AI — Embeddings
What are Embeddings?
Numerical representations of text for similarity comparison.
Generate Embeddings
from openai import OpenAI
client = OpenAI(api_key="your-key")
response = client.embeddings.create(
model="text-embedding-ada-002",
input="Hello, world!"
)
embedding = response.data[0].embedding
Vector Databases
| Database | Description |
|---|---|
| Pinecone | Managed vector DB |
| Weaviate | Open-source |
| Chroma | Lightweight |
| FAISS | Facebook's library |
Similarity Search
from sentence_transformers import SentenceTransformer
import numpy as np
model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = ["Hello world", "Hi there", "Goodbye"]
embeddings = model.encode(sentences)
# Find similar
query = model.encode(["Hello"])
similarities = np.dot(embeddings, query.T)
Mini Practice
- Generate embeddings
- Store in vector database
- Perform similarity search
- Build semantic search
Up Next
Continue with Vector Databases — vector database details.
Related Topics
Frequently Asked Questions about Embeddings
What is Embeddings in AI?
Embeddings is a fundamental concept in AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.
How do I learn Embeddings?
Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Embeddings.
Why is Embeddings important in AI?
Embeddings is essential for AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.