What the data for this category looks like
Feed industry research and investment data primarily comes from agricultural and rural department livestock and poultry breeding monitoring data, monthly operation reports from the China Feed Industry Association, public financial reports of listed feed enterprises, and raw material price quotes from commodity futures markets. Update rhythms vary significantly. Core raw material prices such as corn and soybean meal are updated daily. Industry supply and demand reports are released monthly. Enterprise operating data is disclosed quarterly. Most documents are structured tables and written analysis reports, containing fields such as crude protein content, moisture percentage, unit production cost. Units include percentage, kilogram, ton, yuan/ton, and others.
Constraints on context and token handling
The large differences in update frequencies of multi-source data in the feed industry mean the knowledge base must access real-time quotes, monthly reports, and quarterly financial reports simultaneously. Token usage varies significantly across different document types. Structured raw material ratio tables have high token density. A single table page may occupy more than 1000 tokens. Written analysis reports have more scattered token distribution. Additionally, research and investment scenarios often require associating cross-cycle data. If context is spliced without grouping by data type, token overflow or redundant context is likely to occur. Professional terms in the feed industry such as compound feed, premix, metabolizable energy also increase the complexity of token calculation, requiring precise matching of professional word segmentation rules.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Feed industry requires access to multi-source cross-cycle data; single-round context must cover core indicators of at least 3 cycles to avoid token overflow |
chunkSize | 800–1200 characters | Feed industry documents include structured tables and long-text analysis; this segment length balances token usage and context integrity, preventing table splitting that causes association failure |
apiPassThroughContext | Enabled | Research and investment scenarios require custom splicing of historical research questions and current queries, without relying on system-automatically spliced historical records, to adapt to customized context logic |
similarityTopN | Top 8–10 entries | Feed industry data has multiple dimensions; sufficient raw material price and supply and demand report fragments must be retrieved to avoid missing key information |
parseChunkOverlap | 100–150 characters | Structured feed ratio tables and cost calculation sheets must retain association of adjacent fields after splitting, preventing segmented context breakage |
maxTokenPerChunk | 1500 characters | Prevent excessive token usage in a single segment from making the model unable to process it, adapting to the segmentation needs of long tables in the feed industry |
The parameter values provided on this page are common recommendations for establishing configuration starting points. Actual values are affected by material format, data volume, 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: After a feed industry cost calculation sheet is uploaded, retrieved fragments fail to match crude protein content with corresponding raw material prices. Cause: No reasonable segment overlap rate is configured, causing structured tables to lose context association of adjacent fields after being split.
- Phenomenon: When running a multi-knowledge base classification task on version v4.8.3, classification results always match the fallback category, and do not associate with the context of historical research queries. Cause: Custom context is not passed in via the API, only system-automatically spliced historical records are used, and historical records are not grouped by cross-cycle data in the feed research scenario, causing classification logic to fail.
- Phenomenon: A token overflow error is returned when calling the knowledge base interface, prompting that the context length exceeds the limit. Cause: The
maxContextparameter adapted to feed industry multi-source data is not set, and the default value is used directly, causing spliced cross-cycle raw material prices, supply and demand reports, and other data to exceed the model's token upper limit.
How to confirm correct configuration
- A feed industry monthly supply and demand report and raw material price table should be uploaded, and the segment preview interface checked to confirm that structured tables are not split into adjacent segments with missing associated fields.
- A test query including cross-cycle data should be initiated, and interface logs reviewed to confirm that custom context has been correctly passed in, and that only system-spliced historical records are not used.
- The knowledge base's token statistics panel should be checked, and the actual token usage of a single document compared with the configured
chunkSizeandmaxContextparameters to confirm that the parameter values adapt to the document structure. - A multi-knowledge base classification test should be run, and a query including historical research questions input, to confirm that the classification result does not match the fallback category and conforms to expected classification logic.
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.