What data for this category looks like
Advanced chemical materials internal office data primarily originates from enterprise process manuals, raw material quality inspection ledgers, equipment operation and maintenance records, patent compliance documents, and employee training courseware. Data updates run monthly in alignment with raw material batch adjustments, and update in real time when process modifications or compliance standards change. Individual documents typically include fields such as material numbers, CAS numbers, process parameters, and quality inspection metrics. Common units used include MPa, ℃, %, and kilogram.
Constraints imposed on knowledge base retrieval and recall
Multiple structured fields with specialized units require retrieval to balance precise field matching and semantic understanding, preventing irrelevant results from inconsistent unit usage. Individual documents have lengthy content, so they must be stored in appropriately sized chunks to avoid losing cross-chunk information. Raw material quality inspection data has uneven update frequencies, so index refresh cycles must be adjusted correspondingly to ensure the latest batch data is retrievable. Dense professional terminology requires support for both keyword and semantic retrieval to cover queries for synonymous technical parameters.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Match professional chemical terminology and structured fields, filter low-relevance results |
limit | Top 8–12 entries | For precise query scenarios, a small number of entries covers business needs |
usingReRank | Enabled | Further filter redundant results that are semantically similar but do not match fields |
datasetSearchUsingExtensionQuery | Disabled | Structured data does not require additional query expansion, avoid introducing irrelevant fields |
searchMode | Hybrid retrieval | Balance keyword matching and semantic recall, adapt to professional terminology queries |
Three common misconfigurations
- After importing equipment maintenance contact documents, full-text retrieval and semantic recall fail to match the target number, and a large volume of irrelevant process documents is returned. This occurs because precise matching rules are not configured for structured contact fields, and only global semantic retrieval is used.
- After enabling
usingReRank, online recall testing does not function. This occurs because the re-ranking configuration is not enabled synchronously during the knowledge base index phase, or the re-ranking model is not bound to the currently active dataset. - When
limitis set to 3000, the large language model does not receive retrieval context. This occurs because the total length of retrieval results exceeds the context capacity limit of FastGPT v4.17.0, and no reasonable truncation is applied.
How to verify proper configuration
- Upload one quality inspection document containing a CAS number, retrieve the corresponding CAS number, and confirm that the returned results include the structured fields of this document.
- After enabling
usingReRank, compare the count and ranking of recall results before and after enabling, and confirm that redundant results are effectively filtered. - Set
limitto 8 entries, retrieve high-frequency business queries, and confirm that the number of returned results matches the configured value. - Test the
datasetSearchUsingExtensionQueryswitch state, and confirm that no redundant retrieval results from irrelevant fields are introduced after the switch is disabled.
The values provided are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.