Python Websocket App - Data Collection Failure [closed]

I am developing a Python 3.11 application that connects to Ultravox (a Llama 3-based application) via WebSocket. The application integrates with Twilio to facilitate outbound calls, and it hosts the prompt that is passed to the Ultravox agent. https://docs.ultravox.ai/essentials/sdk https://docs.ultravox.ai/essentials/tools The Problem: The Ultravox agent is not consistently collecting all 20 required data points before ending the call. While it generally understands when it has gathered enough information (based on the prompt definition), it often fails to collect required data points before ending the call. In my use case, I am collecting healthcare information. The calls usually give information about plan coverage first then move onto plan frequencies second. What I’ve Tried: Prompt engineering: I have refined the prompt to explicitly define the 20 data points the agent must collect, but this hasn’t fully resolved the issue. (btw if anyone has tips on Llama 3 prompting that would be amazing) I have also broken up the data that needs to be collected into two categories. Plan Coverage has 9 data points and Plan Frequencies (how often something resets) has 9 data points. My Question: Can I enforce this requirement through Python code within my application? Would an SDK be a better approach? If so, what tools or libraries should I use to build one? I was considering using Pydantic Field Validators but wasn't sure how my code would reach the Ultravox Agent.

Mar 2, 2025 - 21:02

I am developing a Python 3.11 application that connects to Ultravox (a Llama 3-based application) via WebSocket. The application integrates with Twilio to facilitate outbound calls, and it hosts the prompt that is passed to the Ultravox agent.

https://docs.ultravox.ai/essentials/sdk

https://docs.ultravox.ai/essentials/tools

The Problem:
The Ultravox agent is not consistently collecting all 20 required data points before ending the call. While it generally understands when it has gathered enough information (based on the prompt definition), it often fails to collect required data points before ending the call. In my use case, I am collecting healthcare information. The calls usually give information about plan coverage first then move onto plan frequencies second.

What I’ve Tried:

Prompt engineering: I have refined the prompt to explicitly define the 20 data points the agent must collect, but this hasn’t fully resolved the issue.
- (btw if anyone has tips on Llama 3 prompting that would be amazing)
I have also broken up the data that needs to be collected into two categories. Plan Coverage has 9 data points and Plan Frequencies (how often something resets) has 9 data points.

My Question:

Can I enforce this requirement through Python code within my application?
Would an SDK be a better approach? If so, what tools or libraries should I use to build one?
I was considering using Pydantic Field Validators but wasn't sure how my code would reach the Ultravox Agent.