What This Category of Data Looks Like
Refinery industry investment research data primarily comes from unit operation logs, crude oil quality inspection reports, refinery process specifications, industry supply and demand research reports, and customs import and export clearance data. Unit operation data updates hourly or by minute. Process specifications and research reports update quarterly or per project node. Quality inspection reports update with each production batch. Documents include structured process parameter tables, with fields covering reaction temperature, tower pressure, product production capacity, and similar metrics. Common units are degrees Celsius, megapascals, and tons per hour. Documents also include unstructured operation review documents and long-form feasibility study reports. Individual long documents can reach tens of thousands of characters.
What Constraints These Characteristics Impose on Context and Token Management
The multi-type and long-form nature of refinery data creates clear constraints for context and token management. Structured parameter tables have dense fields. The token density of single-segment parameter content is higher than that of general text. Excessively long segments quickly consume context token quotas. Frequently updated unit operation data requires frequent updates to knowledge base context. If the context window is set too small, latest parameters will be crowded out by older documents. For segmenting long-form research reports and operation logs, insufficient overlapping tokens will break logical connections between parameters, reducing answer accuracy. Industry data also has strong unit and field uniqueness. Too many irrelevant parameters in recalled context will add unnecessary token consumption.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 12000-16000 token | Adapts to the total token requirements for segmenting long-form refinery research reports and unit operation logs, preventing premature context truncation |
chunkSize | 800-1200 characters | Balances the integrity of structured parameter tables and token consumption, avoiding token overflow from overly long segments and broken parameter connections from overly short segments |
chunkOverlap | 200-300 token | Preserves front and back logical connections of structured fields, preventing segment breaks from disrupting complete explanations of process parameters |
maxTokens | 2000-3000 token | Meets the long-text generation requirements for parameter derivation and process analysis in refinery investment research responses, adapting to the generation limit of the qwen2.5-14b-int4 quantized model |
recallCount | Top 6-8 entries | Refinery industry data has strong relevance. Excessive recall will exceed the context window quota, while insufficient recall will fail to cover core parameters |
similarityThreshold | 0.75-0.85 | Filters irrelevant unit data and research reports, avoiding invalid token consumption and ensuring response accuracy |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. Testing on local samples is recommended before finalizing settings.
Three Common Configuration Mistakes
- The
token validation failederror with 400 status code appears in the chat interface. The cause is that the configuredmaxContextexceeds the maximum context window supported by the model, or the total token count of knowledge base segments exceeds the input limit. - The
Range of max_tokens shoerror appears, with 400 status code. The cause is incorrect setting of themaxTokensparameter, where the configured generated token count exceeds the support limit of the qwen2.5-14b-int4 quantized model. - Knowledge base responses have truncated content, and complete process parameter analysis cannot be output. The cause is that
chunkSizeis set too small, leading to loss of critical parameter connections in segments, orrecallCountis too high, leading to context being crowded out by irrelevant documents.
How to Confirm Correct Configuration
- Upload a refinery unit operation log document, check the segment preview function, and confirm that segments do not break the front and back connections of core process parameters.
- View the configured values of
maxContextandmaxTokensin the application settings interface, and verify that they match the context and generated token support ranges of the selected model. - Initiate a query containing specific refinery parameters, check the returned context token count via debug logs, and confirm that no abnormal truncation or errors occur.
- Test the voice input function, confirm that no
token validation failederror appears after input, and verify the compatibility of the parameter configuration.
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.