What the data for this use case looks like
Verification data for funding source KYC comes primarily from three types of materials submitted by customers: bank corporate or personal account statements, annual tax returns, and assessment certificates for fixed or financial assets. Data is submitted either in one batch during a single verification, or updated regularly per compliance audit requirements. Most documents take the form of multi-page PDFs or structured CSV files. Bank statements include fields such as transaction date, transaction amount, counterparty, and fund flow. Tax returns include fields such as reporting period, tax payable, and tax actually paid. Asset certificates include fields such as assessed value, issuing institution, and validity period. The unit for transaction amount is uniformly Renminbi yuan. The format for transaction date is YYYY-MM-DD.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Funding source verification data consists mostly of multi-page structured files, and field rules vary by data source type. Multi-turn dialogue must process information in batches grouped by material type, to avoid context overload. Funding source verification requires tracing the compliance of every transaction. Dialogue must retain full historical interaction context, to let the model connect related transaction information. Field differences across different materials are significant. Prompts must clearly distinguish extraction logic for different data sources, to prevent the model from mixing up fields. Funding source verification has compliance requirements. Dialogue must proactively ask for missing key fields, as single questions cannot cover all verification information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Funding source verification requires retaining context for multi-round transaction checks. This length covers roughly 10 rounds of dialogue interaction, and prevents context overflow. |
dialogue_round_limit | 10–15 rounds | Standard funding source verification requires checking transactions one by one and supplementing missing counterparty information. 10–15 rounds covers most verification scenarios. |
prompt_template | For funding source KYC scenarios, first identify the submitted material type, then extract corresponding compliance fields separately for bank statements, tax certificates, and asset certificates, ask for missing key information, and finally generate a standardized verification result | The verification data fields for funding sources vary by material type. A clear template prevents the model from mixing up extraction rules for different data sources. |
recall_top_k | Top 6–8 entries | Funding source verification requires connecting transaction context. Too many recalled entries causes context overload, too few causes omission of key transaction details. |
similarity_threshold | 0.75–0.85 | Accurate matching of counterparty and verification material information is required. This threshold balances matching accuracy and recall rate. |
file_parse_chunk_size | 1000–1500 characters | Statement files for funding sources are mostly long texts. Chunking retains complete transaction context, and improves extraction accuracy. |
The parameter values provided on this page are all common recommendations used as a starting point 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: A call to the dialogue interface returns
400 Bad Requestwith the prompt "Context length exceeded". Cause: ThemaxContextparameter is not configured, and the retention length of dialogue history is not limited. This causes the context from multi-round transaction checks to exceed the maximum length supported by the model. - Symptom: Fields such as
fund flowandissuing institutionreturned by dialogue are empty, and no follow-up questions are triggered. Cause: The prompt does not explicitly require the model to check for completeness when extracting fields, and does not define follow-up question rules for funding source scenarios. This causes the model to miss key compliance fields. - Symptom: Complete dialogue records cannot be obtained via the interface, only partial interaction content is returned. Cause: The
dialogue_history_exportconfiguration item is not enabled, or the correct session ID parameter is not carried when calling the interface. This causes the interface to fail to locate the complete history of the corresponding session.
How to confirm correct configuration
- Launch a test dialogue, input a single-page fragment of a bank statement, and check whether the model can correctly extract the
transaction amountandcounterpartyfields. Adjustsimilarity_thresholdbased on extraction accuracy. - Call the dialogue interface, check whether the returned results include complete session history, and confirm that the
dialogue_history_exportconfiguration item is enabled. - Simulate multi-round follow-up questions about transaction details. For example, first ask about the counterparty of a specific transaction, then ask about related transactions of that counterparty. Check whether the model retains the previous transaction context, and adjust
maxContextbased on context retention performance. - Upload multiple different types of funding source materials, such as bank statements and tax returns. Check whether the model can extract corresponding fields separately by material type, and adjust
prompt_templatebased on field matching performance.
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.