What this category of data looks like
Data sources include creator backends, corporate credit information query channels, account history systems, and contract management systems. Documents processed daily fall into four categories: onboarding application forms, identity qualification certification materials, verification audit records, and cooperation agreements. Each document corresponds to a single onboarding or verification application. Updates are triggered by applications or synced in daily batches.
Document structures are mixed. Content ranges from plain-text application descriptions to qualification materials with structured tables and agreement texts. Fields include account ID, qualification number, audit status, agreement terms, and other content with no unified format.
Constraints on Document Parsing and Chunking
The mixed document types require chunking logic to support multiple content structures including plain text, tables, and structured agreements. This avoids semantic breaks caused by forced splitting.
The update rhythm triggered by applications means the parsing process must be lightweight and fast. This matches the time requirements of the audit workflow.
Dispersed data sources and the small size of individual documents require parsing tools that support on-demand invocation.
Documents contain sensitive identity and qualification fields. Chunking must retain full context integrity to ensure complete information associations can be obtained in subsequent verification steps.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Onboarding applications and qualification materials are mostly short paragraphs. This range preserves the complete context of a single application or qualification item, avoiding split-induced semantic breaks |
chunkSplitter | Split by paragraph + title | Documents include titles such as "Identity Qualification Certification" and paragraph content. Splitting this way preserves logical relevance of content, adapting to semantic retrieval for subsequent qualification verification |
customPdfParse | Enabled | Onboarding qualification materials are mostly in PDF format. Enabling this allows adaptation to MinerU parsing capabilities, extracting complete structures of tables and text |
PARSE_FILE_TIMEOUT_SECONDS | 60–120 seconds | Individual onboarding documents are small in size. This range covers the full process of format parsing and chunking, avoiding audit interruptions due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Individual onboarding materials typically do not exceed this threshold, covering upload requirements for most qualification scan copies and agreement documents |
Three Common Misconfigurations
- Issue: Enhanced PDF parsing is enabled but table content cannot be extracted normally, with a 500 status code returned. Cause: The
customPdfParseconfiguration item is not checked in the FastGPT knowledge base settings, or the local MinerU service port is not correctly mapped to the FastGPT container network. - Issue: Parsed table data is not split correctly, only returning a single paragraph. Cause: Table multi-vector support is not enabled, and the
chunkSplitterdoes not adapt to the splitting logic for table structures. - Issue: A document containing multi-page qualification materials times out during parsing, causing the task to fail. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting is too low, failing to cover the full parsing process for multi-page documents.
How to Confirm Correct Configuration
- Upload a sample onboarding qualification PDF document that includes tables. Check if the parsed result retains the complete structure of table columns and paragraph text.
- Confirm in the FastGPT knowledge base settings that the
customPdfParseswitch is enabled, and that the local MinerU service port matches the configured parameters. - Upload an onboarding agreement document within 1000 MB. Verify that the parsing task completes within 2 minutes with no timeout errors.
- View the parsed chunk list. Confirm that no forced splitting across qualification items has occurred in each chunk, and that content is logically coherent.
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.