What the data for this category looks like
Condiment marketing-related data comes primarily from four types of channels: monthly sales ledgers from regional distributors, SKU detail reports exported from e-commerce platforms, brand marketing material copy libraries, and annual marketing investment reports.
Data update rhythms vary by scenario: offline ledgers are updated monthly, e-commerce SKU reports are updated as products are adjusted, marketing materials are adjusted on demand during campaign cycles, and annual reports are updated per calendar year.
Document structures fall into three categories: structured tables (with specification, unit, and price fields), long-text marketing plans, and mixed-format campaign review reports.
There are subtle differences in fields and units. For example, specifications are often marked in grams, milliliters, kilograms, or liters. Some ledgers use cases or bags as pricing units.
Constraints imposed on document parsing and chunking
The multi-source and multi-structure nature of condiment marketing data creates multiple constraints for the parsing and chunking stage. Mixed-format documents contain both text and embedded tables, so parsing logic must support both content types. Otherwise, numerical values and associated relationships of structured fields will be lost.
Structured tables have fields mixed with multiple unit specifications. The binding relationship between fields and units must be preserved during parsing, otherwise complete product or campaign information cannot be restored after chunking.
Frequently updated ledgers and report data have a large number of rows. Default chunking parameters may generate overly large data blocks, leading to reduced subsequent retrieval efficiency.
Paragraphs in long-text marketing plans are closely linked around campaign themes. Chunking must preserve contextual logic to avoid destroying the integrity of campaign descriptions after splitting.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_chunk_size | 800–1200 characters | Condiment marketing documents include short-text marketing plans and SKU tables with tens of thousands of rows. This range balances information integrity within chunks and retrieval efficiency |
chunk_overlap | 10%–15% | In scenarios with mixed structured tables and long text, this overlap ratio prevents key information such as campaign logic and product fields from being split apart |
parse_timeout | 600 seconds | Parsing large annual marketing reports or batch-exported sales ledgers takes a long time. This duration covers the complete parsing process |
enable_table_parse | Enabled | Most condiment marketing data includes structured tables. Enabling this feature preserves the binding relationship between fields and their corresponding units |
max_upload_size | 1000 MB | Some batch-exported SKU data or annual report files have large file sizes. This upper limit meets upload requirements |
table_parse_mode | Parse by field binding | Condiment tables have scenarios where multiple fields such as specification, unit, and price are associated. Parsing by field binding prevents fields from being separated from their numerical values |
The parameter values provided on this page are common recommended 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 settings.
Three common mistakes
- Phenomenon: After uploading marketing documents in internal Confluence format, the parsing result is empty or only returns a small amount of meaningless text. Cause: Internal resource access permissions are not configured, so the parsing service cannot pull document content from the internal Confluence site, resulting in parsing failure.
- Phenomenon: After uploading an Excel sales ledger with 10,000+ rows of data, the chunking results have overly large blocks, reducing retrieval efficiency. Cause: The
max_chunk_sizeparameter is not adjusted, and the default chunk length is used for ultra-large structured data, resulting in redundant information within blocks. - Phenomenon: After uploading a PDF-format annual marketing report, the parsing process times out or is interrupted. Cause: The
parse_timeoutparameter is not adjusted, and the default timeout duration is insufficient to cover the complete parsing process of large PDF documents.
How to confirm the configuration is correct
- Upload a local condiment SKU table document, and check whether the parsing result retains the field association of specification, unit, and price.
- Upload a marketing PDF document with more than 500 pages, and check whether the parsing progress is completed within the configured timeout duration.
- Retrieve the chunked documents, check whether key campaign descriptions are split into two separate chunks, and adjust the
chunk_overlapparameter until this situation no longer occurs. - Upload a marketing document link accessible from the internal network, confirm that the parsing result contains complete document content, and verify that the internal network access configuration is effective.
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.