What this category of data looks like
Data related to service consultation and response for oil and gas exploration and development comes from field seismic exploration acquisition reports, drilling operation logs, reservoir assessment documents, administrative service approval documents, and project progress ledgers. Data update frequency adjusts based on project phases: operation data is synchronized weekly during the exploration phase, and approval documents are updated along with the service process. Document structures fall into three categories: long-form technical reports, structured parameter tables, and scattered work order records. Fields include well number, operating depth, formation pressure, approval document number, processing progress, and more. Units include meters, megapascals, degrees Celsius, items, and others.
What constraints these characteristics impose on the "document parsing and chunking" link
Long seismic exploration reports have large single-document length, so professional term combinations must be avoided from being split during chunking. Structured tables in drilling logs contain associated fields such as well number, depth, and pressure, so the entire table structure must be retained during chunking to prevent data breaks. Fields in service consultation work orders have strong associated attributes, so contextual logic must be retained during chunking. Real-time collected operation data has a high update frequency, so the parsing link must adapt to incremental synchronization requirements and avoid repeated parsing of full data.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the professional paragraph length of oil and gas exploration reports, avoiding splitting of term combinations |
chunkSplitter | Hierarchical splitting by paragraph + title | Matches the chapter structure of exploration documents and retains logical associations |
customPdfParse | Enable table recognition mode | Adapts to structured table data in drilling logs and assessment reports, avoiding cell splitting |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time consumption requirements of long document parsing and prevents task interruption due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploads of large seismic data PDFs or compressed packages |
chunkSettingMode | Manual configuration | Adjust parameters for different document types such as technical reports, work orders, and tables |
Three common mistakes
- After uploading a large exploration report, the parsing node is unresponsive and the task status shows timeout. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default parameter duration is insufficient to complete long document parsing. - After uploading a drilling log table, only partial data is captured and cell content is split. The cause is that the table recognition mode of
customPdfParseis not enabled, and the default parsing logic will split the table structure and cause data loss. - When the model outputs exploration data tables in Markdown format, the displayed content is truncated. The cause is that the
chunkSizesetting is too small, splitting complete table rows into different chunks and causing information breaks during rendering.
How to confirm the configuration is correct
- A single 500-page exploration report is uploaded, and parsing task logs are reviewed to confirm no timeout errors occur.
- A drilling log document containing structured tables is uploaded, and the parsed text is checked to confirm retention of the complete table structure without cell splitting.
- The chunk preview function is called to check that professional term paragraphs are not split and logical associations remain intact.
- A file of the maximum allowed size is uploaded to confirm the upload and parsing processes do not experience abnormal interruptions.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.