What the data for this category looks like
Core data for text annotation projects includes four types: annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Data sources cover customer-provided corpus, annotation results from internal annotation platforms, custom vocabularies, and deviation records from quality inspection sampling. Data updates occur daily or when triggered by individual batch annotation tasks.
Annotation specifications are organized by chapters and clauses, including tag definitions and boundary explanations. Sample packages store text fragments mapped to their corresponding tags. Quality inspection reports include deviation items and rectification requirements for sampled samples. Problem feedback forms record difficult scenarios encountered during annotation. Fields include sample text, tag ID, quality inspection score, rectification opinions, and more. There are no unified mandatory units, with adjustments made flexibly based on document type.
Constraints on retrieval and recall from these data characteristics
The data characteristics of text annotation projects impose clear constraints on the retrieval and recall link. Multi-source heterogeneous data sources require the retrieval system to support indexing and matching across document formats, adapting to long-text annotation specifications, structured mappings in sample packages, and short-text scenarios in quality inspection reports. The high-frequency update feature requires the retrieval system to support incremental updates, avoiding delays caused by full index rebuilding. The core business requirements of tag boundary explanations and difficult sample arbitration demand higher semantic matching accuracy for recall results, filtering irrelevant documents to reduce the verification workload for annotators. The retrieval module in FastGPT v4.17.0 must be adjusted for these characteristics to ensure recall results directly support annotation, quality inspection, and arbitration workflows.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the semantic boundaries of annotation specifications, avoiding low-relevance results that interfere with tag determination |
limit | Top 10 entries | Adapts to the upper limit of retrieval volume for single-batch annotation tasks, preventing context overload that reduces annotation efficiency |
usingReRank | Enabled | Optimizes ranking accuracy for recall results, supporting long-text annotation specifications and difficult samples |
datasetSearchUsingExtensionQuery | Enabled | Expands synonymous expressions of annotation terminology, covering custom vocabularies and exclusive vocabulary from the annotation platform |
collectionIds | Fill in the knowledge base collection IDs bound to the project | Limits the retrieval scope to only document data exclusive to the current annotation project |
searchMode | embedding | Adapts to the mixed document types of annotation projects, supporting semantic matching for both short-text samples and long-text specifications |
Three common misconfigurations
- Phenomenon: A large number of non-annotation-related documents are returned in retrieval results. Cause: The
collectionIdsparameter is not configured, and the retrieval scope covers the entire platform knowledge base, leading to irrelevant content interfering with annotation judgment. - Phenomenon: After batch importing sample packages in Excel format, indexing and retrieval fail. Cause: The
datasetSearchUsingExtensionQueryparameter is not enabled, making it impossible to adapt to text extraction and mapping logic for table fields. - Phenomenon: Empty results are returned when calling the API interface to connect to the knowledge base. Cause: The
collectionIdsparameter is not filled in correctly, orsearchModeis not set to the mode adapted for annotation documents, resulting in a mismatch between the retrieval scope and target data.
How to confirm successful configuration
- A single fragment of an annotation specification can be uploaded as a retrieval query, a retrieval test performed, and returned results checked to confirm they only include annotation-related documents within the current project.
- Retrieval logs can be viewed to confirm the
usingReRankparameter is enabled, and that returned results are sorted according to the priority requirements of tag explanations. - A batch of annotation sample packages can be imported, the indexing progress page checked, and incremental update tasks confirmed to automatically trigger at a daily or per-batch frequency.
- The API interface can be called with synonymous expressions of annotation terminology, returned results checked to confirm they include explanatory documents for the corresponding tags, and the
datasetSearchUsingExtensionQueryextended query function verified as active.
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.