What the Data for This Category Looks Like
Satellite communications marketing content originates from the marketing asset libraries of satellite communication operators. It includes bandwidth package descriptions, industry solution documents, terminal device brochures, and offline event script templates. Update schedules are irregular, triggered by new package launches and industry policy adjustments. Some general script libraries sync on a monthly basis. The document structure of each asset includes: asset name, applicable frequency band, bandwidth parameters (unit: Mbps), target customer group, effective period, asset type (text/image/video script), and release channel. Some assets include technical parameter tables, with fields that mix structured text and unstructured script content.
What Constraints These Characteristics Impose on Vector Models and Indexing
Satellite communications marketing assets include technical parameters with units, structured package descriptions, and unstructured script templates. Vector models must support semantic matching for both technical terminology and colloquial scripts, and retain unit information. Otherwise, parameter units will reduce semantic matching accuracy. Asset update schedules are irregular, so incremental indexing is required to avoid the time cost of full index reconstruction. Some assets have large length differences, so chunk splitting must preserve full context to avoid semantic breaks in technical parameters after splitting. Additionally, some assets include professional terms such as frequency band and bandwidth. An embedding model adapted for technical text must be selected to ensure accurate vector representations of professional terms.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunk length | 800–1200 characters | Satellite communications marketing assets include technical parameter descriptions and scripts. Chunks of 800–1200 characters preserve the context of technical parameters and avoid semantic breaks after splitting |
embedding model | bce-embedding-v1 | Delivers effective semantic matching for technical terms in the satellite communications field, with verified compatibility from community testing |
recall count | 10–15 entries | Covers most relevant marketing assets, avoids retrieving excessive irrelevant content, and ensures retrieval efficiency |
similarity threshold | 0.78–0.82 | Filters low-relevance marketing assets and ensures the semantic matching accuracy of retrieval results |
incremental indexing toggle | Enabled | Adapts to the irregular update schedule of marketing assets and reduces the time cost of full indexing |
pushdata API call mode | chunk mode | Meets the long-text processing requirements of satellite communications marketing assets and preserves contextual semantics |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are influenced by material format, data volume, and business rules. Each scenario requires individual analysis, and testing on one’s own samples is recommended before finalizing settings.
Three Common Misconfigurations
- Symptom: After uploading assets using the pushdata API in chunk mode, the interface continuously displays the "Indexing" status. Cause: The incremental indexing toggle is not enabled, and the chunk length setting exceeds the system default threshold, leading to excessively long processing time for a single chunk.
- Symptom: Embedding fails when calling the bce-embedding-v1 model. Cause: The model is not specified in the FastGPT
embedding modelconfiguration, or the access key for the corresponding model is not bound. - Symptom: The API returns
error: { message: 'This token does not have permission to use the model: text-embedding-3-large }. Cause: The used key does not have authorization for the corresponding model, or the configured model name does not match the actually available model.
How to Verify Proper Configuration
- Upload a test satellite communications bandwidth package description, and confirm that the embedded vector dimension matches the standard dimension of the selected
embedding model. - Initiate a semantic retrieval request, and confirm that the number of retrieval results matches the configured
recall countvalue. - Upload an updated marketing asset, and verify that when the
incremental indexing toggleis enabled, the indexing task completes automatically without requiring manual full index reconstruction. - Call the embedding API, and check that the returned embedding result has no missing fields and includes semantic vectors for technical parameters.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.