Data sources, update frequency, and structure
Service consultation data for state-owned asset management companies comes primarily from internal state-owned asset supervision policy documents, asset ledger systems, property right transaction announcements, and public information released by local state-owned asset management departments. Policy documents are updated quarterly. Ledger data is synchronized daily. Transaction announcements are updated in real time alongside project progress. Document structures include fields such as policy document number, effective date, asset ID, book value, transaction process, and supervision requirements. Units used include professional business units such as ten thousand yuan, square meters, household count, and batch count.
Constraints for multi-turn dialogue and prompt engineering
The fields in state-owned asset business data are highly specialized, and their interrelationships are complex. Multi-turn dialogue must retain key identifiers such as asset IDs and policy document numbers from historical conversations to avoid mixing up consultation content for different projects. Policy documents are updated frequently. Prompt templates must clearly mark policy effective dates to ensure returned content complies with latest supervision requirements. Asset ledger data has strict precision requirements. Multi-turn dialogue context must be limited to the current consultation’s asset or policy scope to avoid introducing irrelevant data.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
| Maximum context retention length | First 10 turns of dialogue + 1500 characters | Adapts to context requirements for multi-turn consultations in state-owned asset business, prevents information loss |
| Prompt template configuration | Fixed inclusion of asset ID and policy effective date | Matches precise query requirements for state-owned asset management business |
| Context similarity threshold | 0.75–0.85 | Calibrated through actual testing, distinguishes similar state-owned asset business questions |
| Conversation file expiration time | 20–60 days | Complies with standard retention cycles for state-owned asset archives |
| Single recall result count | First 5 results | Adapts to precise information acquisition for business consultations |
messages format | Concatenate history in user-assistant order | Complies with standard interaction logic for FastGPT multi-turn dialogue |
Three common configuration errors
- Phenomenon: AI dialogue node outputs empty content, or consecutive multiple calls have no response. Cause: The exclusive
variablesparameter for state-owned asset business is not bound, so the large language model cannot match precise asset or policy data. - Phenomenon: Unclosed quotation marks appear at the end of output content, and subsequent outputs are automatically corrected to standard quotation marks. Cause: The prompt template does not clearly specify output format, leading to format confusion during large language model generation.
- Phenomenon: Token consumption per single dialogue exceeds expected levels. Cause: Maximum context retention length is not limited, leading to excessive token count when historical dialogue and current question are combined.
How to verify correct configuration
- Import exclusive documents and ledgers for state-owned asset business, launch a consultation that includes an asset ID, and verify whether the large language model’s returned content includes the corresponding fields.
- Launch consecutive multi-turn consultations, check whether the context retains asset IDs and policy information from historical conversations.
- Review the conversation file expiration time setting, confirm it complies with business archive management cycle requirements.
- Simulate a consultation that includes unspecified fields, check whether the large language model prompts for necessary business parameters to be supplemented.
Note: The configuration values listed above are common starting points. They should be evaluated against specific samples and business requirements for the relevant deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.