What the data for this category looks like
The data for the speech annotation and transcription category mainly comes from audio files, annotation platform backends, proprietary vocabularies, and spot-check samples. Daily associated documents include transcription specifications, audio delivery lists, quality inspection reports, and speaker annotation forms. Data updates occur daily or per annotation batch. Fields include audio duration, annotator identifiers, quality inspection judgment results, speaker tags, etc. There are no unified fixed units; values adjust based on the requirements of specific annotation tasks.
What constraints do these characteristics impose on the "re-ranking and hybrid retrieval" link
Data for this category mixes unstructured audio transcription text and structured annotation metadata. Hybrid retrieval must adapt to the recall logic of both types of data. Data updates daily or per batch, so the retrieval pipeline must support incremental sync or full refresh configurations. Fields include speaker tags, quality inspection results, proprietary vocabulary entries, etc. Retrieval weights must be set for different fields. Additionally, noise segment judgment and speaker differentiation are core time-consuming steps. The re-ranking link must prioritize recalling results that contain proprietary terms or clear speaker tags, to avoid invalid content interfering with retrieval effectiveness.
How to set the configurations
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
searchMode | Hybrid retrieval mode | Adapts to scenarios where the category contains both transcription text and structured annotation metadata for retrieval, balancing semantic matching and keyword recall |
similarity | 0.72–0.78 | Calibrated threshold based on actual testing for matching accuracy of proprietary terms and speaker tags, filters low-correlation noise segments |
limit | Top 10 entries | Controls the number of initial recall results, adapts to the result volume of single annotation task retrieval, avoids context overload |
usingReRank | Enabled | Addresses differences between noise segments in transcription text and structured metadata, optimizes result ranking logic via re-ranking |
| Retrieval filtering rules | Filter by speaker tags and proprietary vocabularies | Filters annotation results from non-target speakers and irrelevant proprietary terms, aligns with core business requirements of the category |
Three common mistakes
- Issue: A large number of irrelevant noisy audio segments appear in hybrid retrieval results, and filtering configurations do not take effect. Cause: Exclusive filtering rules are not set for speaker tags and proprietary vocabularies, and only basic similarity threshold filtering is relied on.
- Issue: When
usingReRankis set to false, the re-ranking function does not trigger, and retrieval result ranking is chaotic. Cause: The retrieval pipeline is not connected to the re-ranking plugin, or the re-ranking trigger condition is not bound to the retrieval link. - Issue: Semantic retrieval mode returns more accurate results than hybrid retrieval, and users mistakenly disable hybrid retrieval configurations. Cause: Hybrid retrieval weight configurations are not optimized for proprietary terms and structured metadata, leading to poor initial recall effectiveness.
How to confirm configurations are properly set
- Enter a test question containing proprietary terms or speaker tags on the FastGPT retrieval test page, check whether the initial recall results include target documents.
- After enabling
usingReRank, compare result rankings before and after enabling, confirm whether highly relevant results appear first. - Check retrieval logs, confirm that
searchModeis set to hybrid retrieval mode and thesimilaritythreshold matches the configured value. - For multi-variable workflows, trigger a test request, confirm that specified variables are correctly associated with the retrieval scope of the speech annotation knowledge base.
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.