What Data Looks Like for This Category
This category’s data primarily comes from customer business corpora, sample packages produced by internal annotation platforms, industry general vocabularies, and quality inspection sampling results. Daily maintained documents include annotation specification manuals, batch annotation sample packages, quality inspection reports, and problem feedback forms. Data updates follow a daily incremental update or batch synchronization schedule. Core fields include text content to be annotated, annotation tag sets, quality inspection markers, and sample unique identifiers. No additional complex format constraints apply.
What Constraints These Characteristics Impose on the Reranking and Hybrid Retrieval Link
The daily or batch update rhythm of customer corpora and sample packages requires the reranking and hybrid retrieval link to support fast incremental synchronization. This avoids recalling outdated annotation rules or old batch samples, which could reduce the accuracy of current annotation tasks. Semantic differences between annotation specifications and quality inspection reports require reranking to accurately match user questions with tag boundary explanations and associated content for difficult sample judgments. This ensures Q&A content aligns with the latest annotation requirements. The field attributes of sample IDs and quality inspection markers require hybrid retrieval to support filtering by quality inspection status and sample type. This ensures recalled content complies with the quality inspection standards of the current annotation batch, preventing unqualified samples from being used as references. Format differences across multiple data sources also require the reranking link to unify semantic representations. This avoids retrieval bias caused by inconsistent fields.
How to Set Configurations
These configurations apply to FastGPT v4.17.0 and above:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Reranking Toggle | Enabled | For the semantic matching needs of text annotation projects, enabling reranking improves recall accuracy for content related to tag boundaries and difficult sample judgments |
| Single Recall Limit | 8–12 items | Matches the sample volume range for single retrieval in annotation projects, avoiding excessive redundant content that interferes with annotation rule matching |
| Similarity Threshold | 0.72–0.85 | Calibrated through actual testing, this threshold adapts to semantic similarity matching for annotation specifications, sample descriptions, and similar text |
| Hybrid Retrieval Mode | Enabled | Combines keyword matching and semantic retrieval to cover multiple types of retrieval needs including annotation terminology and sample content |
| Reranking Recall Count | Top 5–8 items | Filters redundant reranking results to focus on the most relevant annotation rules and sample reference content |
Three Common Misconfigurations
- After the reranking model is configured, reranking results display as false during Q&A sessions. This occurs because the required server-side AI Proxy environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not correctly configured, and the AI Proxy root address is not configured as required (no /v1 or other interface suffix added). - The reranking model fails to start properly. Troubleshooting steps include checking whether the reranking model’s input parameters are optimized for short text and terminology-dense content specific to annotation projects.
- Retrieved annotation terminology has low semantic matching with the user’s original question. This occurs because the reranking link is not configured to use the original question as the matching query. The default setup uses rewritten multi-queries for retrieval instead.
How to Confirm Configurations Are Correct
- Verify that the server has correctly configured the
AIPROXY_API_ENDPOINT(AI Proxy service root address, do not add /v1 or other interface suffixes; this is not a single model address) andAIPROXY_API_TOKEN(AI Proxy administrator token, consistent with AI Proxy server configuration and not empty) environment variables, and that no /v1 or other interface suffixes are appended to the path. - Access the FastGPT test chat interface, enter a question related to annotation specifications, and check if the retrieval results include content corresponding to the annotation rules.
- Review the reranking model’s running logs to confirm the model loaded successfully and there are no inference errors.
- Adjust the similarity threshold and observe whether the relevance of retrieval results changes as expected.
The values listed here are common starting points. They should be evaluated against your own deployment 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.