What Data Looks Like for This Category
Daily documents for this category include project plans, milestone lists, weekly reports, and risk issue lists. Data comes from contracts, work breakdown structures (WBS), client schedules, and project management systems. Data is updated weekly or at each milestone node. Most documents contain fields such as task ID, responsible person, deadline, progress status, and risk level. Some content uses structured table format. Other parts are cross-party scheduling coordination records described in natural language.
What Constraints Do These Characteristics Impose on Reranking and Hybrid Retrieval
Project implementation plan management data includes both structured table and natural language text formats. It has fixed update frequencies. Therefore, reranking and hybrid retrieval must adapt to both text formats. It must also ensure the timeliness of search results after data updates. Core business revolves around task decomposition and cross-party scheduling coordination. Retrieval must accurately match core fields such as tasks, responsible persons, and schedules. This avoids retrieving irrelevant historical weekly reports or cross-project documents. Risk issue list content must be retrieved quickly. The retrieval pipeline must balance retrieval speed and accuracy. It must not add excessive unnecessary time consumption due to the reranking step.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
usingReRank | Enabled | For business documents such as project plans and risk issue lists, reranking filters low-relevance scheduling and task descriptions. This improves the accuracy of customer technical support |
similarity | 0.72–0.88 | Matches relatively clear text such as project tasks and risk levels. This avoids retrieving irrelevant cross-project historical documents |
limit | Top 8 entries | Controls the number of tasks and risk items retrieved in a single call. This adapts to the daily information viewing scale of project managers |
searchMode | Hybrid retrieval | Combines vector retrieval and keyword retrieval. This adapts to the hierarchical structure of WBS and keyword matching requirements for contract terms |
| Rerank model recall upper limit | Top 15 entries | Controls the input data volume during the reranking stage. This avoids computing resource overload |
Three Common Mistakes
- After offline deployment of FastGPT v4.17.0, clicking the rerank model test button displays a "request error" prompt. The cause is failure to correctly mount the rerank model offline package to the container’s specified directory, or failure to correctly configure the absolute path of the local model in the configuration file.
- Enabling reranking slows retrieval speed. The cause is that the number of items retrieved in a single call is too high, and the rerank model is not adapted to the short text characteristics of project implementation plans. This increases time consumption for rerank calculations per data entry.
- The proportion of irrelevant content in search results is too high. The cause is a too-low
similarityparameter setting, which retrieves a large number of irrelevant weekly reports or cross-project scheduling records.
How to Confirm Configurations Are Properly Set
- Access the retrieval configuration page of the FastGPT knowledge base. Confirm the
usingReRankswitch is enabled. - Submit a targeted test query, such as "query the current project's risk issue list". Check that returned results include relevant content from project plans and risk issue lists, and that sorting follows business priority.
- Click the test button for the rerank model. Verify the model request returns no errors and produces normal sorting results.
- Check retrieval time consumption. Confirm it meets the response requirements of the business scenario. Adjust the
limitparameter value as needed based on actual conditions.
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.