Parshva Daftari
71 lines
2.9 KiB
Plaintext
71 lines
2.9 KiB
Plaintext
---
|
|
title: Overview
|
|
icon: "arrow-up-arrow-down"
|
|
iconType: "solid"
|
|
---
|
|
|
|
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
|
|
|
|
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
|
|
# Reranking enabled (default)
|
|
results = memory.search("query", user_id="user1")
|
|
|
|
# Explicitly enable reranking
|
|
results = memory.search("query", user_id="user1", rerank=True)
|
|
|
|
# Disable reranking for this specific search
|
|
results = memory.search("query", user_id="user1", rerank=False)
|
|
```
|
|
|
|
## 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
|