What the data for this category looks like
Data for this category primarily comes from asset management systems, procurement systems, financial fixed asset modules, and terminal management platforms. Daily generated documents include asset ledgers, inventory sheets, loan and return forms, and scrap applications. Data update frequencies are mostly monthly, quarterly, or annually. Document fields include core information such as asset ID, name, specification model, user, location, purchase date, and asset status. Most entries are structured with no complex nesting, making field matching during retrieval straightforward.
What constraints do these characteristics impose on the reranking and hybrid retrieval link
The data characteristics of this category impose three core constraints on the reranking and hybrid retrieval link. First, data update frequency is low. Retrieval must prioritize the latest ledger and inventory data to avoid recalling outdated asset information. Second, most documents are structured entries. Hybrid retrieval needs to assign different weights to fields such as asset ownership, location, and user to ensure core business information is displayed first. Third, data sources are scattered. Field alignment across multiple systems is required. The reranking link must perform precise matching for asset-related keywords, avoid interference from irrelevant general IT documents, and align with core business scenarios such as account-physical verification and user confirmation.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Retrieval data source scope | Only structured data from asset management systems, procurement systems, financial fixed assets, and terminal management platforms | Match the data sources of this category to avoid mixing in irrelevant business documents |
| Reranking model trigger threshold | 0.75–0.85 | Target the semantic similarity of asset fields to filter low-match non-core asset entries |
| Single recall result upper limit | Top 20 entries | Adapt to scenarios with moderate asset inventory data volume to avoid result overload |
| Reranked result output upper limit | Top 5 entries | Focus on core asset information to meet the quick review needs of pre-sales Q&A and technical support |
| Retrieval field weight configuration | Set asset ID, user, and location weights to 1.2, and set all other field weights to 1 | Align with the core business concerns of this category, prioritize associating ownership and location information |
| Retrieval timeout threshold | 600 seconds | Adapt to retrieval delays from multi-system data integration to avoid request interruptions |
Three Common Configuration Errors
- Phenomenon: Reranked results return false after retrieval, or no valid output is generated from the reranking link. Cause: The association binding between the reranking model and retrieval results is not configured correctly, or the input format of the reranking model does not match the structured fields of asset data.
- Phenomenon: The reranking function works normally during knowledge base testing, but cannot call the reranking capability after connecting to a large model. Cause: The reranking switch is not enabled in the large model's retrieval configuration, or the deployment path of the reranking model is not synchronized to the large model call link.
- Phenomenon: Hybrid retrieval results only contain asset information from a single data source, and multi-system data is not integrated. Cause: The retrieval data source scope is configured incorrectly, and all compliant data sources are not covered, resulting in some asset ledgers not being recalled.
How to Confirm the Configuration is Complete
- Enter the retrieval configuration page of FastGPT v4.17.0, verify that the retrieval data source scope includes asset management systems, procurement systems, financial fixed assets, and terminal management platforms.
- Initiate a simulated asset query, check whether the recalled result fields include core information such as asset ID, user, and location.
- After enabling the reranking function, compare the sorting difference between the recalled results and the reranked results to confirm that the reranking logic takes effect.
- Check the large model call link, confirm that the reranking model configuration has been synchronized to the channel model's retrieval settings.
The values provided are common starting points and should be measured 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 2026-10-19.