What data for this category looks like
Data for this category typically comes from cloud billing systems, tag data, order systems, and resource monitoring platforms. Update frequencies are divided into daily and monthly cycles. Commonly produced documents include billing details, cost allocation tables, resource utilization reports, and optimization suggestion sheets. Core fields include resource unique identifiers, cost amounts, allocation ratios, timestamps, and tag key-value pairs. Units include yuan, hours, GB, and others. Data structure varies by business scenario. Detailed documents have a large number of entries, while report documents focus on aggregated statistics.
Constraints imposed by these characteristics on reranking and hybrid retrieval
Data sources cover multiple systems, leading to wide variations in field types and semantics. Hybrid retrieval must match multi-dimensional information including billing values, resource identifiers, and tag attributes. Without this, it is difficult to accurately return the target content requested by customers. Daily updated real-time billing data requires low latency for retrieval and reranking processes. Monthly aggregated reports require priority matching of time ranges and aggregation dimensions. Billing details contain a large number of entries. Without reranking filtering, retrieval results include large amounts of irrelevant content, which increases the verification workload for customer service teams. Tag attribution verification and abnormal cost troubleshooting are core time-consuming tasks. Reranking and hybrid retrieval must first associate tag information with abnormal cost information, to prevent interference from invalid results.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | Cloud billing data has many fields and wide semantic variations, requiring filtering of low-relevance retrieval results |
limit | Top 5-10 entries | Billing details contain many entries. Limiting the number of returned results reduces the verification burden for customer service teams |
similarity | 0.75-0.85 | Requires differentiation between similar resource IDs and cost entries to avoid mismatching irrelevant content |
searchMode | Hybrid retrieval | Must combine multi-source information from billing data, tag data, and order data to cover multi-dimensional customer inquiry needs |
| Reranking request timeout threshold | 30 seconds | Adapts to batch retrieval latency for cloud billing data, preventing request interruptions from affecting customer service response times |
Three common mistakes
- Deploying the bge-rerank-base image and receiving a 401 Unauthorized response. The cause is incorrect configuration of access permissions or keys for the reranking model, preventing FastGPT from properly calling the model interface.
- Retrieval results include large numbers of irrelevant billing entries when reranking is not enabled, making it impossible to filter target content. The cause is that basic retrieval only relies on vector similarity, without performing secondary semantic sorting and filtering on results.
- Testing fails after configuring a private reranking model in FastGPT 4.9. The cause is that older versions do not expose full configuration access for private reranking models. Upgrade to FastGPT v4.17.0 or later to complete integration.
How to confirm configurations are correct
- Upload a sample of cloud billing details, submit a test query containing a resource ID or cost keyword, and check if retrieval results prioritize target-relevant entries.
- Compare the number of retrieval results when
usingReRankis enabled and disabled, to confirm that the reranking function has filtered low-relevance content. - Check the reranking model call logs to confirm there are no 401 errors or timeout exceptions, and that interface calls are functioning normally.
- Verify that when
searchModeis set to hybrid retrieval, retrieval results include both tag association and cost amount matching content, covering multi-dimensional retrieval needs.
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.