What the data for this category looks like
Home goods investment research data mainly comes from industry research reports, financial reports of listed home goods companies, supply chain survey documents, e-commerce sales ledgers, patent retrieval files, and so on. Update rhythms vary: quarterly financial reports follow natural quarters, industry research reports are released irregularly, e-commerce sales ledgers are updated daily, and patent documents are updated with application progress. Document formats include PDF deep research reports, XLSX SKU sales data, PPT industry summit materials, and some documents contain embedded tables, charts, and parameter lists. Common fields include SKU code, material composition, unit price, shipment volume, patent number, and so on. Units include yuan, ten thousand pieces, pieces, and others.
What constraints do these characteristics impose on the "document parsing and chunking" link
Mixed formats from multiple sources require the parsing process to support PDF, XLSX, PPT and other document types, and avoid missing valid content in embedded tables and charts. Differentiated update rhythms require parsing results to retain metadata such as document publishing organization and update time, to facilitate subsequent filtering of investment research information by time dimension. The complex field and unit system requires the parsing process to accurately extract unit information, to avoid unit confusion of parameters from different categories. The high proportion of structured tables requires the chunking process to retain row-column associations, and avoid losing data logic after splitting.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_EXTRACT_MODE | Structured retention + cell association | Home goods investment research documents contain a large number of SKU and sales data tables, and row-column associations need to be retained to avoid data breakage |
CHUNK_SIZE | 800–1200 characters | Home goods documents contain long sections of industry analysis and short-field parameters, and this range can balance context completeness and retrieval accuracy |
PARSE_ENCODING_AUTO_DETECT | Enabled | Some older PPT and XLSX documents use non-UTF-8 encodings, and automatic detection can avoid errors such as the argument ‘windows-1252’ is invalid encoding |
UPLOAD_FILE_MAX_SIZE | 1000 MB | A single research report collection may contain multiple PDF and PPT files, and large-volume batch uploads need to be supported |
DOC_META_EXTRACT_ENABLE | Enabled | Home goods investment research documents need to retain metadata such as release time and source organization to facilitate subsequent traceability |
The parameter values given on this page are 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: An error
the argument ‘windows-1252’ is invalid encodingoccurs when uploading PPT or PDF documents. Cause: Automatic encoding detection is not enabled, and the document uses a non-UTF-8 encoding format, resulting in parsing failure. - Phenomenon: After uploading an XLSX-format home goods sales table, the chunking result loses the association of multi-row cells, and only scattered text is extracted. Cause: The structured table extraction mode is not configured, and the default pure text extraction leads to data logic breakage.
- Phenomenon: The retrieval result matches the threshold, but the returned chunk contains irrelevant non-investment research content. Cause: The chunk length is set too large, and the content boundary between industry analysis and parameter fields is not distinguished.
How to confirm the configuration is correct
- Upload a home goods sales data document containing structured XLSX tables, and check whether the parsing result retains the row-column association between SKU and corresponding sales volume.
- Upload an older PPT document with non-UTF-8 encoding, and confirm that no encoding-related errors are triggered during the parsing process.
- View the chunked text fragments, and confirm that each chunk does not contain content across unrelated topics.
- Test retrieval and check the source tags of the returned results, and confirm that the document source information corresponding to the chunk can be displayed.
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.