What This Category of Data Looks Like
The core data for this category comes from the unified budget management system, project declaration materials, fund payment data, business system ledgers, and satisfaction surveys. Performance training materials related to party building learning and party building requirement documents for rectification and implementation are also part of daily processing workflows. Daily processed documents include performance target forms, self-evaluation forms, performance evaluation reports, supporting material lists, and rectification reports, covering performance-related information across the full project lifecycle of initiation, implementation, and acceptance. Data is updated annually. Each individual document includes fixed fields such as project name, performance indicators, fund amount, rectification items, and more. Units used include ten thousand yuan, person-times, completion rate, and other relevant units.
What Constraints These Characteristics Impose on the Document Parsing and Chunking Link
Batch documents updated annually require the parsing and chunking process to have efficient batch processing capabilities, to avoid delaying overall progress due to excessively long parsing time for individual documents. There are large differences in the structure of multiple types of documents: performance reports are primarily paragraph-based, self-evaluation forms and lists are primarily table-based, and rectification requirement documents related to party building learning are primarily itemized. The parsing engine must adapt to multiple text structures at the same time. Data from multiple systems may have inconsistent formatting. During chunking, the association between data from different sources must be preserved, to avoid separating performance indicators from their corresponding supporting materials, or breaking the linked content between party building requirements and performance rectification work. Core business links rely on accurate extraction of performance information. Chunking operations must not lose key content such as performance targets and fund correspondence, otherwise subsequent evaluation work will be affected.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of performance evaluation reports, retains the contextual association between performance indicators and their supporting materials |
chunkSplitter | Split by title + paragraph | Matches the multi-level title structure of performance documents, avoids splitting descriptive content under the same performance indicator |
customPdfParse | Enabled | Targets performance self-evaluation forms and supporting material lists with merged cells, retains the integrity of cell fields |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing duration of large annual individual performance evaluation reports, avoids parsing failure due to timeout |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports batch uploading of supporting material packages for annual projects, covering all business documents |
Three Common Misconfigurations
- The interface displays "marker-pdf call request error" or local Docker deployment shows "connection failed" logs. The cause is that the port mapping of the local parsing service is not correctly configured, and the parsing service port is not exposed to the host network during deployment.
- Knowledge base Q&A returns content that deviates from preset Q&A pairs, with non-preset AI-generated content. The cause is that the
trainingTypeparameter is not correctly set, or core association identification fields of Q&A pairs are lost during chunking. - Parsing result is empty or table content is missing after uploading a performance report. The cause is that the
customPdfParseconfiguration is not enabled, and the default parsing engine cannot fully extract table data with merged cells.
How to Verify Successful Configuration
- Upload a single typical performance evaluation report, check the parsed chunked content, confirm that core performance indicators and fund amount fields are fully retained.
- Trigger preset fiscal performance Q&A tests related to party building learning, verify that the returned content completely matches the answers in the knowledge base's preset Q&A pairs.
- Upload a batch of annual supporting material packages, check that the parsing progress completes normally, with no timeout or connection failure error logs.
- After configuring the export function, generate a test export file, confirm that the file format and content meet expectations.
The values provided are common starting points and should be evaluated against local sample data.
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.