Reranker-enhanced search is available in Mem0 1.0.0 Beta and later versions. This feature significantly improves search relevance by using specialized reranking models to reorder search results.
Overview
Rerankers are specialized models that improve the quality of search results by reordering initially retrieved memories. They work as a second-stage ranking system that analyzes the semantic relationship between your query and retrieved memories to provide more relevant results.
How Reranking Works
- Initial Vector Search: Retrieves candidate memories using vector similarity
- Reranking: Specialized model analyzes query-memory relationships
- Reordering: Results are reordered based on semantic relevance
- Enhanced Results: Final results with improved relevance scores
Configuration
Basic Setup
from mem0 import Memory
config = {
"reranker": {
"provider": "cohere",
"config": {
"model": "rerank-english-v3.0",
"api_key": "your-cohere-api-key"
}
}
}
m = Memory.from_config(config)
Supported Providers
Cohere Reranker
config = {
"reranker": {
"provider": "cohere",
"config": {
"model": "rerank-english-v3.0",
"api_key": "your-cohere-api-key",
"top_k": 10, # Number of results to rerank
"return_documents": True
}
}
}
config = {
"reranker": {
"provider": "sentence_transformer",
"config": {
"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
"device": "cuda", # Use GPU if available
"max_length": 512
}
}
}
Hugging Face Reranker
config = {
"reranker": {
"provider": "huggingface",
"config": {
"model": "BAAI/bge-reranker-base",
"device": "cuda",
"batch_size": 32
}
}
}
LLM-based Reranker
config = {
"reranker": {
"provider": "llm_reranker",
"config": {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4",
"api_key": "your-openai-api-key"
}
},
"top_k": 5
}
}
}
Usage Examples
Basic Reranked Search
# Reranking is enabled by default when configured
results = m.search(
"What are my food preferences?",
user_id="alice"
)
# Results are automatically reranked for better relevance
for result in results["results"]:
print(f"Memory: {result['memory']}")
print(f"Score: {result['score']}")
Controlling Reranking
# Enable reranking explicitly
results_with_rerank = m.search(
"What movies do I like?",
user_id="alice",
rerank=True
)
# Disable reranking for this search
results_without_rerank = m.search(
"What movies do I like?",
user_id="alice",
rerank=False
)
# Compare the difference in results
print("With reranking:", len(results_with_rerank["results"]))
print("Without reranking:", len(results_without_rerank["results"]))
Combining with Filters
# Reranking works with metadata filtering
results = m.search(
"important work tasks",
user_id="alice",
filters={
"AND": [
{"category": "work"},
{"priority": {"gte": 7}}
]
},
rerank=True,
limit=20
)
Advanced Configuration
Complete Configuration Example
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333
}
},
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4",
"api_key": "your-openai-api-key"
}
},
"embedder": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
"api_key": "your-openai-api-key"
}
},
"reranker": {
"provider": "cohere",
"config": {
"model": "rerank-english-v3.0",
"api_key": "your-cohere-api-key",
"top_k": 15,
"return_documents": True
}
}
}
m = Memory.from_config(config)
Async Support
from mem0 import AsyncMemory
# Reranking works with async operations
async_memory = AsyncMemory.from_config(config)
async def search_with_rerank():
results = await async_memory.search(
"What are my preferences?",
user_id="alice",
rerank=True
)
return results
# Use in async context
import asyncio
results = asyncio.run(search_with_rerank())
When to Use Reranking
✅ Good Use Cases:
- Complex semantic queries
- Domain-specific searches
- When precision is more important than speed
- Large memory collections
- Ambiguous or nuanced queries
❌ Avoid When:
- Simple keyword matching
- Real-time applications with strict latency requirements
- Small memory collections
- High-frequency searches where cost matters
# Optimize reranking performance
config = {
"reranker": {
"provider": "sentence_transformer",
"config": {
"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
"device": "cuda", # Use GPU
"batch_size": 32, # Process in batches
"top_k": 10, # Limit candidates
"max_length": 256 # Reduce if appropriate
}
}
}
Cost Management
# For API-based rerankers like Cohere
config = {
"reranker": {
"provider": "cohere",
"config": {
"model": "rerank-english-v3.0",
"api_key": "your-cohere-api-key",
"top_k": 5, # Reduce to control API costs
}
}
}
# Use reranking selectively
def smart_search(query, user_id, use_rerank=None):
# Automatically decide when to use reranking
if use_rerank is None:
use_rerank = len(query.split()) > 3 # Complex queries only
return m.search(query, user_id=user_id, rerank=use_rerank)
Error Handling
try:
results = m.search(
"test query",
user_id="alice",
rerank=True
)
except Exception as e:
print(f"Reranking failed: {e}")
# Gracefully fall back to vector search
results = m.search(
"test query",
user_id="alice",
rerank=False
)
Reranker Comparison
| Provider | Latency | Quality | Cost | Local Deploy |
|---|
| Cohere | Medium | High | API Cost | ❌ |
| Sentence Transformer | Low | Good | Free | ✅ |
| Hugging Face | Low-Medium | Variable | Free | ✅ |
| LLM Reranker | High | Very High | API Cost | Depends |
Real-world Examples
Customer Support
# Improve support ticket relevance
config = {
"reranker": {
"provider": "cohere",
"config": {
"model": "rerank-english-v3.0",
"api_key": "your-cohere-api-key"
}
}
}
m = Memory.from_config(config)
# Find relevant support cases
results = m.search(
"customer having login issues with mobile app",
agent_id="support_bot",
filters={"category": "technical_support"},
rerank=True
)
Content Recommendation
# Better content matching
results = m.search(
"science fiction books with space exploration themes",
user_id="reader123",
filters={"content_type": "book_recommendation"},
rerank=True,
limit=10
)
for result in results["results"]:
print(f"Recommendation: {result['memory']}")
print(f"Relevance: {result['score']:.3f}")
Personal Assistant
# Enhanced personal queries
results = m.search(
"What restaurants did I enjoy last month that had good vegetarian options?",
user_id="foodie_user",
filters={
"AND": [
{"category": "dining"},
{"rating": {"gte": 4}},
{"date": {"gte": "2024-01-01"}}
]
},
rerank=True
)
Migration Guide
From v0.x (No Reranking)
# v0.x - basic vector search
results = m.search("query", user_id="alice")
To v1.0.0 Beta (With Reranking)
# Add reranker configuration
config = {
"reranker": {
"provider": "sentence_transformer",
"config": {
"model": "cross-encoder/ms-marco-MiniLM-L-6-v2"
}
}
}
m = Memory.from_config(config)
# Same search API, better results
results = m.search("query", user_id="alice") # Automatically reranked
Best Practices
- Start Simple: Begin with Sentence Transformers for local deployment
- Monitor Performance: Track both relevance improvements and latency
- Cost Awareness: Use API-based rerankers judiciously
- Selective Usage: Apply reranking where it provides the most value
- Fallback Strategy: Always handle reranking failures gracefully
- Test Different Models: Experiment to find the best fit for your domain
Reranker-enhanced search significantly improves result relevance. Start with a local model and upgrade to API-based solutions as your needs grow.
