Knowledge Base Retrieval and Recall for Chemical Raw

What the data for this category looks like

Data sources for chemical raw materials research reports include industry association monthly statistics, listed company periodic announcements, third-party chemical spot databases, and brokerage special research reports. Update frequencies vary. Spot transaction price data is updated daily. Production capacity and supply-demand balance data is updated quarterly. Policy interpretation reports are updated immediately when related policies are released. Individual documents typically include fields such as product grades, production capacity scale, spot transaction prices, upstream and downstream dependence, and environmental policy impacts. Common industrial measurement units such as ten thousand tons per year, yuan per ton, and ten thousand tons are used. Some documents contain multi-line structured table data.

Constraints imposed by these characteristics on knowledge base retrieval and recall

Differences in update frequencies across data sources require setting differentiated recall time thresholds for different data types. Specialized fields and standardized units require enabling field-level retrieval matching to avoid semantic bias. Individual research reports are lengthy and include multi-line structured tables. Reasonable text segmentation and table parsing rules must be implemented to retain complete quantitative information. Mixed formats across multiple data sources require adaptive parsing for formats such as XLSX tables and PDF documents. Long text splitting must be avoided to prevent professional information from being fragmented.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Chemical raw material research reports often contain continuous quantitative data. This segmentation length preserves the integrity of single sets of information such as production capacity and prices
`recall_count`	Top 10 results	Research report data is highly specialized. A sufficient number of candidate results must be recalled for reranking and filtering
`similarity_threshold`	0.72–0.85	Matching accuracy for specialized terminology has high requirements. This range filters out low-relevance general chemical texts
`rerank_top_k`	Top 5 results	Focus on core research report content, avoiding excessive redundant results that disrupt retrieval experience
`parse_file_max_size`	1000 MB	Adapt to large-scale industrial database XLSX files uploaded in batches
`parse_timeout_seconds`	600 seconds	Parsing large multi-line tables takes significant time. This setting reserves sufficient processing time

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. It is recommended to test on in-house samples before finalizing settings.

Three Common Configuration Mistakes

Retrieval results are empty after uploading multi-line XLSX format tables. Cause: The structured table parsing switch is not enabled, or the chunk_size setting is too short, resulting in table row data being truncated.
Knowledge base search responses time out in version 4.8.20. Cause: The parse_timeout_seconds setting is lower than actual parsing time, or the recall_count setting is too high, resulting in excessive vector retrieval load.
The knowledge base always displays no available content. Cause: The data source update path is not configured correctly, or the parse_file_max_size setting is smaller than the actual uploaded file size, resulting in parsing failure.

How to Verify Correct Configuration

Upload a single typical chemical raw material research report, and check if parsed text segments retain complete quantitative fields such as production capacity and prices.
Enter specialized terminology such as "polyethylene spot price", and verify whether the relevance of recalled results meets expectations.
Upload multiple industry documents in different formats in batches, and check whether parsing tasks complete normally.
Adjust the similarity_threshold parameter, and verify whether the number and relevance of recalled results change reasonably with the parameter.

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.