What this category of data looks like
The data for consulting and reply services for communication towers and infrastructure primarily comes from four data sources: tower operation and maintenance inspection logs, base station parameter manuals, service process specifications, and user repair records. Update frequencies vary by document type: inspection logs are updated weekly, base station parameter manuals are updated quarterly alongside device upgrades, and service process specifications are only updated when policies or processes are adjusted. Individual document fields include unique base station identifier, longitude and latitude coordinates, tower height, maintenance cycle, required materials for services, response time limit, and more. Units include meters, hours, units, square meters, and others.
What constraints these characteristics impose on knowledge base retrieval and recall
The presence of specialized fields and units requires retrieval to accurately match parameter semantics, to avoid generalized recall of irrelevant content. Differences in update rhythms across multiple data sources require incremental update tasks to use different synchronization cycles per document type. Context length limits in service consultation scenarios require strictly controlling the number of recalled results, to avoid exceeding the model's input token limit. Mixed documents combining structured parameters and unstructured process text require retrieval modes that balance semantic matching and field accuracy.
How to configure settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.72–0.85 | Semantic similarity interval for matching communication tower specialized terminology and structured parameters, filters low-relevance recalled results |
limit | Top 6–8 results | Adapts to the single-round context length limit of service consultation scenarios, avoids occupying excessive model input space |
searchMode | Semantic Priority | Balances specialized term matching and semantic association of process text, adapts to mixed document structures |
usingReRank | Enabled | Performs secondary ranking on initial recalled results, further filters non-consultation content in inspection records |
datasetSearchUsingExtensionQuery | Disabled | Strictly limits retrieval scope, prevents the model from generating extended content outside the knowledge base |
collectionIds | Unique ID corresponding to the communication tower service consultation knowledge base | Restricts retrieval to the specified business knowledge base only, avoids cross-library recall of irrelevant data |
Three common configuration mistakes
- Symptom: The number of single-round recalled results deviates from expected values, or occupies excessive context space. Cause: The
limitparameter value is not adjusted based on the single-round context upper limit of service consultation scenarios. - Symptom: The AI returns content outside the configured knowledge base. Cause: The
datasetSearchUsingExtensionQueryconfiguration item is not disabled, allowing the model to automatically expand the query scope. - Symptom: Retrieval results return non-standard JSON format or empty JSON. Cause: The
searchModeconfiguration does not match the structured document characteristics of the current knowledge base, or result formatting verification is not enabled.
How to verify successful configuration
- Submit a service consultation query about the base station maintenance process, and confirm that returned results only contain content from the communication tower knowledge base.
- View the retrieval logs in FastGPT v4.17.0, and confirm that the number of recalled results matches the range set by the
limitparameter. - Check the format of retrieval results, and confirm that returned content uses standard structured format with no abnormal fields or garbled characters.
- Adjust the value range of the
similarityparameter, and verify whether the relevance of recalled results aligns with business expectations.
The values provided 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.