What the Data for This Category Looks Like
Data for feed intelligent due diligence reports primarily comes from batch quality inspection reports of feed manufacturers, raw material purchase ledgers, feeding records from breeding operations, and industry compliance sampling announcements. Update frequency varies by business node: purchase ledgers update with each procurement batch, quality inspection reports generate with each production batch, and industry sampling announcements release irregularly. Documents mostly consist of structured tables with fixed headers paired with long-text detection explanations. They include fields such as raw material name, crude protein content, moisture content, crude fiber value, procurement batch, and supplier qualification. Units include percentage, kilogram, ton, and some documents contain OCR-transcribed content from test images.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The mixed structure of structured tables and long-text explanations means parsing cannot only split table cells. It must associate detection explanation fragments with corresponding raw materials to avoid disconnecting fields and their explanations. Multiple units and detailed fields require retaining the binding relationship between fields and their corresponding values during chunking, to prevent misalignment between fields and units after parsing. OCR-transcribed image content has recognition errors, so original transcribed fragments must be retained instead of only extracting structured data, to facilitate subsequent verification. When uploading multiple batches and multiple suppliers’ documents in bulk, content blocks must be split by batch dimension to avoid mixing information across batches.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CHUNK_SIZE | 800–1200 characters | Feed due diligence reports include long-text detection explanations and structured tables. This range balances the integrity of information per block and retrieval accuracy |
CHUNK_OVERLAP | 100–150 characters | Cross-segment field association information must be retained to avoid disconnecting raw materials from their detected values after splitting |
TABLE_PARSE_STRATEGY | structured_with_caption | Most feed document tables include detection explanation titles. This mode retains the binding relationship between tables and their attached explanations |
OCR_ENABLED | Enabled | Some documents include OCR-transcribed content from test images, so OCR parsing of original transcribed fragments must be enabled |
PARSE_TIMEOUT | 600 seconds | Avoid timeout triggers due to large content volume when parsing bulk multi-batch documents |
SPLIT_BY_HEADER | Enabled | Split content blocks by document header fields, to facilitate distinguishing parsing results by raw material or batch |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: A 403 status code is returned when calling the Feishu multi-dimensional table HTTP interface, or submitted field data is empty. Cause: The permission scope of the Feishu application is not correctly configured, and edit and read permissions for the corresponding table are not granted.
- Phenomenon: After bulk uploading multiple feed due diligence reports, search results mix raw material data from different batches. Cause: The configuration for splitting content blocks by batch or header fields is not enabled, and content is not bound by individual document dimension during parsing.
- Phenomenon: Knowledge base search tests fail to return detected value content for feed raw materials. Cause: Chunk length is set too large, causing detected values and their associated raw material fields to be split into different content blocks, making association and matching impossible during retrieval.
How to Confirm Proper Configuration
- Upload a single feed quality inspection report, view the parsed content block list, and confirm that tables and their attached detection explanations are bound as the same block.
- Bulk upload multiple feed due diligence reports from different batches, view the parsing task list, and confirm that each document’s content blocks carry corresponding batch identifiers.
- Initiate a knowledge base search test, enter the keyword for a raw material name, and confirm that search results include the corresponding fields and detected values.
- Test the Feishu multi-dimensional table HTTP call interface, submit parsed field data, and confirm that the interface returns a successful status and data is written normally.
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.