What data consists of for this category
Typical documents for this category include tank solution analysis records, process parameter cards, fixture records, film thickness and adhesion reports, hazardous waste ledgers, and wastewater monitoring records. Data sources include customer surface treatment specifications, tank solution test results, rectifier operation data, online water quality monitoring records, and hazardous waste management ledgers. Update frequency follows per tank cycle, per shift, or fixed monitoring cycle requirements. Most document structures are formed by concatenating structured fields, including fields such as tank number, analysis time, concentration value, temperature, and current. Some documents include fixed-format tables and numerical units.
What constraints do these characteristics impose on the document parsing and chunking link
The high-frequency updates and structured nature of documents in this category create multiple constraints for document parsing and chunking. First, the structure varies significantly across document types, so custom parsing rules must be supported to adapt to different format records. Second, fields are closely linked (such as tank number and corresponding tank solution concentration). Chunking must retain the full context of individual records to avoid splitting critical associated information. Third, update frequency is high and the number of documents per batch is large, so parsing timeout limits and file upload caps must be controlled to prevent task failures. Fourth, some documents contain hazardous waste-related data, so the parsing process must ensure that association relationships for sensitive fields are not lost.
This guide is written based on the configuration logic of FastGPT v4.17.0. Specific configurations are listed below:
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Documents in this category mostly consist of structured records concatenated from short fields. Chunks that are too long will break the association between tank numbers and corresponding parameters, while chunks that are too short will split complete single process parameter entries. |
chunkSplitter | Split by custom delimiter + paragraph | Documents in this category include fixed-format single-line records. Custom delimiters can retain the field association of individual records. |
customPdfParse | Enabled | Most documents in this category are structured PDFs. Enabling this option preserves table layout and field association information. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing a single batch of tank solution records or hazardous waste ledgers takes extended time. This setting avoids parsing failures due to timeout. |
chunkSettingMode | Custom mode | Document structures vary widely across this category, so chunking rules must be adjusted for different document types. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapts to upload requirements for single-batch batch documents, preventing parsing failures due to oversized files. |
Three common errors
- Phenomenon: During retrieval, returned chunk content has mismatched tank numbers and corresponding concentration values, with mixed-up fields. Cause:
chunkSplitterwas not configured correctly, which destroyed field associations for individual records during splitting. - Phenomenon: Documents uploaded with custom chunking rules locally show inconsistent recall results compared to local tests when retrieved on the server. Cause:
chunkSizeandchunkSplitterconfigurations were not unified. Chunking logic for different vector models relies on consistency of basic configurations. - Phenomenon: Unrendered Markdown table code blocks appear in context references, and structured data cannot be displayed normally. Cause:
customPdfParsewas not enabled, so layout information for structured tables was not correctly parsed and preserved.
How to confirm the configuration is correct
- Upload a typical tank solution analysis record PDF, and verify that parsed chunks retain contextual association between tank numbers, analysis times, and corresponding concentration values.
- Compare local and server-side chunking results, confirm that
chunkSizeandchunkSplitterconfigurations match, and validate that chunking logic is consistent across different vector models. - Review displayed context reference content, and confirm that structured table layout information has been correctly presented.
- Upload a single batch document larger than 50 MB, and check that the parsing task completes normally without timeout errors.
The values provided are common starting points and should be measured against the reader's own samples.