What This Category’s Data Looks Like
The data sources for urban commercial bank intelligent due diligence reports include internal credit management systems, local regulatory reporting databases, corporate credit reporting interfaces, and industrial and commercial public disclosure platforms. Data update frequencies vary: credit data updates on a T+1 basis, regulatory reporting data updates quarterly, and industrial and commercial public information is synchronized in real time. The fixed document structure is divided into four modules: basic overview, credit authorization status, related party transactions, and risk rating. Standardized fields include credit limit (unit: ten thousand yuan), overdue days (unit: days), number of related enterprises (unit: enterprises), and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The scattered data sources and inconsistent update rhythms for urban commercial bank due diligence require multi-turn dialogue to gradually align the time ranges and field standards of each data source, to avoid mixing data from different cycles. The fixed document structure with a large number of fields requires retaining association verification logic between modules during multi-turn interaction, to prevent field matching errors caused by confused context. Fields have clear units, so unified unit conversion rules must be enforced during multi-turn dialogue to avoid calculation deviations in limits. At the same time, due diligence data for different entities is independent, so entity context must be distinguished during multi-turn interaction to prevent interference from cross-entity information.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Urban commercial bank due diligence reports contain multi-module structured fields, and entity information and historical verification logic must be retained during multi-turn interaction to avoid loss of key fields due to context overflow |
recallTopK | Top 6–8 entries | Urban commercial bank due diligence data fields have high relevance, and sufficient historical dialogue and knowledge base entries must be recalled to ensure consistency of multi-turn verification |
similarityThreshold | 0.72–0.78 | Urban commercial bank due diligence data contains a large number of standardized fields, requiring a balance between recall accuracy and coverage to avoid missing key associated information |
promptTemplate | Guide dialogue in modules according to "basic overview - credit authorization status - risk rating", clarify unit unification rules | Adapt to the fixed document structure of urban commercial bank due diligence reports, reduce context confusion during multi-turn dialogue |
autoClearHistoryCondition | Triggered when new entity keywords are detected | Urban commercial bank due diligence is usually carried out according to different enterprise entities, to avoid interference from cross-entity historical context on the current dialogue |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single urban commercial bank due diligence report files have large volume, and sufficient parsing time must be reserved to complete structured splitting |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: After more than 3 rounds of multi-turn interaction, answers to the same question show inconsistent units for credit limits, or miss related enterprise fields. Cause: The
maxContextconfiguration value is too small, and key verification rules from historical dialogue are truncated, causing the model to fail to reuse previously agreed unit unification rules. - Phenomenon: After configuring the automatic context clearing rule, due diligence conversations across enterprise entities still retain historical information from the previous entity. Cause: The
autoClearHistoryConditionis not bound to entity name keyword trigger conditions, or the parameter configuration format of the trigger logic is incorrect. - Phenomenon: After uploading an urban commercial bank due diligence report, complete risk rating data cannot be recalled during multi-turn dialogue. Cause: The
recallTopKconfiguration value is too low, and insufficient historical knowledge base entries are recalled, causing the model to fail to obtain complete risk rating field information.
How to Confirm Proper Configuration
- Initiate a test dialogue with 3 or more interaction rounds, check whether the answer retains the initially agreed unit rules and has no missing fields.
- Switch to a different enterprise entity to initiate a due diligence conversation, verify that historical context is correctly cleared and no cross-entity information interference occurs.
- Upload a single complete urban commercial bank due diligence report, initiate multi-turn field verification questions, and check whether the recalled knowledge base entries cover all core modules.
- View system logs to confirm that log records for context truncation and clearing triggers match the configuration rules, with no abnormal errors.
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.