This document is written based on the document parsing module of FastGPT v4.17.0.
What data in this category looks like
Typical document sources for this category include customer system interface specifications, public API documentation, debugging test data, and phase-by-phase debugging logs. Daily deliverable documents include interface lists, debugging records, field mapping tables, and issue tracking tables. Most documents use a multi-level directory structure, and include core fields such as interface addresses, request methods, response fields, and error codes. Technical units such as bytes, milliseconds, and request counts are used. Update frequencies are typically daily or per project phase.
What constraints these characteristics impose on document parsing and chunking
The multi-level directory structure of this type of document requires retaining hierarchical associations during parsing, to avoid losing the binding relationship between interfaces and their corresponding fields after chunking. Documents from multiple sources combine plain text, tables, and embedded link API formats, so parsing logic must support different formats. The daily or phase-based update feature requires incremental parsing support to avoid repeated processing of full content. Fields and parameters have strong correlation, so context must be retained during chunking. Splitting cross-field call chain descriptions will compromise the completeness of context for subsequent debugging troubleshooting.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Interface debugging documents contain long paragraphs of call chain descriptions and field mapping tables. This range preserves the complete configuration context of a single interface |
chunkSplitter | Split by paragraph + title | Adapt to multi-level directory document structures, retain hierarchical associations between interfaces and their corresponding fields |
customPdfParse | Enabled | Adapt to PDF-format API documents and debugging logs provided by customers, preserve parsing integrity for tables and embedded links |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Address parsing time for large interface lists and debugging logs, avoid timeout interruptions |
chunkSettingMode | Manual configuration | For the feature of strong field correlation, flexibly adjust chunk boundaries to adapt to documents of different formats |
Three common configuration errors
- No return results after calling the API to parse Feishu multi-level directory documents. The root cause is that the hierarchical association rule for
chunkSplitteris not configured. The parsing logic cannot recognize the directory structure, so no valid chunk data is generated. - Unable to normally retrieve doc-format attachments and embedded images in Feishu documents. The root cause is that the
customPdfParseconfiguration is not enabled. The default parsing logic only processes plain text content. - Timeout errors occur when parsing large debugging logs. The root cause is that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is lower than actual parsing time, causing the task to terminate prematurely.
How to verify correct configuration
- Upload an interface list document with a multi-level directory, confirm that parsed chunks retain the title and field content corresponding to each interface.
- Call the parsing API to upload a test document, confirm that the returned result includes complete chunk text and metadata.
- Upload a test document containing Feishu doc-format attachments and embedded images, confirm that associated text for images and attachments is retrievable in the parsing result.
- Upload a large debugging log file, confirm that the parsing task has no timeout errors, and the number of generated chunks matches expectations.
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.