What data for this category looks like
The data used for intelligent due diligence reports in the optical and optoelectronics field comes primarily from publicly available industry association statistics, regular announcements from listed companies, shipment ledgers of supply chain manufacturers, patent databases, and raw material price monitoring platforms for product segments. Data update frequencies cover three categories: weekly (such as panel spot prices), monthly (such as product segment capacity data), and annual (such as industry development white papers). Most documents combine structured reports and narrative analysis, including fields like production capacity, yield rate, peak wavelength, and effective shipment volume. Common units include nanometers, milliampere-hours, ten thousand pieces, cd/㎡, and other professional measurement identifiers. Some documents also include detailed tables showing quarterly quarter-over-quarter changes.
Constraints on document parsing and chunking
The multi-dimensional structured characteristics of optical and optoelectronics due diligence reports require the parsing process to first distinguish between table blocks and narrative text blocks, to avoid forced merging and chunking of cross-type content. The strong correlation between professional measurement fields requires retaining the binding relationship between parameters and their corresponding units during parsing, to prevent mismatches between values and units after splitting. Weekly and monthly periodic data requires the chunking logic to align with the time dimension, grouping capacity and price data from the same cycle into the same chunk unit. Long-text patent analysis and industry trend content must be split by paragraph themes, to avoid mixing cross-theme information that negatively impacts subsequent retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_MAX_SIZE | 500–1000 MB | Optical and optoelectronics due diligence reports often contain large numbers of supply chain detail tables and full patent texts, so individual file sizes are generally large |
maxChunkSize | 800–1200 characters | This category of data includes professional parameters and long explanatory sentences. Excessively long chunks will break the connection between parameters and their context |
chunkOverlap | 150–200 characters | Professional terms spanning chunks need to retain continuity, to avoid semantic breaks that reduce retrieval accuracy |
PARSE_OCR_ENABLE | Enabled | Some supply chain reports and industry white papers are in scanned document format, requiring OCR to extract original text |
ENABLE_TABLE_PARSE | Enabled | A large number of structured tables in due diligence reports need to be fully parsed into retrievable independent units |
PARSE_TIMEOUT_SECONDS | 600 seconds | Parsing and OCR processing of large files takes a long time, so the timeout threshold needs to be extended |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Scenario: After uploading a scanned optical and optoelectronics due diligence report, an
OCR Errorstatus code is returned, and the parsing process terminates. Cause: ThePARSE_OCR_ENABLEconfiguration is not enabled, or the scanned document resolution is lower than 300dpi, causing the OCR engine to fail to recognize professional text. - Scenario: After importing an Excel-format supply chain report, knowledge base search tests fail to return valid results, only showing a generic error prompt. Cause: The
ENABLE_TABLE_PARSEconfiguration is not enabled, so production capacity, price, and other data in the structured table are not fully parsed into retrievable units. - Scenario: After platform updates, the
miner-uparsing function cannot be found, and manual configuration results in a large number of semantic breaks in chunking results. Cause: Theparser_engineis not specified asminer-u, or themaxChunkSizeis set to an excessively small value, causing professional parameters to be forcibly split.
How to Confirm Configuration is Correct
- Upload a single scanned due diligence report of approximately 100 MB, and check if the parsing log contains the
OCR parse completedfield, to confirm that thePARSE_OCR_ENABLEconfiguration is active. - Import an Excel file containing structured tables, access the knowledge base search test, and retrieve professional parameters in the table, to confirm that the search results include the complete data within the table.
- View the chunked text fragments, verify that parameters and their corresponding units are bound, to confirm that the values of
maxChunkSizeandchunkOverlapmeet the requirements of this category. - Access the platform configuration page, confirm that
parser_engineis set tominer-u, to avoid chunking deviations caused by using the default parsing engine.
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.