What this category of data looks like
Data primarily comes from tower operation and maintenance management systems, daily inspection logs, fault work orders, and basic parameter archives. Update cadence falls into two categories: basic parameter archives are synchronized and updated quarterly, while inspection records and fault work orders are updated in real time when events are triggered. The structure of a single document includes fields such as tower unique ID, installation address, longitude and latitude coordinates, tower height, material type, inspection time, fault description, and processing result. Units include meters, decimal longitude and latitude, and year-month-day hour-minute.
What constraints these characteristics place on vector models and indexes
The difference in update cadence between static basic parameters and dynamic inspection and fault work orders requires indexes to support both incremental synchronization and full refresh modes. Documents contain multiple types of fields such as longitude and latitude coordinates, tower height, and fault description. Geographic coordinates must be separately mapped to spatial vectors and fused with text vectors to enable accurate retrieval. A single document has a mixed structure of short identifier fields and long text descriptions. Chunking must avoid splitting critical parameters and their associated context. High-frequency event data requires index query latency to meet the response needs of real-time consulting.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the mixed structure of short fields and long text in a single document, avoiding splitting critical parameters and their associated descriptions |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent chunks, reducing information loss from cross-chunk retrieval |
| Similarity Threshold | 0.72–0.80 | Filters low-relevance retrieval results, adapting to the field specificity of tower data |
| Single Search Limit | Top 10 entries | Balances query efficiency and recall coverage, adapting to high-frequency consulting scenarios |
defaultToken | Calibrated via actual testing | Adapts to the token consumption characteristics of operation and maintenance documents, avoiding request limit violations |
maxToken | 4096 | Matches the context limits of mainstream embedding models, ensuring complete embedding of long text chunks |
Three common configuration errors
- Enabling the Doubao-embedding-large index model, filling in a custom request address and API key, then clicking test directly results in an error. The cause is failure to verify the protocol format of the custom address and the permission scope of the API key, leading to failed request connectivity.
- A large number of irrelevant tower parameter entries appear in retrieval results. The cause is that the similarity threshold is set too low, failing to filter low-relevance matching results, or that critical parameters and their associated descriptions were split during chunking.
- Multiple vectors cannot be generated after importing table data. The cause is that the multi-vector support switch for table data is not enabled, or the chunking rules for table fields are not configured correctly.
How to confirm successful configuration
- Navigate to the knowledge base settings page, confirm that the chunk length and overlap length configuration values match the preset plan.
- Submit a test consultation, check whether the number of retrieval results and similarity scores fall within the set threshold range.
- Import a test document containing table data, confirm that the system automatically generates multiple vectors normally.
- After configuring the custom embedding model’s request address and API key, click the test button, confirm that normal embedding results are returned.
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 2026-09-28.