What does the data for this category look like
This category's data comes from engineering construction project approval management systems, construction drawing review systems, enterprise qualification databases, and credit platforms, with a daily update frequency. Commonly processed documents include construction permit applications, construction drawing review qualification certificates, quality and safety supervision procedures, completion record forms, and more. Most documents are a mix of structured fields and unstructured text. Some contain embedded approval flowcharts and images of participating unit qualification certificates. Fields mostly involve business identifiers without uniform formats, such as unit names, license numbers, and approval nodes.
What constraints do these characteristics impose on the document parsing and chunking link
Multiple system sources lead to large differences in document formats, requiring adaptation to non-standardized form and report structures. The daily update frequency requires the parsing process to have stable fault tolerance, to avoid interrupting overall tasks due to abnormal single documents. Documents with embedded approval flowcharts and qualification certificate images need support for extracting image content and binding it to associated text. Mixed structured and unstructured content requires retaining contextual associations of key business fields such as approval nodes and license numbers during chunking, to avoid breaking critical information after splitting.
How to configure the settings
For FastGPT v4.17.0, adaptation can be completed using the following configuration items:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the text density of engineering documents, retains complete context of approval nodes and qualification information |
chunkSplitter | Split by paragraph + heading | Matches the chapter structure of engineering documents, avoids destroying logical associations of approval processes after splitting |
customPdfParse | Enabled | Supports extracting embedded construction drawings and qualification certificate images, adapts to non-text content in documents |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to the parsing duration of single engineering documents, avoids timeout failures for large approval materials |
UPLOAD_FILE_MAX_SIZE | 100 MB | Matches the common volume upper limit of engineering approval documents, supports large files such as complete construction drawing review qualification certificates |
chunkSettingMode | Manual configuration | Adjusts chunking rules for different types of approval documents, balances structured fields and unstructured text |
Three common mistakes
- Symptom: Embedded image content is missing from uploaded PDF documents after parsing. Cause: The
customPdfParseconfiguration item is not enabled. The default parsing only extracts text and does not associate embedded images. - Symptom: Parsing fails when uploading a public Yuque link. Cause: The link is not confirmed to be a publicly accessible page without permission restrictions, or the link format does not fall within the valid range for knowledge base parsing.
- Symptom: HTTP tool parameters are parsed correctly, but the tool does not perform the corresponding operation. Cause: Reasonable request timeout parameters are not configured, or the target request address does not have open access permissions, causing the request to fail to trigger.
How to confirm the configuration is properly set
- Upload a typical construction permit application PDF, check if the parsed result includes all embedded qualification certificate images.
- Adjust the
chunkSizeconfiguration, confirm that the text length of the chunked results falls within the preset range, and no key approval nodes are split. - Upload the largest single approval document, confirm that the parsing process does not trigger a timeout error.
- Test the parsing process for a public Yuque link, confirm that normal text chunked results are returned.
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 2026-09-28.