What Data Looks Like for This Category
Typical data for this category comes from daily work outputs of CIO offices, PMOs, and financial BPs. It includes annual budget sheets, project proposal documents, project portfolio dashboards, investment review reports, and similar materials. Data sources are mostly financial systems, project management systems, contract ledgers, and business department requirements. Update frequencies are typically monthly, quarterly, or annually. Document structures include structured tables, hierarchical project descriptions, and numeric fields. Common fields include budget amount, project ID, and priority marker. Units include ten thousand yuan, project cycle days, and similar units.
Constraints Imposed on Document Parsing and Chunking
The data characteristics of this category create multiple constraints for document parsing and chunking. Documents from different sources have large format variations. Some are structured table-based budget reports, others are plain-text project review reports. Parsing rules must be adjusted to fit these different formats. A single processed document may contain large volumes of related data. Chunking must not split cross-field related content, such as budget values and their corresponding project names. Splitting this content reduces subsequent retrieval accuracy. The low update frequency means high-frequency parsing is unnecessary. But the system must support single-processing of large documents. Core fields including priority and budget amount must be fully preserved in chunks. This ensures business relevance for subsequent Q&A.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category's documents include structured tables and lengthy project descriptions. 800–1200 characters retains the full context of a single budget table or complete project paragraph. |
chunkSplitter | By paragraph + title | This category's documents have clear project chapters and budget category titles. Splitting by title preserves the logical structure of the document. |
customPdfParse | Enabled | PDFs for this category are mostly table-based reports exported from financial systems. Enabling this setting preserves the relational structure of table cells. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large documents such as annual budget sheets take longer to parse. 600 seconds covers the parsing duration of most files. |
chunkSettingMode | Custom mode | This category's documents have large format variations. Custom mode allows flexible adjustment of chunking rules to fit different types of budget and project documents. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Documents such as annual review reports for this category may include multiple years of data. A larger file size supports single upload requirements. |
Three Common Mistakes
- Symptom: Knowledge base search tests fail to return target budget data. Results are empty or irrelevant. Cause: Chunking splits related budget values and their corresponding project names. This prevents matching complete business logic during retrieval.
- Symptom: After uploading multiple project documents, parsing results cannot distinguish content from different files. Cause: File metadata is not bound in the chunking configuration. Chunk results lack identifying information for their source document.
- Symptom: Parsed chunk content includes large amounts of invalid format markers. These cannot be correctly retrieved. Cause: The
customPdfParsesetting is not enabled. PDF parsing retains redundant table borders and formatting markers.
How to Verify Proper Configuration
- Upload a single annual budget sheet. In the FastGPT v4.17.0 knowledge base parsing preview, confirm each chunk contains a complete project paragraph or single budget table.
- Upload multiple documents of different types. Review the parsing result list. Confirm each chunk is marked with its source file name or ID.
- Search for keywords that include specific budget values. Confirm search results return the corresponding chunk content.
- Test uploading and parsing a large document. Confirm no timeout errors occur during parsing.
The values listed above are common starting points. They should be assessed 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.