What This Type of Data Looks Like
Knowledge base data required for feed industry research and investment work is primarily sourced from industry association published monitoring weekly reports, real-time quotes from feed raw material spot trading platforms, production records from livestock and poultry farming terminals, feed formula patent documents, and national standard specifications. Update frequencies include real-time (spot quotes), monthly (industry supply and demand reports), and irregular (policy updates and patent releases).
Document structures include structured quote lists (with fields such as raw material name, origin, transaction type, etc.), semi-structured industry analysis documents (with embedded data tables and policy excerpts), and unstructured in-depth research report texts. For fields and units: spot quote documents use yuan/ton as the price unit, farming data uses head and ton as statistical units for inventory and feed consumption, and formula documents include numerical fields related to component proportions.
What Constraints Do These Characteristics Impose on the Vector Models and Indexing Link?
Real-time spot quote data has a high update frequency and small per-record size. It requires incremental indexing to avoid excessive time spent on full reconstruction.
Structured quote lists have standardized fields. They are suitable for using fine-grained single records as segmentation indexing units to improve retrieval accuracy.
Semi-structured analysis documents and unstructured research reports coexist. A mixed indexing mode must be configured to balance vector semantic retrieval and keyword matching capabilities.
There are many industry-specific terms. A vector model adapted to the agricultural field must be selected to reduce semantic matching deviations.
Significant field differences exist across different documents. Field-level vector mapping rules must be configured to avoid irrelevant fields interfering with retrieval relevance.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | bge-large-zh-v1.5 | This model has strong semantic matching capabilities for agricultural domain-specific terms, and meets the semantic understanding requirements of feed industry research and investment data |
CHUNK_SIZE | 800–1200 characters | Feed industry research reports mostly contain long sections of analysis content. This segmentation length preserves semantic integrity while avoiding excessive single segment length that impacts retrieval accuracy |
INDEX_TIMEOUT_SECONDS | 600 seconds | The parsing and indexing process for some semi-structured documents in the feed industry takes a long time. This duration covers conventional indexing operations |
RECALL_TOP_K | 10–15 results | Feed industry research and investment needs to balance coverage of multi-source data and retrieval accuracy. This range balances recall efficiency and result relevance |
INDEX_INCREMENTAL_ENABLE | Enabled | Spot quote data in the feed industry is updated frequently. Incremental indexing avoids timeouts and resource consumption issues caused by full reconstruction |
FIELD_MAPPING_RULES | Configure dedicated mappings per document type | Feed industry data includes three categories: structured quotes, semi-structured analysis content, and unstructured research reports. Significant field differences exist across document types, and dedicated mappings improve retrieval relevance |
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 settings.
Three Common Configuration Errors
- Phenomenon: A 60-second timeout error is returned when switching knowledge base indexes, or the index reconstruction status does not update for more than 1 day. Cause: Incremental indexing mode is not enabled. Full index reconstruction is performed on structured quote and research report data from the feed industry. The volume of data processed in a single batch exceeds the system's default threshold.
- Phenomenon: A parameter verification failure is returned when calling the batch add index interface. Cause: The
chunk_sizeandvector_modelfields inbatch_index_paramsare not passed correctly, or the ID list of target document chunks is not specified in the required format. - Phenomenon: A large number of general industry data unrelated to feed industry research and investment are mixed in retrieval results. Cause: Field-level mapping rules are not configured. Fields of different types of documents are mapped uniformly, making it unable to distinguish feed-specific terms from general terms during semantic matching.
How to Verify Correct Configuration
- Enter the index configuration interface, confirm that the switch status of
INDEX_INCREMENTAL_ENABLEmatches the update rhythm of feed data, and configure the incremental trigger time interval as needed. - Upload a single structured quote document from the feed industry, check the parsed segmentation results, and confirm that the segmentation length matches the preset configuration.
- Initiate a small-scale retrieval test, review the number and relevance of recall results, and adjust the number of recalled entries and similarity threshold to a range that meets business requirements.
- View system operation logs, confirm that no timeout errors occur during the index reconstruction process, and verify that the configured timeout duration covers conventional indexing operations.
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.