What this category of data looks like
Telecommunications service intelligent due diligence reports are core materials for financial, insurance, or wealth management institutions to conduct compliance and operational capability checks on telecommunications service suppliers. Data sources include operator operation backends, communication equipment manufacturer operation and maintenance logs, MIIT industry filing databases, public bidding announcements, and customer service ticket systems. Update frequencies vary: operator operation data updates daily, filing information syncs in real time, and bidding announcements update as projects are released. Document structures primarily use structured tables, supplemented by unstructured operation notes and troubleshooting records. Core fields include port occupancy rate, peak bandwidth, user complaint volume, contract validity period, and device online duration. Corresponding units are percentage, Mbps, count, days, and hours.
Constraints on multi-turn dialogue and prompt engineering
Multi-source heterogeneous data sources require multi-turn dialogue to gradually align field names and formats across different data sources, to avoid field confusion. Frequently updated data requires prompts to explicitly specify real-time pulling of the latest data, to ensure the timeliness of due diligence reports. Mixed-structure documents require multi-turn dialogue to first split structured fields and unstructured descriptions, then complete verification and integration separately. Diverse unit systems require prompts to include unified unit conversion rules, to avoid analysis deviations caused by unit mismatches. The context of multi-turn interactions must retain sufficient length to support cross-data-source associated analysis.
How to configure settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Telecommunications service due diligence reports include multi-dimensional structured data and long-text operation notes. Sufficient context must be retained for multi-turn dialogue to avoid loss of critical information |
recallTopK | Top 8–12 entries | Telecommunications service data covers multiple dimensions including ports, bandwidth, and tickets. A sufficient number of reference data must be recalled to support comprehensive analysis |
PROMPT_TEMPLATE | First verify the unit consistency of structured fields, then gradually verify data completeness through multi-turn questioning | Telecommunications due diligence data has diverse unit systems. Multi-turn dialogue must be used for gradual calibration to ensure accurate analysis results |
HTTP_REQUEST_TIMEOUT | 300 seconds | Some operator data source interfaces have high response delays. Sufficient time must be reserved for data pulling and parsing |
PARSE_FILE_MAX_SIZE | 500 MB | Telecommunications operation log documents are usually large in size. Full parsing must be supported to cover all due diligence information |
contextWindowAdjust | Calibrated based on actual testing | Context processing capabilities vary across different deployment environments. Parameters must be adjusted based on actual operational effects |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- An HTTP request node in the workflow is not triggered, and AI dialogue generation results are called directly. The cause is that the return result of the HTTP request is not bound to the input field of subsequent AI dialogue, causing the dialogue node to fail to obtain telecommunications service data retrieved via the network.
- Redundant pre-interaction content is output after multi-turn dialogue is completed. The cause is that non-essential historical dialogue is not filtered in the
maxContextconfiguration, or the prompt does not specify that only the final due diligence conclusion should be output. - ffmpeg cannot be called to process voice input in a container deployment environment. The cause is that system-level dependency packages are not pre-installed during container construction, and container root permissions are not obtained to complete tool installation.
How to confirm successful configuration
- Initiate a test query containing multi-dimensional telecommunications service fields, and verify whether the AI output automatically aligns unit formats across different data sources.
- Trigger the HTTP request node in the workflow, and check node logs to confirm that the request has been sent and valid data has been returned.
- Test continuous multi-turn queries, and confirm that no field confusion or information loss occurs in context association.
- Adjust the
maxContextparameter, compare dialogue coherence across different values, and confirm that it meets business analysis requirements.
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.