Reference only; verify against the target FastGPT release and deployment documentation.
What this category's data looks like
Party building learning data in the fertilizer and pesticide sector primarily comes from party work archives of state-owned and public enterprise chemical divisions, industry compliance party building learning manuals, and policy interpretation documents released by agricultural and rural affairs authorities and local regulatory bodies. Data updates align with industry policy adjustments, typically on a quarterly basis. Document types include official communications, Q&A compilations, and training presentations. Common fields include document number, issuing entity, effective date, and specialized term tags. Statistical units are mostly "article", "copy", or "volume". Some scanned training materials use unstructured text formats.
What constraints these characteristics impose on document parsing and chunking
Fertilizer and pesticide party building learning documents combine policy rigor and professional technicality. A large share of content consists of long official texts. Some documents include cross-page specialized term connections. Chunking must retain contextual logic, and avoid splitting key policy points. Scanned training materials with unstructured formats require parsing workflows that support OCR recognition. Highly repeated policy citations raise redundancy risks after chunking, so targeted handling of duplicate segments is needed. The large average file size of single compliance documents places higher stability requirements on upload and parsing processes.
Configuration Settings
| Configuration Item | Recommended Value | Basis for This Value |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of fertilizer and pesticide party building documents, and avoids splitting specialized term combinations such as "pesticide residue limit standards" |
chunkSplitter | Split by title + paragraph | Retains document hierarchical structure, and ensures chunked content follows the logical order of party building learning |
customPdfParse | Enable OCR optimization mode | Processes scanned industry party building training manuals, and fully extracts unstructured text |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for large single compliance policy documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevents timeout interruptions during large file parsing |
chunkSettingMode | Manual configuration | Defines custom chunking rules based on the characteristics of fertilizer and pesticide specialized documents |
Three Common Configuration Errors
- Uploading a single fertilizer and pesticide party building compliance document larger than 1000 MB triggers an "offset out of range" error. This occurs because the
UPLOAD_FILE_MAX_SIZEparameter is not adjusted, and the upload exceeds the platform's default upper limit. - Scanned party building training manuals fail to recognize valid text content. This occurs because the OCR optimization mode for
customPdfParseis not enabled, and only the basic text parsing function is used. - A large number of repeated policy citation segments appear after chunking, which affects the accuracy of Q&A retrieval. This occurs because no targeted duplicate rate filtering rules are configured, and highly frequent policy original text segments in party building documents are not effectively deduplicated.
How to Confirm the Configuration is Correct
- Upload a typical fertilizer and pesticide party building policy PDF, and check if the parsed chunks retain title hierarchies and complete paragraphs.
- View the knowledge base upload logs, and confirm that parsing time does not exceed the set
PARSE_FILE_TIMEOUT_SECONDSthreshold. - Test the Q&A retrieval function, and confirm that retrieved chunked content does not include meaningless duplicate segments.
- Upload a scanned party building training document, and confirm that text content can be normally extracted after parsing.
The values listed above are common starting points, and should be evaluated against applicable 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.