What Data for This Category Looks Like
The daily documents for this category include terminal trouble tickets, equipment installation inventory lists, software installation records, and remote assistance records. Fields covered include device MAC addresses, operating system versions, error codes, user workstation numbers, repair actions, and operation durations. Data sources are terminal management platforms, asset systems, user complaint channels, and software repositories. Update frequency is either daily incremental updates or real-time updates tied to individual service requests. Document types include structured device information and unstructured troubleshooting steps, with formats primarily plain text and Markdown. Some records include operation log snippets.
Constraints on Knowledge Base Retrieval and Recall
This category’s data characteristics create multiple constraints for the retrieval and recall process. Data is scattered across multiple systems and diverse in type, combining structured device information and unstructured troubleshooting steps. This requires retrieval workflows to balance keyword matching and semantic understanding, to avoid missing key content with a single approach. Update frequencies are flexible, supporting daily incremental updates or real-time updates per request. This means the knowledge base must support near-real-time synchronization, to prevent outdated data from reducing the reference value of troubleshooting content. Documents focus exclusively on terminal scenarios, so limiting the retrieval scope reduces irrelevant interference. At the same time, the accuracy of fault records directly impacts service efficiency, so recalled results must have high matching precision. Additionally, user complaints often use colloquial language, so queries need optimization to align with professional terminology and improve retrieval matching effectiveness.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Descriptions of desktop terminal faults usually include clear error codes and device characteristics. A threshold that is too low will introduce irrelevant non-terminal fault documents, while a threshold that is too high may miss records of the same type but with different details |
limit | Top 6–10 entries | Common troubleshooting steps for desktop terminal support usually focus on limited scenarios. Too many recalled results will exceed the large model context window and affect problem understanding and reply generation |
searchMode | semantic+keyword | This category’s data includes structured device models and error codes (suitable for keyword matching) and unstructured troubleshooting steps (suitable for semantic matching). A mixed mode balances precision and recall rate |
usingReRank | Enabled | There are many similar expressions for desktop terminal faults. For example, "blue screen" may appear in different hardware scenarios. Reranking can prioritize recall of more relevant repair steps |
datasetSearchUsingExtensionQuery | Enabled | User complaints often use colloquial language, such as "computer lag". Optimized queries can add professional terms like "system stuttering" and "high CPU usage" to improve retrieval matching accuracy |
collectionIds | Fill in the collection ID list of the terminal operation and maintenance exclusive knowledge base under the current project | This category’s data only includes terminal-related documents. Limiting the collection can exclude irrelevant IT service desk ticket data and reduce retrieval interference |
Three Common Misconfigurations
- Symptom: Workflow logs indicate no configured HTTP request was triggered, and only standalone content generated by the large model is returned. Cause: The HTTP request node is not properly bound after the knowledge base retrieval node, or retrieval results are not passed as context parameters for the request.
- Symptom: After importing Markdown-format documents, original first- and second-level headings are not retained, and only body content is displayed. Cause: The Markdown format parsing switch was not enabled during knowledge base import, or the chunking configuration does not retain heading hierarchy information.
- Symptom: Replies generated by the large model include the phrase "Citation: [1]". Cause: The citation format filter for retrieval results is not configured, or the large model prompt does not explicitly require hiding the prefix text of citation markers.
How to Verify Successful Configuration
- View the document list for the knowledge base collection, confirm it only includes exclusive documents for this category such as terminal trouble tickets and equipment installation inventory lists, with no irrelevant cross-category data.
- Submit a test query that includes a clear error code, check whether retrieved and recalled results include matching fault troubleshooting records, and that the number of results matches the configured
limitvalue. - Review workflow run logs, confirm the configured HTTP request was triggered after the knowledge base retrieval node completes, and that correct retrieval result context parameters were passed.
- Generate a test reply, check that the reply does not contain incorrectly formatted citation markers, and that content matches retrieved documents as expected.
The values provided 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 2026-10-19.