What the data for this category looks like
Data sources for feed marketing content include internal enterprise R&D documents, aquaculture technology popular science materials, compliance and regulatory documents, and conversion scripts shared by dealers. Material updates proceed irregularly, tied to new product launches, breeding cycle adjustments, and changes to compliance policies. Document structures vary significantly: they include short product selling point phrases (tens of characters), full-category marketing guides spanning dozens of pages, and structured bulk CSV materials. CSV materials contain fields such as feed name, applicable breeding targets, core nutritional parameters, promotion scripts, and compliance reminders.
What constraints do these characteristics impose on vector models and indexing
The wide variation in material length makes unified chunking parameters difficult to adapt to all cases. Overly long marketing manuals may exceed the context window of vector models. Too short selling point phrases may introduce noise after splitting. Structured CSV materials with multiple fields require different recall weights for each field, to avoid non-marketing content such as nutritional parameters interfering with accurate recall. The irregular update schedule for materials requires indexes to support incremental updates instead of full rebuilds, to reduce resource consumption and time spent. The presence of professional breeding terms also requires vector models to have domain semantic understanding, to avoid semantic matching deviations.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the length variations of feed marketing materials, balances semantic completeness of long documents and reasonable chunking of merged short texts |
chunk_overlap | 100–150 characters | Connects contextual professional breeding terms between adjacent chunks, prevents semantic breaks |
embedding_model | Domain-fine-tuned agricultural semantic vector model | Feed marketing content contains professional aquaculture and livestock farming terms; general vector models have insufficient semantic matching accuracy |
recall_top_k | Top 6–8 results | Covers recall needs across multiple feed marketing scenarios, while filtering redundant low-match results |
similarity_threshold | 0.72–0.78 | Filters low-match irrelevant materials, retains content that aligns semantically with user queries |
incremental_index | Enabled | Adapts to the irregular update schedule of feed marketing content, reduces time and resource usage of full index rebuilds |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis. It is recommended to test on samples specific to the deployment context before finalizing settings.
Three common mistakes
- Issue: After upgrading the platform version, uploading bulk feed marketing CSV files returns a
413 Request Entity Too Largeerror. Cause: The new version adjusted the default threshold forUPLOAD_FILE_MAX_SIZE, which does not match the typical size of bulk feed marketing materials. - Issue: Knowledge base indexing progress gets stuck, with the interface showing uncompleted indexing exceeding the preset duration. Cause:
incremental_indexis not enabled. Full indexing of multiple feed marketing documents times out due to excessive data volume. - Issue: A large number of feed nutritional parameter entries are mixed into recall results, with no target marketing scripts matched. Cause: A domain-fine-tuned agricultural semantic vector model is not used. General models cannot distinguish the semantic priority between parameter fields and marketing scripts.
How to confirm configurations are set correctly
- Upload a typical feed marketing CSV material, check the chunk preview interface. Confirm that chunk lengths fall within the preset
chunk_sizerange, with no excessive truncation or overly fragmented chunks. - Submit a simulated user query, such as "Feed marketing scripts suitable for fattening pigs". Check the number and similarity of recall results. Adjust corresponding configuration items to meet business requirements.
- Add a new feed marketing material to trigger incremental indexing. Confirm that indexing progress completes within the preset duration, with no stuck status.
- Check vector model call logs. Confirm that each recall’s semantic matching results align with the professional terminology of feed marketing scenarios as expected.
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.