Knowledge Base Retrieval and Recall for Livestock and

What the data for this category looks like

Data for livestock and poultry farming originates from publicly available livestock industry standard documents, breeding technical manuals, practical operation records from grassroots livestock service stations, and internal training materials of breeding entities. Three update cadences apply:

Industry standard documents are updated annually
Disease prevention and control documents are adjusted dynamically alongside sudden outbreaks
Practical operation records are submitted irregularly by uploaders

Document structures fall into three categories: long text process-based, structured table-based, and text-image combined. Structured fields include livestock and poultry age, feeding volume, epidemic prevention cycle, and others. Units include kilograms, milliliters, days of age, and weeks of age.

What constraints do these characteristics impose on the knowledge base retrieval and recall workflow

Long text process-based documents have significant length. Embedding single segments easily leads to semantic fragmentation. Adjust segmentation logic to preserve the integrity of core operational steps. Structured table-based documents have strong field correlations. Splitting them directly into independent text blocks loses inter-field correlation information, reducing retrieval accuracy. Dynamically updated disease prevention and control documents that are not reindexed in time will have recalled content lag behind the latest epidemic prevention requirements. Ununiform formats of grassroots practical operation records create large differences in embedding vector distributions, affecting the stability of similarity matching.

How to set configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Preserve semantic integrity of long text process-based livestock and poultry farming documents, avoid splitting core operational steps
`similarity_threshold`	`0.72–0.80`	Match field correlation retrieval needs for structured table-based documents, filter low-relevance recall results
`recall_top_k`	`Top 8 results`	Cover multiple marketing scenarios including feeding, epidemic prevention, and market readiness, ensure comprehensiveness of recalled content
`re_rank_top_k`	`Top 3 results`	Focus on core user query needs, reduce redundant recall results to improve content accuracy
`INDEX_REFRESH_INTERVAL`	`Every 7 days`	Adapt to dynamic update cadence of disease prevention and control documents, ensure timeliness of recalled content
`PARSE_TABLE_STRUCTURE`	`Enabled`	Retain field correlation information of structured tables, avoid semantic loss after splitting

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. It is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: The console returns the error invalid configuration parameter name "hnsw.iterative_scan" and fails to start index construction. Cause: A private vector database parameter was mistakenly added to FastGPT's general retrieval configuration items, and a platform-supported parameter name was not used.
Phenomenon: Extra spaces are added when recalled breeding documents are output by the model, and some professional terms are converted to uppercase. Cause: The preserve_original_format parameter is not configured, or its value does not align with the fixed format requirements of breeding documents.
Phenomenon: When attempting to upload livestock and poultry farming practical operation videos to the knowledge base, the system returns the upload file type not supported error. Cause: Video upload permission is not enabled in platform settings, or the video parsing timeout parameter is not configured.

How to confirm the configuration is complete

Access the knowledge base management interface, review core configuration item values, and confirm they match the preset plan.
Submit a breeding-related query, verify that the number of recall results aligns with the recall_top_k configuration, and that similarity scores fall within the set range.
Upload a latest disease prevention and control document, wait for index reconstruction to finish, then submit relevant queries to confirm recalled content includes the latest information.
Check system logs to confirm there are no records of parameter errors, index construction failures, or format parsing exceptions.

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.