What data for this category looks like
The data for this category primarily comes from CRM systems, product usage data, ticket statistics ledgers, and contract management systems. Commonly processed documents include customer health reports, renewal forecast sheets, high-risk customer lists, and cross-department meeting minutes. Data update cycles are mostly weekly or monthly. Most documents contain fields such as customer identifiers, contract periods, risk ratings, and follow-up records, with inconsistent formats: some are structured tables, others are plain-text meeting minutes.
What constraints do these characteristics impose on the document parsing and chunking process
Due to scattered data sources and inconsistent document formats, the parsing process must support multiple document types including tables, plain text, and PDFs with digital signatures, to avoid missing core fields like customer identifiers and contract expiration dates due to format differences. Since updates occur weekly or monthly, parsing tasks must support batch processing, and timeout settings must match reasonable durations for batch jobs to prevent process interruptions caused by large single-parsing data volumes. Additionally, associated fields related to renewals must retain context during chunking, to avoid losing the correspondence between customer identifiers and risk ratings after splitting, which would affect the accuracy of subsequent Q&A.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field density and text length of renewal health management documents, avoiding overloading or overly splitting single chunks |
chunkSplitter | Split by paragraph + title | Matches the division logic of structured paragraphs such as customer information and risk ratings in documents, retaining contextual associations |
customPdfParse | Enable OCR enhancement mode | Compatible with PDF documents with digital signatures, resolving the issue of incomplete content recognition for non-text PDFs |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to large documents such as monthly high-risk customer lists imported in batches, preventing upload failures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the task duration of weekly batch parsing, preventing parsing interruptions caused by large data volumes |
chunkSettingMode | Manual configuration | Targets the specific field priority of renewal health management, allowing flexible adjustment of chunking rules to adapt to different types of business documents |
Three common mistakes
- A "failed to read file" error is returned when calling the Doc2x tool. The cause is that the
customPdfParseconfiguration is not enabled, so the tool cannot support PDF documents with digital signatures. - Uploaded PDF documents have no image content after parsing. The cause is that OCR enhancement mode is not enabled, and only the text layer is extracted without recognizing auxiliary information embedded in images.
- The API returns a read link but the page reports an access error. The cause is that
UPLOAD_FILE_MAX_SIZEis set too small, and the file upload is not completed, resulting in an invalid link.
How to confirm configurations are set correctly
- Upload a customer health report in PDF format with a digital signature, and check if core fields such as contract expiration dates and risk ratings are fully extracted after parsing.
- Batch upload monthly aggregated high-risk customer lists, and confirm that parsing time does not exceed the duration set by
PARSE_FILE_TIMEOUT_SECONDS. - Review the chunked text fragments, and confirm that each chunk retains the correspondence between customer identifiers and corresponding risk descriptions.
- Test uploading a document close to the
UPLOAD_FILE_MAX_SIZElimit, and confirm that the upload and parsing processes have no interruptions or errors.
The values given 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.