parshvadaftari
130 lines
4.5 KiB
Plaintext
130 lines
4.5 KiB
Plaintext
---
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title: Reranking
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description: 'Improve memory search relevance with advanced reranking capabilities'
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icon: "arrow-up-arrow-down"
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iconType: "solid"
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---
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## Overview
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Reranking is an advanced feature that improves the relevance of memory search results by re-ordering them based on more sophisticated relevance scoring. After initial vector similarity search, rerankers use specialized models to provide more accurate relevance scores.
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<Note>
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Reranking operates as a post-processing step after the initial vector search. It takes the top results from vector similarity search and re-scores them using more advanced models or custom logic.
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</Note>
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## How It Works
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1. **Vector Search**: Initial semantic similarity search retrieves candidate memories
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2. **Reranking**: Selected reranker re-scores candidates using advanced models
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3. **Final Results**: Re-ordered results with both vector and rerank scores
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## Quick Start
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Enable reranking by adding a `rerank` section to your memory configuration:
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```python Python
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from mem0 import Memory
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config = {
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"vector_store": {
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"provider": "chroma",
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"config": {
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"collection_name": "my_memories",
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"path": "./chroma_db"
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}
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},
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4o-mini"
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}
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},
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"rerank": {
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"provider": "zero_entropy",
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"config": {
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"model": "zerank-1",
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"top_k": 5
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}
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}
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}
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memory = Memory.from_config(config)
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# Add memories
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messages = [
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{"role": "user", "content": "I love Italian pasta, especially carbonara"},
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{"role": "assistant", "content": "Carbonara is a classic Roman dish!"}
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]
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memory.add(messages, user_id="alice")
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# Search with reranking - results automatically include rerank scores
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results = memory.search("What Italian dishes does the user like?", user_id="alice")
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for result in results['results']:
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print(f"Memory: {result['memory']}")
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print(f"Vector Score: {result['score']:.3f}")
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print(f"Rerank Score: {result['rerank_score']:.3f}")
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```
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## Supported Providers
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Mem0 supports multiple reranking providers:
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- **[Zero Entropy](../../components/rerankers/models/zero_entropy)**: State-of-the-art neural reranking
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- **[Cohere](../../components/rerankers/models/cohere)**: Enterprise-grade with multilingual support
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- **[Sentence Transformer](../../components/rerankers/models/sentence_transformer)**: Local HuggingFace models
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- **[LLM-based](../../components/rerankers/models/llm)**: Custom scoring using any LLM
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## When to Use Reranking
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Reranking is particularly effective for:
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- **Improved Relevance**: When vector search alone doesn't provide sufficiently relevant results
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- **Domain-Specific Queries**: Specialized terminology or context that benefits from advanced models
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- **Customer Support**: Finding the most relevant help articles and documentation
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- **Knowledge Management**: Better search results in internal knowledge bases
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- **Personal AI Assistants**: More accurate memory recall for user queries
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## Configuration Options
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Each reranker has specific configuration options. See the [Rerankers Documentation](../../components/rerankers/overview) for detailed configuration parameters.
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### Basic Configuration
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```python Python
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"rerank": {
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"provider": "zero_entropy", # or "cohere", "sentence_transformer", "llm"
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"config": {
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"top_k": 5, # Limit results after reranking
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"api_key": "your-key" # Provider-specific API key
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}
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}
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```
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### Controlling Reranking
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You can enable or disable reranking per search:
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```python Python
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# Search with reranking (default when configured)
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results = memory.search("query", user_id="alice", rerank=True)
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# Search without reranking
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results = memory.search("query", user_id="alice", rerank=False)
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```
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## Performance Considerations
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- **Latency**: Reranking adds processing time but significantly improves relevance
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- **Cost**: API-based rerankers (Zero Entropy, Cohere, LLM) have per-request costs
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- **Local Options**: Sentence Transformer reranker runs locally with no API costs
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- **Quality vs Speed**: Balance based on your application's requirements
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## Next Steps
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- Explore specific [reranker providers](../../components/rerankers/overview) and their capabilities
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- Learn about [configuration options](../../components/rerankers/config) for fine-tuning
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- Check out [Vector Stores](../../components/vectordbs/overview) for different storage backends
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- See [Async Memory](./async-memory) for non-blocking reranking operations |