What the Data for This Category Looks Like
Data for this category comes from government information disclosure platforms, official archive systems, document management systems, and departmental business ledgers. Update frequency is irregular. Most content consists of batch-archived official government documents and scattered business entries. Typical documents include government information disclosure application forms, supplementary notice letters, response letters, service receipts, and administrative reconsideration materials. These documents contain fixed-format official paragraphs, structured fields, and single-line business ledger entries, mostly in standard office document or structured table formats.
Constraints on Document Parsing and Chunking
The fixed formats and structured fields of government documents require that key metadata and semantic integrity be preserved during parsing, avoiding splitting complete paragraphs that contain application key points or response basis. Scattered single-line ledger entries require chunking logic adapted to single-line separation, avoiding merging unrelated content into one chunk. Irregular batch-archived data requires the parsing process to support large-file batch processing, avoiding timeout interruptions. Retrieval targeting specific information requires precise matching, so chunk size must balance semantic integrity and recall accuracy, avoiding matching failure caused by chunks that are too fragmented or too large.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Must preserve complete semantics of application content and response key points, avoid splitting paragraphs containing key clauses |
chunkSplitter | By official document paragraph + custom line separator | Adapt to the mixed format of government documents, balancing the chunking needs of fixed official paragraphs and single-line ledger entries |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Batch large files from government archives take longer to parse; the default duration is insufficient to cover the processing cycle |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Support batch file uploads exported from government archive systems, avoid being blocked due to oversized files |
customPdfParse | Enabled | Government official PDF files are mostly formatted layout documents; enabling custom parsing preserves text structure and metadata |
Three Common Mistakes
- After uploading a collection of Word-format response letters over 10 MB, parsing progress stagnates for a long time and eventually shows a timeout error. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter; the default timeout duration is insufficient for processing government large files. - After importing an Excel-format business ledger file, the automatic chunking result merges multiple lines of content into one chunk. The cause is failure to configure
chunkSplitterto split by line; the default chunking logic does not adapt to the single-line entry format of Excel. - When retrieving the correction deadline for a supplementary notice letter, the chunk containing this information cannot be recalled. The cause is that
chunkSizeis set too small, splitting the complete paragraph containing the correction deadline into multiple incomplete fragments, resulting in semantic loss.
How to Verify Correct Configuration
- Upload a single government information disclosure response letter of around 10 MB, check whether the parsing task duration is within the preset
PARSE_FILE_TIMEOUT_SECONDSrange. - Import an Excel-format business ledger file, verify that the chunking results correspond one-to-one with the original file's line entries, with no merging or splitting errors.
- Retrieve preset key information (such as the document number of a response letter), confirm that the chunk containing this information can be accurately recalled.
- View the parsed metadata panel, confirm that key fields such as document date and applicant information have been correctly extracted and associated with the corresponding chunk.
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.