What the data for this category looks like
Data for this category mainly comes from code repositories, CI pipelines, Issue links, and contributor-submitted content. Documents processed daily include PR descriptions, code review comments, CI execution results, and merge records. Data updates occur daily or weekly. A single data entry typically includes fields such as change scope description, associated Issue number, test status tag, submitter information, and merge verification result. There is no fixed uniform format template, and details vary based on project maintenance habits. Some data also contains nested JSON-format CI log content.
What constraints these characteristics impose on the "document parsing and chunking" step
Data sources include native code repository text, CI pipeline logs, formatted PR descriptions and other content types. The document parsing link must adapt to content in plain text, console output, Markdown and other formats to avoid parsing failures due to format incompatibility. Data updates occur daily or weekly. Single processing jobs may involve bulk PR data. Set reasonable timeout limits to avoid task interruptions, and adapt to processing needs of different project scales. Each data entry contains multiple associated fields. When chunking, retain the contextual association between change descriptions, associated Issue numbers and test results. Avoid splitting that makes it impossible to confirm the compliance basis of changes, which affects the accuracy of security and compliance reviews.
The following configurations are formulated based on the parameter rules of FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the single-block context length of PR descriptions and review comments, avoid losing the association between changes and associated information after splitting |
chunkSplitter | Split by paragraphs and line breaks | Retain the logical boundaries of code review comments and change descriptions, align with the reading habits of project maintainers |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapt to the parsing time of bulk PR data and long CI logs, avoid task interruptions due to overly long content |
UPLOAD_FILE_MAX_SIZE | 100–200 MB | Cover the upload requirements of large merge records and CI pipeline outputs |
chunkSettingMode | Manual configuration | Customize chunking rules to adapt to the business scenario for fixed document structures of PR reviews |
Three common mistakes
- Logs show OCR-related errors, or file parsing status shows failure. The cause is failing to turn off unnecessary OCR parsing switches for text-based documents for PR reviews, accidentally triggering the OCR process for plain-text PR descriptions and review comments.
- The model responds normally but an error occurs after adding PR document parsing, returning empty or abnormally formatted fields. The cause is that the chunking configuration does not adapt to the paragraph structure of PR data, resulting in the loss of contextual association between associated Issue numbers and change descriptions after chunking.
- An error occurs when parsing JSON-format content in CI logs, causing the task to terminate directly. The cause is failing to set a reasonable
PARSE_FILE_TIMEOUT_SECONDSparameter, with the parsing time of long logs exceeding the limit.
How to confirm the configuration is correct
- Upload a typical mixed file of PR description and CI logs, check if the parsed chunk results retain the logical association of paragraphs, and adjust the configuration until it meets business requirements.
- View the currently configured
chunkSizeandchunkSplitterparameters, confirm their values adapt to the current document type and length being processed. - Submit a test PR containing a large log file, confirm that no timeout or size limit error occurs during the upload and parsing process.
- Verify that the parsed chunked data contains complete content of associated Issue numbers and change descriptions, with no field loss.
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.