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Updated the code to use the correct prompt broken down into smaller pieces

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Dominic
2025-02-20 14:28:02 -08:00
parent b71ad2d082
commit 68587ca4e9
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examples/phone-chatbot/README.md
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@@ -106,12 +106,12 @@ curl -X POST "http://localhost:7860/daily_start_bot" \
-d '{"dialoutNumber": "+18057145330", "detectVoicemail": true}'
```
### New! Using Gemini with Daily
### New! Using Gemini 2.0 Flash Lite with Daily
We have introduced a new example file that uses Gemini. You can find the code within bot_daily_gemini.py.
If you want to spin up a Gemini-based bot for this demo, instead of an OpenAI-based bot, call the same properties above but on the `daily_gemini_start_bot` endpoint instead.
We have introduced support for Google's Gemini 2.0 Flash Lite model in this example. This lightweight model offers faster response times and reduced costs while maintaining good conversational capabilities.
For example:
**Quick Start**
To use the Gemini-based bot instead of OpenAI:
```shell
curl -X POST "http://localhost:7860/daily_gemini_start_bot" \ py pipecat
@@ -119,7 +119,26 @@ curl -X POST "http://localhost:7860/daily_gemini_start_bot" \
-d '{"detectVoicemail": true}'
```
Any request body properties supported by `/daily_start_bot` (such as "detectVoicemail", "dialoutnumber", etc) can also be passed to `/daily_gemini_start_bot`. The only difference is that calling the Gemini endpoint will start a Gemini bot session.
All request body parameters supported by /daily_start_bot (such as detectVoicemail, dialoutNumber, etc.) are also compatible with /daily_gemini_start_bot.
This example uses context switching to help steer the bot in the right direction. As Flash Lite is a smaller model, getting it to consistently call functions was difficult for these longer prompts. Breaking the prompt
down into smaller pieces helped improve the accuracy of the bot.
**Implementation Details**
The implementation is available in bot_daily_gemini.py and features:
Staged prompting approach: Breaking down complex tasks into smaller, more focused prompts to improve the lightweight model's performance
Dynamic context switching: The bot can change its behavior in real-time based on what it detects (voicemail vs. human caller)
Function-based architecture: Uses function calling to trigger context switches and call termination
**Optimizations for Lightweight Models**
Working with Gemini 2.0 Flash Lite required some specific optimizations:
Simplified prompts: Each prompt focuses on a single task with clear instructions
Function-driven state changes: The model calls specific functions to switch between different conversation modes
Reduced context requirements: Each stage maintains only the context needed for its specific purpose
This approach significantly improves the consistency of function calling in this lightweight model, which was challenging with longer, more complex prompts.
### More information