Vector Embeddings Explained: OpenAI, Chroma, t-SNE & LangChain for RAG Systems
In Day 3 of our RAG series, we dive deep into the world of vector embeddings — the heart of any Retrieval-Augmented Generation system.
You’ll learn:
How OpenAI embeddings work with Chroma to power semantic search
How to visualize embeddings using t-SNE to understand high-dimensional data
How to integrate it all into your LangChain-based RAG pipeline
Perfect for AI engineers, data scientists, and developers building LLM apps that understand context, not just keywords.
🧠 Concepts: Semantic similarity, high-dimensional vectors, t-SNE
🛠️ Tools: OpenAI Embeddings, Chroma, LangChain, Scikit-learn (for visualization)
This is the foundation of retrieval intelligence. Master this — and you control what your AI knows.
In Day 3 of our RAG series, we dive deep into the world of vector embeddings — the heart of any Retrieval-Augmented Generation system.
You’ll learn:
How OpenAI embeddings work with Chroma to power semantic search
How to visualize embeddings using t-SNE to understand high-dimensional data
How to integrate it all into your LangChain-based RAG pipeline
Perfect for AI engineers, data scientists, and developers building LLM apps that understand context, not just keywords.
🧠 Concepts: Semantic similarity, high-dimensional vectors, t-SNE
🛠️ Tools: OpenAI Embeddings, Chroma, LangChain, Scikit-learn (for visualization)
This is the foundation of retrieval intelligence. Master this — and you control what your AI knows.
