What this category of data looks like
Chemical raw material research report data sources include publicly available industry statistical datasets, professional chemical industry journals, operating data disclosed by listed companies, and public information from bulk commodity spot trading platforms. In terms of update rhythm, spot price data is updated daily, industry supply and demand research reports are updated monthly, and capacity planning data is updated quarterly. Each individual document includes raw material identification information, core performance parameters, monthly price trends, upstream and downstream supply chain routes, regional supply and demand balance, and policy impact summaries. For fields and units, raw material identification uses unified CAS numbers, performance parameters are labeled as "content" and "density", capacity unit is "tons/year", price unit is "yuan/ton", and transportation radius is labeled as "kilometers".
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Multi-turn dialogue for chemical raw materials must strictly distinguish the CAS numbers of different raw materials to avoid confusion between raw materials with the same name but different grades. Daily updated spot price data requires prioritizing the latest time range during recall, so the time dimension recall range of the context must be limited. The multi-dimensional parameters and units in documents must be clearly and uniformly specified in the prompt to prevent the LLM from mixing different units in outputs. During multi-turn dialogue, upstream and downstream related discussions must retain the previous round's raw material identification to prevent answer deviations caused by context drift. Research reports with different update cycles must specify the recall of datasets corresponding to the cycle in the prompt to avoid confusion between cross-cycle data.
How to configure the settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
recall_top_k | Top 8–12 entries | Chemical raw material research reports have lengthy individual content. Excessive recall will exceed the context window, while insufficient recall will fail to cover detailed indicators |
similarity_score_threshold | 0.75–0.85 | There are numerous similar names and grades for chemical raw materials. A threshold that is too low will introduce irrelevant data, while a threshold that is too high may miss accurately matched detailed parameters |
rerank_count | Top 4–6 entries | Retain core entries after reranking recall results, adapting to the need to quickly locate specific indicators during multi-turn dialogue |
max_context_length | 8000–12000 characters | Chemical raw material research reports contain multi-dimensional data, requiring a sufficient context window to carry historical information and recall content from multi-turn dialogue |
system_prompt | Fixedly associate the currently discussed CAS number, only return content matched in the knowledge base, and uniformly use yuan/ton and tons/year as units for outputs | Avoid confusion between different chemical raw materials during multi-turn dialogue, and unify units to prevent output chaos |
history_max_rounds | Retain the most recent 3–5 dialogue turns | Discussions of chemical raw materials often involve upstream and downstream linkages. Retaining multi-turn context avoids repeated inquiries about basic identification information |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to conduct tests on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: When the
similarity_score_thresholdconfiguration is set below 0.6, the returned results mix research report content from non-target chemical raw materials. Cause: There are numerous similar names and grades for chemical raw materials. A threshold that is too low fails to filter out irrelevant matches. - Phenomenon: Content is truncated when Markdown tables are displayed on the dialogue interface, with
[hide XXXX char]shown at the end. Cause: Themax_context_lengthparameter is not adjusted, and the total of recalled content and historical dialogue exceeds the context window limit, resulting in content truncation. - Phenomenon: The LLM confuses parameters of different batches of chemical raw materials during multi-turn dialogue. Cause: The currently discussed raw material identification is not fixedly associated in the
system_prompt, and key CAS number information is not retained in historical dialogue.
How to verify a correct configuration
- Initiate a single-turn query to verify that the returned results only contain content related to chemical raw materials, with no irrelevant industry data. Adjust the
similarity_score_thresholdto the interval suitable for the current scenario during verification. - Initiate 3 consecutive queries involving parameters of different chemical raw materials to verify that the LLM always associates the currently discussed raw material identification without confusion. Confirm the configuration logic of
history_max_roundsandsystem_promptduring verification. - Upload a single lengthy chemical raw material research report, initiate a query involving a table, and verify that the returned content is fully displayed without truncation prompts. Adjust the
max_context_lengthparameter to an interval suitable for the current document length during verification. - Test modifying the query scope during multi-turn dialogue to verify that the LLM can adjust the answer direction based on historical context without losing context. Confirm whether the configurations of
recall_top_kandrerank_countmatch the needs of dialogue turns during verification.
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.