Enhanced reranker and updated corresponding docs

docs/open-source/features/overview.mdx

@@ -26,6 +26,7 @@ Mem0 open-source provides a powerful, flexible foundation for AI memory manageme
 ### Memory Management
 - **Synchronous & Asynchronous Operations**: Choose between sync and async memory operations based on your application needs
 - **Smart Memory Retrieval**: Intelligent search and retrieval with semantic understanding
+- **Advanced Reranking**: Improve search relevance with Zero Entropy, LLM-based, or custom reranking models
 - **Memory Persistence**: Long-term storage with automatic optimization and cleanup
 
 ### Advanced Organization

docs/open-source/features/reranking.mdx

@@ -0,0 +1,130 @@
+---
+title: Reranking
+description: 'Improve memory search relevance with advanced reranking capabilities'
+icon: "arrow-up-arrow-down"
+iconType: "solid"
+---
+
+## Overview
+
+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.
+
+<Note>
+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.
+</Note>
+
+## How It Works
+
+1. **Vector Search**: Initial semantic similarity search retrieves candidate memories
+2. **Reranking**: Selected reranker re-scores candidates using advanced models  
+3. **Final Results**: Re-ordered results with both vector and rerank scores
+
+## Quick Start
+
+Enable reranking by adding a `rerank` section to your memory configuration:
+
+```python Python
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai", 
+        "config": {
+            "model": "gpt-4o-mini"
+        }
+    },
+    "rerank": {
+        "provider": "zero_entropy",
+        "config": {
+            "model": "zerank-1",
+            "top_k": 5
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+
+# Add memories
+messages = [
+    {"role": "user", "content": "I love Italian pasta, especially carbonara"},
+    {"role": "assistant", "content": "Carbonara is a classic Roman dish!"}
+]
+
+memory.add(messages, user_id="alice")
+
+# Search with reranking - results automatically include rerank scores
+results = memory.search("What Italian dishes does the user like?", user_id="alice")
+
+for result in results['results']:
+    print(f"Memory: {result['memory']}")
+    print(f"Vector Score: {result['score']:.3f}")
+    print(f"Rerank Score: {result['rerank_score']:.3f}")
+```
+
+## Supported Providers
+
+Mem0 supports multiple reranking providers:
+
+- **[Zero Entropy](../../components/rerankers/models/zero_entropy)**: State-of-the-art neural reranking
+- **[Cohere](../../components/rerankers/models/cohere)**: Enterprise-grade with multilingual support
+- **[Sentence Transformer](../../components/rerankers/models/sentence_transformer)**: Local HuggingFace models
+- **[LLM-based](../../components/rerankers/models/llm)**: Custom scoring using any LLM
+
+## When to Use Reranking
+
+Reranking is particularly effective for:
+
+- **Improved Relevance**: When vector search alone doesn't provide sufficiently relevant results
+- **Domain-Specific Queries**: Specialized terminology or context that benefits from advanced models
+- **Customer Support**: Finding the most relevant help articles and documentation
+- **Knowledge Management**: Better search results in internal knowledge bases
+- **Personal AI Assistants**: More accurate memory recall for user queries
+
+## Configuration Options
+
+Each reranker has specific configuration options. See the [Rerankers Documentation](../../components/rerankers/overview) for detailed configuration parameters.
+
+### Basic Configuration
+
+```python Python
+"rerank": {
+    "provider": "zero_entropy",  # or "cohere", "sentence_transformer", "llm"
+    "config": {
+        "top_k": 5,              # Limit results after reranking
+        "api_key": "your-key"    # Provider-specific API key
+    }
+}
+```
+
+### Controlling Reranking
+
+You can enable or disable reranking per search:
+
+```python Python
+# Search with reranking (default when configured)
+results = memory.search("query", user_id="alice", rerank=True)
+
+# Search without reranking
+results = memory.search("query", user_id="alice", rerank=False)
+```
+
+## Performance Considerations
+
+- **Latency**: Reranking adds processing time but significantly improves relevance
+- **Cost**: API-based rerankers (Zero Entropy, Cohere, LLM) have per-request costs
+- **Local Options**: Sentence Transformer reranker runs locally with no API costs
+- **Quality vs Speed**: Balance based on your application's requirements
+
+## Next Steps
+
+- Explore specific [reranker providers](../../components/rerankers/overview) and their capabilities
+- Learn about [configuration options](../../components/rerankers/config) for fine-tuning
+- Check out [Vector Stores](../../components/vectordbs/overview) for different storage backends
+- See [Async Memory](./async-memory) for non-blocking reranking operations