Parth Sharma
69 lines
2.8 KiB
Plaintext
69 lines
2.8 KiB
Plaintext
---
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title: Overview
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---
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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.
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## Usage
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To use a reranker:
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1. **Configure**: Add a `rerank` configuration section in your memory config
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2. **Search**: Reranking is automatically enabled for all searches (default: `rerank=True`)
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If no reranker is configured, search results will rely on vector similarity scoring alone.
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For comprehensive configuration parameters for each reranker, please refer to [Config](./config).
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### Controlling Reranking Per Search
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Once configured, reranking is enabled by default. You can control it per-search:
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```python
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# Reranking enabled (default)
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results = memory.search("query", user_id="user1")
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# Explicitly enable reranking
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results = memory.search("query", user_id="user1", rerank=True)
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# Disable reranking for this specific search
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results = memory.search("query", user_id="user1", rerank=False)
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```
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## How Reranking Works
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1. **Initial Search**: Vector similarity search retrieves candidate memories
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2. **Reranking** (if enabled): 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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<Note>
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Reranking operates as a post-processing step and can significantly improve search relevance at the cost of additional latency and API calls.
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</Note>
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## Supported Rerankers
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See the list of supported rerankers below.
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<CardGroup cols={2}>
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<Card title="Zero Entropy" href="/components/rerankers/models/zero_entropy" />
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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-based" href="/components/rerankers/models/llm" />
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<Card title="LLM Reranker" href="/components/rerankers/models/llm_reranker" />
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</CardGroup>
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## When to Use Reranking
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- **Improved Relevance**: When vector search alone doesn't provide sufficiently relevant results
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- **Domain-Specific Queries**: For specialized terminology or context that benefits from advanced models
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- **Quality vs Speed Trade-off**: When you can accept higher latency for better search quality
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- **Production Systems**: Where search quality directly impacts user experience
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Choose the reranker that best fits your use case:
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- **Zero Entropy**: Best balance of speed and quality for general use
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- **Cohere**: Enterprise-grade with excellent multilingual support
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- **Sentence Transformer**: Local deployment for privacy-sensitive applications
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- **Hugging Face**: Wide variety of pre-trained models for specialized use cases
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- **LLM-based**: Maximum customization with custom prompts and logic
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