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Embeddings & Vector Models
Master the art of embeddings - the numerical representations that power AI. Learn about embedding models, fine-tuning, and choosing the right approach.
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Embeddings & Vector Models
Embeddings are the bridge between human-understandable data (text, images, audio) and machine learning algorithms. They transform data into dense numerical vectors that capture semantic meaning.
What You'll Learn
- Embedding Basics: How neural networks create vector representations
- Model Selection: Choosing between OpenAI, Cohere, open-source, and custom models
- Fine-Tuning: Adapting pre-trained models to your domain
- Multi-Modal: Combining text, image, and audio embeddings
- Optimization: Reducing dimensions, improving quality, and managing costs
How Embeddings Work
Input: "artificial intelligence"
ā
Embedding Model (e.g., text-embedding-3-small)
ā
Output: [0.023, -0.891, 0.432, ..., -0.124] (1536 dimensions)
Similar concepts produce similar vectors, enabling semantic search and similarity matching.
Popular Embedding Models
Text Embeddings
- OpenAI: text-embedding-3-small, text-embedding-3-large
- Cohere: embed-english-v3.0, embed-multilingual-v3.0
- Open Source: sentence-transformers, E5, BGE
Multi-Modal
- CLIP: Text and image embeddings
- ImageBind: Text, image, audio, depth, and more
- BLIP: Vision-language understanding
Specialized
- CodeBERT: Code embeddings
- BioGPT: Medical/biological text
- Legal-BERT: Legal documents
Key Considerations
- Dimensionality: Balance between quality (high dims) and speed (low dims)
- Domain: Generic vs specialized models
- Cost: API-based vs self-hosted
- Language: Multilingual vs single-language models
- Latency: Real-time vs batch processing
Dive into the guides below to master embeddings.
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