What data looks like for this category
The data sources for this category include historical traffic data, service topologies, monitoring metrics, and business growth plans. Update frequency is monthly or when major requirements are released. Daily deliverables include technical solution documents, architecture topology diagrams, capacity assessment reports, and technical review minutes. Fields cover cross-service boundary definitions, resource quotas, evolution roadmap milestones, constraint conditions, and more. Units include CPU cores, storage capacity, request throughput, and others. There is no fixed uniform single document size; adjustments are made based on requirement scale. This document applies to FastGPT v4.17.0.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
Because data update frequency is low, multi-turn dialogue must limit context scope to avoid including historical data unrelated to the current review. The diverse document structure feature requires prompts to clearly distinguish extraction rules for different document types, adapting to mixed content of text, diagrams, and tables. Fields involve core architecture information such as cross-service boundaries. Dialogues must be split by conversation topic to prevent context from mixing review content for different requirements. Additionally, errors in architecture solutions can lead to long-term expansion restrictions. Prompts must strengthen the verification logic for constraint conditions to ensure reviews cover all necessary technical boundaries and resource configurations.
How to configure the settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique value grouped by review session | Distinguish review contexts for different technical solutions to avoid mixing session data |
messages | Retain the first 20 interaction records of the current review session | Match the short-cycle, topic-focused dialogue characteristics of architecture solution reviews |
variables | Inject associated document metadata for the current technical solution | Provide fixed context anchors for prompts |
| Maximum context length | 8000–12000 token | Adapt to the content size of architecture solution documents, avoiding truncation of key constraint conditions |
| Prompt optimization | Calibrate based on actual testing | Adapt to the professional terminology scenario of architecture reviews, reducing invalid output |
| Maximum associated data per request | Top 3 historical review minutes | Focus on relevant review opinions, avoiding introduction of unrelated historical data |
Three common misconfigurations
- Symptom: After configuring multiple dialogue workflows, the output content of a certain node still appears in the front-end interface. Cause: The hidden output marker parameter in
variablesis not set correctly, or the output shielding option for the corresponding node is not checked in the workflow configuration. - Symptom: The dialogue icon does not change as expected in the login-free scenario, and the default icon is still displayed after refresh. Cause: The icon resource path for the login-free scenario is not correctly bound in the custom configuration items, or the path format does not meet system requirements.
- Symptom: A connection error prompt appears when calling the Qianwen model, and the issue persists after enabling a proxy. Cause: The proxy environment variable parameter is not configured in
variables, or the model interface timeout setting is too low (for example, less than 30 seconds), causing network requests to be interrupted before completion.
How to confirm the configuration is complete
- A simulated dialogue for a technical solution review is initiated. Verification is performed to confirm only output content from core nodes is displayed in the interface, while outputs from non-core nodes are hidden.
- The login-free configuration page is accessed. A custom icon is uploaded and saved, then the page is refreshed to confirm the icon has updated to the preset style.
- A test request is initiated using the Qianwen model. System logs are checked to confirm no connection error reports are present, and the request response duration meets expectations.
- The
chatIdparameter is checked to confirm it generates a unique identifier for each review session, preventing context mixing between different sessions.
The values provided here are common starting points, and should be evaluated against relevant sample data.
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.