What the Data for This Category Looks Like
Automated equipment investment research data originates from manufacturer official technical manuals, industry unified equipment standards, on-site operation and maintenance logs, and third-party testing and certification reports. Update rhythms shift with manufacturer firmware updates, industry standard revisions, and operation and maintenance events. Core static documents update at a low frequency. Operation and maintenance logs receive real-time incremental updates. Document structures primarily include structured parameter tables, long-text operation specifications, and fault troubleshooting workflows. These documents contain standardized fields such as rated power (unit: kW), operating speed (unit: rpm), cumulative operating duration (unit: h), and equipment model numbers. Some documents include text descriptions of wiring diagrams and component disassembly schematics.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
The mixed document structure of structured parameters and long text requires retrieval to support both keyword matching and semantic recall. Relying solely on semantic matching may cause missing parameter fields. The difference between real-time updated operation and maintenance logs and static technical manuals requires the retrieval link to distinguish document types. Assign reasonable recall weights to static documents. Set up real-time synchronization indexes for dynamic logs. Parameter differences across multiple equipment models require retrieval to associate model fields for precise matching. This avoids confusion of parameters across different models. Long-text operation steps need to be segmented. Retain the contextual association between parameters and steps during segmentation. Splitting may otherwise lose key matching information.
How to Set the Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
similarity threshold | 0.5–0.7 | Automated equipment investment research documents contain a large number of structured parameters and long-text operation steps. This range balances the accuracy of keyword matching and semantic recall, avoiding false positives or missed detections |
recall count | Top 10–15 results | Equipment investment research needs to cover multi-model parameters and multi-scenario operations. Too many recall results increase subsequent screening costs, while too few cannot cover all relevant documents |
reranked return count | Top 3–5 results | Core investment research information is concentrated in the early recall results. Reranking focuses on highly relevant entries, adapting to the quick review needs of investment research decision-making |
segment length | 800–1200 characters | Equipment documents include long operation steps and parameter-related content. This segment length retains complete context, avoiding loss of key matching fields after splitting |
maxContext | 2000–3000 characters | Complete parameter association information and operation process context need to be transmitted, avoiding truncation that affects the accuracy of responses |
document deduplication threshold | 0.9 | Equipment models and parameters have repeated technical descriptions. This threshold effectively removes redundant entries while retaining differentiated content in different scenarios |
The parameter values given on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: Reranking model calls fail. Recall results are normal during knowledge base testing, but no reranking output appears during formal calls. Cause: The reranking model is not bound in the large model call configuration. The reranking function is only enabled during the knowledge base testing phase.
- Phenomenon: Retrieval results cannot display source information. Cause: The "retain source file metadata" configuration is not enabled. The source document field is not specified when calling the interface.
- Phenomenon: The knowledge base has sufficient content but cannot recall target entries. Even with the similarity threshold set to 0.4, no matching occurs. Cause: The threshold is set too low. Low-relevance entries are recalled first. High-relevance entries are not selected due to low ranking.
How to Confirm the Configuration Is Set Correctly
- Enter the knowledge base testing page. Enter the specified equipment model keyword. Check whether the similarity scores of the recall results fall within the configured threshold range.
- Initiate a large model call. Check whether the returned results include source information such as the source document's file name and paragraph position.
- Upload two documents containing the same equipment parameters. Check whether only one unique entry remains after deduplication.
- Adjust the segment length parameter. Check whether the segmented document blocks retain complete contextual association between parameters and operation steps.
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.