What data for this category looks like
Document sources for urban public transit operations include internal scheduling systems, safety management platforms, and notice announcements from superior transportation authorities. Update cadence falls into three categories:
- Daily operation reports updated daily
- Route adjustment announcements released as needed
- Monthly safety inspection reports and quarterly operation summary reports updated on fixed schedules
Document structures include structured tables (such as operation data CSVs with fields including route number, shift number, passenger volume, and similar fields), official PDF documents, and Word-format complaint and rectification records. Covered fields include route number, departure time, passenger trips, driving mileage, rectification measures, and similar items. Units include "shifts", "trips", "kilometers", and "days".
What constraints do these characteristics impose on document parsing and chunking?
Multi-source, heterogeneous document types require parsing tools to support both structured tables and free text, to avoid breaking field associations during chunking. Frequently updated daily reports require fast parsing workflows to avoid delaying indexing progress. Fixed-format official documents and reports require parsing to filter redundant headers and footers, reducing the proportion of invalid content. Structured data with multiple fields requires retaining contextual associations during chunking, so different fields of the same record are not split into separate chunks. Large quarterly summary documents have larger capacity, so they need to accommodate higher upload and parsing time limits.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed structured field paragraph and free text characteristics of transit documents, retaining complete semantics of a single operation record or single announcement segment |
chunkSplitter | Paragraph + fixed field separation | Targets the mixed table and text structure of transit documents, retaining field associations for the same record during splitting |
customPdfParse | Enabled | Filters fixed headers and footers in official PDF transit documents, reducing redundant parsed content |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Meets parsing time requirements for large documents such as monthly safety reports and quarterly operation summaries |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading multi-file compressed packages for quarterly summaries, meeting the upload needs of large documents from transit companies |
chunkSettingMode | Manual | Retains custom field separation rules, avoiding damage to document structure from automatic mode |
Three common misconfigurations
- Symptom: After upgrading FastGPT v4.17.0, previously functional CSV operation data files trigger chunking errors, with the interface prompting "chunk context lost". Cause: The new version defaults to switching
chunkSettingModefrom manual to automatic, without retaining the original field separation rule configuration. - Symptom: After uploading a monthly safety report PDF, the parsing task times out and fails directly. Cause: The
PARSE_FILE_TIMEOUT_SECONDSvalue is not adjusted to fit large documents, and the default timeout duration is insufficient. - Symptom: After configuring the minerU parsing plugin in a node, uploaded files still return text parsed by the default FastGPT parser. Cause: The API key for the minerU plugin is not correctly bound, or
customPdfParseis not set to call third-party plugin mode.
How to verify correct configuration
- Upload the operation daily report CSV for a single route, check if the parsed text retains complete fields including route number, departure time, passenger count, with no lost field splits.
- Upload a large monthly report PDF, verify that the parsing task status shows completion, with no timeout prompts.
- Upload the configured document in the knowledge base test node, check if the chunking results filter redundant headers and footers, and that content from the same paragraph is not incorrectly split.
- Adjust the
chunkSizevalue, re-upload the same document, and observe if the chunked paragraph integrity meets expectations.
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.