What the data for this category looks like
Financial report and operational data for steel trade primarily comes from internal monthly business ledgers, quarterly report drafts, upstream steel mill supply settlement slips, and downstream customer payment receipts. Monthly operational data is synced weekly, while quarterly official reports are updated at the end of each month. Documents are mostly structured Excel or CSV tables, supplemented by PDF-format report summaries. Some scenarios include scanned images of weight slips and customs declaration forms. Fields include transaction category, unit of measure (ton), purchase or sales unit price, transaction amount, inventory balance, logistics cost, and more. Data granularity covers full lifecycle information for single-batch transactions.
Constraints for multi-turn dialogue and prompt engineering
The large number of structured fields and fixed units require prompts to explicitly instruct the model to use specified units, to avoid unit conversion errors. Monthly operational data updates frequently, so multi-turn dialogue must support dynamic retrieval of the latest ledgers. Static knowledge base reliance must be avoided to prevent the model from using outdated data in responses. Image-based documents require multimodal parsing capabilities, to ensure information such as weighed weights and customs declaration numbers from images can be included in dialogue context. There are many product subcategories, so multi-turn dialogue must support progressive follow-up queries for operational data of specific subcategories, to avoid generic responses that fail to match users' detailed needs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Steel trade financial reports often include bundled monthly ledger files with large individual file sizes, requiring adaptation to batch upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Structured table parsing requires traversing multiple rows and columns of data, and image-based documents require OCR recognition, leading to long processing times |
maxContext | 8000–12000 characters | Financial report data has many fields, and multi-turn dialogue must retain context information from multiple follow-up queries to avoid truncating critical data |
Segment Length | 1000 characters | Single-page data volume of structured tables is moderate, and segmentation facilitates step-by-step parsing and result integration by the model |
Retrieval Count | Top 8 entries | There are many steel trade product subcategories, requiring sufficient retrieved category data to cover specific categories queried by users |
Similarity Threshold | 0.75 | Low-relevance historical data must be filtered, while operational data of the same category across different cycles must be retained for comparative analysis |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Scenario: Uploaded monthly financial report tables are not referenced by the model, and the response contains calculation results inconsistent with actual data. Cause: Context retrieval configuration for uploaded files is not enabled, or the
similarity thresholdis set too high, resulting in structured data not being included in the dialogue context. - Scenario: Calling the multimodal interface to parse weight slip images returns a
400 invalid imageerror, and the dialogue log records image parsing failure. Cause: The uploaded image uses an unsupported format (such as WEBP), or the image resolution is too low for OCR recognition. - Scenario: The model cannot follow up with detailed queries for specific steel product categories in multi-turn dialogue, and only provides generic conclusions. Cause: The prompt does not explicitly require the model to conduct progressive follow-up queries for subcategories, or the
maxContextsetting is too small, resulting in follow-up query context being truncated.
How to Verify Proper Configuration
- Upload a CSV file containing multi-category steel transaction data, initiate a query for revenue calculation of a specific category, and confirm that the model references data from the uploaded file instead of using general knowledge.
- Upload a scanned weight slip image, initiate a query for image content extraction, and confirm that no
400 invalid imageerrors occur, and that the recognition result matches the image content. - Initiate two or more progressive queries: first ask about total revenue, then follow up with the proportion of specific subcategories, and confirm that the model retains context from the previous dialogue and can continue analysis based on prior query results.
- Check system operation logs to confirm that the uploaded file parsing process completes normally, with no timeout or parsing failure records.
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.