What the data for this category looks like
Data for hotline knowledge base maintenance comes from three main sources: daily ticket records of frontline hotline agents, archived policy and regulatory documents, and regularly updated common question repositories. Updates follow a set schedule: completed tickets are added daily, latest policy documents are synced weekly, and common question categories are updated monthly. Most individual documents use structured formats, with fields including ticket ID, call time, problem category, standard answer, processing duration, and more. Units include items, calls, minutes, and others. Some documents are unstructured policy explanation texts, with length ranging from several hundred to several thousand characters.
Constraints Imposed on Document Parsing and Chunking
Structured fields in hotline knowledge bases require parsing processes to preserve the correspondence between fields and their content. This prevents chunking from breaking the binding logic between questions and answers. High-frequency data updates require parsing workflows to support incremental parsing. Workflows process only new or modified documents, to avoid re-parsing existing stored data. Wide variation in document length requires chunking rules to adapt to texts of different lengths. Complete policy answers must not be split into unrelated fragments. Chunks must not be so large that semantic clarity is lost. Ticket timeliness requires parsed results to retain metadata from original documents, including timestamps and category tags. This allows subsequent retrieval and filtering by time or category.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Standard answers in hotline knowledge bases are mostly paragraph-style content. This length preserves the complete semantic meaning of a single answer, avoiding disruption to policy explanations or process steps |
chunkSplitter | Split by paragraph + heading | Documents contain clear problem category headings and segmented answers. This splitting rule preserves the correspondence between questions and answers, avoiding chaotic chunking |
customPdfParse | Enable structured parsing | Some policy documents are in PDF format. Enabling this setting extracts tables and heading hierarchies from text, avoiding messy content layout after parsing |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Large policy compilation PDFs take longer to parse. This range covers parsing durations for most files, avoiding timeout failures |
chunkSettingMode | Manual configuration | Document structures in hotline knowledge bases are uniform. Manual configuration accurately matches chunking requirements, avoiding excessive splitting or merging from automatic modes |
Three Common Configuration Mistakes
- API requests for file parsing return a success status but produce no parsed results. This occurs when correct parsing module parameters are not specified in the API request, or parsed results are not correctly written to the callback interface.
- Logs show split-related errors. This occurs when
chunkSplitteris configured with a splitting rule that does not match the document structure, or when the document contains extremely long plain text paragraphs without natural separators. - Parsing tasks fail due to timeout. This occurs when the value set for
PARSE_FILE_TIMEOUT_SECONDSis too short, or when the file size for a single parsing exceeds theUPLOAD_FILE_MAX_SIZElimit.
How to Confirm Proper Configuration
- Upload a single typical hotline ticket document. Check if the parsed text preserves the correspondence between problem categories and answer content.
- Call the FastGPT v4.17.0 API to send a parsing request. Check if the returned results include chunked text fragments, and if fragment lengths fall within the
chunkSizeconfiguration range. - View parsing logs. Confirm that split operations do not throw errors, and that task execution duration does not exceed the value set for
PARSE_FILE_TIMEOUT_SECONDS. - Compare parsing results from manual uploads and API uploads. Confirm that chunking logic and content completeness are consistent between the two methods.
The values listed are common starting points, and should be evaluated against samples relevant to the specific deployment scenario.
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.