What This Category’s Data Looks Like
This category's data primarily comes from audio files, annotation platforms, proprietary term lists, and spot-check samples. Daily maintained documents include transcription specifications, audio inventories, quality inspection reports, and speaker annotation forms. Data update frequency is daily or per batch task cycle. Data structure includes fields such as audio duration, speaker ID, annotation status, and transcribed text segments. Audio content must first be converted to structured text before annotation and quality inspection work can begin. The most time-consuming processing steps focus on noise segment judgment, speaker differentiation, and proprietary term proofreading. Data processing errors may lead to recognition training deviations, transcription rework, or delivery delays.
Constraints Imposed on Knowledge Base Retrieval and Recall
This category's data characteristics impose multiple constraints on knowledge base retrieval and recall. First, data must be associated with audio metadata. Retrieval must match fields such as speaker ID and audio duration. Pure text keyword matching alone is not sufficient. Second, frequently updated business documents and spot-check samples require the retrieval pipeline to support incremental synchronization of the knowledge base. This avoids using expired transcription specifications or term lists. Third, the high time consumption of the proprietary term proofreading step requires retrieval to prioritize recalling content related to proper nouns and industry terms. This reduces repetitive manual proofreading work. Additionally, structured annotation data requires retrieval results to be groupable by task batch or quality inspection dimension. This fits the daily workflow of annotation teams.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
collectionIds | Bind corresponding knowledge base IDs by business scenario, such as ["trans_rule","quality_check"] | Adapt to the grouped display needs of knowledge bases for different business rules, expert interpretations, and avoid mixing results across knowledge bases |
limit | Top 10 entries | Control the number of results returned by a single retrieval. This prevents page load timeouts from too many recalled contents, and meets the quick access needs of annotation teams |
usingReRank | Enabled | Re-rank initially recalled results, prioritize displaying highly matched content related to proprietary term proofreading and speaker differentiation, and improve retrieval efficiency |
similarity | 0.75–0.85 | Filter low-match retrieval results, reduce invalid content related to noise segment judgment, and focus on core business needs |
searchMode | Field matching | Set exclusive matching rules for structured fields such as speaker ID and proprietary term list, adapting to the structured data characteristics of speech annotation |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand retrieval keywords, supplement synonyms related to transcription specifications and quality inspection standards, and cover more potentially relevant content |
This configuration is based on the parameter rules of FastGPT v4.17.0.
Three Common Mistakes
- Phenomenon: Retrieval results are not grouped and displayed by knowledge bases such as business rules or expert interpretations, and returned results are mixed. Cause: The
collectionIdsparameter is not used to bind exclusive knowledge base collection IDs for different business scenarios, resulting in unified recall of content from all knowledge bases. - Phenomenon: Retrieval tasks frequently time out after uploading a large transcription specification document. Cause: A reasonable
limitparameter is not set. Too many results are recalled in a single retrieval, exceeding system processing thresholds, without considering the batch processing characteristics of speech annotation documents. - Phenomenon: Retrieval results do not prioritize content related to proprietary terms, and manual proofreading workload is not reduced. Cause: The
usingReRankfunction is not enabled, or thesimilaritythreshold is set too low, resulting in low-match noise segment content being displayed first.
How to Verify Proper Configuration
- Log in to the FastGPT v4.17.0 management backend, check whether the
collectionIdsparameter correctly binds the knowledge base IDs of the corresponding business scenario, and ensure that retrieval results of different groups can be displayed independently. - Upload a standard transcription specification document, initiate a retrieval test, and verify whether the number of returned results matches the
limitparameter setting. Confirm that the timeout issue has been resolved. - Initiate a retrieval request containing proprietary terms, check whether the sorting of retrieval results prioritizes content related to proprietary terms, and confirm that the
usingReRankfunction is active. - Trigger an incremental knowledge base update operation, verify whether frequently updated spot-check samples can be synchronized to the retrieval pipeline in a timely manner, and ensure data timeliness.
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.