76 lines
2.4 KiB
Markdown
76 lines
2.4 KiB
Markdown
# Normalized Embeddings Support in LEANN
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LEANN now automatically detects normalized embedding models and sets the appropriate distance metric for optimal performance.
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## What are Normalized Embeddings?
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Normalized embeddings are vectors with L2 norm = 1 (unit vectors). These embeddings are optimized for cosine similarity rather than Maximum Inner Product Search (MIPS).
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## Automatic Detection
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When you create a `LeannBuilder` instance with a normalized embedding model, LEANN will:
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1. **Automatically set `distance_metric="cosine"`** if not specified
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2. **Show a warning** if you manually specify a different distance metric
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3. **Provide optimal search performance** with the correct metric
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## Supported Normalized Embedding Models
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### OpenAI
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All OpenAI text embedding models are normalized:
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- `text-embedding-ada-002`
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- `text-embedding-3-small`
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- `text-embedding-3-large`
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### Voyage AI
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All Voyage AI embedding models are normalized:
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- `voyage-2`
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- `voyage-3`
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- `voyage-large-2`
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- `voyage-multilingual-2`
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- `voyage-code-2`
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### Cohere
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All Cohere embedding models are normalized:
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- `embed-english-v3.0`
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- `embed-multilingual-v3.0`
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- `embed-english-light-v3.0`
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- `embed-multilingual-light-v3.0`
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## Example Usage
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```python
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from leann.api import LeannBuilder
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# Automatic detection - will use cosine distance
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="text-embedding-3-small",
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embedding_mode="openai"
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)
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# Warning: Detected normalized embeddings model 'text-embedding-3-small'...
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# Automatically setting distance_metric='cosine'
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# Manual override (not recommended)
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builder = LeannBuilder(
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backend_name="hnsw",
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embedding_model="text-embedding-3-small",
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embedding_mode="openai",
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distance_metric="mips" # Will show warning
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)
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# Warning: Using 'mips' distance metric with normalized embeddings...
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```
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## Non-Normalized Embeddings
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Models like `facebook/contriever` and other sentence-transformers models that are not normalized will continue to use MIPS by default, which is optimal for them.
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## Why This Matters
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Using the wrong distance metric with normalized embeddings can lead to:
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- **Poor search quality** due to HNSW's early termination with narrow score ranges
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- **Incorrect ranking** of search results
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- **Suboptimal performance** compared to using the correct metric
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For more details on why this happens, see our analysis of [OpenAI embeddings with MIPS](../examples/main_cli_example.py).
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