What this category of data looks like
Data for this category comes primarily from community forums, event registration systems, social media platforms, and project roadmap documents. Daily produced documents include event planning proposals, presentation speeches, event registration lists, and post-event reports. Update frequency aligns with event cycles, typically once monthly or per hosted event. Document fields include event theme, hosting time, participant count, speaker information, topic outline, feedback collection items, and more. Some documents contain structured tables and mixed text and image content.
Constraints on the document parsing and chunking process
The characteristics of documents in this category impose multiple constraints on the parsing and chunking process. First, documents include structured registration tables, mixed-text-and-image speeches and post-event reports. Parsing must adapt to plain text, table, and embedded image content formats. Second, data updates are concentrated around event cycles. Documents produced for a single event are generated in bulk. Parsing must support batch processing and adapt to sudden content loading demands. Third, core document fields such as event theme, speaker information, and participant count must retain relevance after chunking, to avoid splits that break information integrity. Fourth, data sources are scattered. Document formats exported from different platforms vary. Parsing must support multiple import formats.
Configuration settings
These configuration items are compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents mostly consists of narrative and structured event-related content. This range preserves information integrity for a single chunk, avoiding splits that break the coherence of event topics or speaker information. |
chunkSplitter | Split by paragraph + heading | Documents in this category include event plans, speeches, and other content with hierarchical headings. Splitting by headings and paragraphs preserves the logical structure of the document, making it easier to associate corresponding topics during subsequent retrieval. |
customPdfParse | Enable MinerU parsing mode | Some documents in this category are PDF-format speeches or post-event reports. MinerU better preserves the structure of tables and mixed text-image content, adapting to structured data in post-event reports. |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Single event documents may contain multiple pages. A longer timeout period prevents parsing interruptions for large documents, adapting to batch processing needs. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Some post-event reports include high-definition images or multi-page PDFs. This upper limit covers the size requirements of most event-related documents. |
chunkSettingMode | Manual configuration mode | Document formats in this category vary widely. Manual configuration allows flexible adjustment of chunking rules, adapting to different types of event documents. |
Three common configuration errors
- Uploading a document with external image URLs results in empty parsing results or format errors. This occurs because the custom PDF parsing mode is not enabled. The default parsing engine cannot recognize external image URL links, leading to failure to extract image content properly.
- After deploying FastGPT locally and enabling the MinerU parsing feature, knowledge base parsing cannot call the MinerU API. This occurs because the local API address and port of MinerU are not correctly filled in the FastGPT configuration, or the MinerU service has not opened access permissions for the corresponding port.
- After enabling the enhanced PDF parsing feature, table data does not generate multi-vector split results. This occurs because the multi-vector support configuration for table data is not enabled, or the chunking rules do not adapt to the splitting logic for table structures.
How to verify correct configuration
- Upload a test post-event PDF document, and check if the parsed text content retains the table structure and image descriptions from the original document.
- Enable enhanced PDF parsing and table multi-vector support in knowledge base settings, upload an event registration list document containing tables, and check if the parsed chunk results include split content for table fields.
- Upload a single event document with a size not exceeding the configured maximum limit, and check if the parsing task completes within the set timeout period without timeout errors.
- Adjust the chunk length configuration, and check if the character count of a single chunk matches the expected configured range.
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.