Mem0 1.0.0 (#3545)

docs/components/rerankers/overview.mdx

@@ -1,52 +1,70 @@
 ---
 title: Overview
-icon: "info"
+icon: "arrow-up-arrow-down"
 iconType: "solid"
 ---
 
-Rerankers enhance the quality of search results by re-ordering the initial retrieval results using more sophisticated scoring mechanisms. They act as a secondary ranking layer that can significantly improve the relevance of retrieved memories.
-
-## How Rerankers Work
-
-1. **Initial Retrieval**: Vector search returns candidate memories based on semantic similarity
-2. **Reranking**: The reranker evaluates and re-scores these candidates using more complex criteria
-3. **Final Results**: Returns the top-k memories with improved relevance ordering
-
-## Benefits
-
-- **Improved Precision**: Better ranking of relevant memories
-- **Context Awareness**: More sophisticated understanding of query-memory relationships
-- **Performance**: Can improve results without changing the underlying vector store
-
-## Supported Rerankers
-
-Mem0 supports several reranker models:
-
-<CardGroup cols={2}>
-  <Card title="Cohere" href="/components/rerankers/models/cohere" />
-  <Card title="Sentence Transformer" href="/components/rerankers/models/sentence_transformer" />
-  <Card title="Hugging Face" href="/components/rerankers/models/huggingface" />
-  <Card title="LLM Reranker" href="/components/rerankers/models/llm_reranker" />
-</CardGroup>
+Mem0 includes built-in support for various reranking providers to improve the relevance of memory search results. Rerankers post-process initial vector search results by re-scoring and re-ordering them using more sophisticated relevance models.
 
 ## Usage
 
-Rerankers are configured as part of the memory configuration:
+To use a reranker:
+
+1. **Configure**: Add a `rerank` configuration section in your memory config
+2. **Search**: Reranking is automatically enabled for all searches (default: `rerank=True`)
+
+If no reranker is configured, search results will rely on vector similarity scoring alone.
+
+For comprehensive configuration parameters for each reranker, please refer to [Config](./config).
+
+### Controlling Reranking Per Search
+
+Once configured, reranking is enabled by default. You can control it per-search:
 
 ```python
-from mem0 import Memory
+# Reranking enabled (default)
+results = memory.search("query", user_id="user1")
 
-config = {
-    "reranker": {
-        "provider": "cohere",
-        "config": {
-            "api_key": "your-api-key",
-            "top_n": 10
-        }
-    }
-}
+# Explicitly enable reranking
+results = memory.search("query", user_id="user1", rerank=True)
 
-memory = Memory.from_config(config)
+# Disable reranking for this specific search
+results = memory.search("query", user_id="user1", rerank=False)
 ```
 
-For detailed configuration options, see the [Config](./config) page.
+## How Reranking Works
+
+1. **Initial Search**: Vector similarity search retrieves candidate memories
+2. **Reranking** (if enabled): Selected reranker re-scores candidates using advanced models
+3. **Final Results**: Re-ordered results with both vector and rerank scores
+
+<Note>
+Reranking operates as a post-processing step and can significantly improve search relevance at the cost of additional latency and API calls.
+</Note>
+
+## Supported Rerankers
+
+See the list of supported rerankers below.
+
+<CardGroup cols={2}>
+  <Card title="Zero Entropy" href="/components/rerankers/models/zero_entropy" />
+  <Card title="Cohere" href="/components/rerankers/models/cohere" />
+  <Card title="Sentence Transformer" href="/components/rerankers/models/sentence_transformer" />
+  <Card title="Hugging Face" href="/components/rerankers/models/huggingface" />
+  <Card title="LLM-based" href="/components/rerankers/models/llm" />
+  <Card title="LLM Reranker" href="/components/rerankers/models/llm_reranker" />
+</CardGroup>
+
+## When to Use Reranking
+
+- **Improved Relevance**: When vector search alone doesn't provide sufficiently relevant results
+- **Domain-Specific Queries**: For specialized terminology or context that benefits from advanced models
+- **Quality vs Speed Trade-off**: When you can accept higher latency for better search quality
+- **Production Systems**: Where search quality directly impacts user experience
+
+Choose the reranker that best fits your use case:
+- **Zero Entropy**: Best balance of speed and quality for general use
+- **Cohere**: Enterprise-grade with excellent multilingual support
+- **Sentence Transformer**: Local deployment for privacy-sensitive applications
+- **Hugging Face**: Wide variety of pre-trained models for specialized use cases
+- **LLM-based**: Maximum customization with custom prompts and logic