LangChain | Embeddings | Vector Search: Working with Semantic Data | Video #34

Описание: Welcome to the most exciting part of our journey! 🧠 In Video #34 of our LangChain Full Course, we bridge the gap between human language and machine understanding by exploring Embeddings and Vector Search.

An embedding is a way of representing words, sentences, or documents as a list of numbers (vectors). In this video, I explain how these vectors capture the "semantic meaning" of your data. You'll learn how LangChain uses these embeddings to perform Vector Search, allowing your AI to find relevant information based on intent rather than just matching keywords.

✅ In this comprehensive guide, we cover:

What are Embeddings? Understanding text-to-vector transformation.

Semantic Search vs. Keyword Search: Why vector search is the backbone of modern AI.

The LangChain Embeddings Interface: A look at how LangChain integrates with OpenAI, HuggingFace, and Google.

Vector Databases (Vector Stores): An introduction to where these numeric representations are stored (Chroma, FAISS, Pinecone).

The Mathematical "Vibe": A simple explanation of Cosine Similarity and how the computer "calculates" meaning.

Why this matters: Embeddings are the "soul" of RAG. Without them, your AI is just a fancy search engine. Mastering vector search allows you to build applications that truly understand what the user is asking for, even if they use different words than your source document.

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