What the data for this category looks like
Steel trade business data primarily comes from inventory and sales management systems, spot trading platforms, customer inquiry records, and electronic contracts. Update rhythms vary significantly: spot quotes update in real time or daily, inventory and sales data syncs with each transaction, and contracts and historical inquiry records are generated once with minor revisions. Document structures include structured quote sheets, long-text contracts, and unstructured inquiry records. Core fields include material grade, specification dimensions, delivery location, payment term, with units mostly tons and yuan per ton.
What constraints these characteristics impose on deployment and upgrade
Multi-source heterogeneous data sources require cross-system data access and format adaptation during deployment, to avoid parsing failures caused by inconsistent source formats. Differentiated update rhythms require incremental update configurations, rather than universal full-batch update solutions. Full-batch updates would waste compute resources or fail to sync real-time quote data. Mixed document structures require tailored parsing rules for each type, to prevent incorrect truncation of technical terms and long fields. Specialized fields and units need custom extraction configurations, to ensure the model accurately identifies steel trade-specific parameters.
How to set the configurations
| Configuration Key | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Steel trade contracts and quote documents typically have large file sizes, to support bulk upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long-text contracts and bulk quote documents take longer to parse, to avoid interrupting the parsing process due to timeout |
Chunk Length | 800–1200 characters | Steel trade documents contain technical terms and long fields. Excessively long chunks will break contextual connections, while excessively short chunks will increase irrelevant recall |
Recall Count | Top 6–8 results | Steel trade customer demands are precise, so highly relevant inventory and quote data must be matched, to avoid interference from excessive irrelevant content |
Similarity Threshold | 0.75–0.85 | Filter low-relevance non-target steel product data, to ensure recall results align with business scenarios |
Incremental Update Trigger Interval | 1 hour | Adapt to the real-time requirements of spot quotes, to regularly sync the latest inventory and transaction data |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material types, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After local deployment, a file is uploaded successfully, but empty content is returned during data processing. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not set to a sufficient duration, so parsing of long-text contracts times out and is forcibly terminated, and no valid chunked data is generated. - Symptom: After building and running a Docker image, all vector recall scores are identical. Cause: The local vector database persistence directory is not correctly mounted, resulting in randomly generated vector indexes with no differentiation each time the service starts.
- Symptom: When calling a local Qwen2-7B model, the first response is normal, but subsequent multi-turn context conversations trigger errors. Cause: The
maxContextparameter is not adjusted to accommodate long contexts, or the local deployment environment for FastGPT 4.8.10 has insufficient video memory, leading to context overflow.
How to confirm configurations are correct
- Upload the largest-sized single steel trade document, check that parsed chunks are complete, with no truncation or empty content.
- Trigger an incremental update task, verify that synced inventory and quote data matches the latest status of the data source.
- Initiate a test conversation with multi-turn context, confirm that the model can correctly associate historical inquiry requirements with corresponding steel product parameters.
- View the vector database runtime logs, confirm that each recall result has reasonable differences in scores, with no fully identical 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.