What the Data for This Category Looks Like
The due diligence data for this category originates from ERP production logs, raw material purchase receipts, factory quality inspection reports, and industry circulation monitoring data of feed manufacturing enterprises. Data is generated and updated per production batch. Single-batch data is synced immediately once production finishes. Monthly aggregated industry benchmark data is updated per calendar month. Documents include two categories: structured tables and unstructured reports. Structured data has fields including batch number, production date, raw material name, purchase volume (unit: tons/kilograms), crude protein content (unit: grams/kilograms), sales flow region, and more. Unstructured documents include stamped quality inspection PDFs and recipe specification documents.
Constraints for Vector Models and Indexing Posed by Data Characteristics
Mixed structured and unstructured data requires indexes to support associative retrieval of both text semantic vectors and structured numerical vectors. This avoids encoding bias in purchase volume, content value, and other numerical fields from single vector models. Frequently updated data per production batch requires indexes to support incremental writes and version isolation. This prevents retrieval delays caused by full reindexing. Field differences across multiple raw material categories require vector models to adapt to semantic distinction of different entities. Pre-standardization processing for raw material names and content values is also needed, to reduce confusion in semantic retrieval. High field density in single-batch data requires limiting vector chunk length per document. This avoids vector dimension redundancy that impacts retrieval efficiency. Large volume of monthly aggregated industry data requires configuring a partitioned indexing strategy. This reduces the load of single-batch retrieval.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Alibaba text-embedding-v3 or a general-purpose text vector model adapted for multiple entities | The feed category includes multiple types of raw material entities, so the model needs strong semantic distinction capabilities. The encoding effect of this model for industrial category entities has been verified through actual testing |
chunk_size | 800–1200 characters | Single-batch feed due diligence data has high field density. This length balances semantic completeness and vector dimension redundancy |
index_incremental | Enabled | Adapts to the frequent update requirements per production batch, avoiding delays from full reindexing |
recall_top_k | Top 20 results | Feed due diligence data has many associated items, so a sufficient number of candidate results must be recalled for subsequent screening |
vector_store_batch_size | 50 items per batch | Adapts to the volume of single-batch data, avoiding excessive memory usage during single writes |
enable_structured_vector | Enabled | Supports generating independent numerical vectors for purchase volume, content value, and other fields, for associative retrieval with text vectors |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: All similarity scores of semantic retrieval results exceed the preset threshold, and some results are irrelevant to the feed due diligence topic. Cause: Pre-standardization processing for raw material names and content values was not performed, leading the model to overestimate semantic encoding similarity across different raw material categories. A reasonable similarity threshold was also not set to filter low-relevance results.
- Phenomenon: Auxiliary data is not included in the retrieval index, and relevant auxiliary documents cannot be recalled via keywords. Cause: The vectorization configuration for auxiliary data was not enabled, or auxiliary data was not uploaded to the specified index directory.
- Phenomenon: After multiple sets of vectors are generated for a single feed production data entry, complete batch information cannot be recalled associatively during retrieval. Cause: The index mapping rule for multi-vector association was not configured correctly in version v4.8.7. Only vectors generated by a single vector model were used for retrieval, and the association between multiple vector sets and the original data was not bound.
How to Verify Proper Configuration
- Upload a single batch of mixed structured and unstructured feed due diligence documents. Confirm the vector generation task has a status code of 200 and no error logs.
- Run a semantic retrieval test, adjust the similarity threshold, and confirm the coverage and relevance of recalled results meet business requirements.
- Upload newly generated batch data. Confirm the index automatically updates incrementally without triggering a full reindexing task.
- View the index management interface. Confirm structured numerical vectors and text vectors are associated and bound, with no missing fields.
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.