This document is compatible with FastGPT v4.17.0.
What the Data for This Category Looks Like
The data sources for this category are Git repositories, CI pipelines, static scanning reports, and defect databases. Daily processed documents include merge requests, code review comments, commit records, and branch strategy descriptions. Data updates occur daily or are triggered alongside code commits. Document structures include identification fields, change content, review conclusions, and compliance check results. Most fields use text, hash, or timestamp formats. There are no unified fixed units; field units vary based on document type.
What Constraints Do These Characteristics Impose on the "Document Parsing and Chunking" Link
Dispersed data sources and frequent updates require the parsing process to adapt to multi-format text and batch trigger scenarios. Documents such as merge requests and review comments contain both code snippets and natural language compliance descriptions, so precise chunking is needed to distinguish technical content from compliance statements. Data updated in real time alongside code commits requires controllable parsing latency to avoid blocking review workflows. Significant differences in field structures across different documents require retaining contextual associations to maintain the integrity of change impact assessment.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed length of code snippets and natural language descriptions in merge requests, avoiding chunking that breaks code semantics and compliance context |
chunkSplitter | Semantic segmentation | Preserves the complete logic of review comments and change descriptions, adapting to business semantic judgment needs in code review scenarios |
customPdfParse | Enabled | Adapts to compliance documents in PDF format such as static scanning reports, supports custom parsing rules to extract key check results |
PARSE_FILE_TIMEOUT_SECONDS | 60–120 seconds | Adapts to the parsing duration of batch Git commit records and large static scanning reports, avoiding timeout errors |
chunkSettingMode | Manual configuration | Allows adjusting chunking parameters for different document types such as merge requests and review comments, adapting to multi-scenario requirements |
Three Common Misconfigurations
- The symptom is a timeout error triggered when parsing large static scanning PDFs, with a prompt exceeding the 60000ms limit. The cause is failing to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter and using the default short timeout duration. - The symptom is that retrieved data returned by the knowledge base is fewer than the source Excel file, with only some entries returned. The cause is failing to retain data association fields during chunking, leading to loss of some matching context during retrieval.
- The symptom is that code snippets in merge requests are incorrectly split, causing review comments to fail to associate with corresponding changes. The cause is configuring
chunkSplitterfor fixed character splitting instead of semantic segmentation.
How to Confirm Configuration Is Complete
- Upload a single large static scanning report and check that the parsing task completes without timeout errors.
- Upload a merge request document containing code snippets and compliance descriptions and check that the chunking results retain complete change association logic.
- Compare the source document and parsed data entries to confirm no abnormal missing content.
- After adjusting chunking-related parameters, verify that chunked text meets business semantic integrity requirements.
The values provided here 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.