What the Data for This Category Looks Like
Data for this category comes from paper or electronic logs collected during field exploration, ledgers exported from drilling operation systems, well test reports, and similar materials. Update frequency aligns with exploration project progress. Full ledgers are updated in bulk after a single project concludes. Individual documents vary widely in structure. They range from a few pages of single-well operation records to dozens of pages of regional reserve assessment reports. All contain standardized operation parameters, geological descriptions, quantitative data, and accompanying charts such as logging curves and production histograms. Fields include operation numbers, well location coordinates, lithology types, fluid production volumes, equipment runtime, and more. Units are mostly common engineering physical units, such as hours, cubic meters, and others.
Constraints on Document Parsing and Chunking
The document characteristics of this category impose multiple constraints on the parsing and chunking process. First, large volumes of nested tables and accompanying charts can cause standard text chunking to break data correlation. The binding relationship between tables, images and their corresponding text descriptions must be preserved. This prevents splitting that blocks full restoration of data for a single well or single operation. Second, engineering-specific fields are tightly bound to quantitative data. Chunking must not arbitrarily split continuous paragraphs spanning multiple fields. Each chunk must contain a complete operation unit or parameter group. Third, document length varies greatly. Short documents span only a few pages, while long documents can reach dozens of pages. Fixed character-count chunking may disrupt the logical structure of long documents. Flexible chunking by chapter or operation unit must be supported. Additionally, some documents include exploration-related images. The parsing process must synchronously associate images with their corresponding text descriptions.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the length of single-segment operation parameter groups in oil and gas exploration ledgers. Preserves data integrity while controlling the context size of each chunk. |
chunkSplitter | By chapter + fixed character threshold | Balances logical document structure and length control. Prevents logical fragmentation caused by cross-chapter chunking. |
customPdfParse | Enabled | Preserves the binding relationship between nested tables, images and their corresponding text descriptions. Restores the complete data structure of engineering documents. |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Accommodates parsing time for long documents. Prevents parsing interruptions due to timeout. |
chunkSettingMode | Custom mode | Adjusts chunking logic based on binding rules for engineering-specific fields. Adapts to the unique characteristics of oil and gas exploration data. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates upload requirements for long documents such as regional reserve assessment reports. Meets parsing requirements for large file sizes. |
Three Common Configuration Errors
- Phenomenon: After calling the API to create a knowledge base, standard status fields for parsing in progress, ready, or parsing failed are not obtained, or status returns are delayed. Cause: The synchronous status return parameter is not configured in the API request, or the reporting logic for parsing status is not enabled.
- Phenomenon: When parsing operation ledgers that contain HTTP interface response messages, Set-Cookie field content cannot be correctly extracted. Cause: The default document parsing module does not adapt to the special field format of HTTP messages, and custom parsing rules are not enabled for this type of scenario.
- Phenomenon: After uploading a PDF document containing exploration images, the parsed result does not include the corresponding text description of the image, or image association information is lost. Cause: The
customPdfParseconfiguration is not enabled. The default parsing module cannot preserve the binding relationship between images and their corresponding text.
How to Verify Configuration Effectiveness
In FastGPT v4.17.0, verification of configuration effectiveness can be completed via the following actions:
- Upload a typical single-well operation ledger PDF. Verify that parsed text retains table structures and image associations. Confirm that chunked content includes complete operation parameter groups.
- Call the knowledge base parsing status query API. Confirm that status feedback matching the business scenario is returned, and verify that status reporting logic operates correctly.
- Upload a test document containing HTTP response messages. Verify that target fields are correctly extracted in parsed results, and confirm that custom parsing rules adapt to this document type.
- Upload a large-volume regional assessment report. Confirm that upload and parsing processes complete normally, and verify that file size configuration aligns with business requirements.
The configuration values listed above are common starting points. They should be evaluated against sample documents and business requirements specific to the deployment.
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.