1. What Are Embeddings?
Imagine you want AI to understand that “car” and “automobile” are similar in meaning. Computers don’t inherently understand words — they understand numbers.
Embeddings are how we convert words, sentences, or documents into numerical form, so AI can compare them mathematically.
An embedding is:
- A vector (a list of numbers)
- Each number represents a learned feature
- Similar meanings → similar vectors
Example:
cssCopyEditcar → [0.12, -0.44, 0.88, ...]
automobile → [0.10, -0.47, 0.91, ...]
Their numbers are close → AI knows they’re related.
2. Why Do We Need Embeddings?
Without embeddings:
- AI would compare raw text → poor at finding meaning
With embeddings: - We can search by meaning, not exact words
- Example: Search “How to bake bread” → also finds “Steps for making loaf”
Uses:
- Semantic search
- Chatbots with memory
- Recommendation systems
- Clustering similar content
- Detecting spam or sentiment
3. How Are Embeddings Created?
Embeddings come from embedding models trained on huge datasets.
Popular ones:
- OpenAI text-embedding-ada-002
- BERT / Sentence-BERT
- Cohere embeddings
- Hugging Face models
The model:
- Takes your text
- Tokenizes it (breaks into words/pieces)
- Maps tokens into a high-dimensional vector space (often 512–1536 dimensions)
- Ensures semantically similar things are closer
4. How to Use Embeddings in Practice
Basic workflow:
- Create embeddings for all your data
(e.g., product descriptions, FAQs, documents) - Store them in a Vector Database (Pinecone, Weaviate, Milvus, FAISS)
- When user asks a question:
- Create embedding for the question
- Find the nearest embeddings in your database
- Use those as context for your LLM response
5. Key Concepts to Remember
- Dimensionality: How many numbers in the vector (higher = more detail)
- Cosine Similarity: Common way to measure “closeness” between vectors
- Context Window: Embeddings help you extend LLM memory by storing/retrieving past information
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