What data in this category looks like
Data for this category primarily comes from stress testing platforms, APM monitoring tools, database slow logs, and production traffic baselines. The update frequency is monthly or upon major version releases. Daily produced documents include stress testing plans, capacity evaluation reports, stress testing data records, and optimization suggestion sheets. These documents contain standardized fields such as throughput (TPS), latency (milliseconds), resource usage rate (percentage), and peak concurrent users. Some documents are in scanned image format, with a mix of structured metrics and unstructured analysis content.
What constraints do these characteristics impose on document parsing and chunking?
The characteristics of documents in this category create clear constraints for the parsing and chunking process. First, documents include structured performance metrics and unstructured analysis content. Chunking must retain the contextual connection between the two, to avoid semantic fragmentation after splitting that harms subsequent retrieval and matching. Second, some stress testing reports are in scanned format, so support for optical character recognition (OCR) capabilities is required. Finally, individual document sizes vary widely. Ranges span from optimization suggestion sheets with hundreds of characters to tens of megabytes of stress testing data logs. The system must adapt to parsing needs of different scales, align with the monthly or major version update rhythm, and support versioned management of parsing results.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the average length of individual chapters in stress testing plans, retaining complete context for metrics and analysis |
chunkSplitter | Split by paragraph + heading | Matches the chapter structure of stress testing documents, avoiding content splits across chapters |
customPdfParse | Enable OCR recognition | Supports text extraction from scanned stress testing reports, resolving parsing issues for image-format documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time required for large-capacity stress testing data records, avoiding timeout errors |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers the common capacity range of single-batch stress testing data logs |
Three common mistakes
- Symptom: Knowledge base search returns empty results or very low matching scores. Cause: Chunking splits associated performance metrics and corresponding analysis content, leading to broken semantic context.
- Symptom: Parsed content is missing or garbled after uploading a scanned stress testing report. Cause: OCR recognition mode for
customPdfParsewas not enabled, making it impossible to extract text from images. - Symptom: Timeout errors occur when parsing large-capacity stress testing data records. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default value cannot accommodate the parsing time required for large files.
How to confirm configurations are correctly applied
Based on the configuration logic of FastGPT v4.17.0, configurations can be verified as effective through the following actions:
- Upload a scanned stress testing report, and check whether the parsed result includes text content from the image.
- Upload a standard stress testing plan document, and confirm that chunked results are split by chapter and paragraph without disrupting the association between metrics and analysis.
- Upload a large-capacity stress testing data log, and confirm that no timeout errors occur during the parsing process.
- Conduct search tests on the configured knowledge base to verify that relevant performance metrics and analysis content can be correctly retrieved.
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.