What data looks like for this category
Data for text annotation projects comes from customer-provided corpora, annotation samples generated by internal annotation platforms, custom vocabularies, and quality inspection sampling results. Daily maintained documents include annotation specification manuals, batch sample packages, quality inspection reports, and problem feedback forms. Data updates follow a daily cycle or run per annotation task. A single data entry typically includes original text, annotation tags, quality inspection markers, and sample identification fields. No fixed unified unit standard applies. Data is aggregated only by annotation task.
Constraints on vector models and indexing
Text annotation project data has high update frequency and uses batch tasks as units. Vector indexes must support incremental updates and batch import operations, to avoid the time cost of full index reconstruction. Data includes original text and semantic annotation tags. Vector models must adapt to semantic encoding for short to medium-length text, to ensure accurate vector representations of annotation boundaries and entities. Batch sample packages have relatively fixed field structures, but have differences across batches. Indexes must support storage grouped by task dimension, and must be compatible with quality inspection marker fields for subsequent low-quality data filtering.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single samples in text annotation projects, avoids semantic fragmentation from too-short chunks, and prevents exceeding model token limits from too-long chunks |
| Overlap Length | 100–200 characters | Preserves semantic connections between adjacent chunks, supports cross-chunk entity and boundary annotations in annotation tasks |
| Similarity Threshold | 0.75–0.85 | Filters low-match retrieval results, prevents irrelevant annotation samples from interfering with Q&A |
| Maximum Search Results per Query | Top 5 entries | Controls context length, fits the short context requirements of technical documentation and API Q&A for annotation projects |
| Vector Model | Selected from semantic encoding models supported by pgvector | Aligns with semantic matching needs of text annotation projects, matches the semantic characteristics of annotation tags |
defaultToken | 4096 | Matches the default token limit of mainstream open-source vector models, fits the overall length of text annotation data |
Three Common Misconfigurations
- After uploading a batch annotation sample package, the interface shows index completion but no matching results are returned during retrieval. The cause is failure to configure chunking rules according to the annotation task's field structure, leading to incorrect splitting of original text that cannot match retrieval keywords.
- After calling the file upload API, it is not possible to confirm whether indexing is complete. The cause is failure to listen for the task status field returned by the API, or failure to configure asynchronous callback notifications, so indexing progress cannot be obtained.
- After switching vector models, retrieval results have excessive deviation. The cause is failure to re-encode existing annotation samples into vectors, and retrieval still uses vector data from the old model.
How to Confirm Correct Configuration
- Upload a single typical annotation sample in FastGPT v4.17.0, check that chunked text fragments retain complete annotation boundaries and entity information.
- Initiate technical documentation Q&A related to annotation specifications or samples, verify that retrieved results match the semantic meaning of the query.
- View index task logs, confirm that chunk length, vector model parameters, and configuration items are consistent.
- Test batch upload of annotation sample packages, confirm that index task progress updates meet expectations.
The values provided are common starting points and should be evaluated using samples from the actual deployment scenario.
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.