Data format for this category
Data for livestock and poultry farming intelligent due diligence comes from daily breeding ledgers of breeding entities, animal quarantine certificates, settlement documents for livestock outgoing, and breeding environment monitoring logs. Update frequency aligns with breeding cycles: real-time data such as inventory and feed consumption is updated daily before batch slaughter, and summary reports are generated monthly. Each document includes two types of content: structured ledgers and unstructured images and documents.
Structured fields include inventory volume (unit: head/feather), outgoing volume, feed purchase volume, quarantine batch number, quarantine ID, and others. Unstructured content primarily consists of satellite survey images of breeding plots and scanned copies of paper documents.
Constraints on vector models and indexing
The mixed data source of structured ledgers and unstructured images and documents requires vector models to support both text embedding and multimodal feature extraction. Coexisting small-volume real-time updated data and large-volume low-frequency monthly summary data requires indexes to support flexible switching between incremental refresh and full reconstruction. Business logic that links multiple fields — such as quarantine IDs corresponding to outgoing records — requires indexes to support hybrid retrieval with metadata.
Field units are tied to business meanings, so index metadata storage must retain original field units and business identifiers. This prevents semantic confusion during retrieval.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the length of single records in livestock and poultry farming ledgers and the paragraph structure of monthly reports, avoiding semantic fragmentation from being too short, and exceeding model context limits from being too long |
chunk_overlap | 50–80 characters | Retains business-related information across segments, such as preceding and following content for quarantine batches |
vector_store_index_type | HNSW | Supports fast recall for multi-source mixed data, with higher query efficiency than flat indexes |
recall_top_k | Top 10–15 results | Covers the volume of associated data required for a single due diligence report, avoiding excessive redundant information in recall results |
similarity_threshold | 0.72–0.85 | Matches the business semantic similarity range for livestock and poultry farming data, filtering irrelevant breeding records |
metadata_index_fields | Quarantine ID, inventory volume unit | Enables metadata indexing to support precise filtering of recall results by business fields |
The parameter values provided on this page are common recommendations for establishing configuration baselines. Actual values are affected by material formats, data volumes, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: When configuring a vector model without a GPU environment, the interface displays a "hardware incompatible" error, or OneAPI configuration items cannot be saved. Cause: No vector model image supporting CPU inference was selected, or CPU quantization acceleration parameters were not enabled.
- Phenomenon: The exported knowledge base backup only contains a unified compressed package, and cannot be split and exported by business types such as breeding ledgers and quarantine certificates. Cause: Backup configuration by metadata dimension was not enabled, and only the default global knowledge base backup strategy was used.
- Phenomenon: The number of vector recall results is far lower than the set
recall_top_k, or a large number of recall contents with empty fields appear. Cause:metadata_index_fieldswas not configured correctly, making it impossible to filter irrelevant data by business fields.
How to verify correct configuration
- Upload a single livestock and poultry farming ledger document, and verify that the segmented results after vector embedding cover all complete business fields without semantic fragmentation.
- Initiate a retrieval request based on quarantine ID, and confirm that the recall results only include associated data with the corresponding business identifier, and do not include the full dataset.
- Submit incrementally updated breeding records, and check whether the index completes refresh according to preset rules without triggering a full reconstruction process.
- View vector retrieval logs, and confirm that metadata filtering conditions for each query have been correctly applied with no missing parameters.
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.