Data overview for this category
This category of data typically originates from package managers, code repositories, public vulnerability databases, and license databases. Update frequency is daily or upon version release. Daily produced documents include SBOM lists, dependency risk reports, license lists, and repair tickets. Core fields include dependency package names, version numbers, vulnerability CVE IDs, license types, and impact scopes. Data is maintained by security teams, developer productivity teams, or open source governance offices. It supports supply chain risk identification and governance work.
Constraints on multi-turn dialogue and prompt engineering
Data sources for this category are scattered and updated frequently. Multi-turn dialogue must limit context scope to avoid loading irrelevant cross-project dependency data. There are many core document fields. Prompts must explicitly specify required items such as dependency package names and CVE IDs. This prevents generating content with redundancy or missing critical information. Data updates occur at high frequency. Dialogue must support real-time pulling of the latest dependency risk data to avoid reliance on static caching. Complex dependency assessment tasks must be split via multi-turn dialogue. Guide users step-by-step to provide materials such as dependency lists and risk reports. This avoids overloading single requests.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Dialogue context length | 8000–12000 token | Matches the average length of documents such as SBOMs and dependency risk reports, avoids exceeding model context limits |
messages | Keep the most recent 20 history entries | Supply chain governance documents are typically long. Excessive history will crowd out valid context. Calibrated based on actual testing |
variables | Inject the current project's SBOM link and vulnerability database address | Ensures multi-turn dialogue generates content based on the current project's exclusive data |
| Session ID binding | Bind chatId to project identifiers | Avoids confusion of dependency data across different projects, ensures dialogue context matches the corresponding project |
| Maximum single recall count | Top 5 entries | Limits the number of returned dependency risk entries to avoid content overload |
| Prompt validation toggle | Enabled | Automatically checks required fields for generated documents to prevent missing high-risk dependency information |
Three common configuration mistakes
- Symptom: Like or dislike operations do not take effect after a dialogue completes. Cause: The association logic between dialogue sessions and user feedback is not bound, or the feedback synchronization function is not enabled in the configuration.
- Symptom: Dialogue returned content does not meet JSON format requirements. Cause: The prompt does not strictly specify the JSON structure, or the context data contains unescaped special characters.
- Symptom: Uploading attachments fails to generate a pre-signed URL, for example, with the prompt "fail to create post presigned url". Cause: S3 storage configuration was not synchronized after an upgrade, or the storage bucket permissions do not adapt to FastGPT v4.17.0 API parameters.
How to verify successful configuration
- Initiate a test dialogue, input "Generate the current project's SBOM list", confirm the returned content includes core fields such as dependency package names and version numbers, and that field completeness meets business requirements.
- Execute a multi-turn dialogue test: first upload a dependency risk report, then request repair solutions for high-risk dependencies. Confirm the dialogue context retains the uploaded document information, and that the generated answer is based on the uploaded content.
- Confirm
variablesinject the current project's exclusive data, such as the local path or remote link of the SBOM, to ensure the dialogue uses project-specific data. - Trigger an attachment upload operation, verify the pre-signed URL is generated successfully with no error prompts, and confirm the storage configuration adapts to the current version.
- Execute like or dislike operation tests, confirm feedback data is synchronized to the backend, and verify the association logic is configured correctly.
Note: The configuration values listed above are common starting points. Adjust them based on local sample testing.
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.