What the Data for This Use Case Looks Like
Daily processed documents for this use case fall into four categories: recommendation strategy records, content pool lists, experiment reports, and operation reviews. Data sources include content tags, user behavior logs, experiment platforms, and recommendation backends. Most updates run on a daily or weekly schedule. Documents typically include structured fields such as content ID, tag weight, experiment indicator values, and traffic data. Some content pool lists include multi-dimensional filtering conditions. Experiment reports log multiple sets of control data. Overall structure combines structured tables and unstructured text.
Constraints Imposed on Document Parsing and Chunking
Since documents include both structured fields and unstructured text, chunking must balance structured content integrity and text logical coherence. Avoid splitting that breaks associations between experiment indicators and filtering conditions. Frequently updated daily or weekly documents require parsing processes with low latency. This prevents parsing timeouts from disrupting operation scheduling rhythms. Incorrectly split multi-dimensional filtering conditions in content pool lists may cause missing key compliance content during subsequent reviews. Dispersed control data groups in experiment reports will reduce subsequent indicator interpretation accuracy, leading to operational decision bias. Core compliance review requirements also demand that parsing results retain document traceability information. This ensures the source and modification records of original content can be located later.
Configuration Settings
All configurations below follow the parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical text length of recommendation strategy records and experiment reports, avoids splitting that breaks the logical association of experiment indicators and filtering conditions |
chunkSplitter | Split by paragraph + title | Preserves document structural hierarchy, enables quick location of content modules during subsequent compliance reviews, and supports grouping requirements for multiple sets of control data |
customPdfParse | Enable structured parsing | Adapts to tabular content in content pool lists, accurately extracts multi-dimensional filtering condition fields, and prevents accidental scattering of table content |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large weekly experiment reports and batch content pool lists, prevents parsing timeouts caused by large document sizes that disrupt operation scheduling |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploads of historical content pool data and large weekly operation review documents, meets daily batch processing needs |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types, balances control data grouping in experiment reports and retention of structured fields in content pool lists |
Three Common Configuration Mistakes
- Parsing fails to return document traceability information. The root cause is that metadata binding configuration during chunking is not enabled. The vector database does not store identifiers such as the original document’s file name and path. This makes it impossible to associate the original content source during query.
- Field loss occurs when uploading XLSX multi-row tables. The root cause is that the table structured mode of custom document parsing is not enabled. This leads to incorrect splitting or omission of merged multi-row cells and multi-dimensional filtering conditions.
- The error "the argument ‘windows-1252’ is invalid encoding" appears when uploading PPT or PDF documents. The root cause is that the document uses a non-standard encoding format, and parsing fails because automatic encoding detection parameters are not configured.
How to Verify Proper Configuration
- Upload a typical experiment report document, and review the parsed chunk results. Confirm that each chunk retains complete experiment indicator paragraphs and title hierarchy, with no content incorrectly split.
- Run a query test using the knowledge base. Verify that returned content includes traceability information such as the original document’s file name and paragraph location.
- Upload a content pool list with a volume of approximately 500 MB. Confirm that the parsing task completes within 600 seconds, with no timeout errors.
- Upload an XLSX format table document. Check that parsed fields fully retain multi-dimensional filtering conditions, with no field loss or format corruption.
The values listed are common starting points and should be evaluated against the user’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.