What data for this category consists of
Steel trade intelligent due diligence report data mainly comes from enterprise purchase and sales contracts, inventory ledgers, logistics waybills, customs declaration forms, and industry public quotation data. Data update cycles cover three categories: real-time (generated after a single transaction is completed), daily (inventory and quotation updates), and monthly (bulk due diligence report aggregation). Documents are mostly structured, including fields such as trading entity name, traded product specifications (e.g., HRB400E rebar model), transaction volume (unit: ton), transaction amount (unit: yuan), counterparty, logistics nodes, customs declaration number, and others. A small amount of unstructured contract clauses and communication records are also included.
What constraints these characteristics impose on vector models and indexing
Structured data with multiple fields and professional units needs vector models that can encode professional terminology. Generic models may mix up trade parameters for different categories. Data with mixed update cycles requires indexes to support both incremental updates and batch rebuilding, to adapt to the update needs of real-time transaction documents and monthly summary reports. The mixed structured and unstructured text document structure requires distinguishing field types for embedding, to avoid unprofessional text interfering with the matching accuracy of professional fields. The medium-sized data volume of a single due diligence report requires an index sharding strategy that balances query speed and maintenance costs, to avoid excessive shard size causing high query latency or too small shards increasing index maintenance overhead.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | text-embedding-3-large (fine-tuned for steel trade terminology) | Steel trade contains a large number of professional model and unit terms; the fine-tuned model improves the encoding accuracy of professional text |
INDEX_SHARD_SIZE | 256 MB | The structured data volume of a single due diligence report is moderate; a 256 MB shard balances query speed and index maintenance costs |
Recall count | Top 10-15 results | Steel trade due diligence needs to cover multi-dimensional transaction and inventory data; this range avoids recalling excessive redundant information or missing key content |
Similarity threshold | 0.75-0.85 | Professional term matching requires a high similarity threshold, to avoid recalling irrelevant trade data |
PARSE_STRUCTURED_FIELD | Enabled, specify traded product, transaction volume, and counterparty as embedding fields | Structured fields have stronger professional attributes; separate embedding improves matching accuracy |
VECTOR_DB_INDEX_TYPE | IVF_FLAT (Zilliz environment, supported by FastGPT 4.9 and above) | Adapts to the mixed update cycle of steel trade data, balancing query speed and incremental update 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 case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: A large amount of non-steel trade data is mixed in vector recall results. Cause: The
PARSE_STRUCTURED_FIELDconfiguration is not enabled, and no dedicated embedding fields are specified, causing the generic embedding model to confuse trade text across different categories. - Phenomenon: An
INDEX_SHARD_MISMATCHerror occurs after migrating vector storage from PGSQL to Zilliz. Cause: The index sharding configuration from PGSQL is reused, and theINDEX_SHARD_SIZEparameter is not adjusted to adapt to Zilliz's sharding rules. - Phenomenon: No results are returned after accessing the private reranking model of FastGPT 4.9. Cause: The input format of the reranking model is not adjusted to the structured field splicing format of steel trade due diligence reports, causing the model to fail to recognize valid input content.
How to confirm the configuration is correct
- Upload a real steel trade due diligence report, check the embedding task logs to confirm that the specified structured fields are correctly extracted and embedded.
- Execute a retrieval request targeting a steel trade-specific model number, and verify that the number of recall results matches expected outcomes.
- Switch the vector database type and perform an index rebuilding operation, confirm that no errors occur and query latency meets expectations.
- After accessing the private reranking model, input the spliced structured field text, confirm that the model returns valid sorting results.
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.