What data for this use case looks like
Typical data related to code and change review for frontend application development originates from code repositories, interface management platforms, design draft files, and browser compatibility data logs. Daily associated documents include interface parameter descriptions, component library usage specifications, joint debugging issue records, and version release checklists. Data is updated either daily or when a new version is released. Fields cover code commit records, interface return examples, design draft dimensions, compatible browser versions, and similar items. There are no unified fixed units, as units vary based on the data source type.
Constraints for the multi-turn dialogue and prompt engineering workflow
Data sources are scattered and update frequently. Multi-turn dialogue must limit context scope to avoid loading redundant historical parsed data. Daily document types vary widely. Prompts must adapt to formats of different data sources, and differentiate between code snippets, interface documents, and design draft descriptions. High update frequency requires regular syncing of the latest version release checklists and joint debugging records. The dialogue workflow must support filtering data by specified identifiers to avoid calling outdated information. Frontend scenarios prioritize terminal compatibility. Dialogue must be able to call browser compatibility data as a reference. This constrains the range of data sources used for context calls, preventing irrelevant information from interfering with review results.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum context length | First 2000–3000 characters | Adapts to the single-page content length of frontend code and documents, avoids context overflow affecting responses |
| Session isolation identifier | Bind chatId to the current review task | Isolates context for different code review sessions, prevents repeated loading of historical documents |
| Global variable storage | Enable variables global storage | Retains public configuration during the review process, such as specified knowledge base IDs and compatible browser versions |
| Context filtering rules | Filter by currently uploaded attachments | Only loads code or documents for the current review, excludes historical redundant data |
| Single recall count | First 8–10 entries | Balances information density and dialogue response speed in frontend scenarios |
| Prompt template | Match frontend document types | Set exclusive prompt logic for interface documents and code snippets respectively |
Three common configuration mistakes
- Loading historical documents during dialogue: Historical uploaded documents are included in the parsing scope when new attachments are processed. Cause: No exclusive
chatIdis bound, so session context is not isolated, and previous session data is reused. - Abnormal variable settings: An error prompt appears when attempting to pass a knowledge base ID variable, or global configuration cannot be retained across sessions. Cause:
variablesglobal variable storage is not enabled, so variables are reset with each dialogue, and parameter verification fails. - Unstable knowledge base recall: Some dialogues trigger knowledge base recall, while others do not. Cause: No mandatory recall rule is configured, so the system automatically determines whether to call the knowledge base based on context.
How to confirm configuration is properly set up
- Upload a single attachment, start a dialogue, and verify that returned results only relate to the content of the currently uploaded document, with no historical redundant data.
- Pass a knowledge base ID variable, and confirm that the dialogue starts normally without errors.
- Launch multiple identical review dialogues, and confirm that specified knowledge base content is recalled each time.
- View the session context panel in FastGPT v4.17.0, and confirm that the
messageslist only contains interaction content from the current review session.
The values provided are common starting points, and should be evaluated against relevant samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.