Integrate with Mem0 OSS
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Dev-Khant
@@ -15,16 +15,20 @@ The example:
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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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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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$ python examples/foundational/37-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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- 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_SAMPLE_ROOM_URL=daily_sample_room_url
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@@ -32,16 +36,16 @@ Requirements:
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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 argparse
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import os
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from typing import Union
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from dotenv import load_dotenv
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from loguru import logger
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from openai import audio
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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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@@ -60,7 +64,7 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
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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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from mem0 import MemoryClient, Memory
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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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@@ -70,7 +74,7 @@ except ModuleNotFoundError as 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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memory_client: Union[MemoryClient, Memory], 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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@@ -78,13 +82,18 @@ async def get_initial_greeting(
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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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if isinstance(memory_client, Memory):
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filters = {"user_id": user_id, "agent_id": agent_id, "run_id": run_id}
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filters = {k: v for k, v in filters.items() if v is not None}
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memories = memory_client.get_all(**filters)
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else:
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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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# Get all memories for this user
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memories = memory_client.get_all(filters=filters, version="v2", output_format="v1.1")
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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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@@ -96,7 +105,7 @@ async def get_initial_greeting(
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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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for i, memory in enumerate(memories["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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@@ -120,7 +129,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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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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- 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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@@ -145,12 +154,16 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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voice_id="pNInz6obpgDQGcFmaJgB",
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)
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# Initialize Mem0 memory service
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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"),
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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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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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@@ -161,6 +174,37 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
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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(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
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