What this category of data looks like
Typical data for this category comes from community forums, registration systems, social media, and project roadmaps. Most updates occur monthly or per event cycle. Daily processed documents include event plans, speeches, registration lists, and event post-mortems. Event plans contain topics, timelines, and speaker details. Registration lists are structured lists of personnel information. Post-mortem documents include event feedback and data summaries. Supported document formats include structured tables, long text, and fragmented social media content. There is no unified fixed field structure.
What constraints these characteristics impose on document parsing and chunking
Data sources are scattered, and formats vary widely. The parsing module must adapt to structured tables, long text, and fragmented content to avoid losing key information due to format incompatibility. Update frequencies are inconsistent, and single data volume changes with event scale. The parsing module must support flexible triggering and processing to avoid resource waste or timeouts. Documents include core event details such as speakers and topics. Parsed chunks must retain semantic integrity, otherwise subsequent API Q&A accuracy will suffer. Errors in this category of data directly impact event preparation and community connections, so parsing requires high fault tolerance and accuracy.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic integrity of long documents such as event plans and post-mortems, avoiding splitting that disrupts the logical association of core information such as topics and speakers |
chunkSplitter | By paragraph + heading hierarchy | Adapts to speeches and event plans that contain heading hierarchies, retains the original document structure, and facilitates subsequent precise matching for Q&A |
customPdfParse | Enabled | Processes structured files such as event registration list PDFs and speech PDFs, retains original typesetting and field information, and avoids chaotic content after parsing |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to multi-page event post-mortems or large registration summary files, avoids upload failures due to oversized files |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processes long documents containing summaries from multiple platforms, avoids interruptions triggered by excessively long parsing times |
chunkSettingMode | Manual configuration | Adjusts chunking rules for different document types (such as registration lists and event post-mortems) to adapt to the diverse data formats within this category |
Three common mistakes
- Phenomenon: When parsing newly uploaded event documents, content from old event documents is included in the parsing results. Cause: No filtering rule is configured to only parse the currently uploaded file. The default setting loads all historical knowledge base content.
- Phenomenon: After uploading an image file of a Chinese event speech, the parsing result shows Chinese garbled characters. Cause: No correct adaptation rule is configured for OCR parsing, or the dedicated processing logic for image parsing is not enabled.
- Phenomenon: When asking questions about a single PDF, it is impossible to accurately match the content of the corresponding document. Cause: Chunking is not done by heading hierarchy, leading to incorrect merging of content across documents, or the
chunkSizesetting is too large, disrupting the semantic boundary of a single document.
How to confirm the configuration is correct
For the FastGPT v4.17.0 version configuration, verify with the following actions:
- Upload a single event plan PDF, check if the parsed chunk results retain the original document's heading and paragraph logic.
- Upload multiple images of event registration lists, verify that the parsing result correctly extracts structured field information.
- Upload an event post-mortem document larger than 50 MB, confirm that no timeout errors occur during upload and parsing.
- Test uploading an image of a Chinese speech, check that the Chinese content in the parsing result has no garbled characters.
The values provided are common starting points and should be validated against samples relevant to the specific use case.
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.