What the Data for This Category Looks Like
The data for this category mainly comes from the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and report clues. Daily processed documents include audit notices, inspection work papers, suspicious item lists, interview records, handling decision documents, and rectification reports. These cover structured Excel settlement data and unstructured Word and PDF rule documents. Data update frequency is monthly. Fields include violation type, involved amount, rectification requirements, and others. Units vary based on the business scenario of individual data entries.
What Constraints Do These Characteristics Impose on the Document Parsing and Chunking Link
The batch structured settlement data, long-text unstructured documents, and monthly update rhythm of this category impose multiple constraints on document parsing and chunking. If 10,000+ row Excel settlement data uses the default chunking, matching accuracy will decrease due to overly large single chunks. Word audit documents or handling decision documents containing 100,000+ Chinese characters are prone to parsing timeouts or context breaks, making it impossible to associate policy clauses. Multi-source heterogeneous data has strong field correlation. Chunking must retain cross-field business logic to avoid being unable to fully correspond to policy basis after splitting.
How to Set the Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field lengths of audit documents and settlement data for this category, avoids vector matching deviation caused by overly large single chunks, and retains contextual association between policy clauses and business data |
chunkSplitter | Split by title + paragraph | For documents with structured titles such as audit notices and handling decision documents, retains chapter logic and adapts to hierarchical association of policy basis |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses parsing time for 100,000+ Chinese character Word documents and 15,000+ row Excel data, prevents task failure due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers upload requirements for batch settlement data and long-text documents of this category, adapts to monthly batch data imports |
chunkSettingMode | Custom mode | Configures chunking rules separately for different document types (Excel/Word/PDF) to meet parsing needs of multi-source heterogeneous data |
customPdfParse | Enabled | For PDF-format fund supervision rule bases, retains original typesetting and paragraph structure, avoids disordered policy clauses after parsing |
Three Common Misconfigurations
- Using the default
chunkSizeparameter to process 10,000+ row Excel data causes single chunks to exceed a reasonable length range, leading to abnormal vector matching result counts. Cause: Did not adjust chunking length for the structured settlement data of this category, the default chunking granularity is insufficient to match specific policy clauses. - Uploading a 100,000+ Chinese character Word document results in a parsing task timeout error. Cause: Did not adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter, the default timeout duration is insufficient to complete long-text parsing. - Parsing a PDF-format supervision rule base results in disordered policy clause paragraphs, making it impossible to match business suspicious items by chapter. Cause: Did not enable the
customPdfParseconfiguration, the default PDF parsing does not retain original typesetting and title hierarchy.
How to Confirm Configurations Are Properly Set
- Upload a single 100,000+ Chinese character Word document and 15,000+ row Excel data, check whether the parsing task completes within the set
PARSE_FILE_TIMEOUT_SECONDS. - Randomly select two types of documents: audit notices and settlement data, check whether the chunking results retain chapter titles and field correlations.
- For individual chunked content, verify that its length falls within the
chunkSizeconfiguration interval, with no obviously overly long or short chunks. - Simulate a policy basis query, confirm that chunked content can be correctly retrieved and associated with business suspicious items.
The values provided here are common starting points, and should be evaluated against the user'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.