What the data for this category looks like
Data sources for steel trade financing daily reports include daily transaction data from domestic steel spot trading platforms, official factory price announcements from steel mills, port inventory ledgers, and daily settlement documents from traders. Data is updated daily at midnight with full statistics for the previous day. Some real-time market sub-items are synchronized every hour. The core structure uses structured tables, with a daily industry brief included. Standard fields include: product, specification (unit: mm), origin, daily average price (unit: yuan/ton), transaction volume (unit: ton), inventory (unit: 10,000 tons), and price change range (unit: yuan/ton).
What constraints these characteristics impose on multi-turn dialogue and prompting
The structured fields and specialized unit requirements for steel trade financing daily reports mean prompts must clearly specify the standard names and units of each field. This prevents the model from confusing valuation rules across different product categories. Frequently updated real-time sub-items require multi-turn dialogue to limit the context time range. This stops the model from referencing outdated data. The fixed document structure requires prompts to guide the model to prioritize calling structured fields. Free text summaries have a lower priority than structured fields. Users may append queries for real-time market data for specific products during multi-turn dialogue, so the system must support dynamically adjusting data range instructions.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Steel trade financing daily reports include multiple dimension fields. Multi-turn dialogue must retain information such as product and time range. A sufficient context window prevents loss of critical constraints. |
recallTopK | Top 6–8 results | Daily report data has fixed dimensions. Too many recalled entries introduce irrelevant fields. Too few fail to cover all statistical dimensions covered in user queries. |
similarityThreshold | 0.75–0.85 | Filters non-matching products or outdated daily report data. Adapts to the exact matching requirements of structured data, and avoids irrelevant content interfering with dialogue. |
streamResponse | Enabled | Daily reports have large data volumes. Streaming responses reduce user perceived wait time, and align with the streaming response logic of workflow AI components. |
workflowAiNodeTimeout | 600 seconds | Processing data summarization and field validation during multi-turn dialogue takes significant time. This prevents timeout interruptions to the dialogue flow. |
maxConcurrentChat | Calibrated via business testing | Adapts to concurrency limit requirements for version v4.9.14 and above, meeting the needs of batch business queries. |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: The dialogue API returns a 429 status code, or backend logs show concurrency limit exceeded errors. Cause: The
maxConcurrentChatparameter was not adjusted for the high-frequency query scenario of steel trade financing daily reports. The default concurrency threshold is insufficient to support batch business requests. - Issue: The workflow AI component's annotation logic only retrieves partial streaming response content. Cause: The
streamResponseconfiguration was not enabled, or the workflow was not configured with a node to wait for streaming response completion. This results in the annotation triggering before all data is returned. - Issue: The dialogue prompts "no data" when calling the knowledge base, but all content is visible on the knowledge base page. Cause: The steel trade financing daily report knowledge base was not linked to the current dialogue application, or the knowledge base recall range was misconfigured. This results in only documents under certain categories being recalled.
How to Verify Proper Configuration
- Initiate a dialogue with multi-turn product restrictions and time range queries. Verify that the fields returned by the model match the standard fields of the daily report documents.
- Check the workflow AI component's runtime logs. Confirm that subsequent annotation logic is only triggered after the streaming response is complete, with no content truncation.
- Adjust query parameters in the dialogue. Verify that the
recallTopKandsimilarityThresholdconfigurations filter irrelevant data and only return matching steel product daily report content. - Test simultaneous queries from multiple users. Confirm there are no concurrency limit exceeded errors in the backend, meeting the concurrency requirements of the current business.
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.