What the data for this category looks like
Investment research data sources for professional chains include store operation daily reports, supply chain purchase ledgers, competitor store survey documents, brand VI manuals, and other materials. Data sources cover offline store POS systems, supply chain management platforms, and internal survey notes. Update cycles follow daily end-of-day settlements, weekly supply chain updates, and monthly survey summaries. Common document types are Excel files (with headers and thousands to tens of thousands of rows of detailed data), long Word documents (such as regional store opening plans, annual operation plans), and brand manuals in PDF format. Fields include store ID, SKU code, per-store per-square-meter efficiency, customer unit price, and some fields have clear business units.
What constraints do these characteristics impose on the document parsing and chunking link
The structured multi-Excel detailed data requires that chunks do not cover cross-business unit content, otherwise semantic confusion will occur. The chapterized structure of long documents requires that chunking matches the document hierarchy. Hard cutting by character length will destroy the integrity of business logic. Some fields have specific business units. The association between fields and corresponding data must be retained during parsing, otherwise the business meaning cannot be restored after chunking. Frequently updated data sources require the parsing process to support incremental recognition, avoid repeated processing of already parsed content, and improve chunking efficiency.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Matches the semantic integrity of single business units for professional chains (such as single store daily data, single SKU purchase volume), and adapts to the input limits of mainstream vector models |
chunk_overlap | 100–150 characters | Retains business association between adjacent chunks, avoids context breaks in store data and supply chain details |
excel_parse_mode | row_group mode | Adapts to scenarios where Excel contains more than 10,000 rows of detailed data, splits by row groups instead of parsing the entire table, ensuring each chunk focuses on a single business unit |
max_context_length | 1024 tokens | Matches the input upper limit of most public vector models, avoids vector generation failure caused by chunk capacity exceeding limits |
parse_file_timeout | 600 seconds | Reserves sufficient time to process long documents with 100,000 Chinese characters or Excel files with ultra-large data volumes |
enable_header_extract | Enabled | Retains the correspondence between Excel headers and row data, avoids losing the meaning of business fields after chunking |
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. Testing on local samples is recommended before finalizing settings.
Three common mistakes
- Phenomenon: After uploading an Excel file with 15,000 rows, the parsed single chunk exceeds 15,000 characters, and an error occurs during vector generation. Cause: The
excel_parse_modeis not set torow_groupmode. Default full-table parsing leads to excessive chunk size. - Phenomenon: After uploading a Word document with 100,000 Chinese characters, chunking results in cross-chapter content splicing and semantic confusion. Cause: The document structure parsing switch is not enabled, and hard splitting by character length is used instead of chunking by title hierarchy.
- Phenomenon: Some per-store per-square-meter efficiency fields are empty in the parsed chunks. Cause: The
enable_header_extractconfiguration is not enabled, and the association between headers and row data is not retained, resulting in lost field information.
How to verify correct configuration
- A single test Excel file with 10,000 rows can be uploaded, and the parsed chunk list checked to confirm that the character count of each chunk is within the 800–1200 range.
- A single Word document with 100,000 Chinese characters can be uploaded, and checked to confirm chunks are split by title hierarchy, with no cross-chapter content in adjacent chunks.
- The parsing log can be reviewed to confirm that the
excel_parse_modeandenable_header_extractconfigurations have taken effect, and no field loss prompts are present. - The vector generation process can be triggered to confirm that there are no errors related to exceeding context length, and chunk vector generation is successful.
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.