Multi-turn Dialogue and Prompt Engineering for Industrial Metals Financial Report Analysis

Data related to industrial metals financial reports mainly comes from periodic reports of listed companies, publicly disclosed documents from industry

What the data for this category looks like

Data related to industrial metals financial reports mainly comes from periodic reports of listed companies, publicly disclosed documents from industry regulators, and public data from spot trading markets. The update rhythm falls into two categories: scheduled and real-time. Quarterly and annual reports of listed companies are released on fixed cycles. Spot trading data is updated daily, and industry operation data is updated weekly or monthly. The document structure includes core production indicators, inventory data, cost accounting items, revenue and profit items. Fields include primary aluminum output, cathode copper inventory, zinc concentrate processing fees, gross profit per ton, and others. Units are mostly tons, yuan per ton, and ten thousand yuan.

What constraints these characteristics impose on multi-turn dialogue and prompt engineering

The multi-time dimension of the data requires tracking the user-specified time interval during multi-turn dialogue. Prompts must clearly distinguish recall rules for historical financial report data and real-time market data. Exclusive fields such as zinc concentrate processing fees and primary aluminum comprehensive yield rate must have their definitions added to prompts to avoid model ambiguity. The length of a single financial report document is relatively long, so reasonable segmentation and recall length must be configured to ensure relevant fragments can be fully retrieved during multi-turn dialogue. Differences in fields across different industrial metal categories must automatically match the field list of the corresponding category when maintaining dialogue context, to avoid confusion of cross-category indicators.

How to set the configurations

Configuration ItemRecommended ValueRationale
maxContext12000–15000 charactersThe segmented parsed content of a single industrial metals financial report document is relatively long, so sufficient context must be retained to correlate time and category parameters in multi-turn questions
recallTopKTop 6–8 entriesThere are many fields in industrial metals financial reports, so a sufficient number of relevant fragments must be recalled to cover multi-dimensional indicators
similarityThreshold0.72–0.78Distinguish financial report text from unrelated industry content, avoid recalling irrelevant general metal information
chunkSize800–1200 charactersAdapt to long paragraph indicator descriptions in industrial metals financial reports, avoid splitting that destroys field relevance
PARSE_FILE_TIMEOUT_SECONDS300 secondsParsing a single large financial report document takes a long time, reserve sufficient processing time
streamResponseEnabledSupport streaming output to adapt to the waiting experience of long text generation, and meet the interaction requirements of API calls

The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.

Three common mistakes

  • Phenomenon: The AI dialogue output in the workflow cannot be passed to subsequent modules, and content processing cannot be completed. Cause: The output port of the AI dialogue module is not configured, or the cross-module data transfer switch is not enabled, causing the workflow to fail to continue execution.
  • Phenomenon: The API call dialogue returns a complete result without segmented streaming output, and the front-end display freezes. Cause: The streamResponse configuration item is not enabled, and the streaming transmission logic is not activated.
  • Phenomenon: Dialogue sessions automatically clear historical retrieval and interaction records. Cause: The persistent storage parameters of the dialogue session are not configured, causing the session context to be lost after page refresh or process restart.

How to confirm the configuration is correct

  • Upload a single industrial metals financial report document, check that the parsed segment length matches the value range of the chunkSize configuration.
  • Initiate multi-turn questions: first ask about copper output for a specific quarter, then ask about aluminum inventory for the same period, confirm that the dialogue context retains the time parameter from the initial question.
  • Call the API to initiate a dialogue, check that the response is returned in streaming segmented segments, with no delayed loading of complete results.
  • View the dialogue session list, confirm that historical interactions and knowledge base recall fragments are persistently stored.

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.