What this category of data looks like
This category of data mainly comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Daily processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Most fields involve project numbers, fund amounts, performance indicator completion status, supporting material association relationships, etc. The data update frequency is annual. The volume of documents processed in a single batch fluctuates with project scale. The core associated fields need to match the corresponding relationship between budget execution and performance targets.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Low-frequency data updated annually requires that dialogue contexts be permanently associated with historical project information to avoid reloading full annual data. Mixed processing of multiple types of documents requires prompts to clearly distinguish the priorities of performance targets, supporting materials, and rectification content. Cross-stakeholder collaboration scenarios require dialogue contexts to support permission associations for different roles, and strictly match the field formats of the budget management system. Corresponding verification between funds and performance indicators requires multi-turn dialogue to gradually decompose supporting materials, avoiding loading excessive non-associated data at one time.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the exclusive project number returned by the unified budget management system | Ensure that the dialogue context is strongly bound to a specific fiscal project to avoid confusion across projects |
messages | Retain the last 10 rounds of dialogue content | Adapt to the low-frequency annual data update feature, avoid context overflow and loading of invalid information |
variables | Inject the current project's performance indicator list and fund amount range, using structured fields synchronized by the system | Provide accurate category-specific parameters for prompts, ensuring that verification logic matches business rules |
| Maximum context | 8000–12000 tokens, calibrated based on actual testing | Match the average character count of annual project documents, avoid exceeding the model's processing limit |
| Single search limit | Top 3 results | Core supporting materials are mostly project core documents; excessive retrieval will distract the model |
| Question optimization | Enabled | Adapt to consultation scenarios where users mostly involve the corresponding relationship between performance indicators and funds, decompose vague questions into verifiable sub-questions |
Three common mistakes
- Phenomenon: Dialogue contexts include performance data from non-current projects. Cause:
chatIdis not bound to the unique number of the current project, resulting in cross-project reuse of contexts. - Phenomenon: Clicking the quick button in the conversation opening screen cannot directly send preset questions. Cause: The button is only configured as a static text element, not configured to trigger the sending action of preset questions.
- Phenomenon: Conversation history cannot be retained across sessions for login-free users. Cause: The context storage logic based on temporary session identifiers is not configured, and temporary user identifiers are not injected via
variablesto associate historical data.
How to confirm the configuration is correct
- Initiate a test conversation targeting a specific fiscal project, check that the context only contains performance documents and conversation content for that project, and confirm that the
chatIdbinding takes effect. - Click the quick button in the conversation opening screen, check whether the preset performance consultation question is sent directly, and confirm that the button configuration is correct.
- Upload performance evaluation supporting materials, check whether the API returns a successful response, and confirm that the file upload permission and format verification configuration are correct.
- Switch to the login-free test environment to initiate a conversation, restart the session, check whether the historical conversation is retained, and confirm that the temporary session storage configuration is correct.
The values provided are common starting points and should be measured against the reader's own samples.
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.