What this category of data looks like
The core documents for this category include recommendation strategy parameters, content pool tag lists, experimental comparison data, and operation review records. Data sources include content tag libraries, user behavior logs, experimental platform reports, and recommendation backend traffic data. Updates follow a daily or weekly cycle. Individual documents typically contain fields such as strategy configuration items, indicator values, and traffic fluctuation records. There is no fixed mandatory format, but the documents must fit scenarios for operational analysis and algorithm tuning.
Constraints on document parsing and chunking
The structured nature of this category’s documents requires chunking to preserve the relationship between fields, and to avoid splitting cross-indicator comparison records. Mixed document formats from multiple data sources are common, including exported table snippets, PDF experimental reports, and plain text strategy records. Parsing logic must adapt to these varied document types. Daily or weekly update cycles require support for batch parallel parsing, to prevent task backlogs that reduce operational efficiency. Content pool lists include large volumes of consecutive tag entries; chunking must avoid splitting a single complete tag group. Anomaly traffic troubleshooting documents contain time-series data; chunking must preserve time continuity to support subsequent problem identification.
How to configure settings
This guide is written based on the configuration logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Fits the length of single-segment analysis content in recommendation strategy records and experimental reports, avoids splitting core indicator comparisons |
chunkSplitter | Paragraph + semantic boundary | Adapts to mixed documents containing strategy descriptions and experimental data, preserves content logical integrity |
customPdfParse | Enabled | Adapts to multi-page experimental reports and operation review PDFs, preserves text hierarchy and table structures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Fits parsing time for large batch files such as weekly content pool lists, prevents task interruptions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch import of multiple docx and PDF documents, meets parallel parsing requirements |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types such as strategy records vs. content pool lists, fits the category’s unique document structure |
Three common configuration mistakes
- Symptom: After parsing a docx-format content pool list, chunking results contain scattered tag entries with no complete tag groups. Cause: The
customPdfParseconfiguration was not enabled; the default parsing logic split tag rows within tables. - Symptom: After batch parsing multiple documents, returned chunked text appears as unstructured full paragraphs, which cannot be directly used for recommendation strategy tag matching. Cause: The
chunkSplittersetting was not set to paragraph + semantic boundary mode; only fixed character splitting was used, losing the original paragraph logic of the document. - Symptom: Timeout errors with status code 504 occur when parsing large weekly experimental reports. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is lower than the actual document parsing time, failing to fit the category’s batch parsing requirements.
How to confirm correct configuration
- Upload a single typical document such as a single recommendation strategy record, review the parsed chunking results, and confirm that core strategy paragraphs are not split.
- Batch upload multiple documents of different formats, check the parallel execution status of parsing tasks, and confirm that no batch task backlogs occur.
- Call the API to retrieve chunked results, verify that returned content includes the original document’s hierarchical structure or tag group information.
- Adjust the
chunkSizeparameter, compare chunking lengths across different values, and confirm alignment with the category’s document analysis unit requirements.
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.