What the Data for This Category Looks Like
IT service investment research data sources include operation and maintenance logs, CMDB configuration items, fault review reports, vendor architecture documents, performance metric datasets, and more. Data updates trigger either in real time when operational changes or faults occur, or via batch synchronization on fixed schedules. The document structure mixes structured and unstructured formats. Structured data includes fields such as service ID, response time (milliseconds), resource usage values, and change time. Unstructured documents are mostly long-form operation manuals and fault troubleshooting process records.
Constraints on Deployment and Upgrade
Mixed data structures require deployment to support both unstructured document parsing and structured database integration. Dual data source synchronization channels must be configured. Real-time or high-frequency update data requires an incremental synchronization mechanism during deployment to avoid full synchronization consuming excessive cluster resources. Long unstructured documents require adjusted parsing timeout and segmentation parameters to ensure complete parsed content is stored in the database. Diverse structured data fields require configured field mapping rules to ensure consistent feature vectors during vector storage. Upgrade processes must support resume-from-breakpoint for synchronization tasks, to avoid missing critical knowledge base content due to interrupted data synchronization during upgrades.
How to Set Configuration Values
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600–900 seconds | Unstructured documents for IT service investment research are mostly long-form operation manuals and fault review reports, which take longer to parse individually |
SYNC_INCREMENT_INTERVAL | 300 seconds | IT service data updates frequently, so frequent incremental sync is needed to maintain knowledge base timeliness |
RECALL_TOP_K | Top 10–15 entries | Investment research data has many fields and strong correlations, so a sufficient candidate set is required to ensure retrieval accuracy |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filter low-correlation operation metrics and log data to avoid invalid retrieval interfering with investment research results |
RE_RANK_TOP_N | Top 3–5 entries | Focus on high-correlation core investment research data to reduce the context length required for large model input |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Support uploading large vendor architecture design documents and quarterly operation reports |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: After Docker restarts and is reinstalled, data write time exceeds the preset threshold, and the log shows an
ETIMEDOUTerror. Cause: Incremental sync resume-from-breakpoint is not configured, and large-volume operation and maintenance data sets are synced fully without batch splitting, resulting in connection timeout. - Phenomenon: After upgrade, data is visible in the knowledge base, but no output is returned when passed to the large model. The
contextfield in the large model call log is empty. Cause: After upgrade, the vector retrieval output format is not compatible with the field mapping rules of older investment research data, resulting in invalid context being unable to be generated. - Phenomenon: After upgrading to version v4.8.20, the offline reranking model configuration fails, and the interface displays the
MODEL_NOT_FOUNDerror code. Cause: The reranking model cache directory is not correctly mounted during offline deployment, or the configuration itemRE_RANK_MODEL_PATHpoints to an incorrect path.
How to Verify Proper Configuration
- Execute a manual incremental sync task, and verify that the number of documents in the synced knowledge base matches the new entries in the source data.
- Initiate a simulated investment research query, and confirm that the retrieval results include core data such as operation and maintenance metrics and fault records for the corresponding IT service.
- Trigger a large model call, and confirm that the returned content includes valid field information retrieved from the knowledge base.
- Check the reranking model status interface, confirm that it returns a
200 OKstatus code to verify that the model is loaded normally.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.