Knowledge Base Retrieval and Recall for Footwear Industry

What the data for this category looks like

Footwear industry research report data primarily comes from public reports from industry associations, quarterly financial reports from brands, e-commerce sales monitoring platforms, and raw material supplier data from upstream supply chains. Update frequency varies by data source. Brand financial reports are updated quarterly. E-commerce sales monitoring data is updated weekly. Industry special reports are released monthly or quarterly.

Document structures include modules such as category breakdowns, raw material costs, channel sales data, compliance policies, and competitor dynamics. Fields include SKU codes, product unit prices, per-pair units, raw material gram weights, quarterly quarter-over-quarter change identifiers, and more. Some documents include additional breakdown fields such as offline store layout and online traffic conversion.

What constraints these characteristics place on the knowledge base retrieval and recall process

The multi-source heterogeneous nature of footwear industry research reports, combined with large differences in update frequencies, requires the knowledge base to support batch incremental synchronization configurations. This adapts to different update rhythms for quarterly, weekly, and irregularly released data.

The large number of breakdown fields requires retrieval to support filtering recall results by category, channel, raw material type, and other fields. This prevents unrelated data from being included in results.

Differences in units across data sources require configuring unit standardization conversion rules. This ensures no unit discrepancies when matching numeric fields such as unit prices and weights during retrieval.

Diverse document formats require adapting a mixed parsing process that includes PDF text extraction, Excel structured data import, and JSON real-time data access. It is also necessary to limit the time window for recalled data to match the timeliness requirements of footwear industry research reports.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`900 seconds`	Footwear industry research reports contain multi-page tables and long text. Standard timeout durations are insufficient to complete full parsing
`Recall Count`	`Top 12-18 results`	Footwear industry research reports have many breakdown dimensions. A sufficient recall volume is needed to cover relevant data across different categories
`Similarity Threshold`	`0.72-0.80`	Footwear industry research reports contain a large number of technical terms. A higher similarity threshold is needed to filter low-relevance non-target data
`Incremental Sync Interval`	`2:00 AM daily`	Adapts to the rhythms of weekly updates for e-commerce monitoring data and quarterly updates for brand financial reports. Daily synchronization covers most real-time data
`Chunk Length`	`800-1000 characters`	Footwear industry research report paragraphs often contain long sentences and technical terms. Chunk length adapts to semantic coherence and retrieval accuracy
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Supports file volumes for importing large industry research report PDFs or collections of multiple sales data Excel files

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to run tests on internal samples before finalizing settings.

Three common configuration mistakes

Issue: Creating a new knowledge base in version 4.8.9 deployed via docker-compose returns 500 Internal Server Error. Container logs show database connection timeout. Cause: Vector database connection address and port are not configured correctly, or container-mapped ports do not match configuration parameters.
Issue: Retrieval returns research reports that include a large amount of non-footwear category content. The recall count exceeds the configured Recall Count parameter. Cause: The Similarity Threshold is not set, or the threshold is set too low. Filtering rules by category fields are not enabled.
Issue: Knowledge base query takes too long, with a single response exceeding 10 seconds. Cause: Vector database caching functionality is not enabled, or Chunk Length is not adjusted to a reasonable interval for footwear industry research reports. This leads to excessive computation for parsing and recall.

How to confirm configurations are correct

Upload a quarterly financial report PDF for a footwear brand. Verify that parsed text fully extracts core fields such as raw material costs and sales data, with no obvious truncation or garbled characters.
Submit a retrieval request for a footwear sub-category. Confirm that the number of returned results matches the configured Recall Count parameter, and all results include footwear-related technical terms and fields.
Check the vector database cache dashboard. Confirm that a cache index for the target knowledge base has been generated, and the cache update time matches the configured Incremental Sync Interval.
Enable test mode, then submit a non-footwear related query. Confirm that the LLM only returns content from the footwear industry research reports in the current knowledge base, and does not reference external unrelated information.

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.