Mem0 1.0.0 (#3545)
This commit is contained in:
Parshva Daftari
@@ -1,116 +1,147 @@
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---
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title: Cohere
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description: 'Reranking with Cohere'
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icon: "building"
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iconType: "solid"
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---
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Cohere provides state-of-the-art reranking models that can significantly improve the relevance of search results. Cohere's rerankers are optimized for various languages and use cases.
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Cohere provides enterprise-grade reranking models with excellent multilingual support and production-ready performance.
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## Usage
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## Models
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To use Cohere's reranker with Mem0:
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Cohere offers several reranking models:
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```python
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import os
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- **`rerank-english-v3.0`**: Latest English reranker with best performance
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- **`rerank-multilingual-v3.0`**: Multilingual support for global applications
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- **`rerank-english-v2.0`**: Previous generation English reranker
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## Installation
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```bash
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pip install cohere
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```
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## Configuration
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```python Python
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from mem0 import Memory
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os.environ["COHERE_API_KEY"] = "your-cohere-api-key"
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config = {
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"vector_store": {
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"provider": "chroma",
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"config": {
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"collection_name": "my_memories",
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"path": "./chroma_db"
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}
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},
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4o-mini"
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}
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},
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"reranker": {
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"provider": "cohere",
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"config": {
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"api_key": "your-cohere-api-key", # Can also use environment variable
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"model": "rerank-english-v3.0",
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"top_n": 10
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"api_key": "your-cohere-api-key", # or set COHERE_API_KEY
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"top_k": 5,
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"return_documents": False,
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"max_chunks_per_doc": None
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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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## Environment Variables
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Set your API key as an environment variable:
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```bash
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export COHERE_API_KEY="your-api-key"
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```
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## Usage Example
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```python Python
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import os
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from mem0 import Memory
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# Set API key
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os.environ["COHERE_API_KEY"] = "your-api-key"
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# Initialize memory with Cohere reranker
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config = {
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"vector_store": {"provider": "chroma"},
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"llm": {"provider": "openai", "config": {"model": "gpt-4o-mini"}},
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"rerank": {
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"provider": "cohere",
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"config": {
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"model": "rerank-english-v3.0",
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"top_k": 3
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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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# Use memory as usual
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memory.add("I love playing basketball", user_id="alice")
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memory.add("I enjoy watching movies", user_id="alice")
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# Add memories
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messages = [
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{"role": "user", "content": "I work as a data scientist at Microsoft"},
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{"role": "user", "content": "I specialize in machine learning and NLP"},
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{"role": "user", "content": "I enjoy playing tennis on weekends"}
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]
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# Search will now use Cohere reranking
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results = memory.search("What sports does Alice like?", user_id="alice")
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memory.add(messages, user_id="bob")
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# Search with reranking
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results = memory.search("What is the user's profession?", user_id="bob")
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for result in results['results']:
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print(f"Memory: {result['memory']}")
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print(f"Vector Score: {result['score']:.3f}")
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print(f"Rerank Score: {result['rerank_score']:.3f}")
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print()
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```
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## Configuration
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## Multilingual Support
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `api_key` | Cohere API key | Required |
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| `model` | Cohere rerank model | `rerank-english-v3.0` |
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| `top_n` | Number of results to return | `10` |
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For multilingual applications, use the multilingual model:
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## Available Models
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- `rerank-english-v3.0`: Latest English reranking model
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- `rerank-multilingual-v3.0`: Multilingual reranking model
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- `rerank-english-v2.0`: Previous English model
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- `rerank-multilingual-v2.0`: Previous multilingual model
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## Example with Different Models
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### English Reranker
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```python
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```python Python
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config = {
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"reranker": {
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"rerank": {
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"provider": "cohere",
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"config": {
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"api_key": "your-cohere-api-key",
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"model": "rerank-english-v3.0",
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"top_n": 5
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}
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}
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}
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```
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### Multilingual Reranker
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```python
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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-cohere-api-key",
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"model": "rerank-multilingual-v3.0",
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"top_n": 8
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"top_k": 5
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}
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}
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}
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```
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## Environment Variables
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## Configuration Parameters
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You can set your Cohere API key as an environment variable:
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| Parameter | Description | Type | Default |
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|-----------|-------------|------|---------|
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| `model` | Cohere rerank model to use | `str` | `"rerank-english-v3.0"` |
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| `api_key` | Cohere API key | `str` | `None` |
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| `top_k` | Maximum documents to return | `int` | `None` |
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| `return_documents` | Whether to return document texts | `bool` | `False` |
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| `max_chunks_per_doc` | Maximum chunks per document | `int` | `None` |
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```bash
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export COHERE_API_KEY="your-cohere-api-key"
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```
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## Features
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Then use the config without specifying the API key:
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- **High Quality**: Enterprise-grade relevance scoring
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- **Multilingual**: Support for 100+ languages
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- **Scalable**: Production-ready with high throughput
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- **Reliable**: SLA-backed service with 99.9% uptime
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```python
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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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"model": "rerank-english-v3.0",
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"top_n": 10
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}
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}
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}
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```
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## Best Practices
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## Getting Your API Key
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1. Sign up at [Cohere](https://cohere.ai/)
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2. Navigate to the API keys section in your dashboard
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3. Generate a new API key
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4. Use this key in your configuration
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## Performance Considerations
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- Cohere rerankers work best with 10-100 candidate documents
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- Higher `top_n` values provide more comprehensive reranking but may increase latency
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- The v3.0 models generally provide better performance than v2.0 models
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1. **Model Selection**: Use `rerank-english-v3.0` for English, `rerank-multilingual-v3.0` for other languages
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2. **Batch Processing**: Process multiple queries efficiently
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3. **Error Handling**: Implement retry logic for production systems
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4. **Monitoring**: Track reranking performance and costs
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