This document is compatible with FastGPT v4.17.0’s document parsing and chunking features.
What the data for this category looks like
Production ledgers for complete household appliance manufacturing cover multiple document types including bill of materials (BOM), assembly work instructions, refrigerant charging records, electrical safety test reports, performance sampling test reports, and repair records. Data sources include ERP/MES systems, component batch records, tightening and charging equipment, safety compliance testers, performance laboratory data, and after-sales fault records. Updates are performed per single device, work shift, or sampling plan. Single-device ledgers include fields with clear units such as component batch numbers, charging volumes, test voltages, and repair causes.
What constraints these characteristics impose on document parsing and chunking
Scenarios where multiple types of production ledgers for complete household appliance manufacturing are uploaded together require the parsing engine to support format adaptation, to avoid issues where PDFs with digital signatures cannot be recognized. Frequently updated ledger data requires the parsing process to have sufficient response speed, to prevent task failure due to timeout. The multi-field combined structure of single-device ledgers requires retaining field associations during chunking, to avoid field breaks after splitting. In mixed-line production scenarios, documents of different models may be mixed, requiring chunking rules to recognize model identification fields, ensure chunked content is bound to the corresponding product, and prevent cross-model data confusion.
How to configure it
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | The ledgers for complete household appliance manufacturing include multi-field combinations. This length preserves the complete ledger unit for a single device, avoiding field breaks after splitting |
chunkSplitter | Split by paragraph + title | Daily documents include assembly work instruction steps and safety test sub-results. Splitting by title preserves the structural integrity of the document |
customPdfParse | Enabled | Some PDF ledgers with digital signatures cannot be recognized by the default engine. Enabling this allows content extraction for encrypted or signed documents |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing a complete single-device ledger (including multiple test reports and repair records) takes a long time. This duration covers parsing needs for most scenarios |
UPLOAD_FILE_MAX_SIZE | 100 MB | Batch uploaded shift ledger packages may include multiple test reports and repair records. This upper limit meets batch file upload requirements |
Three common mistakes
- Symptom: An error reading the file is returned when calling the Doc2x tool, and the error message includes a prompt that only specified formats are supported. Cause: The uploaded document format exceeds the range allowed by the current configuration, and
customPdfParsewas not correctly configured to adapt to non-standard formats. - Symptom: After uploading a PDF ledger with a digital signature, the parsing result is empty or contains no valid content. Cause: The
customPdfParseparameter was not enabled, and the default parsing engine cannot recognize the file structure of PDFs with digital signatures. - Symptom: After uploading a properly parsed ledger file, the large language model does not include test image content when generating a report. Cause: Chunking rules for associated image parsing were not configured, only text content was extracted without synchronously associating image metadata.
How to confirm the configuration is complete
- Upload one signed electrical safety test PDF, check if the parsing result includes complete test data and fields.
- Upload a batch of shift ledger files, confirm that the parsing time does not exceed the preset
PARSE_FILE_TIMEOUT_SECONDSduration. - Test documents of different formats (BOM tables, repair records, performance reports), confirm that all can be correctly split into chunks.
- Verify that when calling the large language model, it can associate the parsed text with corresponding image content (if any).
The values provided are common starting points and should be measured against the sample sets relevant to the specific deployment.