What the data for this category looks like
Property management intelligent due diligence report data comes from property project archives filed with local housing and urban-rural development departments, daily operation and maintenance ledgers of property service enterprises, monthly inspection reports of elevator and fire protection systems, and data from the owner service ticket system. Project basic archives are synchronized and updated every quarter. Operation and maintenance ledgers are entered and updated daily. Service tickets and rectification records are generated in real time. A single report includes five modules: project overview, facility and equipment list, operation and maintenance records, expense revenue and expenditure, and service evaluation. Each module has detailed fields. Facility and equipment fields include unique device identifier, installation location, last maintenance time, and next maintenance plan. Expense fields include monthly operation and maintenance cost, special maintenance fund usage records. Units are yuan, kilowatt-hours, and standard date format respectively.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources are scattered and have different update rhythms. Multi-turn dialogue must distinguish between static filed data and dynamic operation and maintenance ticket data. Prompts must clearly define context classification rules to avoid mixing information of different timeliness. A single due diligence report contains detailed fields across multiple modules. Multi-turn dialogue must support focused queries by module. Prompts must limit the scope of recalled fields to avoid returning redundant content. Fields have clear unit requirements. Prompts must force the model to match corresponding units in outputs to avoid unit confusion. Operation and maintenance ledgers and ticket data are updated frequently. Multi-turn dialogue must be configured with a real-time data pull mechanism to ensure the information used in conversations is up to date.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Property management due diligence reports include detailed content across multiple modules. An overly long context can cause the model to forget key information. This range adapts to the context carrying requirements of multi-module data |
Recall count | Top 6–8 entries | Core fields of due diligence reports are concentrated in equipment ledgers and expense modules. Too many recalled entries will introduce irrelevant content. This quantity covers core query requirements |
RECALL_SCORE_THRESHOLD | 0.72–0.85 | Due diligence data fields have strong relevance. A threshold that is too low will introduce irrelevant entries. A threshold that is too high may miss key details. This range balances recall accuracy and completeness |
UPLOAD_FILE_MAX_SIZE | 500 MB | A single complete property management due diligence report usually contains a large number of device photos and operation and maintenance records. This value adapts to large file upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large due diligence reports contain multi-page tables and image parsing. This duration covers the parsing process for complex files |
maxTurns | 10–15 turns | Multi-turn due diligence conversations usually focus on subdivided topics such as equipment operation and maintenance and expense accounting. This turn limit avoids invalid dialogue loops and covers complete query links |
The parameter values given on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: When calling the dialogue API via Python code to generate responses, the returned content does not reference the uploaded property management due diligence report document. Cause: The target knowledge base ID is not correctly bound in the request parameters, or the prompt does not explicitly require generating responses based on knowledge base content.
- Phenomenon: In a private deployment environment, an uncaught exception is thrown when running the AI dialogue workflow, and the log shows "file read failed". Cause: Environment variables related to file storage are not correctly configured, causing the system to be unable to access the uploaded due diligence report files.
- Phenomenon: When using FastGPT 4.8.20, the knowledge base page for loading due diligence reports crashes. Cause: This version has a parsing memory overflow issue for multi-module documents, and an exception is triggered when loading a large amount of equipment ledger data.
How to confirm the configuration is complete
- Upload a standard property management due diligence report file, initiate a basic query, and check whether the returned content includes the core module details in the report. Adjust configuration items to match query accuracy requirements.
- Initiate multi-round progressive queries, such as first asking for the last maintenance time of a specific device, then asking for the next maintenance plan of that device. Check whether subsequent replies are associated with the device information from the first query to verify context retention effects.
- View system logs to confirm that there are no abnormal errors in the file upload and parsing process. Adjust timeout configurations to adapt to large file parsing durations.
- Test via API calls to verify that request parameters correctly bind the knowledge base ID, and ensure that returned content references document data in the knowledge base.
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.