What the data for this use case looks like
The internal office data for basic telecommunications operations mainly comes from operation and maintenance monitoring systems, billing and settlement platforms, customer service ticket systems, and base station status collection terminals. Data update cycles cover real-time (base station operating parameters), hourly (customer service ticket circulation), and daily (monthly billing reports). Documents include two categories: structured and unstructured. Structured fields include ticket number, signal strength (unit: dBm), traffic consumption (unit: GB), and user number. Unstructured content includes operation and maintenance fault descriptions and customer service communication records.
What constraints these characteristics impose on multi-turn dialogue and prompt words
The high-frequency updates of real-time base station parameters and customer service tickets require that multi-turn dialogue contexts filter expired data and only retain valid content from the last 24 hours. The diversity of structured fields requires prompt words to clearly specify field matching rules to avoid confusing the units of signal strength and traffic consumption. The high proportion of long texts in unstructured fault descriptions will prolong single-turn dialogue processing time, so the total character count of the context for a single dialogue must be limited. Session association must be bound to a unique identifier, otherwise ticket follow-up across dialogue turns cannot be implemented.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context Length | 800–1200 characters | Matches the average length of telecommunications operation and maintenance documents and tickets, avoids exceeding model processing limits |
| Session Identification Configuration | Enable chatId binding | Retains historical ticket numbers and user identifiers, supports continuous follow-up of the same service request |
| Prompt Word Variable Settings | Bind variables field | Dynamically passes real-time parameters such as current ticket number and base station number, adapts to multi-turn queries |
| Dialogue History Management | Retain the most recent 3 turns via the messages field | Controls total context length, reduces model inference time |
| Context Refresh Frequency | Synchronize every 5 minutes | Adapts to the update cycle of real-time base station data, ensures the latest parameters are used in dialogues |
| Single-turn Output Limit | No more than 500 characters | Adapts to the concise interaction requirements of internal office scenarios, avoids information overload |
Three Common Misconfigurations
- Phenomenon: Excessively long dialogue response time, with loading delay prompts appearing on the interface. Cause: The context length is not limited, and too many expired base station logs and ticket data are included, exceeding the model's processing capacity.
- Phenomenon: Unexpected end of JSON input error pops up during dialogue, which occurs frequently when using the ChatGLM2 model. Cause: No format verification is performed on the
messagesfield, and an unclosed dialogue history array is passed in, causing model interface parsing to fail. - Phenomenon: Historical ticket information cannot be automatically associated after initiating a new dialogue. Cause: The
chatIdbinding configuration is not enabled, and the session identifier is not persisted, causing each dialogue to be recognized as a new request.
How to Confirm Configurations Are Correct
- Initiate a multi-turn dialogue that includes a historical ticket number, check whether the dialogue interface automatically associates the ticket data corresponding to that ticket number.
- Initiate 3 consecutive parameter queries for the same base station, check whether the context retains the base station number information from the first two rounds.
- Observe the dialogue response time, adjust the context length threshold according to the actual business scenario to ensure the time meets the interaction requirements of internal office work.
- Simulate input of abnormally formatted dialogue history, check whether the Unexpected end of JSON input error is triggered, confirm that the format verification logic has taken effect.
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.