What the Data for This Category Looks Like
Data sources include internal marketing material libraries of refining and chemical enterprises, industry compliance documents, and historical customer communication records. Updates are triggered when product grades are adjusted or compliance policies change. There is no fixed update cycle, but updates occur no less frequently than monthly.
Document structure includes structured unit operation parameters, product specifications, and quotation information, as well as unstructured marketing scripts and customer feedback. Fields include unit processing capacity, raw material requirements, product indicators, and quotation range. The corresponding units are tons per year, volume percentage, mass percentage, and yuan per ton, respectively.
Constraints on Multi-turn Dialogue and Prompt Engineering
Structured unit parameters and quotation fields require precise matching of field units during multi-turn dialogue to avoid confusion between different measurement scenarios. Unstructured marketing scripts and customer feedback require retaining context during multi-turn dialogue to accurately reuse past communication logic. Dynamic updates to compliance documents require prompt templates to synchronize the latest compliance clauses regularly, to prevent output that does not meet regulatory requirements. The unstructured nature of historical customer communication records requires the multi-turn dialogue context window to adapt to longer historical texts, to prevent loss of key information. Customers may switch between asking about the production capacity and quotation of different units multiple times. Multi-turn dialogue must clearly track the product dimension associated with the current conversation to avoid context confusion.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 12000–15000 characters | Refining and chemical marketing data includes structured parameters and unstructured scripts, requiring coverage of the full context of multi-turn dialogue |
recallTopK | Top 3–5 entries | Core information for refining and chemical marketing is concentrated in three dimensions: units, quotations, and compliance. Excessive recall will interfere with context |
similarityThreshold | 0.75–0.85 | Parameters and quotation information for refining and chemical products have high similarity, requiring a reasonable threshold to filter irrelevant content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Documents in the refining and chemical marketing material library contain many structured tables and long texts, resulting in longer parsing time |
promptTemplate | "Please combine the current conversation context to accurately reply regarding the unit parameters, quotations, and compliance requirements of refining and chemical products, and match the units and product dimensions mentioned by the user" | Clearly constrain the core direction of the dialogue to avoid confusion between measurements and parameters of different products |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When
conversationMaxContextis set to 6, the model cannot reuse product information from the previous conversation. Cause: Only the conversation count threshold is configured, and the character limit ofmaxContextis not adjusted synchronously, so it cannot carry the full context text. - Phenomenon: Unit confusion occurs in refining and chemical product parameters returned by multi-turn dialogue, such as incorrect unit description for unit processing capacity. Cause: The prompt template does not explicitly require matching the measurement unit mentioned by the user, and no unit verification logic is added.
- Phenomenon: Garbled characters appear in key information extracted after uploading refining and chemical marketing documents. Cause: The encoding format of document parsing is not specified, or the value of
PARSE_FILE_TIMEOUT_SECONDSis too low, causing some content to not be fully loaded.
How to Verify Proper Configuration
- Initiate two consecutive inquiries about refining and chemical product parameters, and check whether the model can correctly reuse the product dimensions and measurement units mentioned in the previous round.
- Upload a refining and chemical marketing document, wait for parsing to complete, and check whether the parsed text is complete and free of garbled characters.
- Adjust the value of
similarityThreshold, test recall results under different thresholds, and confirm that the filtering effect meets business requirements. - Simulate multi-turn dialogue requests, check the system response status code, and confirm that there are no timeouts or abnormal errors.
- Verify that the FastGPT version is v4.8.0 or higher to ensure that all configuration items take effect normally.
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.