This document describes the document parsing and chunking capabilities of FastGPT v4.17.0.
What the Data for This Use Case Looks Like
Party building learning data for nuclear power operations mainly comes from PDF documents exported from internal enterprise party building management systems, Chinese knowledge base platform links, monthly party building learning briefings, and quarterly assessment question banks. Data is updated weekly with current learning materials, and supporting assessment content is updated monthly. Documents mostly use fixed chapter structures, including modules such as learning original texts, supporting exercises, branch study records, and more. They embed a large number of party building themed promotional images, team study flow diagrams, and assessment score tables. Fields include document title, publishing unit, applicable team scope, and units such as "study hours", "assessment score", and "answer duration".
What Constraints These Characteristics Impose on Document Parsing and Chunking
The fixed chapter structure of party building learning documents requires that content relevance be preserved during parsing and chunking, to avoid splitting independent learning points and assessment modules. Embedded diagrams and tables must be fully extracted; extracting only text will prevent the large language model from understanding supporting visual explanatory content. When using Chinese knowledge base platform links as data sources, public permission web scraping must be supported; private links cannot be parsed directly. Parsing long documents such as quarterly assessment question banks takes a long time, so reasonable timeout settings must be adapted to avoid parsing interruptions. Professional terms in party building documents such as "three meetings and one lesson" and "nuclear safety culture learning" must be retained in full, and terms must not be truncated due to character limits during chunking.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the chapter and paragraph length of party building documents, preserves the integrity of single chunk content, and avoids splitting independent learning points |
chunkSplitter | Split by heading | Matches the fixed chapter structure of party building documents, ensures each chunk corresponds to an independent learning module, and does not break content relevance |
customPdfParse | Enable embedded image extraction | Fully extracts party building promotional images and flow diagrams within documents, enabling large language models to call image and text content for Q&A |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Adapts to the parsing time required for long nuclear power party building documents, avoiding timeout interruptions caused by large document size and high content volume |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports bulk import of large documents such as monthly party building briefings and quarterly assessment question banks |
Three Common Misconfigurations
- Symptom: Parsed PDF documents only display text content, and embedded images cannot be called by the large language model. Cause: The embedded image extraction configuration of
customPdfParseis not enabled, and only plain text content is extracted. - Symptom: After importing a publicly shared Chinese knowledge base platform party building learning link, the system prompts that the data source cannot be parsed. Cause: The link is not confirmed to be publicly accessible, or a private link that requires login permissions is used. FastGPT cannot directly crawl web content.
- Symptom: Content breaks appear in chunks after parsing long documents, with content from the same chapter split across multiple chunks. Cause: The
chunkSplitterset to split by heading is not used, and the default character-based chunking logic is applied, which does not match the fixed chapter structure of the document.
How to Verify Correct Configuration
- Upload a nuclear power party building PDF document containing embedded images, and check whether all embedded images and table content are fully retained in the parsing results.
- Import a publicly shared Chinese knowledge base platform party building learning link, and confirm that complete web text and page structure can be extracted after parsing.
- After setting
chunkSplitterto split by heading, check whether the chunk results are divided according to the chapter headings of the party building documents, with each chunk corresponding to an independent learning module. - Upload a party building question bank document larger than 500 MB, and confirm that the parsing process does not trigger a timeout error and the chunk results are complete.
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 2026-09-28.