What This Category of Data Looks Like
Carbon steel data sources include steel mill direct supply quotation systems, bulk commodity spot trading platforms, public industry association reports, and futures exchange delivery data. Update cycles cover daily spot quotations, weekly steel mill inventory data, monthly industry supply and demand reports, and quarterly capacity adjustment announcements. Document formats include structured Excel tables with specification, origin, and price fields, industry research reports in PDF format with embedded charts and data tables, and structured real-time datasets returned via APIs. Fields and units include price data in yuan per ton, inventory and capacity data in 10,000 tons, and section specification parameters in millimeters.
Constraints on Knowledge Base Retrieval and Recall
The mixed structured and unstructured nature of carbon steel data requires retrieval and recall to support both precise matching of structured fields and semantic association of unstructured text. This prevents missed key specification matches that occur when only keyword recall is used. Multiple update cycle data sources require a combination of incremental and full indexes. This avoids excessive time spent on full index reconstruction. Differences in standardized fields and units require support for filtering by origin, specification, unit and other fields during retrieval. This prevents confusion between different steel product categories. Nested chart documents require the recall process to extract embedded chart data in addition to text content. This ensures the integrity of investment research data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall count | top 10-15 results | Carbon steel investment research requires coverage of multi-dimensional data including spot, futures, and industry reports. Too many results increase context processing time, while too few will miss key associated information |
similarity threshold | 0.75-0.85 | Carbon steel specifications have high category differentiation. A threshold that is too low will introduce irrelevant steel product data, while a threshold that is too high will filter out some semantically matching associated research reports |
chunk length | 800-1200 characters | Carbon steel industry research reports often contain long paragraphs of supply and demand analysis. Chunks that are too long will break context association of professional terms, while chunks that are too short will split complete data logic |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large carbon steel industry research report PDFs or multi-sheet Excel files takes significant time. Default parameters may cause parsing interruptions |
rerank return count | top 5-8 results | The reranking step optimizes recall result ranking to meet the precise screening needs of core data for investment research scenarios |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Knowledge base retrieval takes more than 4 seconds, and interface result loading has significant delays. Cause: The
recall countandchunk lengthparameters were not adjusted, leading to loading excessive redundant carbon steel historical data and non-core research reports. - Phenomenon: Cell data misalignment and field loss occur after retrieving uploaded multi-row XLSX tables. Cause: Table structured parsing configuration was not enabled, and multi-sheet XLSX files were processed as plain text chunks, breaking the association logic between cells.
- Phenomenon: Non-carbon steel product data such as stainless steel and special steel is mixed in retrieval results. Cause: The
similarity thresholdwas not set, or the threshold was set below 0.7, and retrieval scope was not restricted via field filtering, leading to irrelevant category data being introduced via semantic matching.
How to Verify Proper Configuration
- Upload a standard carbon steel spot quotation Excel table, check if the parsed fields include core information such as specifications, origin, and price, to confirm that structured parsing configuration is active.
- Initiate a search for "Q235 sheet spot price", check if retrieval time meets expectations, and verify the value range of the
retrieval_timelog field. - Search a dataset containing multiple steel product categories, check if results only include carbon steel-related data, to confirm that field filtering and similarity threshold configuration are active.
- Upload a large carbon steel industry research report PDF, wait for parsing to complete and check for timeout errors, to confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is reasonable.
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.