What this category of data looks like
This category of data originates from centralized treasury payment systems, unified budget management systems, electronic invoice platforms, and contract management systems, and is updated daily. Daily processed documents include payment applications, fund appropriation plans, invoices, contracts, acceptance reports, reimbursement approval forms, and payment vouchers. Core fields cover budget indicator codes, economic classification codes, single payment amounts, approval node statuses, and payment voucher numbers. The data structure is built around the compliance review process for individual fund payments.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Data from multiple systems requires multi-turn dialogue to complete field association verification across different platforms, preventing cross-system data disconnection. A single business involves multiple documents such as invoices, contracts, and acceptance reports. Dialogue must gradually retrieve and verify each document, so context redundancy must be limited. Daily updated data requires prompts to call the latest dataset by default, avoiding reference to outdated information. Strict compliance requirements mean multi-turn dialogue must retain the historical status of review nodes, while filtering irrelevant fields to reduce response latency.
Configuration settings
This configuration uses FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 token | Meets full context requirements for multi-document review |
| Number of Recalled Historical Dialogues per Session | First 8 turns | Balances context completeness and retrieval response speed |
variables | Separate configuration of global variables and session variables | Stores globally unchanged rule variables such as the fiscal economic classification code library, to avoid resetting with each dialogue |
| Knowledge Base Retrieval Timeout | 30–60 seconds | Matches the time expectation for cross-system data retrieval |
messages | Filter non-review-related dialogue content | Reduces redundant context and lowers retrieval latency |
Three common misconfiguration scenarios
- Symptom: Global default variables are automatically reset during multi-turn dialogue, and fixed fiscal rule parameters cannot be retained. Cause: Global variables and session variables are not separated, and global configurations are overwritten during each dialogue initialization.
- Symptom: Response speed decreases significantly as the number of queries increases in the same dialogue window, and knowledge base retrieval time exceeds 1 minute. Cause: The number of recalled historical dialogues is not limited, and excessive redundant context occupies retrieval resources.
- Symptom: A large amount of irrelevant AI dialogue content is still output after tool calls, and only review results cannot be retained. Cause: The output scope is not clearly defined in the prompt, or dialogue content filtering rules are not configured.
How to verify successful configuration
- Initiate a test dialogue containing global fiscal rules to verify that fixed parameters do not change with dialogue turns.
- Launch 10 consecutive relevant queries to observe whether response speed remains stable.
- Call tools to retrieve cross-system data to verify that retrieval latency matches the configured expectation.
- Check dialogue output content to confirm that only review-related compliance results are included, with no redundant AI dialogue content.
The values provided are common starting points and should be validated against local use cases.
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.