What This Category of Data Looks Like
The core data for this category comes from daily work outputs of data service project managers, collection teams, and client algorithm teams. It includes four core document types: collection plans, sample specifications, authorization statements, and task schedules. Data sources cover client requirement documents, public data sources, files exported by collection tools, and internal sample libraries. Updates follow individual project cycles, or weekly syncs of latest task schedules and sample scopes. Document structures include structured tables, fixed clause text, and dynamic task entries. Fields cover sample collection scope, field specification requirements, collection legality check items, and task time nodes. There is no unified unit system. Adjustments are made based on specific project needs.
Constraints Imposed on Document Parsing and Chunking by These Characteristics
The mixed structured and semi-structured nature of daily documents requires parsing processes to retain table layouts and clause boundaries. This avoids damaging the integrity of key compliance information. Differences in data formats across multiple sources require parsing tools to support multiple input formats. The tools adapt to PDF, Excel, and text files exported by collection tools. Frequently updated task schedules and sample scopes require chunking configurations to support quick adjustments. This adapts to dynamically changing document content. Additionally, the core requirement of collection legality checks requires chunking to avoid splitting key clauses in compliance documents such as authorization statements. This prevents infringement risks or unusable samples.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the information density per chunk of collection plans and authorization statements, avoids splitting key compliance clauses |
chunkSplitter | Custom delimiter + line break | Matches user-preserved paragraph boundaries, retains manually segmented document structures |
customPdfParse | Enabled | Compatible with PDF sample specification documents exported by collection tools, retains original layouts and table structures |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to time consumption requirements for batch processing of multiple documents, avoids parsing timeouts for long schedule documents |
chunkSettingMode | Manual mode | Allows adjustment of chunking parameters for different document types such as authorization statements and collection plans, meets project customization needs |
Three Common Misconfigurations
- Phenomenon: After setting a custom delimiter, chunking results either merge multiple preset paragraphs or split a single compliance clause. Cause: The
chunkSplitteris not bound to the custom delimiter, or thechunkSizevalue does not match the preset paragraph length. - Phenomenon: PDF processing errors occur after docker deployment, returning a JSON response containing error information. Cause: Environment variables related to
customPdfParseare not configured correctly, or thePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted for parsing timeouts. - Phenomenon: Chunked documents in the knowledge base cannot be linked to their source documents. Cause: Basic document source tracing configuration is not enabled, or document metadata fields are not retained during chunking.
How to Verify Correct Configuration
- Upload a single authorization statement document, check if the chunking result retains complete authorization clause paragraphs, with no splitting or merging.
- Batch upload collection plans and schedule documents, verify that parsing time does not exceed the value set in
PARSE_FILE_TIMEOUT_SECONDS, with no timeout errors. - View chunked document metadata, confirm that each chunk is linked to the original document’s file name and path.
- In FastGPT v4.17.0, test calling the knowledge base, verify that responses can link to original document information for corresponding chunks.
The values provided are common starting points, and should be evaluated 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.