What this category's data looks like
Three main sources provide declaration materials and file review data for communication towers and infrastructure: daily survey logs in operation and maintenance management systems, project application documents from project declaration platforms, and archived files from approval departments. Two update frequency modes apply to this data. Project declaration data updates in real time alongside project progress. Operation and maintenance survey data syncs daily. Approval files archive in batches once approval finishes. Documents include both standardized fields and unstructured content. Standardized fields include project ID, tower longitude and latitude, tower height, and load threshold. Common units are meters and kilonewtons. Unstructured content includes on-site photo descriptions, approval opinion letters, and rectification requirement texts.
Constraints for vector models and indexing
Multi-source data requirements mandate metadata binding across systems during the indexing stage. This prevents same-project data from different sources from being incorrectly split. Differing update rhythms require configuring both incremental and full indexing modes. These modes adapt to real-time operation data and batched archived files respectively. Mixed standardized fields and unstructured text in documents requires vector models to support both semantic associations of structured parameters and contextual coherence of long descriptive passages. Specific unit fields require retaining unit information during chunking. This prevents loss of precise parameter semantics after embedding. File review additionally requires linking multiple historical datasets across cycles. Indexing must support grouped recall by project ID.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical length of single survey records and approval opinions in tower declaration materials, fully retains core parameters and context |
| Overlap Length | 100–150 characters | Prevents semantic breaks across chunks, ensures embedding semantic coherence for continuous parameters such as tower height and longitude and latitude |
defaultToken | 4096 token | Adapts to the default token limit of most open-source embedding models, meets embedding requirements for single-chunk data |
maxToken | 8192 token | Handles batched file query scenarios with multiple attachments, covers embedding requirements for long texts |
| Similarity Threshold | 0.75–0.85 | Filters standardized template text that appears repeatedly in declaration materials, accurately matches core review content |
| Single Search Limit | Top 10 results | Adapts to the scope of associated material recall for file review, covers common cross-cycle associated data needs |
Three common misconfigurations
- Symptom: After importing declaration materials, some structured parameter fields such as tower height and longitude and latitude are empty. Cause: Structured field binding configuration is not enabled, and chunk embedding is performed directly on the full document, leading to metadata loss.
- Symptom: Timeout errors occur when batch importing historical files. Cause: Incremental indexing strategy is not configured, and all historical data is scanned in full, exceeding system processing thresholds.
- Symptom: A large number of low-match generic approval template texts appear in search results. Cause: Similarity threshold is set too low, failing to filter redundant standardized content.
How to confirm configurations are properly set
- Upload a single tower survey report, check that the chunked text fully contains core parameters with no truncation or loss.
- Run a batch incremental indexing test, verify that the system only updates newly added file data and does not repeatedly process historical archived content.
- Initiate a simulated approval query, check that the number of returned associated documents matches the preset single search limit.
- View the embedding model’s token consumption logs, confirm that the
defaultTokenandmaxTokenconfigurations do not exceed the model’s supported limits.
The values listed above are common starting points. They should be evaluated against the reader’s own sample datasets.
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.