What the data for this category looks like
Carbon steel-related data comes from three categories: steel mill factory quality inspection documents, spot trading platform market data, and industry association supply and demand statistical reports. Update frequencies vary across sources. Batch-level quality inspection documents update in real time with production batches. Public market data updates daily. Monthly industry reports update monthly. Document structures include fields such as batch identifiers, mechanical performance parameters, delivery standard numbers, transaction quotations, and supply and demand gap data. Units include megapascals, newton meters, yuan/ton, and other professional measurement markers. Document formats include PDF quality inspection reports, Excel quotation sheets, structured statistical files, and other types.
Constraints on Knowledge Base Retrieval and Recall
Multiple document formats require the knowledge base to support multiple parsing engines, to avoid field loss from single-format parsing. Data sources with different update frequencies require differentiated synchronization strategies. Real-time batch data needs incremental synchronization, while monthly reports can use full updates. Professional fields and units require professional tokenization during retrieval, to avoid term matching errors from generic tokenization. Document lengths vary widely: single-batch quality inspection documents are short, while monthly reports are long. Different segmentation rules must be supported to retain contextual associations. Additionally, due diligence requirements need to link batch and market data, so retrieval logic must support cross-document field matching.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Carbon steel monthly industry reports have relatively long length. 300 seconds covers parsing time for most large documents, preventing timeout errors |
Segment Length | 800–1200 characters | Carbon steel quality inspection documents have high parameter density. This range balances contextual association and field integrity, avoiding broken parameter logic from overly short segments and redundancy from overly long segments |
Number of Retrieved Results | Top 8 | Carbon steel due diligence requires covering three data source types: batch data, market data, and industry reports. 8 results balances information density and content redundancy |
Similarity Threshold | 0.72–0.78 | Carbon steel professional terms have high recognition accuracy. This range filters irrelevant general data while retaining professional matching results from the same category |
Incremental Sync Trigger Condition | By file modification time | Carbon steel batch data updates in real time with production. Triggering by modification time ensures the latest batch data is included in the knowledge base promptly |
Number of Reranked Results | Top 3 | Due diligence reports primarily rely on the latest batch quality inspection data and daily market data. Reranking prioritizes displaying high-priority data sources |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. Testing against local samples is recommended before finalizing settings.
Three Common Mistakes
- Issue: After uploading bulk carbon steel quality inspection documents, the parsing task returns a
504 Gateway Timeouterror. Cause: ThePARSE_FILE_TIMEOUT_SECONDSconfiguration was not adjusted. The default timeout duration is insufficient for parsing multi-page long documents. - Issue: After deploying a large language model locally, adding a carbon steel knowledge base triggers an
Internal Server Erroralert. Cause: Parsing rules for carbon steel multi-format documents were not adapted, triggering an exception when loading vector data. This applies to scenarios where adding a knowledge base fails for locally deployed models. - Issue: The proportion of returned content exceeds expectations, and the large language model outputs redundant information. Cause: Reasonable
Number of Retrieved ResultsandSimilarity Thresholdwere not set, failing to limit the number of documents returned by a single retrieval and matching accuracy.
How to Confirm Proper Configuration
- Upload a typical carbon steel batch quality inspection PDF, check parsing task status, confirm no timeout errors occur, and verify parsed fields include batch identifiers and mechanical performance parameters.
- Enter a professional search query, such as "Mechanical performance parameters of a certain batch of carbon steel", check the number of retrieved results and field matching accuracy, and adjust the
Similarity ThresholdandNumber of Retrieved Resultsto meet required proportions. - Upload an updated carbon steel market data file, confirm the knowledge base synchronization task is triggered, and check that the update time matches the file modification time.
- Test the reranking function, confirm that the latest batch data in the retrieved results is displayed first.
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.