What data for this category looks like
Data for cosmetics intelligent due diligence comes from brand filing materials, third-party ingredient test reports, supply chain ledgers, e-commerce platform sales data, and regulatory agency public documents. Update frequency fluctuates with new product launches and compliance inspection cycles, with no fixed schedule. Document formats include structured Excel (including product lists, ingredient tables) and unstructured Word/PDF reports (including compliance judgments, efficacy claim statements). Fields include product name, filing number, ingredient CAS number, content percentage, compliance status, and more. Units include mg/100g, %, batch numbers, and others.
What constraints these characteristics impose on document parsing and chunking
Cosmetics due diligence data includes 10,000-row+ structured Excel files and unstructured documents over 100,000 words. Parsing and chunking must adapt to mixed-format data sources. Fields such as ingredient tables and compliance items have precise numerical values and associated judgments. Chunking must retain field boundaries and contextual associations to avoid information fragmentation after splitting. Documents have fixed modules such as filing chapters and ingredient lists. Module boundaries must be identified to maintain logical integrity. Data volume fluctuates widely, so parameters must support dynamic adjustment to fit due diligence files of different sizes.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Cosmetics due diligence documents contain content such as ingredient tables and compliance judgments that require retained context. This range balances information integrity and vector storage efficiency |
chunk_overlap | 150–200 characters | Key information such as ingredient names and content values is easily truncated during chunking. The overlapping interval retains cross-block associated content |
PARSE_EXCEL_HEADER_MODE | Fixed header matching | The headers of cosmetics due diligence Excel files such as product name and filing number have fixed formats, which avoids header recognition errors |
UPLOAD_FILE_MAX_SIZE | 200 MB | Single filing documents or supply chain ledgers can reach hundreds of megabytes. This threshold covers the size of conventional due diligence documents |
rag_split_mode | Split by semantic segment | Cosmetics ingredient reports and regulatory public documents have clear semantic modules. Splitting by semantic segment maintains the logical integrity of the document |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing Excel files with over 100,000 rows or Word documents with 100,000+ words takes a long time. This duration avoids parsing interruptions |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Field misalignment occurs after chunking Excel data, and headers are split into different chunks. Cause:
PARSE_EXCEL_HEADER_MODEis not configured to fixed header matching mode. The default parsing logic incorrectly recognizes multi-row headers as content chunks. - Phenomenon: Data chunks after chunking exceed the upper limit supported by the vector model, resulting in vector generation failure. Cause:
chunk_sizeis not adjusted to the range suitable for cosmetics documents. The default parameter chunk is too large and exceeds the supported range of the vector model. - Phenomenon: Timeout errors (status code 504) occur during parsing. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to the duration suitable for Excel files with over 100,000 rows or Word documents with 100,000+ words. The default timeout threshold is insufficient to complete full parsing.
How to confirm the configuration is complete
- Upload a single 100,000-row Excel due diligence data file, and check if the parsed data chunks retain complete headers and row associations with no field misalignment.
- Upload a single 100,000-word Word ingredient test report, and check if the chunking results retain contextual associations of key information such as ingredient names and content values with no cross-block fragmentation.
- View the vector generation log, and confirm that the character count of each data chunk falls within the preset
chunk_sizerange with no chunks exceeding the upper limit supported by the vector model. - Simulate a user query, enter a question related to ingredient compliance, and check if the recalled chunked content contains complete associated information with no missing or misaligned content.
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.