This document is written for FastGPT v4.17.0.
What the data for this category looks like
Daily documents for this category include four types: onboarding application forms, identity qualification materials, verification audit records, and cooperation agreements. Data sources include creator backends, enterprise credit information platforms, account history systems, and contract management systems. Updates occur daily, or each time an application is submitted.
Document structures vary significantly. Form types consist of structured field combinations. Qualification materials are mostly PDF electronic documents. Audit records are text with status markers. Cooperation agreements include fixed clauses and dynamic supplementary content. Common fields include qualification numbers, application submission times, audit statuses, cooperation periods and other identifying content.
What constraints do these characteristics impose on the document parsing and chunking process?
Differences in document formats across multiple sources require the parsing step to support multiple file types. This avoids field extraction failures due to format incompatibility.
Data volume fluctuates based on daily updates or application submissions. Parsing and chunking must support batch processing of small files. It must also control the time required to parse a single file.
The mixed scenario of structured forms and unstructured documents requires chunking strategies. These strategies must retain the association of structured fields, and split unstructured text to fit vector storage.
Qualification verification is a core time-consuming step. Parsing and chunking must prioritize extracting core field blocks related to qualifications. This improves subsequent verification efficiency.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field length of onboarding applications and qualification materials, avoids semantic splitting |
customPdfParse | Enabled | Adapts to the PDF format of identity qualification materials, supports enhanced parsing to extract text and tables |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the maximum parsing time limit for single qualification materials and cooperation agreements, avoids timeout failures |
chunkSplitter | Semantic chunking | Retains contextual relevance of audit records and cooperation agreements, avoids breaks in clause splitting |
chunkSettingMode | Manual configuration | Adapts to chunking requirements for different document types, for example, use shorter chunks for qualification materials and longer chunks for agreements |
Three common errors
- No enhanced PDF parsing effect after enabling
customPdfParse. Cause: The minerU parsing dependency is not linked in the knowledge base settings, or relevant environment variables are not configured correctly during local deployment. - No multi-vector generation for table data. Cause: Table multi-vector support configuration is not enabled, or the row and column association structure of tables is not retained during chunking.
- No new files appear in the document library after calling the API to sync. Cause: A third-party interface is mistakenly used instead of the FastGPT official API, and the specified knowledge base sync interface is not used.
How to confirm the configuration is properly set
- Upload an onboarding application PDF, review the parsed text blocks to confirm core fields such as qualification numbers and application times are included.
- After enabling
customPdfParse, upload qualification materials containing tables, check if the row and column structure of tables is retained in the parsing results. - Upload a single cooperation agreement under 100MB, verify that the parsing task is completed within the preset time limit.
- Call the official API to sync the document library, confirm that the returned status code indicates success, and the corresponding file is added to the document library.
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 2026-10-19.