What the Data for This Category Looks Like
General equipment investment research data comes from multiple sources. These include industry quarterly reports from industry associations, product technical manuals from equipment manufacturers, operation logs from equipment maintenance teams, patent documents, and performance test reports from third-party testing institutions.
Update cycles vary significantly by data source. Manufacturers update their manuals every 6 months to 1 year alongside model iterations. Industry associations release quarterly reports on a quarterly basis. Equipment maintenance teams generate operation logs in real time. Third-party testing institutions finalize and release performance test reports as work completes. Patent offices update patent documents in real time when new applications are filed.
Document structures typically include fields such as equipment model, core technical parameters, applicable working conditions, fault code comparison table, and maintenance cycle. Most parameter fields include standardized units.
Constraints Imposed on Knowledge Base Retrieval and Recall
The multi-source and multi-structure characteristics of general equipment investment research data create multiple constraints for retrieval and recall workflows.
Parameter content with multiple fields and standardized units requires retrieval to support precise field matching and unit verification. This prevents parameter misalignment caused by fuzzy matching. Heterogeneous data types — including short parameter entries, long fault troubleshooting manuals, and image charts — require retrieval to support segment adaptation and multimodal recall. Data sources with different update frequencies require the retrieval system to support a mixed configuration of incremental indexing and scheduled full updates. This balances data timeliness and retrieval efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers complete parameter groups and logical paragraphs, as general equipment data includes both short parameter entries and long fault troubleshooting manuals |
recall_top_k | Top 10–15 results | Covers multi-dimensional recall results including models, parameters, and fault cases for investment research scenarios, this range balances comprehensiveness and retrieval efficiency |
similarity_threshold | 0.72–0.85 | Requires high similarity matching for parameter content to avoid parameter misalignment, while retaining certain tolerance for fault cases to adapt to fuzzy queries |
field_weight_config | Calibrated based on actual testing | Weights for different fields (such as equipment model, rated power, fault code) must align with query priorities for investment research scenarios |
index_update_interval | 86400 seconds (1 day) | Balances data timeliness and index construction costs, as industry reports update quarterly and manufacturer product iterations have long cycles |
parse_image_embed | Enabled | Embeds parameter charts in product manuals into retrieval vectors, to adapt to investment research queries that include image descriptions |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test with your own samples before finalizing values.
Three Common Mistakes
- Issue: Device parameter images in uploaded Markdown files fail to load, and no corresponding image results appear during knowledge base retrieval. Cause: No image path replacement rules are configured, and local relative paths are not mapped to public access addresses stored in the knowledge base.
- Issue: Only the default text understanding model can be called during retrieval, and newly added multimodal models cannot be selected. Cause: Access keys and model identifiers for newly added models are not bound in the retrieval task configuration, and only call permissions for the default model are enabled.
- Issue: Custom filter fields (such as equipment model) are not included in indexes generated during knowledge base import. Cause: Field extraction configuration is not enabled during document parsing, and rules for extracting core investment research fields are not specified.
How to Confirm Proper Configuration
- Upload a Markdown test file that includes equipment models, parameter charts, and fault cases. Verify that parsed fields are fully extracted.
- Submit precise queries that include equipment models and power parameters. Verify that the number and similarity of recall results match the preset configuration.
- Trigger an incremental index update task. Verify that the update time and number of updated documents in the index update log match the configured update interval.
- Submit queries that include image descriptions. Verify that retrieval results for corresponding parameter charts are returned.
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.