Knowledge Base Retrieval and Recall for Paint and Ink

What Data for This Category Looks Like

Paint and ink industry data sources primarily include public industry association reports, technical parameter documents from upstream suppliers, internal enterprise production records, and test reports issued by compliance testing institutions. Data update frequencies vary: Raw material quotes and performance parameters are updated frequently based on market supply and demand. Industry compliance standards are revised annually. Internal enterprise formula documents are updated in real time alongside production process adjustments. Most documents combine structured parameter lists and long-form descriptions, with some documents being batch parameter summaries in table format. These tables include fields such as raw material brand, viscosity, fineness, VOC emissions, procurement cycle, and each field has a clear physical unit.

Constraints for Retrieval and Recall Workflows

The high proportion of structured parameters with clear units requires the retrieval process to support field matching and unit verification, preventing confusion between parameters using different units. Large differences in data update frequencies across sources require dividing knowledge base update cycles by data type, to ensure the timeliness of recall results. The high proportion of bulk table documents requires preserving the integrity of parameter groups during chunking, avoiding cross-row splits that break the logical connection of parameters. Close association between raw material and downstream application documents requires supporting cross-document associated matching during recall, to improve result relevance and avoid returning isolated single parameters or application descriptions.

How to Set Configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Paint and ink documents mostly contain structured parameters and long-form descriptions. This range preserves the integrity of parameter groups, avoiding chunks that are too fragmented or overly long
`chunk_overlap`	`100–150 characters`	Cross-chunk parameter descriptions need to retain contextual connections, preventing loss of logical explanations before and after parameters during retrieval
`retrieval_top_k`	`Top 8–10 results`	Investment research scenarios require balancing breadth and accuracy. This number covers multiple sets of relevant parameters and application scenarios
`similarity_threshold`	`0.72–0.8`	Low-match irrelevant parameter documents must be filtered out, while retaining the potential to recall niche formula parameters for targeted product categories
`rerank_top_n`	`Top 3–5 results`	Investment research decisions require focusing on core parameters. Simplifying results through reranking improves the response efficiency of large language models
`parse_table_mode`	`Parse by row groups`	Most paint and ink documents are parameter lists in table format. Parsing by row groups preserves the complete logic of individual parameter sets, avoiding parsing errors

The parameter values provided on this page are common starting points for configuration setup. 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

Phenomenon: After the reranking model is deployed and passes local tests, the rerank_score field returns false in online retrieval results, and results are not sorted by relevance. Cause: The reranking model's calling interface is not bound in the retrieval workflow, or the reranking model's input parameters do not include the original list of recall results from retrieval.
Phenomenon: Knowledge base chunks contain incomplete parameter groups, such as a single table row of parameters split across two chunks. Cause: The parsing mode for structured tables is not enabled, and only the default character-length-based chunking logic is used, failing to preserve the integrity of table rows.
Phenomenon: Retrieval results only return a small number of irrelevant documents, and core parameter content for target categories cannot be recalled. Cause: The configured similarity_threshold value is too high, filtering out most parameter documents that meet the matching requirements.

How to Confirm Proper Configuration

Upload a single table document containing structured parameters, review the chunked results, and confirm that parameter groups are not split.
Submit a retrieval request for a specific parameter, verify that the rerank_score field exists in returned results and that results are sorted by relevance.
Export knowledge base retrieval logs, confirm that the bound reranking model is called for each retrieval, and that the number of returned results falls within the range set by retrieval_top_k.
Attempt a retrieval with a keyword that includes a unit qualifier, confirm that the parameter units in recalled results match the retrieval keyword.

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.