What this category of data looks like
Data for declaration materials and archived file review at state-owned capital operation companies originates primarily from formal declaration documents and archived files of individual projects. Updates align with the timeline of individual review project cycles, with no fixed batch update frequency. Most documents are 10–20 MB Word or PDF files, with fixed fields including: unified social credit code of the declaring entity, project number, funding amount (unit: ten thousand yuan), approval node records, attachment list, and more. Structures typically use multi-chapter nesting, with core review content concentrated in financial and compliance sections.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The above data characteristics create three constraints for multi-turn dialogue and prompt configuration. First, large document size and nested structure require limiting the context length of messages in multi-turn dialogue. This prevents loading excessive redundant content that causes performance degradation. Second, fixed fields and units require mandatory format validation in prompts. For example, funding amounts must use ten thousand yuan as the unit to avoid parsing errors. Third, the independent nature of individual projects requires each review session to use a unique identifier. This ensures dialogue contexts from different projects are not mixed. Additionally, prompt scope should be optimized for state-owned capital professional terminology.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Generate a unique identifier for each declaration project | Distinguish between different review sessions and prevent context mixing |
messages maximum context length | First 6 turns of dialogue | Match the typical review rounds of a single declaration project and control context load |
variables bound fields | Unified social credit code of the declaring entity, project number | Restrict dialogue to processing only the exclusive fields of the current file to avoid confusion |
| Chunk overlap length | 150–200 characters | Retain field associations between adjacent paragraphs and prevent loss of key information such as project number after chunking |
| Single recall count | Top 5 entries | Match the number of core review fields in declaration materials and improve retrieval accuracy |
| Question optimization toggle | Enabled | Optimize queries for professional terminology related to state-owned capital declarations and improve field matching accuracy |
Three common configuration errors
- Phenomenon: Parsing a 10+ MB Word declaration document takes over 30 seconds, and the interface shows stalled parsing progress. Cause: Chunk parameters were not adjusted for the long-paragraph structure of state-owned capital operation declaration materials, leading to chunks that are either too large or too small.
- Phenomenon: Fields from different declaration files are mixed in multi-turn dialogue. For example, the unified social credit code of Project A is incorrectly applied to the review of Project B. Cause: A unique
chatIdwas not configured for each project, resulting in incorrect context isolation. - Phenomenon: State-owned capital declaration professional terminology is not correctly identified even after prompt optimization. For example, "property right transfer" is parsed incorrectly. Cause: The prompt was not limited to only handling declaration and archived file review scenarios related to state-owned capital operations, causing the model to drift from the target task.
How to verify correct configuration
- Upload a single 15 MB state-owned capital operation declaration Word document, and check if parsing time meets expectations.
- Switch between two different declaration project sessions, submit a query that includes the project number, and confirm that dialogue contexts do not interfere with each other.
- Submit a query that includes the funding amount field, and check if the model’s returned results strictly use ten thousand yuan as the unit.
- View session logs, and confirm that each session generates a unique
chatIdidentifier.
The values provided here are common starting points and should be evaluated 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.