What the data for this category looks like
Data sources for special steel intelligent due diligence reports include steel plant production logs, third-party quality inspection reports, industry association supply and demand statistics, customs import and export data, and downstream customer order records. Data update frequencies fall into three categories: real-time (production logs, order data), weekly (industry supply and demand statistics), and monthly (customs data). Document structures include structured CSV logs (containing parameters such as composition and strength), semi-structured PDF quality inspection reports (containing tables and test data), and unstructured due diligence description documents. Fields include carbon content, yield strength, tensile strength, and others, with units mostly using industrial standard units such as %, MPa, and ppm.
What constraints do these characteristics impose on the "vector models and indexing" link?
Structured fields for special steel due diligence data are numerous and have strict unit requirements. Vector models must adapt to the encoding logic of numeric fields to avoid vector space deviations caused by unit differences. Single quality inspection reports contain multi-page tables and long text descriptions, which require adaptive settings for segmented indexing length to avoid breaking the association logic between fields during splitting. The volume of batch-imported CSV data can reach 100,000 entries, so the indexing system needs to support efficient batch vectorization and storage processes. Some data is updated monthly, so incremental indexing must be supported instead of full reconstruction to reduce computing resource consumption.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the table row and paragraph length of special steel quality inspection reports, avoids splitting that breaks the contextual association of fields such as composition and furnace number |
overlap_ratio | 10–15% | Retains field association information between adjacent segments, ensures that retrieval can fully cover the parameter description of a single batch of products |
vector_store_batch_size | 500–1000 entries | Adapts to the batch import efficiency of 100,000-level CSV data, avoids server memory overflow caused by excessive single batch data volume |
similarity_threshold | 0.72–0.85 | Matches the accuracy requirements of special steel product parameters, filters irrelevant recall results with insufficient similarity |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets the parsing time requirement for a single long quality inspection report, avoids premature termination of long document parsing due to timeout |
RECALL_TOP_K | Top 8–12 entries | Covers the multi-dimensional product parameters required for due diligence reports, avoids excessive irrelevant data affecting retrieval efficiency |
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 local samples before finalizing settings.
Three common mistakes
- Phenomenon: After importing 100,000 CSV files into the knowledge base, the total data volume after vectorization is several thousand entries less than the source file. Cause: Some fields are empty or their format does not meet parsing rules, so they are not included in the vectorization process.
- Phenomenon: Vector calculation scores are abnormal, with excessively large values and multiple results with identical scores. Cause: Structured numeric fields are not normalized, causing numerical differences to be amplified and destroying the reasonable distribution of the vector space.
- Phenomenon: After importing files on a deployed FastGPT instance, the system remains in the indexing state for a long time. Cause:
vector_store_batch_sizeis set too large, causing excessive server memory usage and blocking the indexing process; orPARSE_FILE_TIMEOUT_SECONDSis set too small, causing long document parsing to time out without completion.
How to confirm the configuration is correct
- Check the total number of entries in the vector store and compare it with the number of valid data entries in the source file to confirm that there is no large-scale data loss.
- Randomly select multiple special steel quality inspection reports and production logs, perform retrieval, and check the similarity distribution of recall results to confirm that the scores meet business expectations.
- Import a single test CSV file with 100,000 rows, observe the indexing completion time, and confirm that it matches the resource carrying capacity of the current server.
- Check the system logs for any vector calculation exception errors, confirm that there are no numerical overflow or format parsing errors.
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.