What Data for This Category Looks Like
Data for this category typically comes from contracts, work breakdown structure (WBS), client schedules, and project management systems. Four core types of daily generated documents include project plans, milestone lists, weekly reports, and risk issue lists. Fields cover task ID, responsible person, duration, risk level, scheduling nodes, and more. Units are mostly days and risk percentage. Update frequency follows a weekly cycle, or is triggered by project milestone nodes. The volume of documents updated in a single operation fluctuates with project scale.
Constraints on Vector Models and Indexes
Data for this category comes from multiple systems and manually created documents. Format differences across sources increase preprocessing costs for vector conversion. Fixed document structures and clear field rules require retaining complete core business fields such as task ID, responsible person, and duration during chunking. This prevents semantic splitting that breaks business relationships. Weekly or milestone-based update rhythms require index configurations to adapt to non-frequent incremental updates. This reduces resource consumption from full index rebuilds. Data volume varies with project scale. Vector index capacity reservations must match business elasticity requirements. The system must also support filtering retrieval results by task dimension.
How to Configure Settings
This configuration is based on interface parameters in FastGPT v4.17.0.
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxToken | 8000–12000 | Matches the average length of documents such as project plans and weekly reports, avoids truncating core business fields |
defaultToken | 1500 | Adapts to semantic vector generation requirements for single task-related documents |
| Chunk Length | 800–1200 characters | Retains complete semantics of core fields including tasks, responsible persons, and duration, avoids splitting business relationships across chunks |
| Overlap Length | 100–200 characters | Ensures cross-chunk task scheduling and risk association information is not fragmented |
| Similarity Threshold | Calibrated via actual testing | Adapts to semantic judgment standards for task association and risk matching in project implementation scenarios |
| Single Search Limit | Top 10 entries | Controls the volume of returned retrieval results, avoids excessive non-core task information interfering with Q&A |
Three Common Misconfigurations
- Phenomenon: Vector retrieval returns universally high similarity scores and cannot distinguish irrelevant task entries. Cause: Chunking strategy is not adjusted for the field characteristics of project documents, leading to over-weighted semantic vectors of short fields such as responsible person and risk level.
- Phenomenon: Index status shows normal but retrieval returns no matching results. Cause: Incremental update trigger rules are not configured, so weekly updated documents are not synchronized to the vector index library.
- Phenomenon: Empty results or timeouts are returned after calling the interface. Cause: The
defaultTokenvalue is not adjusted based on project document volume, leading to exceeding the model's capacity limit during vector generation.
How to Confirm Proper Configuration
- Upload a complete project plan document, check if chunked text retains complete semantics of core fields including task ID, responsible person, and duration.
- Manually trigger an incremental update, verify that the number of new documents in the index library matches the actual number of updated documents.
- Enter a query statement targeting a specific task, check if the sorting logic and business relevance of returned results meet expectations.
- Call the interface to test the vector generation function, confirm that the returned token consumption value matches the configured
maxTokenvalue.
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.