Split features/ into audio/, observability/, and rag/ subfolders
Extract focused example groups from the catch-all features/ folder: - audio/: audio recording, background sound, sound effects - observability/: observer, heartbeats, sentry metrics - rag/: mem0, gemini-rag, gemini grounding metadata Update README to document the new folders.
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Mark Backman
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examples/rag/mem0.py
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examples/rag/mem0.py
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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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"""Mem0 Personalized Voice Agent Example with Pipecat.
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This example demonstrates how to create a conversational AI assistant with memory capabilities
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using Mem0 integration. It shows how to build an agent that remembers previous interactions
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and personalizes responses based on conversation history.
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The example:
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1. Sets up a video/audio conversation between a user and an AI assistant
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2. Uses Mem0 to store and retrieve memories from conversations
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3. Creates personalized greetings based on previous interactions
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4. Handles multi-modal interaction through audio
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5. Demonstrates two approaches for memory management:
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- Using Mem0 API (cloud-based memory storage)
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- Using local configuration with custom LLM (self-hosted memory)
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Requirements:
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- OpenAI API key
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- ElevenLabs API key (for text-to-speech)
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- Daily API key (for video/audio transport)
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- Mem0 API key (for cloud-based memory storage)
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- [Optional] Anthropic API key (if using Claude with local config)
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Environment variables (set in .env or in your terminal using `export`):
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DAILY_ROOM_URL=daily_room_url
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DAILY_API_KEY=daily_api_key
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OPENAI_API_KEY=openai_api_key
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ELEVENLABS_API_KEY=elevenlabs_api_key
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MEM0_API_KEY=mem0_api_key
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ANTHROPIC_API_KEY=anthropic_api_key (if using Claude with local config)
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The bot runs as part of a pipeline that processes audio frames and manages the conversation flow.
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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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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.frames.frames import LLMRunFrame
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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.deepgram.stt import DeepgramSTTService
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from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
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from pipecat.services.mem0.memory import Mem0MemoryService
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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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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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load_dotenv(override=True)
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async def get_initial_greeting(memory_service: Mem0MemoryService) -> str:
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"""Fetch all memories for the user and create a personalized greeting.
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Args:
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memory_service: The Mem0 memory service instance.
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Returns:
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A personalized greeting based on user memories.
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"""
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try:
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results = await memory_service.get_memories()
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if not results:
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logger.debug("No memories found for this user.")
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return "Hello! It's nice to meet you. How can I help you today?"
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# Create a personalized greeting based on memories
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greeting = "Hello! It's great to see you again. "
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greeting += "Based on our previous conversations, I remember: "
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for i, memory in enumerate(results[:3], 1):
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memory_content = memory.get("memory", "")
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# Keep memory references brief
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if len(memory_content) > 100:
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memory_content = memory_content[:97] + "..."
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greeting += f"{memory_content} "
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greeting += "How can I help you today?"
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logger.debug(f"Created personalized greeting from {len(results)} memories")
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return greeting
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except Exception as e:
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logger.error(f"Error retrieving initial memories from Mem0: {e}")
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return "Hello! How can I help you today?"
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# We use lambdas to defer transport parameter creation until the transport
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# type is selected at runtime.
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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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"twilio": lambda: FastAPIWebsocketParams(
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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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async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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"""Main bot execution function.
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Sets up and runs the bot pipeline including:
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- Daily video transport
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- Speech-to-text and text-to-speech services
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- Language model integration
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- Mem0 memory service (using either API or local configuration)
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- RTVI event handling
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"""
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# Note: You can pass the user_id as a parameter in API call
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USER_ID = "pipecat-demo-user"
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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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# Initialize text-to-speech service
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tts = ElevenLabsTTSService(
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api_key=os.getenv("ELEVENLABS_API_KEY"),
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settings=ElevenLabsTTSService.Settings(
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voice="pNInz6obpgDQGcFmaJgB",
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),
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)
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# =====================================================================
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# OPTION 1: Using Mem0 API (cloud-based approach)
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# This approach uses Mem0's cloud service for memory management
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# Requires: MEM0_API_KEY set in your environment
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# =====================================================================
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memory = Mem0MemoryService(
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api_key=os.getenv("MEM0_API_KEY"), # Your Mem0 API key
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user_id=USER_ID, # Unique identifier for the user
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agent_id="agent1", # Optional identifier for the agent
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run_id="session1", # Optional identifier for the run
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params=Mem0MemoryService.InputParams(
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search_limit=10,
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search_threshold=0.3,
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api_version="v2",
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system_prompt="Based on previous conversations, I recall: \n\n",
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add_as_system_message=True,
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position=1,
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),
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)
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# =====================================================================
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# OPTION 2: Using Mem0 with local configuration (self-hosted approach)
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# This approach uses a local LLM configuration for memory management
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# Requires: Anthropic API key if using Claude model
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# =====================================================================
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# Uncomment the following code and comment out the previous memory initialization to use local config
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# local_config = {
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# "llm": {
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# "provider": "anthropic",
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# "config": {
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# "model": "claude-3-5-sonnet-20240620",
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# "api_key": os.getenv("ANTHROPIC_API_KEY"), # Make sure to set this in your .env
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# }
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# },
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# "embedder": {
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# "provider": "openai",
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# "config": {
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# "model": "text-embedding-3-large"
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# }
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# }
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# }
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# # Initialize Mem0 memory service with local configuration
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# memory = Mem0MemoryService(
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# local_config=local_config, # Use local LLM for memory processing
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# user_id=USER_ID, # Unique identifier for the user
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# # agent_id="agent1", # Optional identifier for the agent
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# # run_id="session1", # Optional identifier for the run
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# )
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# Initialize LLM service
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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 personal assistant. You can remember things about the person you are talking to.
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Some Guidelines:
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- Make sure your responses are friendly yet short and concise.
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- If the user asks you to remember something, make sure to remember it.
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- Greet the user by their name if you know about it.
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""",
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),
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)
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# Set up conversation context and management
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# The context_aggregator will automatically collect conversation context
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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(),
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stt,
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user_aggregator,
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memory,
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llm,
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tts,
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transport.output(),
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assistant_aggregator,
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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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idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
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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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# Get personalized greeting based on user memories
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greeting = await get_initial_greeting(memory)
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# Add the greeting as an assistant message to start the conversation
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context.add_message({"role": "developer", "content": greeting})
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# Queue the context frame to start the conversation
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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 compatible with Pipecat Cloud."""
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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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