What Data Looks Like for This Category
The data sources for this category are financial systems, project management systems, contract ledgers, and business department requirements. Daily documents include annual budget sheets, project proposal documents, project portfolio dashboards, and investment review reports. Most are structured tables paired with a small amount of unstructured explanatory text. Fields cover budget amount, project cycle, project initiation priority, return on investment, and other items, with some fields having clear units. Data update frequency is monthly, quarterly, or annually. Document formats from different sources have minor differences.
Constraints Imposed on Reranking and Hybrid Retrieval
The data characteristics of this category impose multiple constraints on the reranking and hybrid retrieval process. First, data sources are scattered and formats vary. Hybrid retrieval must match both structured fields and unstructured text semantics to avoid retrieval deviations caused by inconsistent field formats. Second, update frequency is low. The vector database synchronization cycle should not be too short, requiring a balance between data freshness and system resource usage. In addition, operations duty tickets and internal Q&A mostly focus on core needs such as budget adjustments and project priorities. Retrieval results must prioritize documents that match key fields. The reranking model must adjust weight allocation for structured fields to improve the recall priority of core information.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | Documents in this category contain both structured and unstructured content. Reranking filters low-correlation results and improves retrieval accuracy |
limit | Top 10 entries | Operations duty and ticket issues usually focus on a small number of core projects or budget items. Too many results increase screening costs |
similarity | 0.72–0.85 | Fields in documents of this category are clearly defined. This threshold matches the matching accuracy of structured fields and avoids mismatching low-correlation data |
searchMode | Hybrid Retrieval | Data in this category includes both structured fields and unstructured text descriptions. Hybrid retrieval balances semantic and field matching |
Three Common Configuration Mistakes
- Issue: An "incompatible model" error is returned when calling the gte-rerank-v2 or Qwen3-Reranker-8B model, or the reranking result is false. Cause: The API key and correct request endpoint for the corresponding reranking model have not been configured in FastGPT, or the model input format does not match the structured fields of the category documents.
- Issue: The docker-deployed reranker service cannot be called by FastGPT, and a request failure prompt is returned. Cause: The internal port of the docker container has not been correctly mapped to the host machine port, or the custom request address filled in FastGPT does not include the correct API path.
- Issue: Low-correlation non-core documents account for too high a proportion in retrieval results, or key budget project documents are not recalled. Cause: The
similaritythreshold orlimitparameter has not been set correctly, resulting in a threshold that is too low or too many recalled entries.
How to Confirm Successful Configuration
- Enter the knowledge base configuration page of FastGPT v4.17.0, confirm that
usingReRankis enabled andsearchModeis set to Hybrid Retrieval. - Submit a test ticket, such as "2024 IT Project Budget Priority Adjustment", and check whether the retrieval results prioritize returning the key field content of the corresponding annual budget sheet.
- Check the reranking model call logs to confirm that the returned sorting results meet expectations, with no obvious low-correlation results mixed in.
- Verify that the custom reranker request address can be accessed normally, with no timeout or 404 errors.
The values provided here are common starting points and 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.