What the data for this category looks like
Auto parts intelligent due diligence report data sources primarily include host factory supporting technical documents, supply chain BOMs, third-party quality inspection reports, customs declarations, and quarterly production capacity update documents. Update rhythms vary by document type: host factory supporting documents are updated quarterly, third-party inspection reports are released with part batches, and BOMs are adjusted with vehicle model iterations. Most documents are multi-page Word or PDF files, containing core fields such as part numbers, material parameters, tolerance ranges, supplier qualifications, and compliance certification numbers. Common units are millimeters (mm), megapascals (MPa), and kilograms (kg). Some documents include embedded real part photos and test data tables.
What constraints do these characteristics bring to the document parsing and chunking link
Multi-page documents with embedded charts require the parsing workflow to retain the association between tables and images, to avoid breaking the link between core parameters and their context. Core fields such as part numbers and compliance certification numbers have strong relevance, so chunking operations must avoid splitting across fields. Some documents use mixed units, so the parsing workflow must automatically identify and unify unit formats to prevent unit information loss after chunking. Batch processing handles large numbers of documents with large individual file sizes, so the system must support large file parsing and configure appropriate timeout settings for batch tasks to prevent task interruptions.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | true | Auto parts due diligence reports contain a large number of part parameter tables. Enabling this option preserves table structure and cell association relationships |
chunk_size | 800–1200 characters | Adapts to the context length of core parameters such as part numbers and materials, to avoid splitting critical parameter groups |
chunk_overlap | 100–150 characters | Retains part batch information across chunks, preventing core related information from being broken |
PARSE_FILE_MAX_SIZE | 500 MB | Covers the typical size of a complete vehicle model supporting due diligence report, preventing parsing failures for large documents |
PARSE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for long multi-page documents, preventing timeout interruptions |
image_parse_enable | Enable based on document type | Embedded real part photos and test images in due diligence reports need parsing to supplement compliance information beyond text |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After enabling
image_parse_enable, the knowledge base can identify image links, but cannot call image-related content during question answering. Cause: Theimage_embedding_enableparameter is not enabled, and the text content extracted from parsed images is not embedded into the vector database. - Phenomenon: When copying parsed chunked content, the browser displays a prompt reading "Unable to use browser automatic copy, please manually copy the content below". Cause: Parsed content is mounted on a non-same-origin iframe container, and browser cross-origin restrictions block automatic copy permissions.
- Phenomenon: In chunked documents, part numbers and their corresponding material parameters are split into different chunks, and no association can be established during recall. Cause: The
chunk_overlapvalue is too small, failing to retain core related information across chunks.
How to confirm the configuration is correct
- Upload a single typical auto parts due diligence report, review the parsed document structure to confirm that table content is not scattered into fragmented text.
- Randomly select due diligence reports of different formats, check whether the parsed chunked content retains the contextual association between part numbers and their corresponding parameters.
- Verify the image parsing function to confirm that the text content of embedded real part photos and test images is correctly extracted.
- Test batch uploading multiple large-size due diligence reports, confirm that no timeout or file size limit errors occur during parsing tasks.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.