Personalized Search Example Docs (#3259)
This commit is contained in:
Antaripa Saha
@@ -220,6 +220,7 @@
|
||||
"examples/ai_companion_js",
|
||||
"examples/collaborative-task-agent",
|
||||
"examples/llamaindex-multiagent-learning-system",
|
||||
"examples/personalized-search-tavily-mem0",
|
||||
"examples/eliza_os",
|
||||
"examples/mem0-mastra",
|
||||
"examples/mem0-with-ollama",
|
||||
|
||||
+7
-3
@@ -39,10 +39,14 @@ Explore how **Mem0** can power real-world applications and bring personalized, i
|
||||
|
||||
<Card title="Customer Support Agent" icon="headset" href="/examples/customer-support-agent">
|
||||
Build a **Customer Support AI** that recalls user preferences, past chats, and provides context-aware, efficient help.
|
||||
</Card>
|
||||
</Card>
|
||||
|
||||
<Card title="LlamaIndex + Mem0" icon="book-open" href="/examples/llama-index-mem0">
|
||||
Combine **LlamaIndex** and Mem0 to create a powerful **ReAct Agent** with persistent memory for smarter interactions.
|
||||
<Card title="Personalized Search Assistant" icon="magnifying-glass" href="/examples/personalized-search-tavily-mem0">
|
||||
Build a **Personalized Search Assistant** that tailors search according to user preferences.
|
||||
</Card>
|
||||
|
||||
<Card title="LlamaIndex + Mem0 Learning System" icon="book-open" href="/examples/llama-index-mem0">
|
||||
Multi-agent learning system powered by memory.
|
||||
</Card>
|
||||
|
||||
<Card title="Chrome Extension" icon="puzzle-piece" href="/examples/chrome-extension">
|
||||
|
||||
@@ -0,0 +1,190 @@
|
||||
---
|
||||
title: 'Personalized Search with Mem0 and Tavily'
|
||||
---
|
||||
|
||||
<Snippet file="security-compliance.mdx" />
|
||||
|
||||
Imagine asking a search assistant for "coffee shops nearby" and instead of generic results, it shows remote-work-friendly cafes with great wifi in your city because it remembers you mentioned working remotely before. Or when you search for "lunchbox ideas for kids" it knows you have a **7-year-old daughter** and recommends **peanut-free options** that align with her allergy.
|
||||
|
||||
That's what we are going to build today, a **Personalized Search Assistant** powered by **Mem0** for memory and [Tavily](https://tavily.com) for real-time search.
|
||||
|
||||
|
||||
## Why Personalized Search
|
||||
|
||||
Most assistants treat every query like they’ve never seen you before. That means repeating yourself about your location, diet, or preferences, and getting results that feel generic.
|
||||
|
||||
- With **Mem0**, your assistant builds a memory of the user’s world.
|
||||
- With **Tavily**, it fetches fresh and accurate results in real time.
|
||||
|
||||
Together, they make every interaction **smarter, faster, and more personal**.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
Before you begin, make sure you have:
|
||||
|
||||
1. Installed the dependencies:
|
||||
```bash
|
||||
pip install langchain mem0ai langchain-tavily langchain-openai
|
||||
```
|
||||
|
||||
2. Set up your API keys in a .env file:
|
||||
```bash
|
||||
OPENAI_API_KEY=your-openai-key
|
||||
TAVILY_API_KEY=your-tavily-key
|
||||
MEM0_API_KEY=your-mem0-key
|
||||
```
|
||||
|
||||
## Code Walkthrough
|
||||
Let’s break down the main components.
|
||||
|
||||
### 1: Initialize Mem0 with Custom Instructions
|
||||
|
||||
We configure Mem0 with custom instructions that guide it to infer user memories tailored specifically for our usecase.
|
||||
|
||||
```python
|
||||
from mem0 import MemoryClient
|
||||
|
||||
mem0_client = MemoryClient()
|
||||
|
||||
mem0_client.project.update(
|
||||
custom_instructions='''
|
||||
INFER THE MEMORIES FROM USER QUERIES EVEN IF IT'S A QUESTION.
|
||||
|
||||
We are building personalized search for which we need to understand about user's preferences and life
|
||||
and extract facts and memories accordingly.
|
||||
'''
|
||||
)
|
||||
```
|
||||
Now, if a user casually mentions "I need to pick up my daughter", or "What's the weather at Los Angeles", Mem0 remembers they have a daughter or user is somewhat interested/connected with Los Angeles in terms of location, those will be referred for future searches.
|
||||
|
||||
### 2. Simulating User History
|
||||
To test personalization, we preload some sample conversation history for a user:
|
||||
|
||||
```python
|
||||
def setup_user_history(user_id):
|
||||
conversations = [
|
||||
[{"role": "user", "content": "What will be the weather today at Los Angeles? I need to pick up my daughter from office."},
|
||||
{"role": "assistant", "content": "I'll check the weather in LA for you."}],
|
||||
[{"role": "user", "content": "I'm looking for vegan restaurants in Santa Monica"},
|
||||
{"role": "assistant", "content": "I'll find great vegan options in Santa Monica."}],
|
||||
[{"role": "user", "content": "My 7-year-old daughter is allergic to peanuts"},
|
||||
{"role": "assistant", "content": "I'll remember to check for peanut-free options."}],
|
||||
[{"role": "user", "content": "I work remotely and need coffee shops with good wifi"},
|
||||
{"role": "assistant", "content": "I'll find remote-work-friendly coffee shops."}],
|
||||
[{"role": "user", "content": "We love hiking and outdoor activities on weekends"},
|
||||
{"role": "assistant", "content": "Great! I'll keep your outdoor activity preferences in mind."}],
|
||||
]
|
||||
|
||||
for conversation in conversations:
|
||||
mem0_client.add(conversation, user_id=user_id, output_format="v1.1")
|
||||
```
|
||||
This gives the agent a baseline understanding of the user’s lifestyle and needs.
|
||||
|
||||
### 3. Retrieving User Context from Memory
|
||||
When a user makes a new search query, we retrieve relevant memories to enhance the search query:
|
||||
|
||||
```python
|
||||
def get_user_context(user_id, query):
|
||||
filters = {"AND": [{"user_id": user_id}]}
|
||||
user_memories = mem0_client.search(query=query, version="v2", filters=filters)
|
||||
|
||||
if user_memories:
|
||||
context = "\n".join([f"- {memory['memory']}" for memory in user_memories])
|
||||
return context
|
||||
else:
|
||||
return "No previous user context available."
|
||||
```
|
||||
This context is injected into the search agent so results are personalized.
|
||||
|
||||
### 4. Creating the Personalized Search Agent
|
||||
The agent uses Tavily search, but always augments search queries with user context:
|
||||
|
||||
```python
|
||||
def create_personalized_search_agent(user_context):
|
||||
tavily_search = TavilySearch(
|
||||
max_results=10,
|
||||
search_depth="advanced",
|
||||
include_answer=True,
|
||||
topic="general"
|
||||
)
|
||||
|
||||
tools = [tavily_search]
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages([
|
||||
("system", f"""You are a personalized search assistant.
|
||||
|
||||
USER CONTEXT AND PREFERENCES:
|
||||
{user_context}
|
||||
|
||||
YOUR ROLE:
|
||||
1. Analyze the user's query and context.
|
||||
2. Enhance the query with relevant personal memories.
|
||||
3. Always use tavily_search for results.
|
||||
4. Explain which memories influenced personalization.
|
||||
"""),
|
||||
MessagesPlaceholder(variable_name="messages"),
|
||||
MessagesPlaceholder(variable_name="agent_scratchpad"),
|
||||
])
|
||||
|
||||
agent = create_openai_tools_agent(llm=llm, tools=tools, prompt=prompt)
|
||||
return AgentExecutor(agent=agent, tools=tools, verbose=True, return_intermediate_steps=True)
|
||||
```
|
||||
|
||||
### 5. Run a Personalized Search
|
||||
The workflow ties everything together:
|
||||
|
||||
```python
|
||||
def conduct_personalized_search(user_id, query):
|
||||
user_context = get_user_context(user_id, query)
|
||||
agent_executor = create_personalized_search_agent(user_context)
|
||||
|
||||
response = agent_executor.invoke({"messages": [HumanMessage(content=query)]})
|
||||
return {"agent_response": response['output']}
|
||||
```
|
||||
|
||||
### 6. Store New Interactions
|
||||
Every new query/response pair is stored for future personalization:
|
||||
|
||||
```python
|
||||
def store_search_interaction(user_id, original_query, agent_response):
|
||||
interaction = [
|
||||
{"role": "user", "content": f"Searched for: {original_query}"},
|
||||
{"role": "assistant", "content": f"Results based on preferences: {agent_response}"}
|
||||
]
|
||||
mem0_client.add(messages=interaction, user_id=user_id, output_format="v1.1")
|
||||
```
|
||||
|
||||
### Full Example Run
|
||||
|
||||
```python
|
||||
if __name__ == "__main__":
|
||||
user_id = "john"
|
||||
setup_user_history(user_id)
|
||||
|
||||
queries = [
|
||||
"good coffee shops nearby for working",
|
||||
"what can I make for my kid in lunch?"
|
||||
]
|
||||
|
||||
for q in queries:
|
||||
results = conduct_personalized_search(user_id, q)
|
||||
print(f"\nQuery: {q}")
|
||||
print(f"Personalized Response: {results['agent_response']}")
|
||||
```
|
||||
|
||||
## How It Works in Practice
|
||||
Here’s how personalization plays out:
|
||||
|
||||
- Context Gathering: User previously mentioned living in Los Angeles, being vegan, and having a 7-year-old daughter allergic to peanuts.
|
||||
- Enhanced Search Query:
|
||||
Query -> "good coffee shops nearby for working"
|
||||
Enhanced Query -> "good coffee shops in Los Angeles with strong wifi, remote-work-friendly"
|
||||
- Personalized Results: The assistant only returns wifi-friendly, work-friendly cafes near Los Angeles.
|
||||
- Memory Update: Interaction is saved for better future recommendations.
|
||||
|
||||
## Conclusion
|
||||
With Mem0 + Tavily, you can build a search assistant that doesn’t just fetch results but it understands the person behind the query.
|
||||
|
||||
Whether for shopping, travel, or daily life, this approach turns a generic search into a truly personalized experience.
|
||||
|
||||
Full Code: [Personalized Search GitHub](https://github.com/mem0ai/mem0/blob/main/examples/misc/personalized_search.py)
|
||||
Reference in New Issue
Block a user