This document is written for FastGPT v4.17.0.
What Data Looks Like for This Category
Data for this category typically comes from contracts, work breakdown structures (WBS), client schedules, and project management systems. Daily documents include project plans, milestone lists, weekly reports, and risk issue lists. Data updates run on a weekly cycle or are triggered by individual milestone nodes. Document structures use fields grouped by task nodes, responsible persons, time nodes, and risk levels. No uniform fixed units of measurement apply. Some fields require association with project numbers and client information.
Constraints These Characteristics Impose on Reranking and Hybrid Retrieval Workflows
Data for this category is scattered across multiple systems. Hybrid retrieval must support recall logic for multi-source heterogeneous data sources. Documents are structured by task nodes, responsible persons, and time nodes. The reranking stage must prioritize matching task keywords and responsible person fields to raise the sorting weight of relevant results. Data updates run weekly or are triggered by milestone nodes. Real-time incremental synchronization is not required, but regular full index refreshes must be triggered to maintain data timeliness. Cross-party schedule coordination and risk issue tracking are core time-consuming processes. Retrieval must quickly locate documents related to schedules and risks. The reranking model must therefore adapt to semantic matching of long-text task descriptions and risk level fields.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Reranking Toggle | Enabled | Most documents for this category are long-text task descriptions. Reranking filters semantically irrelevant recall results |
| Similarity Threshold | 0.72–0.85 | Adapts to the semantic association strength of project documents, filters non-core task documents with low matching degrees |
| Single Search Limit | Top 10 entries | Balances retrieval efficiency and recall coverage, matches the single retrieval needs of cross-party schedule coordination |
| Retrieval Mode | Hybrid Retrieval | Combines keyword and semantic retrieval, adapts to the multi-source heterogeneous project document data structure |
| Reranking Batch Size | 2–4 entries per run | Controls video memory usage, avoids video memory overflow when processing long-text project documents |
Three Common Configuration Mistakes
- Symptom: Retrieval response times out, and the interface displays a request timeout status code. Cause: The reranking batch size is not limited. Processing too many long-text project documents in a single run causes excessive video memory usage, triggering the system timeout mechanism.
- Symptom: Irrelevant task documents account for too high a proportion in retrieval results. Cause: The reranking function is not enabled. Only basic semantic retrieval is used to recall non-relevant entries, and content with low semantic matching degrees is not filtered.
- Symptom: Retrieval results are not sorted by task priority. Cause: The reranking model is not configured to adapt to the semantic weight of task node and responsible person fields. The sorting logic only relies on basic similarity, and does not combine the core field characteristics of category documents.
How to Verify Successful Configuration
- Access the FastGPT knowledge base configuration page, and confirm the reranking toggle is enabled.
- Submit a test query, and verify the number of returned retrieval results matches the single search limit configuration.
- View the system monitoring panel, and confirm video memory usage during the reranking stage does not exceed the preset threshold.
- Manually adjust the similarity threshold, and verify the matching degree of retrieval results changes reasonably with the threshold adjustment.
The values provided 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.