Merge pull request #1388 from deshraj/user/dyadav/mem0-integration
Added mem0 service.
This commit is contained in:
Kwindla Hultman Kramer
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examples/foundational/35-mem0.py
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242
examples/foundational/35-mem0.py
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#
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# Copyright (c) 2024–2025, 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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Example usage (run from pipecat root directory):
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$ pip install "pipecat-ai[daily,openai,elevenlabs,silero,mem0]"
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$ python examples/foundational/35-mem0.py
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Requirements:
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- OpenAI API key (for GPT-4o-mini)
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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 memory storage and retrieval)
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Environment variables (set in .env or in your terminal using `export`):
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DAILY_SAMPLE_ROOM_URL=daily_sample_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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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 asyncio
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import os
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import sys
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import aiohttp
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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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.openai_llm_context import OpenAILLMContext
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from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
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from pipecat.services.elevenlabs import ElevenLabsTTSService
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from pipecat.services.mem0 import Mem0MemoryService
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from pipecat.services.openai import OpenAILLMService
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from pipecat.transports.services.daily import DailyParams, DailyTransport
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logger.remove(0)
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logger.add(sys.stderr, level="DEBUG")
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load_dotenv(override=True)
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try:
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from mem0 import MemoryClient
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except ModuleNotFoundError as e:
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logger.error(f"Exception: {e}")
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logger.error(
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"In order to use Mem0, you need to `pip install mem0ai`. Also, set the environment variable MEM0_API_KEY."
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)
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raise Exception(f"Missing module: {e}")
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async def get_initial_greeting(
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memory_client: MemoryClient, user_id: str, agent_id: str, run_id: str
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) -> str:
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"""Fetch all memories for the user and create a personalized greeting.
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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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# Create filters based on available IDs
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id_pairs = [("user_id", user_id), ("agent_id", agent_id), ("run_id", run_id)]
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clauses = [{name: value} for name, value in id_pairs if value is not None]
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filters = {"AND": clauses} if clauses else {}
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# Get all memories for this user
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memories = memory_client.get_all(filters=filters, version="v2")
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if not memories or len(memories) == 0:
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logger.debug(f"!!! No memories found for this user. {memories}")
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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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# Add some personalization based on memories (limit to 3 memories for brevity)
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if len(memories) > 0:
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greeting += "Based on our previous conversations, I remember: "
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for i, memory in enumerate(memories[: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(memories)} 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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async def main():
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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
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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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async with aiohttp.ClientSession() as session:
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(room_url, token) = await configure(session)
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# Set up Daily transport with video/audio parameters
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transport = DailyTransport(
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room_url,
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token,
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"Chatbot",
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DailyParams(
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audio_out_enabled=True,
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vad_enabled=True,
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vad_analyzer=SileroVADAnalyzer(),
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transcription_enabled=True,
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),
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)
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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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voice_id="pNInz6obpgDQGcFmaJgB",
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)
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# Initialize Mem0 memory service
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memory = Mem0MemoryService(
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api_key=os.getenv("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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# Initialize LLM service
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llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
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messages = [
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{
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"role": "system",
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"content": """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 = OpenAILLMContext(messages)
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context_aggregator = llm.create_context_aggregator(context)
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rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
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pipeline = Pipeline(
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[
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transport.input(),
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rtvi,
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context_aggregator.user(),
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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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context_aggregator.assistant(),
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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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allow_interruptions=True,
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enable_metrics=True,
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enable_usage_metrics=True,
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),
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observers=[RTVIObserver(rtvi)],
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)
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@rtvi.event_handler("on_client_ready")
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async def on_client_ready(rtvi):
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await rtvi.set_bot_ready()
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@transport.event_handler("on_first_participant_joined")
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async def on_first_participant_joined(transport, participant):
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await transport.capture_participant_transcription(participant["id"])
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# Get personalized greeting based on user memories. Can pass agent_id and run_id as per requirement of the application to manage short term memory or agent specific memory.
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greeting = await get_initial_greeting(
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memory_client=memory.memory_client, user_id=USER_ID, agent_id=None, run_id=None
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)
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# Add the greeting as an assistant message to start the conversation
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context.add_message({"role": "assistant", "content": greeting})
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# Queue the context frame to start the conversation
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await task.queue_frames([context_aggregator.user().get_context_frame()])
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@transport.event_handler("on_participant_left")
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async def on_participant_left(transport, participant, reason):
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print(f"Participant left: {participant}")
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await task.cancel()
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runner = PipelineRunner()
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await runner.run(task)
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if __name__ == "__main__":
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asyncio.run(main())
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