What the data for this category looks like
Data for this category mainly comes from large-scale farming manuals, disease prevention and control guides, feed formula documents, marketing scripts from farmer communities, and statistical reports on slaughter and inventory. Update rhythm varies by business scenario: feed formulas adjust irregularly with raw material market trends, disease prevention and control guides update with notifications of new virus strains, and marketing materials iterate with quarterly marketing campaigns. Document formats include plain-text farming tips, immunization or proportion plans with tables, and PDF-format industry briefings. Most fields relate to livestock inventory, slaughter cycle, and feed conversion ratio. Common units are head/feather, kilogram, and day age.
What constraints do these characteristics impose on document parsing and chunking
The data characteristics of this category impose three constraints on parsing and chunking:
- A large number of documents contain numerical tables with units. Parsing must retain row and column associations of tables, to avoid separating fields from their units after splitting.
- Marketing scripts and technical documents coexist. Chunking boundaries must be distinguished between short-text marketing materials and long-text technical solutions, to prevent forced splitting of marketing scripts into overly long segments.
- Update frequency is uneven. Some documents require incremental parsing. Parsing logic triggered by file modification time must be supported, to avoid repeated processing of already parsed content.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Tables such as feed ratios and immunization schedules are common in documents for this category. Retaining table structure prevents fields from being separated from their units |
CHUNK_SIZE | 800–1200 characters | Balances complete context for long-text technical documents and semantic coherence for short-text marketing materials |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Meets parsing duration requirements for large industry farming reports, avoids interrupting the parsing process due to timeout |
ENABLE_INCREMENTAL_PARSE | Enabled | Document update frequency varies across this category. Incremental parsing reduces repeated processing of already parsed files |
UPLOAD_FILE_MAX_SIZE | 500 MB | Matches the file size range common for large industry briefings and farming manuals in this category |
The parameter values provided on this page are common starting points for configuration setup. Actual values are influenced by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: No parsing progress prompt displays in the interface after uploading a PDF, and search tests return empty results. Cause: The
PARSE_TABLE_ENABLEconfiguration is not enabled. Table-based farming documents cannot have text correctly extracted, so no valid data is available for subsequent vectorization. - Symptom: The vectorization step takes an extended period after uploading documents to the knowledge base. Cause: The
CHUNK_SIZEparameter is not adjusted. Using an excessively small segment length generates too many chunks, increasing vectorization computing overhead. - Symptom: Non-document-related text questions trigger the document parsing process when starting a conversation. Cause: The global
PARSE_QUESTION_ENABLEconfiguration is not disabled, causing all input content to enter the document parsing pipeline.
How to Verify Configuration Correctness
- Upload a test document containing structured tables, and confirm that the parsed text retains the association between fields and units.
- View parsing logs to confirm that the parsing duration of a single document does not exceed the configured timeout threshold, and no timeout interruption records exist.
- Upload the same document after it has been updated, and confirm that the system only parses the modified content and does not repeatedly process historical files.
- Submit a non-document-related text question, and confirm that the input content does not trigger the document parsing process and only enters the standard question and answer pipeline.
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.