What the data for this category looks like
Rural commercial bank investment research data primarily comes from internal credit management systems, publicly available business entity directories from county-level agricultural and rural bureaus, industry monitoring reports submitted by local financial supervision bureaus, and credit statistics data from county-level branches of the People's Bank of China. Update frequencies vary: internal customer files update in real time with credit adjustments, industry monitoring data updates monthly, and policy documents sync on demand. Each document follows a single structured record format, corresponding to one county-level business entity or one research trip. Each record includes fixed fields: entity ID (numeric format), registered address, credit balance (unit: ten thousand yuan), business category, most recent revenue data (unit: ten thousand yuan), and contact person name.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Structured single records include numeric entity IDs. Do not return internal IDs directly to users during multi-turn dialogue. Use IDs to associate context, to avoid mixing information from different entities. Update frequencies vary. Real-time credit data must be retrieved during every conversation. Monthly industry data can use a caching cycle to reduce repeated retrieval overhead. Fields include numerical values with units. Prompts must explicitly require LLMs to include corresponding units in responses, to avoid numerical ambiguity. Most investment research scenarios involve queries about county-level business entities. Multi-turn dialogue must retain context-based entity associations, to ensure subsequent questions accurately match corresponding knowledge base entries. Strictly limit LLMs to replying only using knowledge base content, to avoid generating unauthorized external information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 3 | Single records in rural commercial bank investment research knowledge bases have complete information. Excessive recall leads to redundant context, which reduces LLM response accuracy |
similarity threshold | 0.75–0.85 | Names, addresses and other fields of county-level business entities have high similarity. A threshold that is too low will retrieve irrelevant entries, while a threshold that is too high will miss matching results |
maxContext | 800–1200 characters | Single knowledge base records have short length. Multi-turn dialogue requires retaining integrated information from 3-5 recalled records. This range covers typical dialogue lengths |
disable external knowledge generation | enabled | Strictly limit LLMs to reply using only investment research data from the knowledge base, to avoid generating unauthorized external information |
cache expiration time | 24 hours | Monthly updated industry data does not require frequent retrieval. Real-time updated credit data automatically skips the cache for direct retrieval |
hide source file identifiers | enabled | Entity IDs in the knowledge base are internal identifiers, and do not need to be displayed to users, to avoid exposing internal management details |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: Retrieval results include internal identifiers such as entity IDs, which are sent to LLMs or displayed to users. Cause: The
hide source file identifiersconfiguration is not enabled, causing internal fields of the knowledge base to be recalled along with other content. - Symptom: Custom models have inconsistent calling effects between the dialogue interface and the workspace. Cause: The
maxContextandsimilarity thresholdparameters are not unified across the two scenarios, leading to differences in retrieval and generation logic between scenarios. - Symptom: LLM responses exceed the scope of the knowledge base, generating unauthorized external information. Cause: The
disable external knowledge generationconfiguration is not enabled, or the prompt does not explicitly limit LLMs to using only information from the knowledge base.
How to confirm configurations are set correctly
- Initiate a retrieval test that includes an entity ID. Confirm the returned results do not include the ID, to verify the
hide source file identifiersconfiguration is active. - Initiate the same retrieval request in both the dialogue interface and the workspace. Confirm the returned results are consistent across both, to verify unified parameter configuration.
- Initiate a query that exceeds the scope of the knowledge base. Confirm the LLM does not generate irrelevant content, to verify the
disable external knowledge generationconfiguration is active. - Initiate a multi-turn dialogue. Confirm entity information associated with context does not become mixed, to verify the
maxContextconfiguration covers dialogue length requirements.
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.