What the data for this category looks like
General equipment financial report data mainly comes from listed companies' periodic reports and exchange public disclosure platforms, with updates following quarterly and annual report disclosure cycles. Most documents are in PDF format, with structures including sections such as business performance discussions and financial statement notes. Equipment-related data is scattered across segments like production equipment details, capacity operations, and main business costs. Fields include production equipment book value, unit production cost, equipment operating hours, and others, with units mostly being yuan, units, and hours.
What constraints do these characteristics impose on the knowledge base retrieval and recall workflow
The long-text nature of general equipment financial reports requires chunk lengths to fit the input limits of vector models, to avoid single chunks exceeding processing limits. The scattered data structure requires retrieval to accurately match equipment-specific fields, to avoid recalling irrelevant content. The fixed update schedule requires the knowledge base to support incremental updates, to reduce resource consumption from full re-parsing. Fields with specific naming require higher retrieval matching accuracy, so similarity thresholds need adjustment to filter low-relevance results.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Fits the input length limits of most vector models, preventing single chunks from exceeding model-supported ranges |
recall_top_k | Top 10 results | General equipment financial report data is scattered, requiring sufficient retrieved segments to filter valid content via reranking |
similarity_threshold | 0.75–0.85 | General equipment financial reports have many specifically named fields, requiring higher matching accuracy to filter irrelevant content |
rerank_top_k | Top 3–5 results | Focuses on core equipment-related data, avoiding interference from redundant information during AI generation |
incremental_update_enabled | Enabled | Adapts to the quarterly update rhythm of financial reports, reducing resource consumption from full document uploads |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single financial report PDFs have large parsing volumes, requiring extended timeout periods to avoid parsing failures |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and testing on appropriate samples is recommended before finalizing settings.
Three common configuration errors
- Phenomenon: Token limit exceeded errors returned by the vector model. Cause: Long financial report documents were not properly chunked, with single chunks exceeding the model's maximum supported input length.
- Phenomenon: The knowledge base cannot perform automatic incremental updates, requiring manual re-upload of all documents. Cause: The
incremental_update_enabledconfiguration was not enabled, or update tasks were not triggered based on document disclosure times. - Phenomenon: Redundant knowledge base citation identifiers are included in AI responses, or output speed is slow. Cause: Citation annotation switches were not disabled, and too many segments were retrieved, leading to excessive model context load.
How to verify correct configuration
- Upload a single complete general equipment listed company financial report PDF, and verify that the length of text chunks generated after parsing falls within the
chunk_sizeconfiguration range. - Input specific retrieval terms such as "production equipment book value" and "unit production cost", and check that the similarity scores of retrieved results fall within the configured threshold interval.
- Upload a newly released quarterly financial report file, and verify that the system only processes the new content without performing full re-parsing of all documents.
- Trigger the retrieval workflow, and check that the number of reranked results matches the
rerank_top_ksetting, with all content focusing on general equipment-related segments.
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.