What data for this category looks like
Footwear financing daily report data is sourced from daily inventory ledgers, bank credit systems, and supply chain repayment reports of footwear brand owners and distributors. Updates run every early morning, covering full data from the previous day. Each daily report is categorized and summarized by shoe SKU, and includes fields such as SKU code, shoe name, daily available credit limit, pledged inventory quantity, daily repayment amount, and credit term duration. Credit limit units are ten thousand yuan, inventory units are pairs, and credit term units are calendar days.
Constraints imposed on vector models and indexing by these characteristics
The daily update requirement for footwear financing daily reports means indexes must support near-real-time incremental refreshing, to avoid performance loss from full index rebuilding. Each document contains multiple SKU entries, with structured fields mixed with unstructured financing description text. Vector models must support both structured field encoding and natural language semantic understanding. The large overall number of entries requires indexes to support precise recall under high cardinality, to avoid redundant matching. Multilingual fields from some cross-border footwear brands increase dimension adaptation requirements for vector encoding, so the input specifications of corresponding multilingual vector models must be matched.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
RECALL_TOP_K | Top 20 entries | Each daily report contains multiple SKU entries; sufficient related entries must be covered to ensure recall completeness |
VECTOR_MODEL_NAME | bge-large-zh-v1.5 | Adapts to mixed encoding of Chinese structured fields and natural text, and supports recognition of professional terminology for footwear categories |
INDEX_INCREMENTAL_REFRESH | Enabled | For the daily full update data feature, near-real-time incremental refreshing reduces resource usage from index rebuilding |
PARSE_CHUNK_SIZE | 800–1200 characters | Individual SKU entries are short, but overall documents have many entries; chunk length adapts to semantic integrity of mixed fields |
MONGODB_INDEX_COLLECTION | rag_dataset | Corresponds to the MongoDB storage collection for FastGPT's default knowledge base dataset, and matches the associated storage table for custom indexes |
EMBEDDING_DEVICE | cuda:0 | Adapts to deployment environments equipped with 3090 GPUs; a single GPU meets the encoding requirements for daily incremental updates |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: Vector models fail to load in Docker deployment environments, with
model not founderrors in logs. Cause: The vector model was not mounted to the model storage directory of the Docker container, or the model path was not configured in environment variables. - Issue: The exported knowledge base dataset.csv only contains the index field, with no content field. Cause: The content field switch for knowledge base export was not enabled, or SKU names and financing description text fields of the daily report were not correctly associated during import.
- Issue: Chunk truncation occurs during text import, leading to semantic loss and low matching accuracy of recall results. Cause: The
PARSE_CHUNK_SIZEparameter was not adjusted, and the default chunk length does not match the short entries of footwear SKUs.
How to Confirm Successful Configuration
- Review vector model loading logs to confirm that the model file corresponding to the configuration item has been successfully loaded, with no error messages.
- Import a single footwear financing daily report document, check the index generation progress, and confirm that the incremental refresh function triggers normally, with no full index rebuilding prompts.
- Export the knowledge base dataset, check that the generated csv file contains the expected fields, and that field content matches the daily report data.
- View GPU usage monitoring to confirm that the vector encoding task has called the specified GPU device, and resource usage meets the configuration requirements of the deployment environment.
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.