What the data for this category looks like
The data used for construction machinery financial report analysis primarily comes from listed companies’ annual and quarterly reports, monthly operation data from industry associations, and official operating briefings released by enterprises. Data update cycles are split into regular and irregular: annual reports are updated once per year, quarterly reports are updated every quarter, and industry operation data is updated monthly. Most documents use structured tables, including consolidated balance sheets, income statements, cash flow statements, and special indicators such as segmented equipment revenue share, excavator sales volume, and overseas business revenue. Fields cover operating revenue, attributable net profit, R&D investment, equipment utilization rate, and other metrics. Common units are ten thousand yuan, units, and hours.
What constraints these characteristics impose on knowledge base retrieval and recall
Construction machinery financial reports have numerous structured fields with high professional specificity. Retrieval requires precise matching of segmented indicators to avoid retrieving irrelevant general financial data. The combination of long documents and multi-dimensional indicators means that segmentation must not break the associated integrity of financial indicators, otherwise retrieval results will lose necessary context. Data update cycles vary across different sources, so retrieval must prioritize returning the latest quarterly and monthly data while retaining historical data for comparative analysis. Additionally, the retrieval logic for industry-specific indicators in the construction machinery sector differs from general financial indicators, requiring separate configuration of field weights to raise the recall priority of specialized indicators.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Single segments of construction machinery financial reports must retain complete financial indicator specifications to avoid losing associated data after splitting |
recallTopK | Top 10–15 results | Financial report analysis requires covering multi-dimensional indicators; too many or too few results will impact context integrity |
similarityThreshold | 0.72–0.85 | Fields in construction machinery financial reports have high professional specificity, requiring a balance between precision and recall coverage |
rerankTopN | Top 5–8 results | Filter redundant retrieval results to focus on core financial data and industry-related indicators |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large annual report PDF files takes significant time, so sufficient time must be reserved |
UPLOAD_CSV_ENCODING | UTF-8 | Adapt to the import of Chinese financial report data to avoid garbled characters |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: No optional values are available for variable references in the knowledge base selection interface. Cause: The field mapping function is not enabled in the knowledge base configuration, or the uploaded structured data does not include referenceable variable fields.
- Phenomenon: Significant differences exist between results returned by online conversations and API calls. Cause: Retrieval parameters are not unified; API calls do not explicitly specify
similarityThresholdandrecallTopKconsistent with online conversations, or online conversations have context caching enabled by default. - Phenomenon: Chinese garbled characters appear after importing CSV files into the knowledge base. Cause:
UPLOAD_CSV_ENCODINGis not configured as UTF-8, or the actual encoding of the original file does not match the set value.
How to confirm correct configuration
- Upload a sample construction machinery financial report file, check that parsed segments retain complete financial indicator rows with no truncation or splitting errors.
- Initiate a retrieval test, enter specified financial report field keywords, and verify that the field matching degree and quantity of retrieval results align with the expected configuration.
- Initiate identical retrieval requests via the API interface and online conversation, compare the consistency of returned results to confirm that parameter configurations are unified.
- Perform re-parsing and index reconstruction operations on the existing knowledge base, verify that the vector retrieval function returns results 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.