What Data for This Category Consists Of
The data for this category typically comes from customer interviews, bidding documents, vendor product materials, and historical project proposals. Update frequency is per order or weekly. Commonly processed documents include technical proposal books, customer requirement survey forms, quotation lists, and demo scripts. Most documents contain technical route descriptions, implementation boundary explanations, quotation details, and demo process steps. There are no unified fixed fields, and content is dynamically adjusted based on customer requirements and project phases.
Constraints Imposed on Document Parsing and Chunking
Diverse document sources and formats require the parsing step to support multiple file types including technical proposal books, quotation lists, and demo scripts. This prevents loss of compliance-related content due to format incompatibility. The per-order or weekly update frequency means chunking configurations must support rapid adjustments to accommodate length differences across different project documents. Documents include technical routes, implementation boundaries, and compliance clauses, so chunking must retain semantic integrity and avoid splitting critical compliance content. The lack of unified fixed fields means chunking logic must not rely on preset fields, to adapt to dynamically changing document structures.
Configuration Settings
This setup is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the semantic length of technical paragraphs and compliance clauses in pre-sales technical proposals, avoiding overly fragmented or overly long chunks |
chunkSplitter | Prioritize paragraphs | Retains semantic integrity of technical proposals and compliance clauses in documents, and adapts to the paragraph structure of multiple document types |
customPdfParse | Enabled | Adapts to PDF documents common in pre-sales technical proposals that contain tables, headers, and footers, improving parsing accuracy |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch upload of multiple documents for large-scale projects, preventing upload failures due to file size limits |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time required for complex pre-sales documents, preventing parsing interruptions due to timeouts |
chunkSettingMode | Manual configuration | Adapts to the differentiated chunking needs of different project documents, allowing flexible parameter adjustments to meet compliance review requirements |
Three Common Configuration Errors
- The error "Cannot redefine property: toString at Object.defineProperty" appears. This occurs when the
customPdfParseparameter is not configured correctly, causing the document parsing engine to repeatedly bind object properties. - Indexing fails after the chunk count exceeds 3000. This happens when the
chunkSizeparameter is not adjusted to fit the document length, leading to a chunk count that exceeds the system's default processing limit. Resolve this by splitting large documents or adjusting chunking parameters. - CSV file chunking fails after a version upgrade. This is caused by the default chunking rules not matching the new version's CSV parsing logic. Reconfirm the configuration of the
chunkSplitterparameter.
How to Verify Correct Configuration
- Upload a typical pre-sales technical proposal document. Check the number of parsed chunks and content integrity, then adjust the
chunkSizeparameter until chunks have coherent semantics. - Review parsing logs for format errors or timeout prompts. Confirm that the value of
PARSE_FILE_TIMEOUT_SECONDSmatches the current document's parsing time requirements. - Select a paragraph containing compliance clauses. Verify that chunking did not split critical content, and confirm the
chunkSplitterconfiguration is active. - Confirm that the uploaded file size does not exceed the
UPLOAD_FILE_MAX_SIZElimit to avoid upload failures.
The values provided here are common starting points and should be evaluated 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.