What the Data for This Category Looks Like
Data for urban public transport party building learning has four primary sources: policy documents issued by superior party committees, internal party course materials, monthly party building activity records, and party member learning ledgers. Updates to this data align with the release of superior documents and quarterly party building activities, with no fixed schedule. Supported document formats include PDF, Word, scanned files, and Excel spreadsheets. Policy documents follow standard official document structures. Party course materials have clear title hierarchies. Party member ledgers include structured fields such as name, job position, and learning duration.
Constraints on Document Parsing and Chunking
The multi-format nature of party building documents requires the parsing pipeline to support scanned file OCR, table extraction, and plain text parsing. Party course materials with clear title hierarchies and scattered activity records coexist. This requires chunking to balance thematic consistency and content integrity, and avoid splicing content across topics. Structured party member ledgers require complete retention of field information. Column names and corresponding data must not be lost during parsing. The non-fixed update cycle of document uploads requires the parsing pipeline to support batch processing and flexible configuration.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Party building learning documents often contain original policy text and proper nouns. This length ensures complete context for each chunk, and avoids breaking clauses or proper nouns mid-sentence. |
chunkSplitter | Split by title + paragraph | Party building documents have clear title hierarchies. Splitting by title ensures each chunk has a unified theme, and avoids splicing content across topics. |
customPdfParse | Enable OCR mode | Some party building documents are scanned files or image-format party course materials. Enabling OCR allows full extraction of text content. |
PARSE_FILE_TIMEOUT_SECONDS | 120–180 seconds | Single party building documents may include multiple scanned pages or long content. Extending the timeout prevents interruptions during long document parsing. |
UPLOAD_FILE_MAX_SIZE | 50–100 MB | Matches the size limit for single party building learning course materials and ledger files. Prevents upload failures due to oversized files. |
chunkSettingMode | Manual configuration | Chunking rules must be adjusted for the unique formats of party building documents. Manual configuration allows flexible adaptation to different types of learning materials. |
Three Common Errors
- When a party building-related Word document is uploaded, the parsing node shows no activity, and the backend returns a timeout status code. The root cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. Long document parsing times out and is interrupted by the system. - When an Excel file containing party member ledgers is uploaded, only the first few rows of structured data are captured. The root cause is failure to optimize parameters for table parsing, or failure to retain the complete table structure during chunking.
- Parsed chunk content is truncated mid-proper noun such as "three meetings and one lesson" or "theme party day". The root cause is a
chunkSizevalue that is too small, and failure to split chunks using title rules.
How to Confirm Correct Configuration
- Use the parsing node in FastGPT v4.17.0, upload a standard party building PDF course material, and confirm that the parsed text includes all main body content with no obvious truncation.
- Upload an Excel file containing party member ledgers, and confirm that the parsed result retains the complete table structure and all field information.
- Randomly select a set of chunked content, and confirm that each chunk has a unified theme, with no content spliced across titles.
- Upload a party building document of approximately 80 MB, and confirm that no timeout errors occur during parsing.
The values provided are common starting points, and should be assessed against sample data relevant to the specific deployment context.
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.