What the data for this category looks like
Commercial property financing daily report data is sourced from lease ledgers of property operators, lease filing information from local housing and urban-rural development departments, and loan ledgers and credit approval records from cooperating financial institutions. Updates sync all full financing dynamics from the previous day every early morning. Documents are split by individual commercial property project. Each document includes fields such as project name, location, operating business type, financing subject, financing amount, financing method, disbursement date, annualized interest rate, and repayment term. The unit for amount is ten thousand yuan, the unit for interest rate is annualized percentage, and the unit for term is month.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Multi-field structured data for commercial property financing daily reports requires multi-turn dialogue to first lock in a specific property project. Without this step, the model cannot accurately match corresponding financing data. Daily updates mean prompts must include rules to call the latest daily report data, preventing use of expired information. Fields with units such as amount and interest rate require unified unit standards across multi-turn dialogue to avoid mixed unit outputs. There are many detailed categories of financing methods, so multi-turn conversation context must retain the user’s previous screening conditions. This ensures subsequent follow-up questions link back to the initially specified project. Each daily report includes a large number of projects, so prompts must restrict the model to generate responses only using project data associated with the current session, avoiding cross-project confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 16000 tokens | Commercial property financing daily reports have many fields. Multi-turn dialogue requires sufficient context to retain project screening and detailed follow-up questions. 16000 tokens cover 3 to 4 complete conversation rounds |
promptTemplate | First confirm the target property project name, then answer based on the latest financing daily report data for that project. The output must strictly use the units specified in the documents | Commercial property financing daily reports have scattered projects and fields with units. Pre-confirmation avoids data matching errors. Clearly specifying unit requirements unifies output standards |
relevantChunks | Top 3–5 | Each project corresponds to one daily report entry. Too many recalled entries introduce irrelevant project information. Too few entries may miss key details |
similarityThreshold | 0.75–0.85 | Project names have abbreviated and full name variations. A threshold that is too low introduces irrelevant projects. A threshold that is too high fails to match abbreviated project names |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Batch daily report files contain data for multiple projects. A longer timeout ensures complete parsing of all entries |
UPLOAD_FILE_MAX_SIZE | 50 MB | Meets storage and parsing requirements for monthly full-volume batch daily report files |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: The Human field is null in the response record preview when calling the dialogue API. Cause: The
userfield that complies with FastGPT V4.9.1 requirements is not correctly passed in the request parameters, resulting in missing session user identification. - Symptom: Individual users cannot view their own historical conversations when multiple users share the application. Cause: The
enable_user_historyconfiguration item is not enabled, or an independent session ID is not generated for each user. - Symptom: The financing amount unit in the model's output does not match the document during multi-turn dialogue. Cause: The prompt does not explicitly require strict use of the units specified in the documents, leading to deviations caused by the model converting units on its own.
How to Verify Proper Configuration
- Initiate a single-turn test dialogue, specify a specific property project name, and verify that the response only links to financing data for that project.
- Initiate a multi-turn dialogue, first specify the project, then follow up with questions about financing method details, and verify that the context retains the initial project screening conditions.
- Call the dialogue API, check that the response includes the correct
userfield and session ID, and confirm that the Human field is not null. - Upload a test commercial property financing daily report file, verify that parsed fields are complete, and confirm that the parsing process did not time out.
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.