What the data for this category looks like
The data for specialized equipment intelligent due diligence reports primarily comes from equipment factory qualification certificates, annual operation and maintenance logs, third-party quality inspection reports, and industry regulatory filing documents. Static data includes factory parameters such as equipment model, serial number, rated power, operating pressure, and similar items. Dynamic data covers operation information such as daily operating duration, number of faults occurred, records of repaired or replaced components, and similar items. Most document structures are a mix of multi-page structured tables and long text. Fields include quantifiable parameters with clear units — for example, rated power is measured in kW, operating duration in hours — and uniquely identified items such as equipment serial numbers, compliance inspection numbers, and similar items. Update rhythm is divided into two categories: static data with fixed updates, and dynamic data with daily or batch updates.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The separation of static and dynamic attributes of specialized equipment due diligence data requires that multi-turn dialogue must clearly distinguish the equipment type associated with historical queries in the context, to avoid confusing parameters of different batches or models of equipment. The mixed structure of long documents requires prompts to specify a recall range, only extracting operation and maintenance records or factory parameters related to the current query, to prevent irrelevant content from interfering with model output. Fields with clear units require prompts to forcibly retain the original units, to avoid data distortion caused by the model performing arbitrary conversions. For dynamically updated operation and maintenance data, the dialogue process must prioritize calling the latest uploaded log files, and old version historical records may only be used when explicitly specified.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Specialized equipment due diligence reports often contain multiple pages of operation logs and parameter documents. An overly long context may cause the model to forget key fault records |
UPLOAD_FILE_MAX_SIZE | 500 MB | Due diligence reports for a single piece of equipment often include multiple quality inspection reports and operation and maintenance logs, so support for large file uploads is required |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing due diligence documents for large equipment requires reading multi-page structured data, so the timeout period must be adapted to long document parsing |
Recall count | Top 10 entries | Fault records and compliance items for specialized equipment are scattered throughout the document. A sufficient number of recalled entries is needed to cover multi-dimensional due diligence requirements |
Similarity threshold | 0.75 | Distinguish equipment model parameters from those of other devices in the same category, to avoid recalling due diligence data from unrelated equipment |
rerank_top_n | Top 5 entries | Rerank the recalled entries, prioritize matching the equipment serial number and fault type of the current conversation, to improve accuracy |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- API calls return file parsing failure with status code 413 | The
UPLOAD_FILE_MAX_SIZEparameter was not adjusted. The default configuration cannot adapt to the large-volume attachments of specialized equipment due diligence reports. - Discrepancies exist between answers from platform testing and API calls | The
maxContextandprompt_templateparameters consistent with platform settings were not specified in API requests, leading to inconsistent context and prompt configuration. - Irrelevant model numbers of specialized equipment data are recalled during dialogue | The
Similarity thresholdparameter was not set to filter low-match document fragments, leading to confusion of due diligence data.
How to confirm configurations are properly set
- Upload the largest single specialized equipment due diligence report, check whether the parsing status shows completed with no error prompts.
- Call the API with parameters consistent with platform testing, compare whether the answer content returned by the two attempts matches.
- Enter a query containing a specific equipment serial number, check whether the recalled document fragments only correspond to the due diligence data of that serial number.
- Initiate multi-round progressive queries, confirm that the model can retain the equipment model and fault type information from the previous round of dialogue.
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.