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Fix CI issues related to missing dependency (#3096)

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This commit is contained in:
Deshraj Yadav
2025-07-03 18:52:50 -07:00
committed by GitHub
parent 2c496e6376
commit 7484eed4b2
32 changed files with 6150 additions and 828 deletions
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532
examples/graph-db-demo/neo4j-example.ipynb
View File

@@ -1,271 +1,267 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ApdaLD4Qi30H"
},
"source": [
"# Neo4j as Graph Memory"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l7bi3i21i30I"
},
"source": [
"## Prerequisites\n",
"\n",
"### 1. Install Mem0 with Graph Memory support\n",
"\n",
"To use Mem0 with Graph Memory support, install it using pip:\n",
"\n",
"```bash\n",
"pip install \"mem0ai[graph]\"\n",
"```\n",
"\n",
"This command installs Mem0 along with the necessary dependencies for graph functionality.\n",
"\n",
"### 2. Install Neo4j\n",
"\n",
"To utilize Neo4j as Graph Memory, run it with Docker:\n",
"\n",
"```bash\n",
"docker run \\\n",
" -p 7474:7474 -p 7687:7687 \\\n",
" -e NEO4J_AUTH=neo4j/password \\\n",
" neo4j:5\n",
"```\n",
"\n",
"This command starts Neo4j with default credentials (`neo4j` / `password`) and exposes both the HTTP (7474) and Bolt (7687) ports.\n",
"\n",
"You can access the Neo4j browser at [http://localhost:7474](http://localhost:7474).\n",
"\n",
"Additional information can be found in the [Neo4j documentation](https://neo4j.com/docs/).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DkeBdFEpi30I"
},
"source": [
"## Configuration\n",
"\n",
"Do all the imports and configure OpenAI (enter your OpenAI API key):"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "d99EfBpii30I"
},
"outputs": [],
"source": [
"from mem0 import Memory\n",
"\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = (\n",
" \"\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QTucZJjIi30J"
},
"source": [
"Set up configuration to use the embedder model and Neo4j as a graph store:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "QSE0RFoSi30J"
},
"outputs": [],
"source": [
"config = {\n",
" \"embedder\": {\n",
" \"provider\": \"openai\",\n",
" \"config\": {\"model\": \"text-embedding-3-large\", \"embedding_dims\": 1536},\n",
" },\n",
" \"graph_store\": {\n",
" \"provider\": \"neo4j\",\n",
" \"config\": {\n",
" \"url\": \"bolt://54.87.227.131:7687\",\n",
" \"username\": \"neo4j\",\n",
" \"password\": \"causes-bins-vines\",\n",
" },\n",
" },\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OioTnv6xi30J"
},
"source": [
"## Graph Memory initializiation\n",
"\n",
"Initialize Neo4j as a Graph Memory store:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "fX-H9vgNi30J"
},
"outputs": [],
"source": [
"m = Memory.from_config(config_dict=config)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kr1fVMwEi30J"
},
"source": [
"## Store memories\n",
"\n",
"Create memories:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "sEfogqp_i30J"
},
"outputs": [],
"source": [
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"I'm planning to watch a movie tonight. Any recommendations?\",\n",
" },\n",
" {\n",
" \"role\": \"assistant\",\n",
" \"content\": \"How about a thriller movies? They can be quite engaging.\",\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"I'm not a big fan of thriller movies but I love sci-fi movies.\",\n",
" },\n",
" {\n",
" \"role\": \"assistant\",\n",
" \"content\": \"Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.\",\n",
" },\n",
"]\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gtBHCyIgi30J"
},
"source": [
"Store memories in Neo4j:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "BMVGgZMFi30K"
},
"outputs": [],
"source": [
"# Store inferred memories (default behavior)\n",
"result = m.add(\n",
" messages, user_id=\"alice\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lQRptOywi30K"
},
"source": [
"![](https://github.com/tomasonjo/mem0/blob/neo4jexample/examples/graph-db-demo/alice-memories.png?raw=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LBXW7Gv-i30K"
},
"source": [
"## Search memories"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UHFDeQBEi30K",
"outputId": "2c69de7d-a79a-48f6-e3c4-bd743067857c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loves sci-fi movies 0.3153664287340898\n",
"Planning to watch a movie tonight 0.09683349296551162\n",
"Not a big fan of thriller movies 0.09468540071789466\n"
]
}
],
"source": [
"for result in m.search(\"what does alice love?\", user_id=\"alice\")[\"results\"]:\n",
" print(result[\"memory\"], result[\"score\"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "2jXEIma9kK_Q"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ApdaLD4Qi30H"
},
"source": [
"# Neo4j as Graph Memory"
]
},
"nbformat": 4,
"nbformat_minor": 0
{
"cell_type": "markdown",
"metadata": {
"id": "l7bi3i21i30I"
},
"source": [
"## Prerequisites\n",
"\n",
"### 1. Install Mem0 with Graph Memory support\n",
"\n",
"To use Mem0 with Graph Memory support, install it using pip:\n",
"\n",
"```bash\n",
"pip install \"mem0ai[graph]\"\n",
"```\n",
"\n",
"This command installs Mem0 along with the necessary dependencies for graph functionality.\n",
"\n",
"### 2. Install Neo4j\n",
"\n",
"To utilize Neo4j as Graph Memory, run it with Docker:\n",
"\n",
"```bash\n",
"docker run \\\n",
" -p 7474:7474 -p 7687:7687 \\\n",
" -e NEO4J_AUTH=neo4j/password \\\n",
" neo4j:5\n",
"```\n",
"\n",
"This command starts Neo4j with default credentials (`neo4j` / `password`) and exposes both the HTTP (7474) and Bolt (7687) ports.\n",
"\n",
"You can access the Neo4j browser at [http://localhost:7474](http://localhost:7474).\n",
"\n",
"Additional information can be found in the [Neo4j documentation](https://neo4j.com/docs/).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DkeBdFEpi30I"
},
"source": [
"## Configuration\n",
"\n",
"Do all the imports and configure OpenAI (enter your OpenAI API key):"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "d99EfBpii30I"
},
"outputs": [],
"source": [
"from mem0 import Memory\n",
"\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QTucZJjIi30J"
},
"source": [
"Set up configuration to use the embedder model and Neo4j as a graph store:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "QSE0RFoSi30J"
},
"outputs": [],
"source": [
"config = {\n",
" \"embedder\": {\n",
" \"provider\": \"openai\",\n",
" \"config\": {\"model\": \"text-embedding-3-large\", \"embedding_dims\": 1536},\n",
" },\n",
" \"graph_store\": {\n",
" \"provider\": \"neo4j\",\n",
" \"config\": {\n",
" \"url\": \"bolt://54.87.227.131:7687\",\n",
" \"username\": \"neo4j\",\n",
" \"password\": \"causes-bins-vines\",\n",
" },\n",
" },\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OioTnv6xi30J"
},
"source": [
"## Graph Memory initializiation\n",
"\n",
"Initialize Neo4j as a Graph Memory store:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "fX-H9vgNi30J"
},
"outputs": [],
"source": [
"m = Memory.from_config(config_dict=config)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kr1fVMwEi30J"
},
"source": [
"## Store memories\n",
"\n",
"Create memories:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "sEfogqp_i30J"
},
"outputs": [],
"source": [
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"I'm planning to watch a movie tonight. Any recommendations?\",\n",
" },\n",
" {\n",
" \"role\": \"assistant\",\n",
" \"content\": \"How about a thriller movies? They can be quite engaging.\",\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"I'm not a big fan of thriller movies but I love sci-fi movies.\",\n",
" },\n",
" {\n",
" \"role\": \"assistant\",\n",
" \"content\": \"Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.\",\n",
" },\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gtBHCyIgi30J"
},
"source": [
"Store memories in Neo4j:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "BMVGgZMFi30K"
},
"outputs": [],
"source": [
"# Store inferred memories (default behavior)\n",
"result = m.add(messages, user_id=\"alice\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lQRptOywi30K"
},
"source": [
"![](https://github.com/tomasonjo/mem0/blob/neo4jexample/examples/graph-db-demo/alice-memories.png?raw=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LBXW7Gv-i30K"
},
"source": [
"## Search memories"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UHFDeQBEi30K",
"outputId": "2c69de7d-a79a-48f6-e3c4-bd743067857c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loves sci-fi movies 0.3153664287340898\n",
"Planning to watch a movie tonight 0.09683349296551162\n",
"Not a big fan of thriller movies 0.09468540071789466\n"
]
}
],
"source": [
"for result in m.search(\"what does alice love?\", user_id=\"alice\")[\"results\"]:\n",
" print(result[\"memory\"], result[\"score\"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "2jXEIma9kK_Q"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

13
examples/misc/diet_assistant_voice_cartesia.py
View File

@@ -45,11 +45,7 @@ def get_food_recommendation(user_query: str, user_id):
"""Get food recommendation with memory context"""
# Search memory for relevant food preferences
memories_result = memory_client.search(
query=user_query,
user_id=user_id,
limit=5
)
memories_result = memory_client.search(query=user_query, user_id=user_id, limit=5)
# Add memory context to the message
memories = [f"- {result['memory']}" for result in memories_result]
@@ -71,6 +67,7 @@ def get_food_recommendation(user_query: str, user_id):
# Save audio file
if response.audio:
import time
timestamp = int(time.time())
filename = f"food_recommendation_{timestamp}.mp3"
write_audio_to_file(
@@ -118,7 +115,11 @@ def initialize_food_memory(user_id):
# Initialize the memory for the user once in order for the agent to learn the user preference
initialize_food_memory(user_id=USER_ID)
print(get_food_recommendation("Which type of restaurants should I go tonight for dinner and cuisines preferred?", user_id=USER_ID))
print(
get_food_recommendation(
"Which type of restaurants should I go tonight for dinner and cuisines preferred?", user_id=USER_ID
)
)
# OUTPUT: 🎵 Audio saved as food_recommendation_1750162610.mp3
# For dinner tonight, considering your love for healthy spic optionsy, you could try a nice Thai, Indian, or Mexican restaurant.
# You might find dishes with quinoa, chickpeas, tofu, and fresh herbs delightful. Enjoy your dinner!

13
examples/misc/test.py
View File

@@ -1,4 +1,4 @@
from agents import Agent, Runner, function_tool, handoffs, enable_verbose_stdout_logging
from agents import Agent, Runner, function_tool, enable_verbose_stdout_logging
from dotenv import load_dotenv
from mem0 import MemoryClient
@@ -35,7 +35,7 @@ travel_agent = Agent(
understand the user's travel preferences and history before making recommendations.
After providing your response, use store_conversation to save important details.""",
tools=[search_memory, save_memory],
model="gpt-4o"
model="gpt-4o",
)
health_agent = Agent(
@@ -44,7 +44,7 @@ health_agent = Agent(
understand the user's health goals and dietary preferences.
After providing advice, use store_conversation to save relevant information.""",
tools=[search_memory, save_memory],
model="gpt-4o"
model="gpt-4o",
)
# Triage agent with handoffs
@@ -55,7 +55,7 @@ triage_agent = Agent(
For health-related questions (fitness, diet, wellness, exercise), hand off to Health Advisor.
For general questions, you can handle them directly using available tools.""",
handoffs=[travel_agent, health_agent],
model="gpt-4o"
model="gpt-4o",
)
@@ -74,10 +74,7 @@ def chat_with_handoffs(user_input: str, user_id: str) -> str:
result = Runner.run_sync(triage_agent, user_input)
# Store the original conversation in memory
conversation = [
{"role": "user", "content": user_input},
{"role": "assistant", "content": result.final_output}
]
conversation = [{"role": "user", "content": user_input}, {"role": "assistant", "content": result.final_output}]
mem0.add(conversation, user_id=user_id)
return result.final_output

77
examples/misc/vllm_example.py
View File

@@ -34,96 +34,91 @@ config = {
"api_key": "vllm-api-key",
"temperature": 0.7,
"max_tokens": 100,
}
},
"embedder": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small"
}
},
},
"embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
"vector_store": {
"provider": "qdrant",
"config": {
"collection_name": "vllm_memories",
"host": "localhost",
"port": 6333
}
}
"config": {"collection_name": "vllm_memories", "host": "localhost", "port": 6333},
},
}
def main():
"""
Demonstrate vLLM integration with mem0
"""
print("--> Initializing mem0 with vLLM...")
# Initialize memory with vLLM
memory = Memory.from_config(config)
print("--> Memory initialized successfully!")
# Example conversations to store
conversations = [
{
"messages": [
{"role": "user", "content": "I love playing chess on weekends"},
{"role": "assistant", "content": "That's great! Chess is an excellent strategic game that helps improve critical thinking."}
{
"role": "assistant",
"content": "That's great! Chess is an excellent strategic game that helps improve critical thinking.",
},
],
"user_id": "user_123"
"user_id": "user_123",
},
{
"messages": [
{"role": "user", "content": "I'm learning Python programming"},
{"role": "assistant", "content": "Python is a fantastic language for beginners! What specific areas are you focusing on?"}
{
"role": "assistant",
"content": "Python is a fantastic language for beginners! What specific areas are you focusing on?",
},
],
"user_id": "user_123"
"user_id": "user_123",
},
{
"messages": [
{"role": "user", "content": "I prefer working late at night, I'm more productive then"},
{"role": "assistant", "content": "Many people find they're more creative and focused during nighttime hours. It's important to maintain a consistent schedule that works for you."}
{
"role": "assistant",
"content": "Many people find they're more creative and focused during nighttime hours. It's important to maintain a consistent schedule that works for you.",
},
],
"user_id": "user_123"
}
"user_id": "user_123",
},
]
print("\n--> Adding memories using vLLM...")
# Add memories - now powered by vLLM's high-performance inference
for i, conversation in enumerate(conversations, 1):
result = memory.add(
messages=conversation["messages"],
user_id=conversation["user_id"]
)
result = memory.add(messages=conversation["messages"], user_id=conversation["user_id"])
print(f"Memory {i} added: {result}")
print("\n🔍 Searching memories...")
# Search memories - vLLM will process the search and memory operations
search_queries = [
"What does the user like to do on weekends?",
"What is the user learning?",
"When is the user most productive?"
"When is the user most productive?",
]
for query in search_queries:
print(f"\nQuery: {query}")
memories = memory.search(
query=query,
user_id="user_123"
)
memories = memory.search(query=query, user_id="user_123")
for memory_item in memories:
print(f" - {memory_item['memory']}")
print("\n--> Getting all memories for user...")
all_memories = memory.get_all(user_id="user_123")
print(f"Total memories stored: {len(all_memories)}")
for memory_item in all_memories:
print(f" - {memory_item['memory']}")
print("\n--> vLLM integration demo completed successfully!")
print("\nBenefits of using vLLM:")
print(" -> 2.7x higher throughput compared to standard implementations")