Technical Documentation and Interface Q&A for Incident Management and Root Cause Analysis: Document Parsing and Chunking

What the data for this category looks like

The data sources for this category are incident timelines, logs, monitoring metrics, and code commit records. The update frequency is either after each major incident or once per month. Daily processed documents include RCA reports, incident tickets, review minutes, and rectification tracking forms. Most document structures contain fields such as incident timestamps, impact scope, troubleshooting steps, root cause conclusions, and rectification items. Some table-based documents include numeric unit fields such as incident duration and number of affected users.

What constraints do these characteristics impose on the "document parsing and chunking" link

Incident-related documents may have a large single volume, such as long RCA reports or multi-page log summaries. Chunking must be adapted to long text parsing capabilities to prevent critical root cause information from being split. Documents with mixed multi-source data structures require accurate identification of field boundaries and paragraph logic, avoiding chunking that breaks the contextual association of incident troubleshooting. The monthly or incident-triggered update rhythm requires the parsing process to support on-demand triggering, preventing repeated parsing of historical data from consuming resources. The table structure of rectification tracking forms requires retaining the correspondence between cells and entire rows, otherwise rectification items cannot be accurately associated with root cause conclusions.

How to set the configurations

This configuration is based on FastGPT v4.17.0.

Three common mistakes

• Phenomenon: A timeout error is triggered when parsing a Word document containing 100,000 Chinese characters, with a 504 status code. Cause: The PARSE_FILE_TIMEOUT_SECONDS value is not adjusted to the range suitable for large-volume documents, and the default timeout duration is insufficient to complete full parsing.
• Phenomenon: After importing an Excel-format rectification tracking form, the chunking result loses the cell-to-row correspondence, and fields are empty. Cause: The chunkSplitter is not configured to adapt to the table structure, and the default chunking rule splits by row, destroying the horizontal association of the table.
• Phenomenon: After generating vectors, the retrieval results have low relevance to root causes, and irrelevant historical incident records are returned. Cause: The chunkSize value is too small, splitting complete root cause hypothesis verification steps into multiple independent chunks, losing critical contextual logic.

How to confirm that the configuration is correct

• Upload a single test document containing 100,000 Chinese characters, check that the parsing task status does not show a timeout error, and confirm that the PARSE_FILE_TIMEOUT_SECONDS value is adapted to the current document volume.
• Import a rectification tracking form containing structured tables, check that the chunking result retains the correspondence between cells and entire rows, and confirm that the chunkSplitter configuration matches the document structure.
• Select a single root cause conclusion text, check that the chunking result completely includes the conclusion and the associated troubleshooting steps before and after, and confirm that the chunkSize value is reasonable.
• Upload a test document of the maximum allowed volume, confirm that the upload and parsing processes do not trigger capacity limit errors, and confirm that the UPLOAD_FILE_MAX_SIZE configuration meets requirements.

The values provided here are common starting points and should be evaluated against the sample documents relevant to the specific use case.

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.