What the data for this category looks like
General equipment research report data primarily originates from securities firm machinery industry research reports, public statistical data released by domestic general machinery industry associations, and periodic reports of relevant listed companies. The core update cycle is quarterly, with temporary research reports published alongside industry policies and major bidding events. Document structures usually include modules such as overall industry operation status, production and sales data for segmented product categories, downstream application demand analysis, and key enterprise operating data. Fields include statistical cycle, production volume, sales volume, revenue, year-over-year change range, and similar metrics. Common units include pieces, ten thousand yuan, percentage, and others.
What constraints these characteristics impose on model access and configuration
General equipment research reports contain large amounts of structured statistical data, professional industry terminology, and update cycles including regular quarterly reports and temporary event reports. These factors impose clear constraints on model access and configuration. First, structured data includes multiple types of numerical fields with units. Recall logic that accurately matches field semantics and units must be configured to avoid results where numerical values and units do not match. Second, temporarily updated research report content must support incremental synchronization, and full periodic update processes should not be relied on. Finally, long document chapters must be split by content module to ensure contextual coherence between professional terminology and associated data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | The length of a single general equipment research report typically ranges from 5000 to 12000 characters, so the context window must fully accommodate the chapter content and associated data of one document |
RECALL_TOP_N | Top 8–12 results | Segmented data from general equipment research reports is scattered across multiple documents, so a sufficient number of recalled contents are needed to cover cross-chapter associated information |
SIMILARITY_THRESHOLD | 0.75–0.85 | High precision is required for matching structured numerical values and units to avoid recalling low-relevance data |
PARSE_CHUNK_MODE | Split by chapter | Chapter content of general equipment research reports has independent logic; splitting by chapter preserves contextual coherence between data and terminology |
UPLOAD_INCREMENTAL_ENABLE | Enabled | Rapid updates for temporary event research reports are required, and incremental synchronization reduces resource overhead from repeated parsing |
PROMPT_CUSTOM | Calibrate via actual testing | Adaptation to general equipment industry terminology is needed to calibrate the model's understanding of professional data associations |
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 specific analysis, and it is recommended to test against your own samples before finalizing.
Three common configuration errors
- Phenomenon: Recall results only contain scattered fragments matched by keywords, and do not associate complete numerical data from the same chapter. Cause: The parse-by-chapter mode is not configured, leading to broken context that prevents the model from associating multiple sets of associated data within a single research report.
- Phenomenon: The locally deployed version cannot enable the image understanding model, resulting in missing key data when parsing research reports containing industry charts. Cause: The
IMAGE_PARSER_ENABLEconfiguration item is not enabled, or the corresponding image parsing dependencies are not pre-installed in the deployment package. - Phenomenon: Relevant data exists in the knowledge base, but the model directly refuses to answer questions that do not explicitly hit keywords. Cause: The
SIMILARITY_THRESHOLDis set too high, causing semantically compliant recall results to be filtered out, or a generalized reasoning template adapted to the industry scenario is not configured.
How to verify successful configuration
- Upload a single general equipment research report, check the parsed segmented modules, and confirm that the segments match the chapter structure of the research report.
- Enter a query containing production and sales data for segmented product categories, and check whether the recall results cover matching content for relevant fields and units.
- Upload a temporarily released industry research report, verify that the knowledge base can complete incremental synchronization without triggering a full parsing process.
- Review the answers returned by the model, confirm that no content with mismatched numerical values and units appears, and that cross-chapter associated data can be linked.
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.