What data for this category looks like
Daily documents for content recommendation operations fall into four main categories: recommendation strategy records, content pool lists, experiment reports, and operation reviews. Data sources include content tag libraries, user behavior logs, experiment platform data, and recommendation backend operation data. Most documents are updated daily or weekly. Typical document structures include core strategy entries, content identification fields, metric statistics items, and anomaly annotation items. Common fields include content ID, tag category, impression count, click-through rate, experiment group, and others. There are no mandatory uniform units. Some metrics are stored as numeric values, while others use text classification formats.
What constraints do these characteristics impose on document parsing and chunking?
Diverse document types mix structured metrics and text-based strategies. Parsing must support extracting mixed-format content without losing structured fields. High update frequencies require support for frequently imported documents. Parsing timeouts and file size limits must align with batch processing needs for daily or weekly updates. Content pool lists often include multi-column structured data. Chunking must avoid cutting off cross-column associated information, while preserving contextual links between metrics and their corresponding strategies. Experiment reports and operation reviews contain long-form text analysis. Chunk lengths must support semantic integrity for subsequent retrieval, avoiding splitting critical experiment conclusions and metric interpretation content.
How to configure settings
The following configurations use standard settings for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Ensures semantic integrity for long-form recommendation strategy and experiment report text, avoids splitting content linked to critical metrics |
chunkSplitter | Split by paragraph + heading hierarchy | Matches the hierarchical structure of strategy entries and experiment conclusions in documents, preserves contextual associations |
customPdfParse | Enabled | Adapts to content pool lists and experiment reports that include tables and multi-column content, prevents loss of structured data |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch importing weekly updated content pool lists, meets needs for importing multiple documents in a single operation |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing durations for long-form operation review documents, prevents interruptions during parsing of large files |
chunkSettingMode | Manual mode | Allows adjustment of chunking parameters for different document types (strategy records / content pool lists) to fit specialized scenario needs |
Three common misconfigurations
- Issue: Uploading Excel format files triggers an unsupported prompt, or only the first two columns of data are retained after importing a CSV file. Cause: Custom parsing configuration is not enabled, or parsing rules are not adjusted for structured table documents. The default parsing logic only supports basic text and two-column CSV formats.
- Issue: Calling marker-pdf to parse PDF documents returns a connection failure, even when using a local address. Cause: Ports are not correctly mapped during Docker deployment, or the in-container parsing service fails to start normally, preventing external requests from reaching the parsing node.
- Issue: Knowledge base question answering returns content that deviates from predefined question-answer pairs, or does not use original source responses. Cause: Chunking fails to retain complete context for question-answer pairs, or retrieval parameters do not limit recall to associated chunks of predefined question-answer pairs, leading the model to call irrelevant content.
How to confirm configurations are properly set
- Upload a typical document such as a content pool list, verify that all columns of structured information are retained in the parsed data with no missing fields.
- Import multiple documents in bulk, confirm that the upload and parsing process does not trigger timeout or file size limit error messages.
- Test chunking effects for different document types, confirm that long-form text is not unreasonably truncated, and that critical strategy entries and metric links are intact.
- For documents with predefined question-answer pairs, verify that retrieved chunks include complete context of original source responses, with no cross-chunk splitting.
The values provided are common starting points and should be evaluated using local sample data.
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.