What the data for this category looks like
Sources of dairy industry investment research data include supply chain reports released by industry associations, public financial reports of dairy enterprises, raw milk purchase price monitoring data, consumer survey results, quality inspection and regulatory announcements, and more. Update frequencies vary: raw milk purchase prices and market circulation data are updated weekly. Corporate financial reports are released quarterly and annually. Industry research reports are updated monthly or quarterly. Quality inspection announcements are released immediately alongside test results. Document types include structured CSV reports, long-form PDF research reports, short news web pages, photo documents with test data, and others. Core fields include milk fat content, protein content, raw milk purchase price, milk source supply volume, and more, with corresponding units of grams/100 grams, yuan/kilogram, tons, and others.
What constraints do these features impose on the document parsing and chunking link?
Documents with differing update frequencies require retaining timestamp associations during chunking to avoid mixing data across cycles. The coexistence of multiple document types requires parsing tools to support structured tables, long-text nested structures, and image OCR extraction simultaneously. Failure to do so will result in lost field correspondence. Differences in units of core fields may cause confusion in parsed data, so unit formats must be unified before chunking. The nested chapter structure of long-form financial reports requires that chunking does not break contextual logic. Otherwise, the connection between product analysis and supply chain data will be disrupted.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxChunkSize | 800–1200 characters | Dairy documents contain a large number of professional terms and structured tables. This length balances contextual integrity and chunk granularity |
chunkOverlap | 100–150 characters | Retains the front-back association of time series data, preventing continuous data such as raw milk prices and product sales from being truncated by chunking |
PARSE_TABLE_ENABLE | true | A large number of structured tables such as raw milk purchase prices and product ingredient tables exist in dairy documents. This needs to be enabled to retain the correspondence between fields and values |
PARSE_IMAGE_OCR_ENABLE | true | Some quality inspection reports and on-site survey photos contain printed or handwritten test data, requiring OCR to extract valid content |
PARSE_TIMEOUT | 300 seconds | For large dairy enterprise annual financial reports, this duration avoids parsing timeout interruptions, and adapts to the default service configuration of FastGPT open source version 4.9 |
UPLOAD_FILE_MAX_SIZE | 200 MB | Covers the file size upper limit of most dairy enterprise supply chain databases and annual financial reports |
The parameter values provided on this page are common starting points 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: After uploading a large dairy enterprise financial report PDF, the knowledge base parsing status shows
504 Gateway Timeout. Cause: ThePARSE_TIMEOUTparameter is not adjusted, and the default timeout duration is insufficient to complete long document parsing. - Phenomenon: After uploading a CSV file containing raw milk price tables, the core fields in the parsed result are empty. Cause: The
PARSE_TABLE_ENABLEparameter is not enabled, and the structured table is not correctly identified and extracted. - Phenomenon: The pdf-marker tool functions normally when deployed locally, but there is no parsing option when uploading dairy quality inspection report PDFs to the FastGPT knowledge base. Cause: The HTTP port configuration of pdf-marker is not consistent with the parsing service port of FastGPT, resulting in service call failure.
How to confirm the configuration is correct
- Upload a small dairy quality inspection report PDF, and check whether the parsed text blocks retain the complete correspondence between test items, values and units.
- Enter the FastGPT knowledge base settings page, and verify that the configuration values of parameters such as
PARSE_TABLE_ENABLEandPARSE_IMAGE_OCR_ENABLEare consistent with the preset plan. - Upload a dairy enterprise annual financial report with a volume exceeding 100 MB, wait for the parsing to complete, and check that there are no timeout errors in the parsing task log.
- For structured CSV format raw milk price data, verify that the parsed text blocks retain the timestamp and corresponding values of each row.
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.