an MCP trigger test. Input keywords for a specified device model. Check if the corresponding MCP call is triggered and exclusive content is returned.
- Adjust the similarity threshold. Test the number of recall results under different thresholds. Confirm that matching results match expected device parameter documents.
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.