Vector Models and Indexing for Livestock and Poultry Farming

What the Data for This Category Looks Like

Livestock and poultry farming investment research data sources include weekly inventory and slaughter reports released by industry associations, daily feed raw material price reports, animal disease monitoring bulletins, agricultural policy documents, and research reports from third-party farming institutions. Data update schedules cover multiple dimensions: feed prices and slaughter quotations are updated daily, disease monitoring data is aggregated weekly, and in-depth industry reports are released quarterly. Document structures include structured tables with fields such as inventory volume, slaughter volume, unit price (units: 10,000 head, yuan/ton, per unit, etc.), unstructured research report paragraphs, and policy announcement texts. Some data sources are Excel spreadsheets or PDF industry analysis documents.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The multi-frequency update schedule of livestock and poultry farming data requires indexes to support flexible switching between incremental updates and full reconstruction, to avoid excessive computing resource consumption from full index reconstruction. Structured fields and clear unit requirements mean vector models must retain semantic details, and must not lose field association information during splitting or conversion. Mixed-format data sources (tables, research reports, announcements) require indexing systems to adapt to both structured text and unstructured long text vector generation, and support adjustments to segmentation strategies for different formats. The high information density of investment research scenarios requires ensuring accurate vector recall, and avoiding irrelevant data interfering with retrieval results.

Configuration Settings

Configuration Item	Recommended Values	Rationale
`embedding_model`	`bge-large-zh-v1.5` or `text-embedding-3-small`	Adapts to the mixed semantics of livestock and poultry farming data (structured fields, research report text), balances accuracy and call costs, and is compatible with the configuration logic of FastGPT v4.8.21-fix
`chunk_size`	`600–1000 characters`	Splits livestock industry research reports and structured table entries, avoids excessively long segments exceeding model input limits, and retains field context association
`recall_top_k`	`Top 8–12 results`	Matches the information density of livestock and poultry farming investment research scenarios, avoids retrieving too many irrelevant data entries
`similarity_threshold`	`0.72–0.78`	Filters low-correlation scattered price quotations or policy documents, and retains core investment research information
`index_incremental_update`	`Enabled`	Adapts to daily updated slaughter quotations and real-time disease data, avoids resource consumption from full index reconstruction
`parse_structured_table`	`Enabled`	Identifies structured tables in livestock farming data, generates vectors for individual cell content to improve retrieval accuracy

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 case-by-case analysis. It is recommended to test on your own samples before finalizing.

Three Common Misconfigurations

Phenomenon: The exported knowledge base dataset.csv only contains the index field, with no content field. Cause: The parse_structured_table configuration is not enabled, so table content in livestock farming data is not correctly extracted as indexable text blocks.
Phenomenon: Index model calls fail after Docker deployment, with logs returning 500 Internal Server Error. Cause: The index model API address is not configured in docker-compose.yml, or the OneAPI key is not correctly bound to the embedding_model configuration item.
Phenomenon: Vector recall results contain a large number of irrelevant feed price data that do not match the livestock investment research theme. Cause: recall_top_k is set too high, and similarity_threshold is not adjusted for mixed data scenarios, leading to low-correlation entries being included in results.

How to Verify Proper Configuration

Upload a structured spreadsheet document for livestock and poultry farming, check that the parsed text blocks contain complete cell content and field descriptions.
Initiate a search using investment research keywords, verify that the number and similarity of recall results match the expected configuration.
View the FastGPT index management page, confirm that incremental update tasks are automatically triggered according to the set cycle.
Export the knowledge base dataset.csv, check that both content and index fields are included, and that the content matches the source document.

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.