Parshva Daftari
52 lines
1.6 KiB
Plaintext
52 lines
1.6 KiB
Plaintext
---
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title: Overview
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icon: "info"
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iconType: "solid"
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---
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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.
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## How Rerankers Work
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1. **Initial Retrieval**: Vector search returns candidate memories based on semantic similarity
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2. **Reranking**: The reranker evaluates and re-scores these candidates using more complex criteria
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3. **Final Results**: Returns the top-k memories with improved relevance ordering
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## Benefits
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- **Improved Precision**: Better ranking of relevant memories
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- **Context Awareness**: More sophisticated understanding of query-memory relationships
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- **Performance**: Can improve results without changing the underlying vector store
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## Supported Rerankers
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Mem0 supports several reranker models:
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<CardGroup cols={2}>
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<Card title="Cohere" href="/components/rerankers/models/cohere" />
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<Card title="Sentence Transformer" href="/components/rerankers/models/sentence_transformer" />
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<Card title="Hugging Face" href="/components/rerankers/models/huggingface" />
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<Card title="LLM Reranker" href="/components/rerankers/models/llm_reranker" />
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</CardGroup>
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## Usage
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Rerankers are configured as part of the memory configuration:
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```python
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from mem0 import Memory
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config = {
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"reranker": {
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"provider": "cohere",
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"config": {
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"api_key": "your-api-key",
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"top_n": 10
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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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```
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For detailed configuration options, see the [Config](./config) page. |