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95 lines
3.5 KiB
Plaintext
95 lines
3.5 KiB
Plaintext
---
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title: Overview
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description: 'Pick the right reranker path to boost Mem0 search relevance.'
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---
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Mem0 rerankers rescore vector search hits so your agents surface the most relevant memories. Use this hub to decide when reranking helps, configure a provider, and fine-tune performance.
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<Info>
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Reranking trades extra latency for better precision. Start once you have baseline search working and measure before/after relevance.
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</Info>
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## Supported Rerankers
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<CardGroup cols={3}>
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<Card title="Cohere" icon="/images/provider-icons/cohere.svg" href="/components/rerankers/models/cohere" />
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<Card title="Sentence Transformers" icon="vector-square" href="/components/rerankers/models/sentence_transformer" />
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<Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/rerankers/models/huggingface" />
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<Card title="LLM Reranker" icon="wand-magic-sparkles" href="/components/rerankers/models/llm_reranker" />
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<Card title="Zero Entropy" icon="/images/provider-icons/zeroentropy.svg" href="/components/rerankers/models/zero_entropy" />
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</CardGroup>
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<Note>
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All five rerankers are available in both the Python and the [TypeScript](/open-source/features/reranker-search#typescript-sdk) self-hosted SDKs. Each provider page has a **TypeScript (self-hosted)** section with the camelCase config.
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</Note>
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## Reranking Workflow
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<CardGroup cols={3}>
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<Card
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title="Understand Reranking"
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description="See how reranker-enhanced search changes your retrieval flow."
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icon="search"
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href="/open-source/features/reranker-search"
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/>
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<Card
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title="Configure Providers"
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description="Add reranker blocks to your memory configuration."
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icon="gear"
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href="/components/rerankers/config"
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/>
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<Card
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title="Optimize Performance"
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description="Balance relevance, latency, and cost with tuning tactics."
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icon="gauge"
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href="/components/rerankers/optimization"
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/>
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<Card
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title="Custom Prompts"
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description="Shape LLM-based reranking with tailored instructions."
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icon="code"
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href="/components/rerankers/custom-prompts"
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/>
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<Card
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title="Zero Entropy Guide"
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description="Adopt the managed neural reranker for production workloads."
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icon="sparkles"
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href="/components/rerankers/models/zero_entropy"
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/>
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<Card
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title="Sentence Transformers"
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description="Keep reranking on-device with cross-encoder models."
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icon="microchip"
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href="/components/rerankers/models/sentence_transformer"
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/>
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</CardGroup>
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## Picking the Right Reranker
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- **API-first** when you need top quality and can absorb request costs (Cohere, Zero Entropy).
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- **Self-hosted** for privacy-sensitive deployments that must stay on your hardware (Sentence Transformer, Hugging Face).
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- **LLM-driven** when you need bespoke scoring logic or complex prompts.
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- **Hybrid** by enabling reranking only on premium journeys to control spend.
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## Implementation Checklist
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1. Confirm baseline search KPIs so you can measure uplift.
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2. Select a provider and add the `reranker` block to your config.
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3. Test latency impact with production-like query batches.
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4. Decide whether to enable reranking globally or per-search via the `rerank` flag.
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<CardGroup cols={2}>
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<Card
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title="Set Up Reranking"
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description="Walk through the configuration fields and defaults."
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icon="gear"
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href="/components/rerankers/config"
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/>
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<Card
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title="Example: Reranker Search"
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description="Follow the feature guide to see reranking in action."
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icon="rocket"
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href="/open-source/features/reranker-search"
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/>
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</CardGroup>
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