What Data Looks Like for This Category
Daily documents for text annotation projects primarily consist of annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Data sources include corpus provided by clients, internal annotation platforms, specialized vocabularies, and quality inspection sampling results. Updates occur on a daily basis or per batch of annotation tasks. Data fields mostly contain text snippets, annotation tags, quality inspection scores, feedback problem descriptions, and similar items. The core focuses are semantic boundaries, entity recognition, and intent classification, differing from the visual feature processing logic of image annotation projects.
What Constraints These Characteristics Impose on Reranking and Hybrid Retrieval
Text annotation projects have high-frequency dynamically updated data, requiring hybrid retrieval to adapt to quickly refreshed samples and quality inspection results. Multiple document types have significant semantic differences, so reranking models must distinguish relevance weights across different documents. Retrieval needs are concentrated in core scenarios such as tag boundary explanations and difficult sample arbitration. High-match annotation specifications and quality inspection records must be recalled first. A single retrieval must cover information calls across the entire annotation workflow, so the retrieval upper limit must adapt to content requirements across multiple links.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the semantic matching accuracy requirements of text annotation projects, filters low-relevance non-annotation content |
limit | 1200–1500 entries | Covers document call volume across the entire annotation workflow, prevents insufficient results from failing to support tag explanations and arbitration |
searchMode | hybrid | Adapts to the hybrid retrieval logic for multiple document types, combines semantic and keyword matching to recall content of different types such as annotation specifications and sample packages |
usingReRank | Enabled | For documents with large semantic differences in annotation projects, the reranking model optimizes result relevance sorting and prioritizes recalling quality inspection reports and specification content |
| Retrieval Refresh Interval | 1 day / per batch of annotation tasks | Matches the daily data update rhythm of text annotation projects |
Three Common Misconfigurations
- Reranking model test errors occur. Troubleshooting shows that the access path for the private model was not configured in FastGPT v4.17.0, leading to failed model calls.
- When the reranking function is not enabled, retrieval results are only sorted by basic similarity. They cannot distinguish semantic differences between annotation specifications and irrelevant samples, leading to irrelevant content interfering with ticket processing.
- The hybrid retrieval configuration
limitis set to 1500 entries, but actual returned results are insufficient. The cause is that the retrieval library data for the annotation project was not updated synchronously, so the recall pool cannot cover all annotation documents.
How to Verify Proper Configuration
- Access the FastGPT v4.17.0 project backend, view the retrieval configuration panel, and confirm that
usingReRankis enabled. - Input a test question such as "How to define tag boundaries", check whether returned results prioritize displaying annotation specification documents in the sorting order.
- Adjust the
similaritythreshold, verify whether low-relevance non-annotation content is filtered out. - Check whether the number of retrieval results falls within the 1200–1500 entry range configured by
limit.
The values given 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.