What the data for this category looks like
The data for declaration materials and file review for urban investment platforms mainly comes from internal project declaration systems, archive management platforms, and external qualification verification interfaces. There are two data update rhythms: batch upload triggers during the project declaration phase, and monthly scheduled synchronization for daily file archiving. A single document’s structure includes four types of content: declaration forms, qualification scans, financial statements, and approval circulation forms. Core fields include project ID, declaration subject, declaration amount (unit: ten thousand yuan), approval status, and submission time (format: YYYY-MM-DD). The size of a single attached file is usually within 100 MB.
What constraints these characteristics impose on the workflow orchestration link
Decentralized data sources require the workflow to be configured with multi-source data pull nodes to integrate declaration materials and file information from different systems. Complex document structures with long-text attachments require the workflow to support segment parsing and attachment extraction capabilities. Fixed units and format requirements for core fields require adding field verification links in the workflow to prevent format errors from entering the review process. The update rhythm of batch declaration and archiving requires the workflow to support scheduled trigger or event trigger modes to adapt to execution timing for different business scenarios.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapts to the single-paragraph text length of urban investment declaration materials, avoids context window overflow |
| Single question limit | Top 3 candidate files | Matches the conventional number of files in a single batch of urban investment declarations, reduces redundant computing overhead |
| Context window | 16000 token | Accommodates complete declaration materials and full-process text of approval circulation |
| Variable scope | User-level | Corresponds to exclusive task variables for different declaration specialists, avoids global variable conflicts |
| Node timeout | 600 seconds | Adapts to the parsing and review duration of large files, prevents task interruption mid-process |
| Output node filtering | Specified by nodeId | Only syncs replies from target AI conversation nodes to the chat window, hides redundant node outputs |
Three common errors
- Phenomenon: Replies from multiple AI conversation nodes in the workflow all appear in the final chat interface, and it is impossible to hide the output of specified nodes. Cause: The output filtering rule is not configured via
nodeId, and the context of all nodes is synced to the chat window. - Phenomenon: Only one question classification query can be initiated each time, and multi-round follow-up questions and probing inquiries are not supported. Cause: The workflow does not have a loop trigger node, only executes a single AI call, and does not retain conversation context.
- Phenomenon: Task variables from different declaration specialists are overwritten, leading to confusion of declaration data. Cause: The scope of
variablesis not set to user-level, and global variable configuration is used.
How to confirm the configuration is correct
- Launch a simulated declaration task, submit test data containing multiple files, check that only the replies from the target AI conversation node are displayed in the chat interface.
- Switch to different test user identities, submit task variables with the same name, confirm that each variable configuration does not interfere with each other, and no data overwriting occurs.
- Upload a single declaration material with 10000 characters, check that the workflow can complete segment parsing and review process normally, without timeout errors.
- View the workflow node logs, confirm that the values of all configuration items match the preset values, and no abnormal parameters are triggered.
The values listed 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.