What data for this category looks like
This category of data primarily comes from crowdsourcing platform backends, annotation quality statistical reports, annotator training assessment archives, and financial settlement details. Daily maintained documents include personnel access rules, training materials, performance calculation sheets, and settlement reconciliation lists. Data update cadence is daily for personnel qualification changes, and monthly for performance and settlement data. Document fields include annotator employee IDs, access qualification levels, single-annotation accuracy rates, monthly settlement amounts, and more. Data structures vary across sources: some are structured tables, others are unstructured text descriptions.
What constraints do these characteristics impose on the knowledge base retrieval and recall workflow
Mixed data structures from multiple sources require the retrieval system to support both structured and unstructured hybrid retrieval. This avoids recall failures caused by field mismatches. Daily updated personnel qualification data needs an incremental update mechanism. This ensures retrieval results reflect the latest personnel status in real time. Monthly updated performance and settlement data do not require frequent updates. A scheduled full synchronization can be configured for these datasets. Document types cover rules, archives, reports, and other formats. Targeted adjustments to retrieval modes are needed to ensure recall accuracy across different document types. Additionally, fields include sensitive content such as personnel qualifications, performance, and settlement information. Retrieval scope must be strictly controlled to avoid leaking unrelated data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | This category of documents includes structured fields such as qualifications and performance. A threshold that is too low will introduce unrelated annotator data. A threshold that is too high will miss matching personnel archive content |
limit | Top 10 entries | Knowledge base entries for crowdsourced annotator management are mostly personnel archives and rule documents. Too many single recall results will exceed the context window and reduce LLM response efficiency |
searchMode | Semantic Retrieval | This category requires matching unstructured text such as personnel qualification descriptions and performance rules. Semantic retrieval supports semantic matching needs for natural language queries |
usingReRank | Enabled | Redundancy exists across multi-source data. Re-ranking filters low-correlation results and improves retrieval accuracy |
datasetSearchUsingExtensionQuery | Enabled | This optimizes user queries semantically. It supports fuzzy query needs in annotator management scenarios, such as imprecise queries like "annotators with intermediate qualifications" |
collectionIds | Divided by business module, such as annotator qualification library, performance rule library, settlement process library | Data for this category comes from multiple independent systems. Separated database configuration enables precise recall of content from corresponding business modules, avoiding cross-library recall of unrelated data |
Three common misconfigurations
- Issue: Retrieval results return unrelated content from both annotator qualifications and settlement processes. Root cause: The
collectionIdsparameter is not configured correctly. Knowledge base collections are not divided by business module, leading to cross-library recall of documents from unrelated businesses. - Issue: After deploying FastGPT v4.17.0 using Docker Compose, clicking "New Knowledge Base" triggers an error "Failed to create directory". Root cause: The correct knowledge base storage volume is not mounted, or the dataset path in the configuration file does not match the actual mounted directory. This prevents the system from reading or writing knowledge base files.
- Issue: The LLM response includes annotator settlement rules not present in the knowledge base. Root cause: The
similaritythreshold is set too low, orusingReRankis not enabled. This leads to recall results that do not cover the core needs of the user query, or lack of configured strict context filtering rules.
How to confirm proper configuration
- Navigate to the knowledge base management page of FastGPT v4.17.0. Check if the configured
collectionIdscorrespond to exclusive knowledge base IDs for business modules such as annotator qualifications, performance rules, and settlement processes. - Submit a test query. Enter "annotators with intermediate qualifications". Verify that retrieval results only include relevant fragments from the annotator qualification library, and that the number of returned results does not exceed 10.
- View the system retrieval logs. Confirm that the
similarityscores of returned results fall within the 0.75–0.85 range. There should be no abnormal cross-business module recall records. - Check the storage volume mount status for the Docker Compose deployment. Confirm there are no log entries for new knowledge base creation errors, and that knowledge base files can be read and written normally.
The values listed here are common starting points. They should be evaluated 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.