Technical Documentation and Interface Q&A for On-site Outsourced Operations and Maintenance Services: Document Parsing and Chunking

This configuration is based on the feature logic of FastGPT v4.17.0.

What Typical Data for This Category Looks Like

Typical data for this category originates from customer ITSM systems, monitoring platforms, asset inventories, and on-duty records. Daily generated documents fall into four categories: on-site daily reports, service desk tickets, inspection reports, and SLA monthly reports.

Document structure varies significantly by type:

• On-site daily reports include fields such as duty period, problem description, and processing result
• Service desk tickets include business identifiers such as number, priority, and response time limit
• Inspection reports include operation and maintenance fields such as device number, status, and inspection time
• SLA monthly reports include service achievement statistics

Data update frequencies are divided into daily updates (on-site daily reports, service desk tickets) and monthly updates (SLA monthly reports).

Constraints Imposed on Document Parsing and Chunking

Scattered data sources contain mixed structured and unstructured content. Parsing engines must adapt to both text paragraphs and table fields. Chunking must retain the association of business fields, and avoid splitting key identifiers.

Differences in update frequencies across documents require chunking strategies to match timeliness needs: daily updated documents need information granularity suitable for high-frequency queries, while monthly updated documents must retain the integrity of statistical logic.

On-site service documents often include professional operation and maintenance fields such as device numbers and response durations. Chunking must avoid splitting cross-paragraph business units. This prevents information fragmentation that prevents subsequent Q&A from associating complete business scenarios.

How to Configure the Settings

Three Common Misconfigurations

• Phenomenon: Connection failure error occurs when calling customPdfParse deployed in Docker Desktop. Attempting to use a local address still fails to connect. Cause: The parsing service inside the Docker container did not expose ports correctly, or the container network mode restricts local access.
• Phenomenon: After uploading documents such as on-site daily reports and tickets, the parsing result is empty or cannot recognize table fields. Cause: The customPdfParse configuration is not enabled. The default parsing engine has insufficient adaptability for structured tables.
• Phenomenon: Timeout error occurs when parsing SLA monthly reports. Cause: The value set for PARSE_FILE_TIMEOUT_SECONDS is lower than the actual parsing time of the document, failing to adapt to the processing rhythm of large monthly summary documents.

How to Verify Proper Configuration

• Log in to the FastGPT backend, enter the document parsing configuration page, and check that the setting values of configuration items such as PARSE_FILE_TIMEOUT_SECONDS and UPLOAD_FILE_MAX_SIZE match the preset parameters.
• Upload a typical on-site daily report document, review the field integrity in the parsing result, and confirm that key business identifiers are not split.
• Try uploading a single monthly summary document under 500 MB, verify that the upload and parsing process has no timeout errors.
• For different types of business documents, switch chunkSettingMode to manual configuration, adjust chunkSize and preview the chunking effect, confirm that it meets the business information granularity requirements.

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.