What This Type of Data Looks Like
Refining and chemical research report data mainly comes from industry special research reports released by financial institutions, public annual reports of domestic refining and chemical enterprises, and monthly operation briefings from industry associations. Update rhythm follows monthly and quarterly cycles. Some device dynamic reports are updated alongside project progress. Most documents are long-form PDFs, containing process flow diagrams, material balance sheets, and energy consumption indicator tables. Fields cover device capacity, crude oil properties, product yield, and unit energy consumption. Units use industrial standard specifications such as tons/hour, cubic meters, and kcal/kg.
What Constraints Do These Characteristics Impose on Deployment and Upgrade
The long-form PDF structure of refining and chemical research reports requires adjusting parsing timeout and segmentation parameters during deployment, to avoid parsing interruptions or content truncation. Multiple structured fields and special industrial units require building dedicated indexes for numeric fields when configuring the vector database, and presetting unit mapping rules. Monthly and quarterly update rhythms require synchronizing cycle parameters for scheduled pull tasks during upgrades, to adapt to data update frequencies. Some research reports include embedded charts, requiring enabling the image OCR parsing module during deployment to ensure non-text content can be retrieved.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Refining and chemical research reports are mostly long-form PDFs, with typically long parsing times, to avoid mid-run interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Refining and chemical research reports contain multi-page charts and data tables, with typically large single-file sizes |
maxContext | 8000–12000 characters | Sufficient context must be retained after long document segmentation, to link process parameters and product information |
| Number of Retrieved Results | Top 8–12 results | Refining and chemical research reports have many structured fields, requiring sufficient retrieved matching passages to cover different indicator dimensions |
| Similarity Threshold | 0.75–0.85 | Filters low-match irrelevant content, while retaining weak matches for specific process parameters |
SCHEDULE_INTERVAL | 2592000 seconds | Adapts to the regular monthly update rhythm of refining and chemical research reports |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Errors
- After deploying the open-source version v4.8.17, calling a model returns
{"detail":"Error message: ｜ "message": "403 This token is not authorized to use the model"}. The cause is that the model permission scope of the token was not correctly associated in the FastGPT configuration, or the token was not granted access permissions for the corresponding large model. - Multiple accounts cannot be created after private deployment. The cause is that the multi-account configuration switch of FastGPT was not enabled, or relevant parameters for account permission verification were not configured.
- Content truncation occurs when parsing refining and chemical research reports. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted to a sufficient duration, or the segmentation length was set too small, causing content to be lost during splitting.
How to Verify Proper Configuration
- A typical refining and chemical research report PDF is uploaded, and the parsed text is checked for complete process parameters and unit information to confirm the parsing module is working properly.
- A retrieval request for specific refining and chemical indicators is initiated, and the number of returned results is verified to match the preset value range.
- A call to the associated large model is tested, and the returned results are checked for token permission-related errors to confirm the model access configuration is correct.
- The logs of the scheduled pull task are reviewed, and the task trigger timing is verified to align with the preset cycle to confirm the update configuration is effective.
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.