What the data for this category looks like
Document data for communication towers and infrastructure primarily comes from daily operation and maintenance work orders, tower foundation inspection reports, lease contracts, maintenance logs, and compliance inspection documents. Update rhythms vary: work orders and real-time inspection data update when events are triggered, while annual inspection reports and lease contracts update on a scheduled basis. Each single document contains a unique identifier field (e.g., 6-digit tower number), physical attribute fields (e.g., tower height, inclination, units: meters, ‰), business fields (e.g., risk level, processing duration, units: hours), and associated responsible person information.
Constraints imposed on vector models and indexing workflows
The mixed multi-field data, differential update rhythms, and high proportion of long texts in this category create multiple constraints for vector model and indexing workflows. Both numerical attributes and text semantics must be included in the vectorization process to avoid association errors caused by relying solely on text matching. Incremental and full data must be processed separately to ensure real-time work orders can be retrieved without delay from full index updates. When splitting long documents, retain key identifiers across chunks to prevent critical information such as tower numbers or tower location addresses from being separated, which reduces retrieval accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average text length of single inspection reports and work orders, avoiding over-splitting or redundancy |
| Overlap Length | 50–80 characters | Retains key associated information such as tower numbers and risk levels across chunks, preventing semantic breaks |
| Similarity Threshold | 0.72–0.85 | Adapts to mixed semantic matching needs for numerical fields and text, balancing recall rate and accuracy |
| Single Search Limit | Top 6 results | Matches the volume of associated data required for a single document, avoiding excessive redundant results that interfere with generation |
| defaultToken | 4096 | Adapts to the vectorization context length of single long documents, ensuring complete semantic capture |
| maxToken | 8192 | Meets context requirements after splicing multiple associated documents, supporting information retrieval for complex document drafting |
Three Common Misconfigurations
- All search results have extremely low relevance: Unique identifiers such as tower numbers and tower location addresses are not bound as metadata to chunked text, causing vector matching to fail to associate with correct tower foundation business data.
- No data is ever present in the index: Incremental index trigger rules are not configured, and only full index updates are performed, so new operation and maintenance work orders and real-time inspection reports are not added to the index library.
- The last set of index chunks fails to complete vectorization: Associated numerical attributes such as inclination and tower height are not retained when splitting long documents, resulting in incomplete chunk semantics and the vector model being unable to generate valid embeddings.
How to verify proper configuration
- Upload a single tower foundation inspection report to FastGPT v4.17.0, view the chunk preview interface, and confirm that key information such as tower numbers and risk levels is not split across chunks.
- Submit a search request for a specific 6-digit tower number, verify that the recall results include all associated work orders, inspection reports, and lease contracts for that tower.
- Submit a long document vectorization task, check that the task log has no context truncation or timeout errors.
- Adjust the similarity threshold, verify that recall results under different thresholds fall within a reasonable range matching business needs.
The values provided are common starting points and should be evaluated against your own specific samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.