What data for this category looks like
Data for this category primarily comes from pressure testing platforms, APM monitoring tools, database slow logs, and production traffic baselines. Regularly produced documents include pressure testing plans, capacity evaluation reports, pressure testing data records, and optimization suggestion sheets. Update frequency is monthly or when major versions are released. Documents typically have multi-level directory structures, with fields including throughput (QPS), latency (milliseconds), CPU and memory usage, bottleneck indicators, and more. The body length of individual documents varies widely.
What constraints these characteristics impose on document parsing and chunking
Documents come from diverse sources and have inconsistent formats. The parsing link must adapt to multiple formats, including Word for pressure testing plans, PDF for capacity evaluation reports, and CSV for pressure testing data. The multi-level directory structure of documents requires retaining hierarchical associations during parsing. This avoids losing the binding relationship between chapters and corresponding data. The wide variation in single document body length requires that each chunk contains complete indicator context when splitting. This prevents core performance data such as throughput and latency from being split apart. Parsing duration must also be controlled to prevent subsequent processes from being blocked due to timeouts. Monthly updated documents do not need high-frequency incremental parsing. Full parsing can cover updated content.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Performance testing documents contain large amounts of indicators and context. This length retains the complete association of a single set of pressure testing data |
chunkSplitter | Split by paragraph + semantics | Performance data and analysis conclusions are tightly bound. Splitting by semantics avoids losing the association between indicators and analysis content after splitting |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Parsing large pressure testing reports or multi-batch data records takes a long time. This range covers parsing needs for most scenarios |
customPdfParse | Enable external parser configuration | The built-in parser has insufficient adaptability to table formats in pressure testing reports. External parsing better preserves original typesetting and data structure |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Archive files for large pressure testing datasets are usually large in size. This threshold covers upload needs for most business scenarios |
Three common configuration mistakes
- Failing to correctly set the external parser key after configuring
customPdfParse, resulting in failed parsing of PDF-format pressure testing reports. The interface displays file parsing timeout or empty results. - Not adjusting parsing configurations for multi-level directories in Feishu documents, resulting in only the first chapter content being parsed. Pressure testing data under multi-level directories cannot be fully retrieved.
- Failing to correctly mount the file parsing module during Docker deployment, or not correctly passing file parameters when calling the API for parsing, resulting in the interface returning a successful call but no parsing results.
How to verify correct configuration
- Upload a typical pressure testing report in PDF format, check if the parsed text retains the original multi-level directory structure and table data.
- Call the API to upload a small pressure testing document, confirm that the parsing results returned by the interface contain complete text content, with no empty fields or truncation.
- Check the parsing module logs for FastGPT v4.17.0, confirm that the configured value of
PARSE_FILE_TIMEOUT_SECONDSdoes not trigger timeout errors. - Test uploading a pressure testing data archive file larger than 1000 MB, confirm that the
UPLOAD_FILE_MAX_SIZElimit error is not triggered.
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.