What this category of data looks like
Home goods investment research data mainly comes from category operation reports released by industry associations, annual and quarterly operating announcements of listed home goods enterprises, real-time SKU data from e-commerce platforms, and factory price ledgers of supply chain manufacturers. Update rhythms vary across sources. Industry reports are updated quarterly. Operating announcements are updated at disclosure dates. E-commerce SKU data is updated weekly. Supply chain ledgers are updated in real time with order changes. Document structures include fields such as SKU code, material specifications, dimension parameters, suggested retail price, wholesale purchase price, quarterly sales trends, and competitive benchmarking information. Units include millimeters, centimeters, yuan, units, and other physical measurement and transaction units.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
Multi-source heterogeneous data sources lead to inconsistent formats and field standards across different documents. The retrieval link must support combined logic of exact field matching and semantic recall to avoid cross-data-source semantic confusion. Different update rhythms of data sources have varying timeliness requirements. E-commerce SKU data needs to prioritize recalling content updated in the last 7 days. Industry reports can retain quarterly historical data. The retrieval link must support configuration items for filtering by update time. Documents contain a large number of parameter information with specific units. Retrieval must retain field-level semantic association. For example, when a user queries "sofa dimensions", documents containing the dimension field must be recalled. Content only containing the keyword "sofa" does not meet retrieval matching requirements. Long paragraphs of product descriptions and short entry-style price data coexist. Segmentation processing must balance semantic integrity and retrieval accuracy.
How to set the configurations
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Home goods documents contain long texts such as product parameters and industry analysis. This segmentation range balances semantic integrity and retrieval granularity, and avoids splitting parameter information |
similarity_threshold | 0.72–0.85 | Product descriptions and industry terms for home goods have high semantic similarity differentiation. This threshold filters irrelevant results while retaining relevant content |
recall_top_k | 15–20 items | A single home goods investment research document may cover information for multiple SKUs. Sufficient recall volume covers multi-dimensional investment research query needs |
rerank_top_k | 5–8 items | Investment research scenarios require accurate output of core information. Reranking retains highly relevant results for decision reference |
parse_chunk_overlap | 100–150 characters | Long paragraphs of home goods documents are mostly parameter lists. Overlapping segmentation avoids semantic breaks across paragraphs |
vector_db_connection_timeout | 30 seconds | Batch import of multi-source data carries high vector database connection timeout risk. This duration covers conventional import and query processes |
The parameter values provided on this page are general recommendations for establishing configuration baselines. 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 misconfigurations
- Phenomenon: Semantic retrieval and full-text retrieval tools return "Connection error". Cause: Vector database connection configuration does not adapt to batch query load of home goods multi-source data, or network policies restrict access ports of the vector database.
- Phenomenon: The maximum number of createable knowledge bases is 30, and no additional knowledge bases can be added. Cause: The default configured pgvector storage engine is used, and the corresponding storage upper limit parameter is not adjusted.
- Phenomenon: Irrelevant building materials category documents appear in recall results. Cause: Field-level retrieval configuration is not enabled. Only global semantic recall is used, leading to confusion with other category content that has similar semantics to home goods.
How to confirm the configuration is correctly set
- Upload a single home goods product manual document, check if the parsed segment length matches the preset
chunk_sizerange. Adjust segmentation parameters until segment semantics are complete. - Enter a query containing specific home goods product parameters, verify whether the number of recall results falls within the preset
recall_top_kinterval, and confirm the recall logic is effective. - Check vector database connection logs, confirm no frequent timeout errors. Adjust the
vector_db_connection_timeoutparameter until the connection is stable. - Compare result rankings before and after reranking, confirm that highly relevant documents have expected rankings, and verify the reranking configuration is effective.
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.