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How to Save and Retrieve a Vector Database using LangChain, FAISS, and Gemini Embeddings Efficient storage and retrieval of vector databases is foundational for building intelligent retrieval-augmented generation (RAG) systems using large language models (LLMs). In this guide, we’ll walk through a professional-grade Python implementation that utilizes LangChain with FAISS and Google Gemini Embeddings to store document embeddings and retrieve similar information. This setup is highly suitable for advanced machine learning (ML) and deep learning (DL) engineers who work with semantic search and retrieval pipelines. Why Vector Databases Matter in LLM Applications Traditional keyword-based search systems fall short when it comes to understanding semantic meaning. Vector databases store high-dimensional embeddings of text data, allowing for approximate nearest-neighbor (ANN) searches based on semantic similarity. These capabilities are critical in applications like: Question Ans...

Vector Database (Vector DB): A Deep Dive for ML/DL Engineers What is a Vector Database? A Vector Database (Vector DB) is a specialized type of database designed to efficiently store, index, and query high-dimensional vectors. These vectors often represent embeddings from deep learning models—semantic representations of data such as text, images, audio, or code. Unlike traditional relational databases that rely on exact key-based lookups or structured queries, vector databases are optimized for approximate or exact nearest neighbor (ANN or NNS) searches, which are fundamental to tasks such as semantic search, recommendation systems, anomaly detection, and generative AI retrieval-augmented generation (RAG). Core Components of a Vector Database A production-grade vector database typically comprises the following components: Embedding Store: A storage engine for high-dimensional vectors with metadata. Indexing Engine: Structures like HNSW, IVF, PQ, or ANNOY to support f...