What the data for this category looks like
Data sources include publicly disclosed credit operation information from state-owned large banks, official public financial statistics datasets from the central bank, and publicly disclosed interbank market transaction data. Daily updates aggregate full bank financing-related data from the previous trading day. Core scale indicators are aggregated for cross-institution comparison on a weekly basis. Documents use structured multi-column tables, with fields including transaction subject, financing term, interest rate benchmark, investment scale, business channel, and more. Field units follow these rules: investment scale uses units of RMB 100 million, financing term uses natural days or natural months, and interest rate benchmark uses annualized interest calculation standards.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering?
Financing daily report data for state-owned large banks is sourced from multiple dispersed channels, updated at high frequency, and uses a structured format. These traits create multiple constraints for multi-turn dialogue and prompt engineering. Disparate data sources have differing statistical definitions. Multi-turn dialogue processes must include gradual guidance to clarify these definitions, to avoid confusion between official disclosure standards and interbank statistical standards. High-frequency updates require prompt engineering to explicitly limit data calls to public information from the current day and previous trading day, and prohibit use of outdated information. The structured multi-field document format requires multi-turn dialogue processes to gradually filter for the specific fields of interest, to avoid returning redundant content. Uniform data unit rules must be embedded into prompt engineering to ensure consistent unit descriptions across all fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxHistory | 3–5 turns | Aligns with multi-turn dialogue requirements for state-owned large bank financing daily reports. 3-5 turns covers common business scenarios including definition confirmation and field filtering, and avoids token overflow caused by overly long context windows |
chunk_size | 1000–1500 characters | Aligns with the structured table field length of state-owned large bank financing daily reports. Split text segments can fully carry a single set of business data, facilitating model calls |
recall_top_k | Top 6–8 results | Covers core business fields of state-owned large bank financing daily reports. Excessively large recall volumes lead to redundant information, while insufficient recall may miss critical data |
similarity_threshold | 0.75–0.85 | Accurately matches standardized field names for state-owned large bank financing daily reports, avoids irrelevant unstructured data recall, and ensures accuracy of context retrieval |
custom_system_prompt | Only use publicly disclosed financing daily report data from state-owned large banks. Clearly label data sources and statistical definitions. Sort returned results according to specified fields. Unify units to RMB 100 million and natural days | Aligns with compliance requirements for state-owned large bank data, unifies field units and description rules, and prevents inconsistent output |
max_output_tokens | 1500–2000 characters | Supports output requirements for multi-field comparative analysis of financing daily reports, and prevents truncation of critical information due to insufficient output length |
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 targeted analysis. Testing using samples tailored to the specific deployment should be conducted before finalizing settings.
Three Common Misconfigurations
- Issue: Previously confirmed financing statistical definitions cannot be reused in multi-turn dialogue. Cause: The
maxHistoryparameter is not set correctly, so historical dialogue context is not included in the current request's context window. - Issue: Intermediate AI dialogue content during tool calling cannot be hidden. Cause:
custom_system_promptdoes not explicitly require hiding redundant tool calling logs, or tool calling result filtering rules are not configured. - Issue: After configuring markdown format prompts, output appears as plain markdown source code instead of rendered content. Cause: The system prompt does not explicitly specify rendering markdown syntax, or the built-in FastGPT markdown rendering switch is not enabled.
How to Confirm Proper Configuration
- Initiate a test dialogue, ask about core indicators of state-owned large bank financing daily reports, and verify that returned results include clear statistical definition explanations.
- Initiate 3 consecutive dialogue turns, verify that each reply links to the prior interaction's questions, and confirm that context reuse works correctly.
- After configuring markdown format requirements, check that output appears as visualized tables or lists, instead of plain source code format.
- Check dialogue logs to confirm that input and output token count statistics function properly, and adjust corresponding thresholds based on business needs.
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.