Knowledge Base Retrieval and Recall for Home Goods

What the data for this category looks like

Core data for home goods comes from light industry manufacturing industry spot check reports, brand supply chain ledgers, e-commerce platform product filing information, and public data from third-party quality inspection institutions. Update cycles are triggered irregularly alongside new product launches, quarterly compliance spot checks, and price adjustments. Each individual data entry includes fields such as SKU number, material composition, physical dimensions, compliance certification number, supplier qualification, batch number, and individual quality inspection results. Units include millimeters, kilograms, milligrams per cubic meter, percentage, and others. Most documents are structured tables or PDF/CSV files with fixed fields.

What constraints these characteristics impose on knowledge base retrieval and recall

The home goods category has a high proportion of structured fields, a large number of SKUs, and mixed use of multiple units. The retrieval link must prioritize field-level precise matching; general-purpose full-text retrieval is not suitable for this scenario. Multiple batches and multiple suppliers’ data will create duplicate entries, so deduplication rules need to be configured. The timeliness of quality inspection data requires the knowledge base to regularly sync the latest spot check results to avoid recalling expired compliance information. Additionally, due diligence reports need to link products to their corresponding batch quality inspection records, so recall results must be bound to the unique identifiers of SKUs and batches to prevent information misalignment.

How to configure settings

Configuration Item	Recommended Setting	Rationale
`PARSE_TABLE_MODE`	`Structured extraction priority`	Home goods documents mostly contain quality inspection tables and SKU parameter tables. Prioritizing structured field extraction improves retrieval accuracy
`RECALL_TOP_N`	`Top 10-15 results`	Home goods have a large number of SKUs. This range covers associated data for different batches and suppliers of the same category, avoiding missing key compliance information
`SIMILARITY_THRESHOLD`	`0.75-0.85`	Structured field matching requires a relatively high similarity threshold to avoid recalling irrelevant SKU data while covering approximate entries of the same material and category
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Home goods quality inspection reports are mostly multi-page PDFs or bulk CSV ledgers. 50 MB can accommodate full quality inspection data for a single batch, preventing upload truncation
`PARSE_FILE_TIMEOUT_SECONDS`	`120 seconds`	Bulk parsing of multi-page quality inspection tables requires additional time to complete structured extraction, preventing parsing timeout failures
`ENABLE_FIELD_INDEX`	`Enabled`	Home goods have unique identifier fields such as SKU number and batch number. Enabling field indexing enables precise matching and improves the linking accuracy of due diligence reports

The parameter values provided on this page are common starting points for configuration. Actual values are affected by document format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Scenario: When creating a knowledge base on a local deployment of version 4.9.0, the image understanding model option is not displayed. Cause: This version does not compile image parsing dependency packages by default. The corresponding plugin must be manually installed before enabling the feature.
Scenario: Non-target SKU home goods data is mixed in recall results, and field matching accuracy is insufficient. Cause: The SIMILARITY_THRESHOLD parameter is set too low, failing to filter low-relevance approximate entries.
Scenario: After batch uploading quality inspection PDF reports, the parsing task times out and returns status code 504. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter value is too short, not adapting to the parsing time of multi-page structured documents.

How to verify correct configuration

Upload a single home goods quality inspection PDF, check whether core fields such as SKU number and material composition are extracted in the parsing results, confirming that the PARSE_TABLE_MODE configuration takes effect.
Enter a SKU number keyword to initiate a retrieval, verify whether recall results prioritize entries matching the number, confirming that the field indexing function works properly.
Initiate a bulk data retrieval, count whether the number of recall results falls within the preset range, confirming that the RECALL_TOP_N configuration meets category requirements.
Upload a quality inspection ledger file larger than 20 MB, confirm that the upload is not truncated, verifying that the UPLOAD_FILE_MAX_SIZE configuration is reasonable.

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.