Multi-turn Dialogue and Prompt Engineering for Coal Chemical Industry Research Report Retrieval

Coal chemical industry research reports draw from public materials from industry associations, publicly disclosed documents of listed companies, and

What Data for This Category Looks Like

Coal chemical industry research reports draw from public materials from industry associations, publicly disclosed documents of listed companies, and public research reports from professional consulting firms. Updates follow a quarterly cadence for special reports, with supplementary updates when policies or project changes occur. Documents are typically split into four sections: industry overview, core project parameters, upstream and downstream supply and demand data, and policy interpretation. Core parameters are concentrated in the project parameters section, including raw coal quality parameters, project production capacity scale, product pricing ranges, industrial chain supporting conditions, and similar content. Most parameter units are tons, ten thousand yuan, gigajoule, mg/kg, and other standard units.

How These Characteristics Impact Multi-turn Dialogue and Prompt Engineering

Coal chemical research reports contain large volumes of professional process parameters and industrial chain data. Multi-turn dialogue must retain professional terminology and project references within the context to avoid broken conversations. Research report updates do not follow a fixed cycle, so prompts must support dynamic matching of the latest public project data. Core parameters are scattered across different sections, so multi-turn dialogue must guide users to clarify parameter types to avoid retrieving irrelevant content. Some parameters involve cross-section associations, so prompts must specify retrieval ranges to ensure the relevance of returned content. Parsing and retrieving long documents requires reasonable control of the context window to avoid exceeding model processing limits.

Configuration Settings

Configuration ItemRecommended ValueRationale
maxContext8000–12000 charactersIndividual coal chemical research reports have relatively long lengths, requiring sufficient context to handle project references in multi-turn dialogue
recall_top_kTop 8–12 resultsCoal chemical research report parameters are scattered, requiring sufficient retrieval volume to cover cross-section associated content
prompt_templateFixed prefix: "This assistant is a coal chemical industry research report retrieval assistant, only answers questions based on provided research report content, and must clearly mark the cited research report sections", suffix: user questionMust limit the scope of responses to avoid generating irrelevant content, and clearly define the assistant's identity
PARSE_FILE_TIMEOUT_SECONDS300 secondsCoal chemical research report document structures are complex, requiring sufficient time to complete chunking and field extraction
similarity_threshold0.75–0.85Requires filtering irrelevant research report content while retaining matching accuracy for professional parameters
rerank_top_nTop 3–5 resultsRequires focusing on core parameter content to avoid excessive redundant information

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 individual analysis, and it is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

  • Issue: The human field is empty in the API call response record preview. Cause: The human parameter was not correctly passed in the request body, or the parameter format does not meet interface requirements.
  • Issue: The custom dialogue interface has poor interactive experience, and retrieval results are displayed illogically. Cause: The rerank_top_n parameter was not configured, or retrieval results were not sorted by relevance.
  • Issue: When using a third-party deployed large model, response content does not follow preset retrieval rules. Cause: The prompt was not correctly configured in prompt_template, or research report context was not passed to the model context window.

How to Confirm Correct Configuration

  • Send a single-turn test request, check if the response record contains the correct human parameter content, and confirm that parameter transmission is normal.
  • Conduct a multi-turn dialogue test, verify that the context is correctly retained and project references are accurately identified.
  • View the retrieval result list, confirm that the number and sorting of results match the preset configuration, with no obvious irrelevant content.
  • Trigger a file parsing task, check if parsing time is within the configured PARSE_FILE_TIMEOUT_SECONDS range, with no timeout errors.

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.