What the Data for This Category Looks Like
Financial, insurance, and wealth management practitioners use feed industry research reports as core reference materials for agricultural-related business decisions. Sources include public monitoring data from the Ministry of Agriculture and Rural Affairs’ Animal Husbandry and Veterinary Bureau, weekly and monthly industry reports released by the China Feed Industry Association, special research reports from securities firm agriculture, forestry, animal husbandry and fishery teams, and internal technical documents from feed production enterprises.
Update rhythms vary: public monitoring data updates weekly, industry association reports release monthly or quarterly, securities firm research reports update irregularly alongside raw material price fluctuations and policy announcements, and internal enterprise documents update in real time with formula adjustments.
Documents primarily use text analysis combined with structured tables. Most reports include core data modules, market analysis modules, and policy interpretation modules. Fields cover raw material names, addition amounts, cost values, and similar items. Units include kilograms, yuan, tons, and other standard units.
Constraints on Vector Models and Indexing
The multi-source update rhythm of feed research reports requires indexes to support on-demand refreshing, avoiding resource waste from full reindexing. The document structure combining structured tables and professional terminology requires vector models to adapt to Chinese professional text. Segmentation strategies must retain complete table semantics, avoiding splits that break the relevance of single data groups.
Multi-field numeric content requires indexes to support both unstructured text vector retrieval and structured field filtering, improving recall accuracy. Large differences in fields across different research reports require index schemas to have sufficient flexibility to adapt to different types of feed research report content.
Additionally, research report retrieval for the financial field requires accurate recall results, avoiding irrelevant content that interferes with decision-making. Domain adaptation of the vector model and recall rules for the index must be configured specifically.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Feed research reports contain a large number of structured tables. Excessively long segments will destroy the semantic integrity of tables, while excessively short segments will lose contextual relevance. This range can cover the complete rows of a single table or paragraph logic |
chunk_overlap | 150–200 characters | Feed research reports have many professional terms and cross-paragraph logical connections. The overlapping interval can retain the context of terms and avoid semantic breaks |
embedding_model | bge-large-zh-1.5 or domain fine-tuned models with the same embedding dimension | The feed industry has a large number of professional terms. This model adapts to Chinese professional text, with an embedding dimension of 1024, which can meet the indexing requirements of most vector databases |
structured_data_enabled | Enabled | Feed research reports contain a large number of quantifiable structured fields. When enabled, it supports both vector retrieval and structured filtering, improving recall accuracy |
top_k | 10–15 entries | The professional content of feed research reports is highly concentrated. Too many recalls will introduce irrelevant documents, while too few will fail to cover relevant information |
auto_refresh_mode | On-demand trigger | Feed research reports have no fixed update cycle. On-demand triggering avoids invalid full index reconstruction, and adapts to the configuration logic of version v4.8.7 |
The parameter values provided on this page are conventional recommendations used to establish a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. Testing should be performed using local samples before finalizing configuration values.
Three Common Misconfigurations
- Phenomenon: Retrieval results fail to cover preset auxiliary industry data (such as soybean meal price trends), or auxiliary data cannot be accurately retrieved. Cause: The auxiliary dataset is not bound to the vector index configuration, only the main text of the research report is vectorized, and the index entry for auxiliary data is not associated.
- Phenomenon: Structured tables in a single feed formula research report are split into multiple scattered vector segments, and recall results cannot fully match formula parameters. Cause: The segmentation overlap parameter is not configured correctly, or the structured data indexing function is not enabled, causing table content to be split arbitrarily and losing the complete semantic association of single sets of data.
- Phenomenon: Retrieval results meet expectations during local debugging, but a vector dimension mismatch error (status code 400) occurs after deployment to the server, or recall accuracy drops significantly. Cause: The vector model embedding dimensions used locally and on the server are inconsistent, or the text preprocessing rules of the model are not unified, resulting in generated vectors that cannot match the vector space of the existing index.
How to Verify Successful Configuration
A single feed research report with structured tables is uploaded. The number of segments after vector splitting is checked to confirm that segmentation parameters match preset configurations. Professional terms from the feed industry (such as "aquafeed metabolic energy") are entered. The fields of recall results are checked against those in the research report to confirm that the structured data indexing function is enabled. After replacing the vector model, vector generation time consumption and dimension are tested to confirm that the embedding dimension matches the indexing dimension of the vector database. The index refresh operation is triggered. The index update log is reviewed to confirm that the on-demand refresh configuration is active.
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.