Remove quickstart example from repo
This commit is contained in:
Mark Backman
51
.github/workflows/sync-quickstart.yaml
vendored
51
.github/workflows/sync-quickstart.yaml
vendored
@@ -1,51 +0,0 @@
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name: Sync Quickstart to pipecat-quickstart repo
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on:
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push:
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branches: [main]
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paths:
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- 'examples/quickstart/**'
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workflow_dispatch: # Manual trigger
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jobs:
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sync-quickstart:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout main repo
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Checkout quickstart repo
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uses: actions/checkout@v4
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with:
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repository: pipecat-ai/pipecat-quickstart
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token: ${{ secrets.QUICKSTART_SYNC_TOKEN }}
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path: quickstart-repo
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- name: Sync files (excluding uv.lock and README.md)
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run: |
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# Copy all files except uv.lock and README.md
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find examples/quickstart -type f \
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-not -name "README.md" \
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-not -name "uv.lock" \
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-exec cp {} quickstart-repo/ \;
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- name: Commit and push changes
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run: |
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cd quickstart-repo
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git config user.name "GitHub Action"
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git config user.email "action@github.com"
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git add .
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# Only commit if there are changes
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if ! git diff --staged --quiet; then
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git commit -m "Sync from pipecat main repo
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Updated files from examples/quickstart/
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Commit: ${{ github.sha }}
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"
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git push
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else
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echo "No changes to sync"
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fi
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@@ -8,7 +8,7 @@
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**Pipecat** is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
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> Want to dive right in? Try the [quickstart](https://docs.pipecat.ai/getting-started/quickstart).
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> Want to dive right in? Run `pipecat init quickstart` or follow the [quickstart guide](https://docs.pipecat.ai/getting-started/quickstart).
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## 🚀 What You Can Build
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@@ -6,7 +6,6 @@ This directory contains examples to help you learn how to build with Pipecat.
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New to Pipecat? Start here:
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- **[Quickstart](quickstart/)** - Get your first voice AI bot running in 5 minutes _(coming soon)_
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- **[Client/Server Web](client-server-web/)** - Learn to build web applications with Pipecat's client SDKs _(coming soon)_
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- **[Phone Bot with Twilio](phone-bot-twilio/)** - Connect your bot to a phone number _(coming soon)_
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@@ -1,16 +0,0 @@
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FROM dailyco/pipecat-base:latest
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# Enable bytecode compilation
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ENV UV_COMPILE_BYTECODE=1
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# Copy from the cache instead of linking since it's a mounted volume
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ENV UV_LINK_MODE=copy
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# Install the project's dependencies using the lockfile and settings
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RUN --mount=type=cache,target=/root/.cache/uv \
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--mount=type=bind,source=uv.lock,target=uv.lock \
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--mount=type=bind,source=pyproject.toml,target=pyproject.toml \
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uv sync --locked --no-install-project --no-dev
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# Copy the application code
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COPY ./bot.py bot.py
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@@ -1,150 +0,0 @@
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# Pipecat Quickstart
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Build and deploy your first voice AI bot in under 10 minutes. Develop locally, then scale to production on Pipecat Cloud.
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**Two steps**: [🏠 Local Development](#run-your-bot-locally) → [☁️ Production Deployment](#deploy-to-production)
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> 🎯 Quick start: Local bot in 5 minutes, production deployment in 5 more
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## Step 1: Local Development (5 min)
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### Prerequisites
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#### Environment
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- Python 3.10 or later
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- [uv](https://docs.astral.sh/uv/getting-started/installation/) package manager installed
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#### AI Service API keys
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You'll need API keys from three services:
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- [Deepgram](https://console.deepgram.com/signup) for Speech-to-Text
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- [OpenAI](https://auth.openai.com/create-account) for LLM inference
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- [Cartesia](https://play.cartesia.ai/sign-up) for Text-to-Speech
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> 💡 **Tip**: Sign up for all three now. You'll need them for both local and cloud deployment.
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### Setup
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Navigate to the quickstart directory and set up your environment.
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1. Install dependencies:
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```bash
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uv sync
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```
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2. Configure your API keys:
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Create a `.env` file:
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```bash
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cp env.example .env
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```
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Then, add your API keys:
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```ini
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DEEPGRAM_API_KEY=your_deepgram_api_key
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OPENAI_API_KEY=your_openai_api_key
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CARTESIA_API_KEY=your_cartesia_api_key
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```
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### Run your bot locally
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```bash
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uv run bot.py
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```
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**Open http://localhost:7860 in your browser** and click `Connect` to start talking to your bot.
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> 💡 First run note: The initial startup may take ~20 seconds as Pipecat downloads required models and imports.
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🎉 **Success!** Your bot is running locally. Now let's deploy it to production so others can use it.
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---
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## Step 2: Deploy to Production (5 min)
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Transform your local bot into a production-ready service. Pipecat Cloud handles scaling, monitoring, and global deployment.
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### Prerequisites
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1. [Sign up for Pipecat Cloud](https://pipecat.daily.co/sign-up).
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2. Install the Pipecat CLI:
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```bash
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uv tool install pipecat-ai-cli
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```
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> 💡 Tip: You can run the `pipecat` CLI using the `pc` alias.
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### Configure your deployment
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The `pcc-deploy.toml` file tells Pipecat Cloud how to run your bot.
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```ini
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agent_name = "quickstart"
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secret_set = "quickstart-secrets"
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[scaling]
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min_agents = 1
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```
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**Understanding the TOML file settings:**
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- `agent_name`: Your bot's name in Pipecat Cloud
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- `secret_set`: Where your API keys are stored securely
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- `min_agents`: Number of bot instances to keep ready (1 = instant start)
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### Log in to Pipecat Cloud
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To start using the CLI, authenticate to Pipecat Cloud:
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```bash
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pipecat cloud auth login
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```
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You'll be presented with a link and six-digit code that you can click to authenticate your client.
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### Configure secrets
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Upload your API keys to Pipecat Cloud's secure storage:
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```bash
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pipecat cloud secrets set quickstart-secrets --file .env
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```
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This creates a secret set called `quickstart-secrets` (matching your TOML file) and uploads all your API keys from `.env`.
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### Deploy
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Deploy to Pipecat Cloud:
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```bash
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pipecat cloud deploy
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```
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This pushes your project files to Pipecat Cloud where a docker image is built and deployed into production.
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### Connect to your agent
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1. Open your [Pipecat Cloud dashboard](https://pipecat.daily.co/)
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2. Select your `quickstart` agent → **Sandbox**
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3. Allow microphone access and click **Connect**
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---
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## What's Next?
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**🔧 Customize your bot**: Modify `bot.py` to change personality, add functions, or integrate with your data
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**📚 Learn more**: Check out [Pipecat's docs](https://docs.pipecat.ai/) for advanced features
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**💬 Get help**: Join [Pipecat's Discord](https://discord.gg/pipecat) to connect with the community
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### Troubleshooting
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- **Browser permissions**: Allow microphone access when prompted
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- **Connection issues**: Try a different browser or check VPN/firewall settings
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- **Audio issues**: Verify microphone and speakers are working and not muted
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@@ -1,145 +0,0 @@
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""Pipecat Quickstart Example.
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The example runs a simple voice AI bot that you can connect to using your
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browser and speak with it. You can also deploy this bot to Pipecat Cloud.
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Required AI services:
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- Deepgram (Speech-to-Text)
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- OpenAI (LLM)
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- Cartesia (Text-to-Speech)
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Run the bot using::
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uv run bot.py
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"""
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import os
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from dotenv import load_dotenv
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from loguru import logger
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print("🚀 Starting Pipecat bot...")
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print("⏳ Loading models and imports (20 seconds, first run only)\n")
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logger.info("Loading Silero VAD model...")
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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logger.info("✅ Silero VAD model loaded")
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from pipecat.frames.frames import LLMRunFrame
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logger.info("Loading pipeline components...")
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from pipecat.pipeline.pipeline import Pipeline
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from pipecat.pipeline.runner import PipelineRunner
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from pipecat.pipeline.task import PipelineParams, PipelineTask
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.aggregators.llm_response_universal import (
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LLMContextAggregatorPair,
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LLMUserAggregatorParams,
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.openai.llm import OpenAILLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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logger.info("✅ All components loaded successfully!")
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load_dotenv(override=True)
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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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logger.info(f"Starting bot")
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stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
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tts = CartesiaTTSService(
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api_key=os.getenv("CARTESIA_API_KEY"),
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settings=CartesiaTTSService.Settings(
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voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
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),
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)
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llm = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY"),
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settings=OpenAILLMService.Settings(
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system_instruction="You are a friendly AI assistant. Respond naturally and keep your answers conversational.",
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),
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)
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context = LLMContext()
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user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
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context,
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user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
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)
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pipeline = Pipeline(
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[
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transport.input(), # Transport user input
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stt,
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user_aggregator, # User responses
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llm, # LLM
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tts, # TTS
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transport.output(), # Transport bot output
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assistant_aggregator, # Assistant spoken responses
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]
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)
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task = PipelineTask(
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pipeline,
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params=PipelineParams(
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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)
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@transport.event_handler("on_client_connected")
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async def on_client_connected(transport, client):
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logger.info(f"Client connected")
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# Kick off the conversation.
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context.add_message(
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{"role": "developer", "content": "Say hello and briefly introduce yourself."}
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)
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await task.queue_frames([LLMRunFrame()])
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@transport.event_handler("on_client_disconnected")
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async def on_client_disconnected(transport, client):
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logger.info(f"Client disconnected")
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await task.cancel()
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runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
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await runner.run(task)
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async def bot(runner_args: RunnerArguments):
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"""Main bot entry point for the bot starter."""
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transport_params = {
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"daily": lambda: DailyParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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"webrtc": lambda: TransportParams(
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audio_in_enabled=True,
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audio_out_enabled=True,
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),
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}
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transport = await create_transport(runner_args, transport_params)
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await run_bot(transport, runner_args)
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if __name__ == "__main__":
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from pipecat.runner.run import main
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main()
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@@ -1,6 +0,0 @@
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DEEPGRAM_API_KEY=your_deepgram_api_key
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OPENAI_API_KEY=your_openai_api_key
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CARTESIA_API_KEY=your_cartesia_api_key
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# Optional: Connect via Daily WebRTC locally
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DAILY_API_KEY=your_daily_api_key
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@@ -1,6 +0,0 @@
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agent_name = "quickstart"
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secret_set = "quickstart-secrets"
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agent_profile = "agent-1x"
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[scaling]
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min_agents = 1
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@@ -1,20 +0,0 @@
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[project]
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name = "pipecat-quickstart"
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version = "0.1.0"
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description = "Quickstart example for building voice AI bots with Pipecat"
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requires-python = ">=3.10"
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dependencies = [
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"pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,runner]",
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"pipecat-ai-cli"
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]
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[dependency-groups]
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dev = [
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"pyright>=1.1.404,<2",
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"ruff>=0.12.11,<1",
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]
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[tool.ruff]
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line-length = 100
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[tool.ruff.lint]
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select = ["I"]
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Reference in New Issue
Block a user