On this tutorial, we discover how we are able to construct a totally useful conversational AI agent from scratch utilizing the Pipecat framework. We stroll via organising a Pipeline that hyperlinks collectively customized FrameProcessor lessons, one for dealing with person enter and producing responses with a HuggingFace mannequin, and one other for formatting and displaying the dialog move. We additionally implement a ConversationInputGenerator to simulate dialogue, and use the PipelineRunner and PipelineTask to execute the info move asynchronously. This construction showcases how Pipecat handles frame-based processing, enabling modular integration of parts like language fashions, show logic, and future add-ons corresponding to speech modules. Take a look at the FULL CODES right here.
!pip set up -q pipecat-ai transformers torch speed up numpy
import asyncio
import logging
from typing import AsyncGenerator
import numpy as np
print("🔍 Checking accessible Pipecat frames...")
strive:
from pipecat.frames.frames import (
Body,
TextFrame,
)
print("✅ Fundamental frames imported efficiently")
besides ImportError as e:
print(f"⚠️ Import error: {e}")
from pipecat.frames.frames import Body, TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.process import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from transformers import pipeline as hf_pipeline
import torchWe start by putting in the required libraries, together with Pipecat, Transformers, and PyTorch, after which arrange our imports. We herald Pipecat’s core parts, corresponding to Pipeline, PipelineRunner, and FrameProcessor, together with HuggingFace’s pipeline API for textual content technology. This prepares our surroundings to construct and run the conversational AI agent seamlessly. Take a look at the FULL CODES right here.
class SimpleChatProcessor(FrameProcessor):
"""Easy conversational AI processor utilizing HuggingFace"""
def __init__(self):
tremendous().__init__()
print("🔄 Loading HuggingFace textual content technology mannequin...")
self.chatbot = hf_pipeline(
"text-generation",
mannequin="microsoft/DialoGPT-small",
pad_token_id=50256,
do_sample=True,
temperature=0.8,
max_length=100
)
self.conversation_history = ""
print("✅ Chat mannequin loaded efficiently!")
async def process_frame(self, body: Body, route: FrameDirection):
await tremendous().process_frame(body, route)
if isinstance(body, TextFrame):
user_text = getattr(body, "textual content", "").strip()
if user_text and never user_text.startswith("AI:"):
print(f"👤 USER: {user_text}")
strive:
if self.conversation_history:
input_text = f"{self.conversation_history} Consumer: {user_text} Bot:"
else:
input_text = f"Consumer: {user_text} Bot:"
response = self.chatbot(
input_text,
max_new_tokens=50,
num_return_sequences=1,
temperature=0.7,
do_sample=True,
pad_token_id=self.chatbot.tokenizer.eos_token_id
)
generated_text = response[0]["generated_text"]
if "Bot:" in generated_text:
ai_response = generated_text.break up("Bot:")[-1].strip()
ai_response = ai_response.break up("Consumer:")[0].strip()
if not ai_response:
ai_response = "That is attention-grabbing! Inform me extra."
else:
ai_response = "I would love to listen to extra about that!"
self.conversation_history = f"{input_text} {ai_response}"
await self.push_frame(TextFrame(textual content=f"AI: {ai_response}"), route)
besides Exception as e:
print(f"⚠️ Chat error: {e}")
await self.push_frame(
TextFrame(textual content="AI: I am having bother processing that. May you strive rephrasing?"),
route
)
else:
await self.push_frame(body, route)We implement SimpleChatProcessor, which masses the HuggingFace DialoGPT-small mannequin for textual content technology and maintains dialog historical past for context. As every TextFrame arrives, we course of the person’s enter, generate a mannequin response, clear it up, and push it ahead within the Pipecat pipeline for show. This design ensures our AI agent can maintain coherent, multi-turn conversations in actual time. Take a look at the FULL CODES right here.
class TextDisplayProcessor(FrameProcessor):
"""Shows textual content frames in a conversational format"""
def __init__(self):
tremendous().__init__()
self.conversation_count = 0
async def process_frame(self, body: Body, route: FrameDirection):
await tremendous().process_frame(body, route)
if isinstance(body, TextFrame):
textual content = getattr(body, "textual content", "")
if textual content.startswith("AI:"):
print(f"🤖 {textual content}")
self.conversation_count += 1
print(f" 💭 Change {self.conversation_count} completen")
await self.push_frame(body, route)
class ConversationInputGenerator:
"""Generates demo dialog inputs"""
def __init__(self):
self.demo_conversations = [
"Hello! How are you doing today?",
"What's your favorite thing to talk about?",
"Can you tell me something interesting about AI?",
"What makes conversation enjoyable for you?",
"Thanks for the great chat!"
]
async def generate_conversation(self) -> AsyncGenerator[TextFrame, None]:
print("🎭 Beginning dialog simulation...n")
for i, user_input in enumerate(self.demo_conversations):
yield TextFrame(textual content=user_input)
if i We create TextDisplayProcessor to neatly format and show AI responses, monitoring the variety of exchanges within the dialog. Alongside it, ConversationInputGenerator simulates a sequence of person messages as TextFrame objects, including brief pauses between them to imitate a pure back-and-forth move through the demo. Take a look at the FULL CODES right here.
class SimpleAIAgent:
"""Easy conversational AI agent utilizing Pipecat"""
def __init__(self):
self.chat_processor = SimpleChatProcessor()
self.display_processor = TextDisplayProcessor()
self.input_generator = ConversationInputGenerator()
def create_pipeline(self) -> Pipeline:
return Pipeline([self.chat_processor, self.display_processor])
async def run_demo(self):
print("🚀 Easy Pipecat AI Agent Demo")
print("🎯 Conversational AI with HuggingFace")
print("=" * 50)
pipeline = self.create_pipeline()
runner = PipelineRunner()
process = PipelineTask(pipeline)
async def produce_frames():
async for body in self.input_generator.generate_conversation():
await process.queue_frame(body)
await process.stop_when_done()
strive:
print("🎬 Operating dialog demo...n")
await asyncio.collect(
runner.run(process),
produce_frames(),
)
besides Exception as e:
print(f"❌ Demo error: {e}")
logging.error(f"Pipeline error: {e}")
print("✅ Demo accomplished efficiently!")In SimpleAIAgent, we tie all the things collectively by combining the chat processor, show processor, and enter generator right into a single Pipecat Pipeline. The run_demo technique launches the PipelineRunner to course of frames asynchronously whereas the enter generator feeds simulated person messages. This orchestrated setup permits the agent to course of inputs, generate responses, and show them in actual time, finishing the end-to-end conversational move. Take a look at the FULL CODES right here.
async def fundamental():
logging.basicConfig(stage=logging.INFO)
print("🎯 Pipecat AI Agent Tutorial")
print("📱 Google Colab Appropriate")
print("🤖 Free HuggingFace Fashions")
print("🔧 Easy & Working Implementation")
print("=" * 60)
strive:
agent = SimpleAIAgent()
await agent.run_demo()
print("n🎉 Tutorial Full!")
print("n📚 What You Simply Noticed:")
print("✓ Pipecat pipeline structure in motion")
print("✓ Customized FrameProcessor implementations")
print("✓ HuggingFace conversational AI integration")
print("✓ Actual-time textual content processing pipeline")
print("✓ Modular, extensible design")
print("n🚀 Subsequent Steps:")
print("• Add actual speech-to-text enter")
print("• Combine text-to-speech output")
print("• Join to raised language fashions")
print("• Add reminiscence and context administration")
print("• Deploy as an internet service")
besides Exception as e:
print(f"❌ Tutorial failed: {e}")
import traceback
traceback.print_exc()
strive:
import google.colab
print("🌐 Google Colab detected - Able to run!")
ENV = "colab"
besides ImportError:
print("💻 Native setting detected")
ENV = "native"
print("n" + "="*60)
print("🎬 READY TO RUN!")
print("Execute this cell to start out the AI dialog demo")
print("="*60)
print("n🚀 Beginning the AI Agent Demo...")
await fundamental()We outline the primary perform to initialize logging, arrange the SimpleAIAgent, and run the demo whereas printing useful progress and abstract messages. We additionally detect whether or not the code is operating in Google Colab or regionally, show setting particulars, after which name await fundamental() to start out the total conversational AI pipeline execution.
In conclusion, we now have a working conversational AI agent the place person inputs (or simulated textual content frames) are handed via a processing pipeline, the HuggingFace DialoGPT mannequin generates responses, and the outcomes are displayed in a structured conversational format. The implementation demonstrates how Pipecat’s structure helps asynchronous processing, stateful dialog dealing with, and clear separation of considerations between totally different processing levels. With this basis, we are able to now combine extra superior options, corresponding to real-time speech-to-text, text-to-speech synthesis, context persistence, or richer mannequin backends, whereas retaining a modular and extensible code construction.
Take a look at the FULL CODES right here. Be at liberty to take a look at our GitHub Web page for Tutorials, Codes and Notebooks. Additionally, be happy to observe us on Twitter and don’t neglect to hitch our 100k+ ML SubReddit and Subscribe to our Publication.
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its reputation amongst audiences.
