What the data for this category looks like
The data for specialized equipment intelligent due diligence reports comes primarily from equipment factory certificates, operation and maintenance logs, third-party statutory inspection reports, financial lease ledgers, and ownership certificates. Static data such as equipment model, factory serial number, rated power, and factory date stays fixed long-term. Operation and maintenance data including cumulative operating hours, fault counts, and maintenance records is updated monthly. Annual inspection reports provide the most recent safety performance parameters. The document structure uniformly includes four modules: unique equipment identifier, core parameter ledger, operation and maintenance cycle records, and third-party verification conclusions. Most fields have clear units: power is measured in kW, operating hours in hours, and valuation in RMB yuan.
What constraints these characteristics impose on tool calling and plugins
The characteristics of specialized equipment due diligence data — large number of static parameters, fixed update cycles for incremental operation and maintenance data, clear units on fields, and requirement for traceability — create multiple constraints on tool calling and plugins. Static core parameters can be cached and reused. Configure a parameter cache validity period to avoid repeated requests to third-party interfaces. Incremental operation and maintenance data must support pulling by time range. Plugins must adapt to monthly or quarterly incremental interface formats. Fields have standardized units. Automatically verify unit consistency before tool calling. Also record data sources and pull timestamps to meet due diligence traceability requirements. Single due diligence documents have lengthy content. Tool calling must support batch processing of long text fields to prevent single request timeouts.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
plugin_request_timeout | 600 seconds | Specialized equipment due diligence data includes long-text operation and maintenance records and multi-interface associated requests. Sufficient timeout time is required to ensure complete data pulling |
embedding_model | bge-large-zh-1.5 | This model has high semantic coding accuracy for professional equipment parameters and operation and maintenance terminology, and is compatible with mainstream vector database interfaces |
plugin_content_type | application/json, application/x-www-form-urlencoded | Specialized equipment data source interfaces support both request formats. Compatible configuration can adapt to different third-party data sources |
recall_top_k | Top 8 entries | Specialized equipment due diligence reports have a large number of core parameter entries. A sufficient number of associated fields must be recalled to support due diligence analysis |
field_unit_check_switch | Enabled | Most specialized equipment fields have clear units. Enabling verification can avoid call failures caused by parameter format mismatches |
cache_static_param_ttl | 2592000 seconds | Static equipment parameters are not updated within 30 days. Caching configuration can reduce the frequency of repeated calls to third-party interfaces |
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 the settings.
Three common mistakes
- Symptom: The plugin call returns a
400 Bad Requesterror, prompting that the request body format is invalid. Cause:plugin_content_typeis not configured to a compatible format. Only specifyingapplication/jsonfails to adapt to theapplication/x-www-form-urlencodedrequest requirements of some equipment data sources. - Symptom: Vector recall results have unexpected matching degrees, or dimension mismatch errors occur when storing vectors. Cause: The embedding dimension of the target service is not confirmed to match the 1024 dimensions of
bge-large-zh-1.5. A model interface with other embedding dimensions is incorrectly used. - Symptom: The intelligent question answering interface returns abnormal results after being called. Local debugging works normally, but the call fails in the online environment. Cause: The interface domain name whitelist is not correctly configured, or cross-origin request restrictions in the online environment are not handled, resulting in request interception.
How to confirm that the configuration is correct
- Initiate a plugin call request, check the returned request header and request body format, and confirm that they match the configured
plugin_content_type. - Call the embedding interface to generate a vector of the equipment parameter text, and check that the vector dimension matches the 1024 dimensions of
bge-large-zh-1.5. - Trigger a tool call for a long document, check the request timeout log, and confirm that the elapsed time does not exceed the configured
plugin_request_timeout. - Check the pull record of static parameter cache, and confirm that the cache validity period matches the setting of
cache_static_param_ttl.
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.