What this category of data looks like
Data for this category typically comes from CRM systems, product usage backends, ticket statistics ledgers, and contract management systems. Update cycles run weekly or monthly. Daily processed documents include customer health reports, renewal forecast sheets, high-risk customer lists, and cross-department meeting minutes. Document fields usually include customer identifiers, renewal deadlines, risk levels, service response times, contract expiration dates, and more. There is no unified fixed format. Some documents embed tables and nested paragraphs.
Constraints for document parsing and chunking
Mixed-format documents from multiple sources require the parsing process to adapt to mixed layouts, and avoid losing table nesting or paragraph hierarchy information. Weekly or monthly batch document updates require parsing tools to support batch tasks and timeout fault tolerance, preventing single-process overload. Non-standardized fields require retaining contextual associations during chunking, to avoid splitting cross-field related information into different chunks. Spoken content from meeting minutes and structured data from renewal forecast sheets are mixed. This requires chunking logic to balance long text splitting and structured information retention, preventing key risk nodes from being truncated. Documents for compliance review require retaining original reference associations. Chunking must not break cross-paragraph compliance basis associations.
Configuration settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Documents related to renewal health mostly have mixed structures. This range balances contextual integrity and chunk granularity, and meets the need to retain key information for compliance reviews |
chunkSplitter | Separator + adaptive length | Daily processed documents include tables, paragraphs, and spoken meeting minutes. Mixed splitting logic balances chunking effects for structured content and natural text |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Batch processing of monthly compliance document collections requires sufficient parsing time to avoid timeout errors |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Monthly documents uploaded in a single batch usually do not exceed this threshold, which meets batch processing requirements |
customPdfParse | Enabled | Customer health reports are mostly PDF documents with custom layouts. Enabling custom parsing retains table and nested paragraph information |
Three common configuration mistakes
- When uploading a 10 MB-level monthly compliance document collection, the interface displays a timeout error. The error message includes
timeout of 360000ms exceeded. Cause: ThePARSE_FILE_TIMEOUT_SECONDSconfiguration is not adjusted. The default timeout duration is insufficient to complete batch document parsing. - Table nested field information is lost in the parsed chunk results, and some paragraphs are incorrectly split. Cause: The
customPdfParseconfiguration is not enabled. The default parsing logic cannot adapt to custom-layout customer health reports. - Ultra-long segments after chunking are not split further, causing single chunk length to exceed the preset threshold. Cause: The adaptive splitting logic of
chunkSettingModeis not enabled. Only fixed separator processing is applied, and no ultra-long text splitting rules are added.
How to verify correct configuration
- Upload a typical customer health report PDF, check the parsed text structure, confirm that tables and nested paragraphs are not lost.
- Run a batch upload test, upload a monthly compliance document collection, confirm that the task does not trigger a timeout error.
- View the chunk result list, confirm that single chunk length meets the preset range, and ultra-long segments have been split further.
- Test the custom parsing logic binding, confirm that parsing results can be processed according to preset rules.
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 2026-10-19.