This document is based on FastGPT v4.17.0 configuration.
What the data for this category looks like
Typically, data for this category comes from CRM systems, contract order databases, identity management platforms, and product backends. Daily processed documents include onboarding application forms, tenant configuration sheets, administrator account lists, and delivery confirmation forms. Fields include customer identifiers, tenant IDs, permission configuration items, onboarding statuses, associated contract numbers, and more. Data is updated either after each tenant initialization task or via daily batch synchronization. Individual data entries are small in size but have strong field correlation. Precise matching between customer identifiers and tenant configuration items is required.
What constraints do these characteristics impose on the re-ranking and hybrid retrieval link
Data for this category comes from multiple disconnected systems, with strong field correlation. Hybrid retrieval must support precise cross-data-source field matching. Individual data entries are small, but core correlation items such as customer identifiers and tenant IDs must be matched. The re-ranking step must prioritize aligning core fields to avoid interference from irrelevant configuration items in search results. Data updates fluctuate with ticket processing rhythms. Retrieval must adapt to near-real-time synchronization frequencies, while limiting the number of recalled entries to reduce redundant information. Additionally, search results must be linked to delivery confirmation and permission initialization workflows. Re-ranking weights must prioritize key items such as onboarding status and administrator account configurations. This helps operations staff quickly locate core information.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | This scenario requires filtering redundant tenant configuration items. Re-ranking improves the matching priority of core fields to meet the needs of rapid troubleshooting by operations staff |
limit | Top 6–10 entries | The volume of ticket data per batch is limited. Limiting the number of recalled entries reduces redundant information and supports quick review requirements |
similarity | 0.78–0.82 | Balances the precision of customer information matching and recall scope, avoiding omission of valid configuration items |
searchMode | Hybrid Retrieval | Adapts to retrieval needs across multiple data sources, integrating associated information from CRM and identity management systems |
| Re-ranking model request timeout | 30–60 seconds | Adapts to latency for local deployment or cross-cloud calls, preventing interruption of the retrieval process |
Three common errors
- Phenomenon: The re-ranking model returns empty results or no matching content, with a 200 status code but an empty result array. Cause: The re-ranking model request address is not configured correctly, or the input retrieval text does not match the model's input format. No valid results are generated.
- Phenomenon: The number of search results does not match the configured
limitparameter, with excessive redundant entries. Cause: Re-ranking filtering is not enabled, or thesimilaritythreshold is set too low. Irrelevant configuration items are recalled as a result. - Phenomenon: A locally deployed re-ranking model via docker cannot be called normally, with a connection timeout prompt on the interface. Cause: The custom request address does not point to the port exposed by the docker container, or network access permissions for the corresponding port are not enabled.
How to confirm successful configuration
- Review the number of recalled search results to confirm they match the configured
limitparameter. Adjust thresholds to align with business requirements. - Test cross-data-source retrieval to verify that customer information from CRM and tenant configuration items from the identity management system can be recalled simultaneously. Confirm that the
searchModeconfiguration takes effect. - Trigger the re-ranking process. Check that the sorting priority of core fields such as tenant ID and customer identifier in the results meets expectations. Adjust the
similarityparameter to optimize matching precision. - View the re-ranking model call logs to confirm that the request address and timeout settings match the deployment environment. Troubleshoot connection exception issues.
The values provided here 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.