What data looks like for this category
Daily documents for text annotation projects include annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Data mainly comes from corpus provided by clients, content produced by internal annotation platforms, specialized vocabularies, and quality inspection sampling results. Data updates happen daily or after each batch of annotation tasks is completed. Core documents focus on semantic boundaries, entity recognition, and intent classification. Fields cover annotation tags, sample content, quality inspection scores, and problem records. All content is generated to support standardization and quality control across the full annotation workflow.
What constraints these characteristics impose on knowledge base retrieval and recall
Documents such as annotation specifications and sample packages have large structural differences. They include both structured quality inspection report fields and unstructured sample text. This requires retrieval tools to adapt to mixed-format content. Data updates happen in batches or daily, so incremental recall logic must support matching the latest annotation results. Queries about tag boundary explanations and difficult samples make up a high share of requests. Retrieval must first return annotation rules and historical arbitration cases with high semantic matching. Errors directly affect model training and delivery progress. Retrieval result accuracy must strictly align with the annotation team’s business logic. Irrelevant content must not be recalled to interfere with on-duty personnel’s decision-making.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Meets precise matching needs for semantic boundaries and entity recognition in text annotation scenarios, and avoids recalling low-relevance non-annotation documents |
limit | Top 6 entries | Adapts to fast decision-making for operations on-duty and work order Q&A, and controls the amount of information returned per query |
searchMode | Semantic priority | Prioritizes matching semantic associations between annotation specifications and sample content, without limiting matching to keywords |
usingReRank | Enabled | Runs secondary ranking on initial recall results, and raises the display priority of content related to difficult sample arbitration and consistency spot checks |
datasetSearchUsingExtensionQuery | Enabled | Automatically expands synonyms and industry terms related to annotations, covering edge terms in specialized vocabularies |
collectionIds | Specify the exclusive dataset ID of the current annotation project | Isolates documents from different annotation teams, and prevents retrieval results from mixing annotation data from unrelated projects |
Three common mistakes
- Phenomenon: Knowledge base retrieval works normally during debugging and preview, but fails to trigger after deployment to a custom page. Cause: The annotation project’s exclusive
collectionIdswas not correctly specified during page integration, so retrieval points to the wrong dataset. - Phenomenon: Images linked to annotation samples uploaded in the cloud knowledge base fail to load. Cause: Imported Markdown files or documents use image links from internal private networks. Public access permissions meeting platform requirements are not configured, so resources cannot be pulled normally during retrieval.
- Phenomenon: Retrieval results for work order questions about tag boundary explanations are incomplete. Cause:
datasetSearchUsingExtensionQuerywas not enabled, so synonyms for annotation professional terms cannot be automatically expanded. Some semantically similar documents are not recalled.
How to confirm the configuration is correct
- Log in to the FastGPT v4.17.0 backend, go to the exclusive dataset page for the annotation project, manually run a retrieval test, and verify that returned results include target documents such as annotation specifications and sample packages.
- Simulate an operations on-duty scenario, enter query statements containing "tag boundary" and "difficult sample", and check if recall results are sorted according to business priorities.
- Deploy a test page, send a retrieval request, and confirm that the number of returned results matches the configured
limitparameter. - Check that integration code correctly binds the current annotation project’s
collectionIdsto avoid mixing data from cross-project sources.
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.