Mem0 1.0.0 (#3545)
This commit is contained in:
Parshva Daftari
@@ -1,162 +1,161 @@
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---
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title: Sentence Transformer
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description: 'Local reranking with HuggingFace cross-encoder models'
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icon: "server"
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iconType: "solid"
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---
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Sentence Transformer rerankers use cross-encoder models that are specifically designed for ranking tasks. These models can run locally and provide good reranking performance without external API calls.
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Sentence Transformer reranker provides local reranking using HuggingFace cross-encoder models, perfect for privacy-focused deployments where you want to keep data on-premises.
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## Usage
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## Models
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To use Sentence Transformer reranker with Mem0:
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Any HuggingFace cross-encoder model can be used. Popular choices include:
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```python
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- **`cross-encoder/ms-marco-MiniLM-L-6-v2`**: Default, good balance of speed and accuracy
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- **`cross-encoder/ms-marco-TinyBERT-L-2-v2`**: Fastest, smaller model size
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- **`cross-encoder/ms-marco-electra-base`**: Higher accuracy, larger model
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- **`cross-encoder/stsb-distilroberta-base`**: Good for semantic similarity tasks
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## Installation
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```bash
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pip install sentence-transformers
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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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config = {
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"reranker": {
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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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"rerank": {
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"provider": "sentence_transformer",
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"config": {
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"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
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"device": "cpu",
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"top_n": 10
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"device": "cpu", # or "cuda" for GPU
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"batch_size": 32,
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"show_progress_bar": False,
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"top_k": 5
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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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## GPU Acceleration
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For better performance, use GPU acceleration:
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```python Python
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config = {
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"rerank": {
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"provider": "sentence_transformer",
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"config": {
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"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
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"device": "cuda", # Use GPU
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"batch_size": 64 # high batch size for high memory GPUs
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}
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}
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}
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```
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## Usage Example
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```python Python
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from mem0 import Memory
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# Initialize memory with local 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": "sentence_transformer",
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"config": {
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"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
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"device": "cpu"
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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 love reading science fiction novels"},
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{"role": "user", "content": "My favorite author is Isaac Asimov"},
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{"role": "user", "content": "I also enjoy watching sci-fi movies"}
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]
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# Search will now use Sentence Transformer reranking
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results = memory.search("What sports does Alice like?", user_id="alice")
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```
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memory.add(messages, user_id="charlie")
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## Configuration
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# Search with local reranking
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results = memory.search("What books does the user like?", user_id="charlie")
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `model` | Sentence Transformer cross-encoder model | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
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| `device` | Device to run on (`cpu`, `cuda`, `mps`) | `cpu` |
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| `top_n` | Number of results to return | `10` |
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## Popular Models
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### Lightweight Models
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- `cross-encoder/ms-marco-MiniLM-L-6-v2`: Fast and efficient
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- `cross-encoder/ms-marco-MiniLM-L-4-v2`: Even faster, slightly lower accuracy
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- `cross-encoder/ms-marco-MiniLM-L-2-v2`: Fastest, good for real-time applications
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### High-Performance Models
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- `cross-encoder/ms-marco-electra-base`: Better accuracy, larger model
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- `ms-marco-MiniLM-L-12-v2`: Balanced performance and speed
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- `cross-encoder/qnli-electra-base`: Good for question-answering tasks
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## Device Configuration
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### CPU Usage
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```python
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config = {
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"reranker": {
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"provider": "sentence_transformer",
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"config": {
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"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
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"device": "cpu",
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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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### GPU Usage (CUDA)
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```python
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config = {
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"reranker": {
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"provider": "sentence_transformer",
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"config": {
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"model": "cross-encoder/ms-marco-electra-base",
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"device": "cuda",
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"top_n": 15
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}
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}
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}
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```
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### Apple Silicon (MPS)
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```python
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config = {
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"reranker": {
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"provider": "sentence_transformer",
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"config": {
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"model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
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"device": "mps",
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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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## Installation
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The sentence-transformers library is required:
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```bash
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pip install sentence-transformers
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```
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For GPU support with CUDA:
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```bash
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pip install sentence-transformers torch
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```
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## Performance Optimization
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### Model Selection
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- Use MiniLM models for faster inference
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- Use larger models (electra-base) for better accuracy
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- Consider the trade-off between speed and quality
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### Device Optimization
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- Use GPU (`cuda` or `mps`) for larger models
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- CPU is sufficient for MiniLM models
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- Batch processing improves GPU utilization
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### Memory Considerations
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```python
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# For memory-constrained environments
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config = {
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"reranker": {
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"provider": "sentence_transformer",
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"config": {
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"model": "cross-encoder/ms-marco-MiniLM-L-2-v2", # Smallest model
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"device": "cpu",
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"top_n": 5 # Fewer results to process
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}
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}
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}
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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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## Custom Models
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You can use any Sentence Transformer cross-encoder model:
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You can use any HuggingFace cross-encoder model:
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```python
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```python Python
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# Using a different model
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config = {
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"reranker": {
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"provider": "sentence_transformer",
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"rerank": {
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"provider": "sentence_transformer",
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"config": {
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"model": "your-custom-model-name",
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"device": "cpu",
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"top_n": 10
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"model": "cross-encoder/stsb-distilroberta-base",
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"device": "cpu"
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}
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}
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}
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```
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## Configuration Parameters
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| Parameter | Description | Type | Default |
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|-----------|-------------|------|---------|
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| `model` | HuggingFace cross-encoder model name | `str` | `"cross-encoder/ms-marco-MiniLM-L-6-v2"` |
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| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
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| `batch_size` | Batch size for processing documents | `int` | `32` |
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| `show_progress_bar` | Show progress bar during processing | `bool` | `False` |
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| `top_k` | Maximum documents to return | `int` | `None` |
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## Advantages
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- **Local Processing**: No external API calls required
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- **Privacy**: Data stays on your infrastructure
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- **Cost Effective**: No per-request charges
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- **Fast**: Especially with GPU acceleration
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- **Customizable**: Can fine-tune on your specific data
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- **Privacy**: Complete local processing, no external API calls
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- **Cost**: No per-token charges after initial model download
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- **Customization**: Use any HuggingFace cross-encoder model
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- **Offline**: Works without internet connection after model download
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## Performance Considerations
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- **First Run**: Model download may take time initially
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- **Memory Usage**: Models require GPU/CPU memory
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- **Batch Size**: Optimize batch size based on available memory
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- **Device**: GPU acceleration significantly improves speed
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## Best Practices
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1. **Model Selection**: Choose model based on accuracy vs speed requirements
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2. **Device Management**: Use GPU when available for better performance
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3. **Batch Processing**: Process multiple documents together for efficiency
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4. **Memory Monitoring**: Monitor system memory usage with larger models
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