What data for this category looks like
Coal chemical industry financial report data mainly comes from annual and quarterly reports of listed companies publicly disclosed by domestic and overseas stock exchanges, monthly operation data released by industry associations, and project commissioning announcements independently disclosed by enterprises. Data update cycles fall into three categories: annual reports are disclosed before April each year, quarterly reports are disclosed within 15 days after the end of the quarter, and industry monthly data is updated before the 10th day of the following month. Most documents are in PDF format, and include modules such as core corporate financial indicators, coal chemical product production capacity and output, raw coal and energy consumption, and revenue share of segmented products. Fields use specialized units including ten thousand tons, tons of standard coal, and 100 million yuan. Some product data is nested within body paragraphs in table form.
Constraints on workflow orchestration from these characteristics
The multi-source, scattered data sources of coal chemical financial reports require the workflow to deploy multiple HTTP request nodes to connect to exchange disclosure interfaces, industry association data platforms, and enterprise official websites separately. Request frequency and timeout parameters for each node must be adapted individually. Differentiated data update cycles require the workflow to support a mixed timing strategy: triggering full financial report analysis quarterly, and updating industry auxiliary data monthly. Nested tables and specialized terminology in documents require the workflow’s document parsing node to adapt to coal chemical industry expression formats, avoiding field extraction deviations caused by general parsing rules. Additionally, the long text nature of financial report data requires the workflow to properly split the context window to avoid exceeding the AI model’s processing limits.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
http_request_timeout | 300 seconds | Coal chemical financial report PDFs usually contain multi-page industry data and financial details. Sufficient response time must be reserved for retrieving external auxiliary data over the network |
parse_chunk_size | 1000–1200 characters | Coal chemical financial reports include extensive technical parameters and product revenue details. The segment length aligns with context association requirements for specialized terminology |
rag_recall_top_k | Top 8–12 entries | Financial report data covers multiple types of segmented fields. A sufficient number of relevant segments must be retrieved to cover all analysis dimensions |
cron_expression | 0 0 1 */3 * | Trigger full financial report analysis quarterly, matching the quarterly report disclosure cycle for listed companies |
field_extraction_confidence | 0.75–0.85 | Field naming in coal chemical financial reports uses differentiated expressions. This range balances extraction accuracy and coverage |
multi_source_merge_strategy | Merge after deduplicating by timestamp | Coal chemical data from different sources has varying update times. This strategy prevents duplicate data from interfering with subsequent 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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three common errors
- Phenomenon: The workflow skips the HTTP request node and proceeds directly to the AI dialogue stage. Cause: The output of the HTTP request node is not connected to the context input port of the subsequent AI node, or the node connection order is reversed.
- Phenomenon: The workflow returns a
429 Too Many Requestsstatus code when making HTTP requests. Cause: No request interval parameter is configured, and public data interfaces of exchanges or industry associations are called frequently, triggering interface rate limiting rules. - Phenomenon: Parsed coal chemical financial report fields (such as tons of standard coal consumption, olefin production capacity) are empty. Cause:
field_extraction_confidenceis set too high, failing to match the differentiated field expression formats in financial reports, resulting in valid data not being extracted.
How to confirm the configuration is correct
- Manually trigger a single workflow run, view the log details of the HTTP request node, and confirm that the returned results contain target coal chemical industry data or enterprise financial report fragments.
- View the input context panel of the AI dialogue node, and confirm that the context list includes the content returned by the HTTP request node.
- Export the parsed structured data, and verify that all preset coal chemical financial report fields have been successfully extracted.
- After configuring the scheduled trigger task, view the workflow execution logs to confirm that the task starts automatically and completes execution according to the preset cycle.
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.