Ep: 05 - Vector Stores in LangChain Explained | | LangChain Playlist
Welcome to Episode 05 of the LangChain Playlist! 🚀
📁 GitHub Code Repository: https://github.com/deepakat002/langchain
📺 Full Playlist: • Ep: 01 - What is LangChain? | LangChain P...
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In this video, we explore *Vector Stores* in LangChain — an essential building block that allows you to store and retrieve embedding vectors efficiently using libraries like **FAISS**, **Chroma**, and **Weaviate**.
Vector stores are the backbone of RAG (Retrieval-Augmented Generation) pipelines, enabling fast and accurate similarity searches across large document collections.
🧠 What you'll learn in this episode:
What is a vector store and why it's important
How embeddings and vector stores work together
Different types of vector stores in LangChain (FAISS, Chroma, etc.)
How to create, store, and search documents using vector stores
Real-world RAG pipeline usage examples
Next Episode: Retrievers in LangChain 🔍🧠
#LangChain #VectorStore #LangChainVectorStores #FAISS #ChromaDB #OpenAI #RAGPipeline #LLMApps #LangChainTutorial #GenerativeAI #PythonAI #DocumentSearch #LangChainBeginners