What this category's data looks like
The data sources for this category are change management systems, CI pipelines, monitoring data, and business calendars. Update frequency is weekly or per single change. Daily processed documents fall into four types: change requests, risk assessments, release window plans, and rollback plans. Most documents use a structure of fixed fields plus semi-structured content. Fields include change ID, window time period, risk level, rollback trigger conditions, and others. Some documents embed CI log snippets or tabular release plan content.
What constraints these characteristics impose on document parsing and chunking
Because data updates are frequent and document content for a single change is strongly correlated, chunking must retain relational links between fields. Do not truncate paragraphs that include risk levels and their corresponding assessment bases. The tabular structure of release window plans requires the parsing process to support table content extraction. Otherwise, key information such as time periods and approval nodes will be lost. CI log snippets embedded in documents are non-core business content. Filter these snippets before chunking, otherwise redundant information will be added to subsequent retrieval. When uploading multiple change documents in bulk, support independent chunking per single document to avoid content confusion across documents.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the average length of core single-paragraph information in change documents, avoiding breaking the link between risk assessment entries and their supporting bases |
chunkSplitter | Split by paragraph + title | Adapts to the hierarchical structure of change documents, retaining contextual links for fields such as change request titles and risk levels |
customPdfParse | Enable table recognition mode | Adapts to the tabular format of release window plans, accurately extracting structured data such as window time periods and approval statuses |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Matches the average parsing duration of large single rollback plan documents, avoiding parsing failures due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the total capacity limit for bulk uploading multiple change documents, meeting the requirement of uploading complete document packages for a single change |
chunkSettingMode | Manual configuration | Adjusts chunking rules for the fixed fields of change documents, avoiding automatic chunking from damaging the integrity of core business information |
Three common mistakes
- Symptom: Multiple change documents are merged into a single batch for parsing after upload, and information cannot be split and extracted per single document. Cause: The single-document independent parsing switch for
customPdfParsewas not enabled, resulting in bulk uploaded documents being merged during chunking. - Symptom: Parsed chunks contain a large number of redundant CI pipeline log snippets, and non-core business content cannot be filtered out. Cause: No filtering rules were configured for
chunkSplitter, resulting in non-business fields being included in the chunking scope. - Symptom: Upload failures are triggered when uploading change document packages larger than 1000 MB. Cause: The
UPLOAD_FILE_MAX_SIZEparameter was not adjusted, exceeding the default upload capacity limit of FastGPT v4.17.0.
How to confirm correct configuration
- Upload a single change request PDF, and check if parsed chunks retain complete links between core fields such as change ID and application time.
- Upload multiple change documents in bulk, and confirm that chunking results for each document are displayed independently, with no mixed content from cross-document merging.
- Upload a large rollback plan document, and check that no timeout errors are triggered during parsing, and that chunking results fully cover all content in the document.
- Enable table recognition mode, upload a release window plan table, and confirm that time periods and approval statuses in the table are correctly extracted as independent chunk content.
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.