What This Category's Data Looks Like
Data for this category primarily comes from monitoring systems, ticket systems, contract documents, and customer communication records. Commonly processed documents include SLA clauses, monthly service reports, availability statistics reports, and customer review minutes. Update cycles are mostly monthly or quarterly. Most document structures include fields such as service period, indicator thresholds, actual achieved values, abnormal event records, and compensation clauses. Most fields are text-based numerical values and status identifiers, with no unified fixed format. Some documents contain cross-page duplicate content and scattered notes.
Constraints on Document Parsing and Chunking
Dispersed data sources create inconsistent document formats. Parsing must adapt to clause-based contract texts, structured ticket system logs, and free-form customer minutes. Monthly or quarterly update cycles require batch processing of parsing tasks, with no acceptable long wait times. Documents include core fields such as SLA indicator thresholds and actual achieved values. Parsing must retain field relevance to avoid breaking indicator context. Abnormal event records and compensation clauses are strongly correlated. Chunking must not split these related contents, as this will disrupt subsequent SLA indicator calculation and attribution. Some documents have cross-page duplicate headers and scattered notes. Invalid redundant content must be filtered out during parsing.
Configuration Settings
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Documents in this category mostly contain coherent clauses and indicators. This length preserves complete context for SLA clauses and indicator groups, avoiding splitting related content |
chunkSplitter | Split by paragraph + title | Adapts to the hierarchical structure of SLA documents, ensuring each chunk corresponds to an independent service clause or indicator item, facilitating subsequent alignment of standards |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Monthly service reports typically contain multi-page statistical data. This timeout range covers the waiting needs of batch parsing, avoiding task interruption due to timeout |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Quarterly summary reports in this category may combine multiple historical documents. This upper limit meets batch upload requirements |
customPdfParse | Enabled | Documents in this category mostly use standardized PDF formats. Enabling custom parsing preserves original typesetting and field structure, improving parsing accuracy |
Three Common Mistakes
- Phenomenon: A timeout error with status code 504 is returned after calling the file parsing API. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default timeout duration is insufficient for processing large monthly summary documents, which corresponds to queuing timeout scenarios after integrating third-party parsing tools. - Phenomenon: File parsing functionality fails after enabling it on a local deployment version, with no error logs. Cause: The
customPdfParseconfiguration was not enabled, and the corresponding parsing dependencies were not installed, making it impossible to process standard PDF format SLA documents. - Phenomenon: Parsed chunks split SLA indicators and their corresponding compensation clauses into different blocks. Cause: The
chunkSizewas set too small, andchunkSplitterwas not configured to split by title, breaking the contextual correlation between clauses.
How to Verify Configuration
- A standard SLA clause PDF is uploaded. Parsed chunks are verified to retain the integrity of independent clauses.
- The
PARSE_FILE_TIMEOUT_SECONDSparameter is adjusted. A monthly service report with more than 100 pages is uploaded. Parsing tasks are confirmed to complete normally without timeout. - The
UPLOAD_FILE_MAX_SIZEconfiguration is checked to ensure that the maximum volume of documents required for business needs can be uploaded. - Parsed chunk metadata is viewed. Core fields such as service period and indicator thresholds are confirmed to have been correctly extracted and retained within the chunks.
The values provided are common starting points and should be evaluated against local 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.