What this category of data looks like
Common daily documents include patrol records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Data is sourced from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint tickets. Update frequency is daily. Document structures include standardized fields and unstructured content. For example, patrol records contain patrol time, location, problem description, and grid inspector signature. Filing approval forms contain case number, violation type, and on-site photo association information. Some documents include image attachments.
What constraints do these characteristics impose on the document parsing and chunking link
Documents from multiple sources have diverse formats, including structured tables, plain text, and image attachments. Parsing links must support multiple formats. Daily updated business data requires efficient parsing processes. Chunk size should not be too large to ensure real-time performance. Documents contain strongly associated core fields, such as case numbers bound to penalty decisions. Chunking must retain semantic associations to avoid splitting key information. Documents related to on-site disposal have strong timeliness. They need to cover complete information at key nodes including patrol, filing, rectification, and case closure.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the length of single-segment key information in patrol records and penalty decisions, avoids splitting core case fields |
chunkSplitter | Split by paragraph + semantic chunking | Retains the semantic association of time-location-problem in patrol records, supports parsing of multi-format documents |
customPdfParse | Enable structured parsing | Extracts structured table fields from filing approval forms and penalty decisions, prevents table content from being incorrectly split as plain text |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Meets time requirements for batch on-site photo uploads and long document parsing |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Matches single-file upper limit requirements for batch on-site photo uploads |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types, adapts to format differences between patrol records and penalty decisions |
Three common errors
- After uploading files, the parsing node is unresponsive, and background logs show timeout errors. The cause is failure to adjust
PARSE_FILE_TIMEOUT_SECONDSto match the time required for batch on-site photo uploads. - Only partial data is retrieved from database tables, and core fields such as case number and violation type are empty in parsing results. The cause is failure to enable
customPdfParsestructured parsing, leading to table content being treated as plain text and split, losing associated information. - Model output Markdown tables are truncated, and hidden character prompts appear in the conversation interface. The cause is chunk size set too small, failing to retain complete table structure and causing information loss.
How to confirm the configuration is correct
- Upload a single penalty decision PDF, check if parsing results extract core fields including case number, violation type, penalty basis, and other key information.
- Upload batch patrol record documents, verify that parsed chunks retain complete semantics of time, location, and problem description for each record.
- Adjust the
chunkSizeparameter, then upload a long document, confirm that parsing results have no key information splitting or truncation. - Check FastGPT v4.17.0 background parsing logs, confirm no timeout errors occur, matching the
PARSE_FILE_TIMEOUT_SECONDSsetting.
The values listed here are common starting points. They should be evaluated against the samples relevant to the target deployment.
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.