What the data for this category looks like
Special steel investment research data comes from four main sources. These include internal production ledgers from steel mills, special steel special reports from industry associations, technical requirement documents from downstream equipment manufacturing enterprises, and alloy formula test records from R&D centers. Production ledgers update daily. Industry reports release monthly. R&D documents update irregularly alongside test progress. Most documents combine structured tables and technical descriptions. They include fields such as heat number, batch number, alloy element content, mechanical performance parameters, delivery status, and downstream application scenarios. Some older documents are scanned files.
Constraints for Model Integration and Configuration
The multi-dimensional structured fields and mixed document formats of special steel data require configuring both structured data parsing and OCR text extraction links during model integration. This prevents information loss from unstructured or scanned documents. Frequently updated production data and infrequently updated industry reports require differentiating incremental indexing trigger rules by data type. This avoids resource waste from full indexing operations. Multi-parameter technical documents need models to accurately match investment research needs. The recall link must support filtering by field dimension to improve the targeting of investment research results.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Special steel technical documents often contain multiple sets of alloy composition and mechanical performance parameters per document. A long context can fully carry the core investment research information of a single document |
recall count | 10–15 items | Special steel investment research requires comparing multi-dimensional parameters. Too many recall results will exceed the context capacity limit, too few will fail to cover all necessary technical details |
RERANK_MODEL_ENABLE | Enabled | Special steel document fields have high similarity. A reranking model filters redundant matching results to improve the accuracy of recall results |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | OCR parsing of scanned special steel documents takes a long time. This setting avoids early timeout causing document parsing failure |
Incremental update trigger threshold | Production data: 100 items/time, industry reports: 10 items/time | Production data has a large daily update volume, industry reports update monthly. Differentiated configuration by data type avoids resource waste |
Similarity threshold | 0.75–0.85 | Special steel technical parameters have high accuracy requirements. Low-correlation matching results are filtered to avoid irrelevant information interfering with investment research judgments |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Configuration Mistakes
- Phenomenon: After enabling
RERANK_MODEL_ENABLEand selecting the reranking option in index configuration, online recall test results do not display the reranked order. Cause: The reranking model call is not enabled in the model configuration of the conversation link. Only enabling the reranking switch during the indexing phase does not take effect. - Phenomenon: When
recall countis set to 3000, the conversation log does not show context content being passed to the large model. Cause: FastGPT has a context length limit. The total character count of 3000 recall results exceeds the capacity range configured bymaxContext. The system automatically truncates the content and does not pass valid content. - Phenomenon: When using a locally deployed large model,
Request Timeouterrors occur frequently during conversations. Cause: The context length after parsing special steel documents is long. The inference time of the locally deployed large model exceeds the FastGPT defaultREQUEST_TIMEOUTconfiguration, causing a timeout.
How to Confirm Successful Configuration
- Enter the online recall test page of the knowledge base, upload a single special steel technical document, enter a query containing alloy composition or mechanical performance, and verify that the returned results are sorted as expected.
- View the parsing task logs of the knowledge base, confirm that scanned special steel documents have completed OCR parsing and structured fields have been correctly extracted.
- Adjust
recall countto different values, observe the incoming context length in the conversation log, and ensure it does not exceed the range configured bymaxContext. - Test the incremental index trigger, upload new special steel production ledger data, confirm that only newly added data is included in the index, and no full reindexing is triggered.
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.